A multi-stage wind and solar energy absorption method for DC channels

Abstract

This invention discloses a multi-stage wind and solar power integration method for DC transmission channels. The method involves acquiring DC transmission channel data; establishing models of the DC transmission channel, sending-end grid, and receiving-end grid based on the data; and establishing a decision model for DC transmission channel renewable energy integration. This invention uses surrogate affine approximation technology to solve the model, outputting two-region planning, trading, generation, and transmission dispatch schemes. This invention demonstrates advantages in solving performance indicators and the feasible region of the problem, and also illustrates the effectiveness of the algorithm in assisting renewable energy integration. It can be applied to DC transmission channel planning, the coordinated allocation of sending and receiving end flexibility resources in the operational field, renewable energy integration, renewable energy integration boundary solving, and DC transmission channel (tethering line) power planning, etc.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and dispatch automation technology, and in particular, it relates to a method, device and medium for multi-stage wind and solar power integration in DC channels. Background Technology

[0002] The rapid development of DC transmission is driving a significant shift in renewable energy consumption strategies. Due to resource endowments, large-capacity wind and solar power generation is often geographically separated from load centers. This contradiction has led to long-distance DC transmission becoming the primary strategy for renewable energy consumption.

[0003] With the continuous large-scale integration of wind power and other new energy units and the ongoing expansion of thermal power unit heating system upgrades, the pressure on power grid peak shaving and frequency regulation is increasing year by year, leading to frequent wind and solar power curtailment due to difficulties in peak shaving. Furthermore, due to the uncertainties of wind and solar power, the large-scale grid connection of new energy sources also poses greater challenges to the stability and security of the power system.

[0004] In dealing with uncertainties, two-stage optimization methods are commonly used in current literature and have been widely applied to the operation of distribution networks, microgrids, and transmission networks containing renewable energy sources. However, two-stage methods have some limitations. Existing literature has pointed out that models such as two-stage robust optimization and scenario-based two-stage stochastic programming cannot simultaneously satisfy the unpredictability and robustness of solutions.

[0005] Currently, actual power systems not only include wind and solar power generation, but also flexible resources such as thermal power generation after flexibility upgrades, energy storage, and demand-side response. These flexible resources significantly impact renewable energy absorption. If utilized rationally, they can greatly facilitate new energy absorption. However, academic research rarely mentions or analyzes the role of flexible resources in DC transmission renewable energy absorption under market trading conditions.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of the present invention, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention fully utilizes the multi-stage model as an effective modeling method that simultaneously satisfies unpredictability and robustness, proposing a multi-stage wind and solar power integration method, device, and medium for DC channels. A multi-stage decision optimization model for DC channel renewable energy integration considering market transactions is proposed, while also introducing the uncertainty of renewable energy output. The multi-stage decision model and surrogate affine approximation algorithm are used to solve the DC channel renewable energy integration problem considering flexibility resources. Based on the physical models of DC channels (tethering lines), wind and solar turbines in the sending and receiving grids, and various flexibility resources, as well as a multi-stage decision model for flexibility resources considering the uncertainty of wind and solar power integration, a multi-stage decision optimization model for DC channel renewable energy integration considering market transactions is established. Surrogate affine approximation technology is used to quickly solve the optimization model, realizing large-scale cross-regional energy allocation and clean energy cross-regional integration, and optimizing my country's current DC channel (tethering line) power transmission configuration mode.

[0008] The objective of this invention is achieved through the following technical solutions:

[0009] A multi-stage wind-solar energy consumption method via DC transmission includes the following steps:

[0010] S100: Acquire data from the DC channel and acquire characteristic data of the power system at the sending and receiving ends, wherein one end of the DC channel is connected to the sending power grid and the other end is connected to the receiving power grid;

[0011] S200: Based on the data from the DC channel and the characteristic data of the sending and receiving power systems, establish models for the DC channel, the sending-end power grid, and the receiving-end power grid;

[0012] S300: Establish a decision model for the consumption of new energy in DC channels based on the models of DC channels, sending-end power grids, and receiving-end power grids;

[0013] S400: Solve the new energy consumption decision model of the DC channel, and determine the planned operation of the power results of the sending-end grid and the receiving-end grid based on the solution results, so as to realize the multi-stage wind and solar consumption of the DC channel.

[0014] In the aforementioned multi-stage wind and solar energy absorption method via DC channel:

[0015] The data for the DC channel includes at least one of the following: DC channel transmission power limit, daily transmission power, maximum number of daily adjustments, and minimum adjustment time.

[0016] In the aforementioned multi-stage wind and solar energy absorption method via DC channel:

[0017] The power system characteristic data at the sending and receiving ends include wind and solar power output forecasts, output forecast confidence intervals, load forecasts, declared supply from power suppliers, declared demand from power demanders, the proportion of load that can be reduced, the ramp-up capability of thermal power units, peak-shaving depth, energy storage discharge depth, and charge / discharge power limits.

[0018] In the aforementioned multi-stage wind and solar energy absorption method via DC channel,

[0019] The decision model for renewable energy consumption in the DC channel is rewritten as follows regarding problem P1:

[0020]

[0021]

[0022]

[0023] Where x represents a vector containing DC transmission power, thermal power unit output, capacity, new energy unit output, net energy storage discharge power, electricity seller transaction volume, load transaction volume, and load shedding optimization variables;

[0024] ∈ indicates the uncertainty of new energy output;

[0025] u represents the optimal range for absorbing the uncertainties of new energy sources;

[0026] G represents a multi-stage affine strategy;

[0027] c represents the vector of relevant parameters in the objective function;

[0028] A, E, b represent relevant constraint parameters that do not contain ∈;

[0029] K, L, M, d represent the inequality constraint parameters containing ∈ during the rescheduling of traditional controllable energy after the generation of uncertainty in new energy sources, and F, H, J, h represent the equality constraint parameters containing ∈ during the rescheduling of traditional controllable energy sources after the generation of uncertainty in new energy sources.

[0030] Solving the decision model for renewable energy consumption in the DC channel includes:

[0031] For the aforementioned decision-making model for renewable energy consumption in the DC channel, proxy variables and proxy functions are introduced:

[0032] 0≤δ LB ≤1, 0≤δ UB ≤1,

[0033] s(U LB U UB )=U UB δ UB -ULB δ LB ,

[0034] Where, δ LB δ UB U represents the introduced proxy variable. LB =diag(u low ), U UB =diag(u up ), diag represents a diagonal matrix with the corresponding elements as diagonal elements, s represents the constructed corresponding surrogate function, u up u low These represent the upper and lower limits of the optimal absorption range for new energy uncertainties, respectively.

[0035] The decision model for renewable energy consumption in the DC channel is processed using strong duality and a proxy affine strategy, wherein:

[0036] Based on strong duality, we obtain:

[0037] Kx+π·1≤d,

[0038]

[0039] π≥0,

[0040] Where π represents the matrix formed by nonnegative dual multipliers. The new surrogate strategy, in which the rescheduling process of traditional controllable energy sources after the introduction of uncertainty in new energy sources is represented as:

[0041]

[0042] right All of them are:

[0043] Fx = h,

[0044]

[0045] Establish the following surrogate affine approximation model for problem P2:

[0046]

[0047] stAx+Eu≤b

[0048] Kx+π·1≤d

[0049]

[0050] π≥0

[0051] Fx = h

[0052]

[0053] The surrogate affine approximation model for problem P2 is solved to achieve a simplified solution for the DC channel renewable energy consumption decision model for problem P1.

[0054] In the aforementioned multi-stage wind and solar energy absorption method via DC channel,

[0055] The aforementioned multi-stage wind and solar power integration method for DC channels is designed to integrate renewable energy sources within the confidence interval of the output of photovoltaic and wind power generation units as defined below:

[0056]

[0057]

[0058] in, Let represent the set of time periods, and t represent the corresponding number of periods. These represent the actual output of photovoltaic and wind power, respectively. C represents the projected expected output of photovoltaic and wind power, respectively. pv C w These represent the installed capacity of photovoltaic and wind power, respectively. These represent the upper limits of the confidence intervals for the predicted output deviation of photovoltaic and wind power, respectively. These represent the lower bounds of the confidence intervals for the predicted output deviations of photovoltaic and wind power, respectively.

[0059] Define a set by a set of variable uncertainties. The range within which the uncertainty of new energy sources can be best absorbed during the mid-t period is as follows:

[0060]

[0061] in, Let represent the lower and upper limits of the uncertainty interval, respectively. Represents a set The uncertainty of new energy in the middle t period is most easily absorbed, and it changes with the installed capacity of new energy.

[0062] The following constraints are introduced to ensure that Including fluctuations in the output of all new energy sources:

[0063]

[0064]

[0065]

[0066]

[0067] in, These represent the upper and lower limits of the uncertainty range for photovoltaic power output, respectively. These represent the upper and lower limits of the uncertainty range of wind power output, respectively.

[0068] In the aforementioned multi-stage wind and solar energy absorption method via DC channel:

[0069] The following multi-objective function is constructed to introduce an optimization objective into the decision model for renewable energy consumption in DC channels, so as to maximize the installed capacity of renewable energy and minimize the overall cost:

[0070]

[0071] in:

[0072] ω1 and ω2 are weighting coefficients, and ω1 + ω2 = 1;

[0073] F g F ls F n F lt Let these represent the cost functions for thermal power transactions, load shedding, electricity seller transactions, and load transaction costs, respectively.

[0074] F pv F w F sto These represent the construction costs of photovoltaic units, wind turbine units, and energy storage, respectively.

[0075] C pv C w These represent the installed capacity of photovoltaic and wind power, respectively;

[0076] C sto Indicates the installed capacity of energy storage at both the sending and receiving ends;

[0077] This represents the thermal power supply of the power supplier in the kth segment of the power supply period t, where k represents the kth supply interval of the power supplier.

[0078] and These represent the load shedding response during time period t, the electricity demand of the demand side in the m-th segment, and the electricity supply of the supplier in the v-th segment, respectively.

[0079] In the aforementioned multi-stage wind and solar energy absorption method via DC channel:

[0080] In the decision-making model for renewable energy consumption in DC channels, the transmission power of DC channels remains stable at least during the minimum adjustment time of DC channels.

[0081] In the aforementioned method for multi-stage wind and solar energy absorption via a DC channel;

[0082] The constraints of the DC channel model include DC channel adjustment state ramp-up constraints, DC channel transmission power upper and lower limit constraints, DC channel minimum adjustment time constraints, DC channel maximum adjustment times constraints, and DC channel daily transmission power constraints.

[0083] In the aforementioned multi-stage wind and solar energy absorption method via DC channel:

[0084] In the decision-making model for renewable energy consumption in DC transmission channels, the constraints at the sending and receiving ends include:

[0085] Power balance constraints at the sending end, energy storage constraints at the sending end, constraints on thermal power units, and related constraints on new energy units;

[0086] The constraints include: power balance at the receiving end, energy storage-related constraints at the receiving end, demand-side response constraints, power demand constraints at the receiving end, and power supply constraints at the receiving end.

[0087] In the aforementioned multi-stage wind and solar energy absorption method via DC channel,

[0088] The constraints of the DC channel renewable energy consumption decision model are:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] in:

[0115] v t This is a 0-1 variable representing whether the DC channel's transmission power is adjusted during time period t; it is set to 1 if adjustment is performed and 0 if no adjustment is performed.

[0116] R dc This indicates the maximum ramp conversion rate for adjusting the power delivery of the DC channel;

[0117] T m Indicates the minimum interval time for DC channel power adjustment;

[0118] X represents the maximum number of times the DC channel is allowed to adjust its power in a day;

[0119] These represent the decision values ​​for thermal power units, sending-end energy storage, receiving-end energy storage, DC transmission power, and load shedding power during the rescheduling process of traditional controllable energy after the emergence of uncertainties in new energy sources.

[0120] τ represents the corresponding optimization time stage;

[0121] G t，τ This represents an affine strategy that maps uncertainty to the values ​​of rescheduling decisions;

[0122] ∈ t This indicates uncertainty regarding new energy sources;

[0123] Q represents the daily power transmission volume pre-planned by the power supply schedule for that day;

[0124] Δt represents the stabilization time phase of the transmitted power in this segment;

[0125] R g This indicates the maximum ramp rate of the thermal power unit;

[0126] P t l P t ls , and These represent the load demand at the receiving end during time period t, the load shedding amount in the demand-side response, the electricity demand of the demand side in the m-th segment, the net discharge power of the energy storage at the receiving end, and the electricity supply of the supplier at the receiving end in the v-th segment, respectively.

[0127] M represents the set of demand-side load demand segments, and m represents the corresponding segment number;

[0128] V represents the set of power segments of the receiving end supplier, and v represents the corresponding segment number;

[0129] γ represents the upper limit coefficient for load shedding;

[0130] This represents the electrical energy demand of segment m. The upper limit;

[0131] This indicates the amount of electricity supplied by the supplier at the receiving end, segment v. The upper limit;

[0132] η r α r These represent the receiving-end energy storage capacity, the upper limit coefficient of net discharge power, and the depth of discharge, respectively.

[0133] Let k represent the set of segments of the power supply, where k represents the corresponding segment number.

[0134] C g ,β,R g , and These represent the thermal power unit capacity, peak shaving depth, maximum ramp rate, power supply of the k-th segment, and upper limit of power supply of the k-th segment, respectively.

[0135] C pv C w These represent the installed capacity of photovoltaic and wind power, respectively;

[0136] η s α sThese represent the capacity of the energy storage resources at the sending end, the upper limit coefficient of the net discharge power, and the depth of discharge, respectively.

[0137] E 0，s This indicates the power level of the energy storage resources at the sending end at the start of the scheduling cycle.

[0138] and P t g The subscript s indicates the planned net discharge power of energy storage and thermal power output at the sending end during time period t;

[0139] Power is supplied to the DC channel during time period t;

[0140] C sto Indicates the installed capacity of energy storage at both the sending and receiving ends;

[0141] This represents the thermal power supply from the power supplier in the kth segment of time period t.

[0142] These represent the upper and lower limits of the uncertainty range for photovoltaic power output, respectively.

[0143] These represent the upper and lower limits of the uncertainty range of wind power output, respectively. The upper and lower limits of the confidence interval for the prediction deviation of power output from photovoltaic and wind power, two new energy sources;

[0144] These represent the uncertain output of photovoltaic and wind power, respectively.

[0145] Represents a set The optimal absorption range of new energy uncertainties during the mid-t period.

[0146] In the aforementioned multi-stage wind-solar energy integration method for DC channels, step S300 includes:

[0147] S301: Establish a new energy consumption model based on the DC channel model, the sending-end power grid model, and the receiving-end power grid model; among them,

[0148] When renewable energy sources fluctuate, the renewable energy consumption model is as follows:

[0149]

[0150] in, G represents the decision values ​​of thermal power units, sending-end energy storage, receiving-end energy storage, DC transmission power, and load shedding power during the rescheduling process of traditional controllable energy sources after the emergence of uncertainties in new energy sources. τ represents the corresponding scheduling time stage. t，τ This represents the affine policy that maps uncertainty to the rescheduling decision value, ∈t This indicates uncertainty regarding new energy sources;

[0151] S302: Establishing a decision-making model for DC channel renewable energy consumption based on a renewable energy consumption model; whereby...

[0152] In the decision-making model for renewable energy consumption in DC channels, a Gaussian distribution is introduced to model the uncertainty of renewable energy output prediction deviation.

[0153] In the aforementioned multi-stage wind and solar energy absorption method via DC channel,

[0154] The objective function for modeling the decision-making model for renewable energy consumption in DC channels is modeled as follows:

[0155] max∑(C pv +C w ),

[0156]

[0157] in,

[0158] The objective function for minimizing overall costs includes the transaction costs of thermal power plants at the sending end, load shedding compensation, power purchase costs at the receiving end, load transaction costs, and the construction costs of wind and solar power units and energy storage.

[0159] F g F ls F n F lt Let these represent the cost functions for thermal power transactions, load shedding, electricity seller transactions, and load transaction costs, respectively.

[0160] F pv F w F sto These represent the construction costs of photovoltaic units, wind turbine units, and energy storage, respectively.

[0161] C sto Indicates the installed capacity of energy storage at both the sending and receiving ends;

[0162] This represents the thermal power supply of the power supplier in the kth segment of the power supply period t, where k represents the kth supply interval of the power supplier.

[0163] and These represent the load shedding response during time period t, the electricity demand of the demand side in the m-th segment, and the electricity supply of the supplier in the v-th segment, respectively.

[0164] Furthermore, this invention also discloses a multi-stage wind and solar energy absorption device with a DC channel, comprising,

[0165] The acquisition unit is used to acquire data from the DC channel and acquire characteristic data of the power systems at the sending and receiving ends, wherein one end of the DC channel is connected to the sending-end power grid and the other end is connected to the receiving-end power grid;

[0166] The power grid model building unit is used to build models of the DC channel, the sending-end power grid, and the receiving-end power grid based on the data of the DC channel and the characteristic data of the sending and receiving end power systems.

[0167] The DC channel renewable energy consumption decision model establishment unit is used to establish a DC channel renewable energy consumption decision model based on the DC channel model, the sending-end grid model, and the receiving-end grid model.

[0168] The execution unit is used to solve the decision model for renewable energy consumption in the DC channel, and to determine the planned operation of the power results in the sending-end grid and the receiving-end grid based on the solution results, so as to realize the multi-stage wind and solar power consumption in the DC channel.

[0169] Furthermore, the present invention discloses a computer-readable storage medium configured to perform the methods described above.

[0170] Furthermore, the present invention also discloses an electronic device comprising:

[0171] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0172] When the processor executes the program, it implements the method described above.

[0173] Compared with the prior art, the present invention has the following advantages:

[0174] 1. This invention provides a physical model encompassing DC transmission lines (tethering lines), wind and solar power units at both the sending and receiving ends of the grid, and various flexible resources. It also proposes a corresponding multi-stage decision optimization model for DC transmission line renewable energy consumption, considering market transactions. Implementing power system planning and operation based on the power system planning results of the interconnected DC transmission lines calculated by the model helps increase the proportion of renewable energy transmission power, improve renewable energy utilization, and facilitate renewable energy consumption. This has guiding significance and application value for relevant departments such as power grid planning and construction. Furthermore, this invention balances the power system investment and operating costs with renewable energy installed capacity during the renewable energy consumption process.

[0175] 2. This invention provides a multi-stage decision-making model for mitigating the uncertainty of wind and solar power output. This method can also be extended to situations where load forecasting is uncertain. A Gaussian distribution is introduced to establish the relationship between actual renewable energy output and predicted expectations. Through the linear relationship between renewable energy output and installed capacity, the confidence interval for renewable energy output uncertainty is derived and transformed into a variable uncertainty set, expressed as follows:

[0176]

[0177] Based on the flexible and controllable technical characteristics of resources, a new energy consumption and reschedule problem based on a linear affine strategy was established to overcome the negative impact of uncertainty in new energy output.

[0178] Meanwhile, considering the relationship between uncertainties at different time periods, a multi-stage decision-making model is introduced. This model can effectively solve the problem of handling time-coupled constraints in uncertainty mitigation, ensuring the unexpectedness and robustness of the rescheduling process, making the problem modeling more realistic, and facilitating the implementation and application of the invention.

[0179] 3. This invention provides a fast solution method for a multi-stage decision-making model for DC channel renewable energy consumption based on surrogate affine approximation technology. The multi-stage decision optimization model is difficult to solve because the uncertainty in renewable energy output introduces infinitely many sets of constraints, and the nonlinear factors introduced by the variable uncertainty set. The surrogate affine approximation algorithm can effectively solve this problem. By introducing a set of surrogate variables and surrogate affine functions, and utilizing strong duality, the original nonlinear problem is transformed into a linear problem, which can then be solved quickly using existing optimization solvers such as Gurobi.

[0180] In summary, this method has broad application prospects in the planning and operation of DC transmission lines, including: overall allocation of flexible resources at the sending and receiving ends, renewable energy consumption, solving the renewable energy consumption boundary, and planning the transmission power of DC transmission lines (tethering lines). Attached Figure Description

[0181] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0182] In the attached diagram:

[0183] Figure 1 This is a flowchart illustrating a multi-stage wind and solar energy absorption method for DC channels according to an embodiment of the present invention.

[0184] Figure 2This is a schematic diagram of the interconnected power grid of the DC channel, the sending and receiving end power grids, and the model of a multi-stage wind and solar power integration method according to an embodiment of the present invention.

[0185] Figure 3 This is a schematic diagram of typical load forecast data of the receiving-end power grid in different seasons for a multi-stage wind and solar power integration method for DC channels according to an embodiment of the present invention.

[0186] Figure 4 This is a schematic diagram of the predicted output and confidence interval of new energy sources according to a multi-stage wind and solar energy integration method for DC channels according to an embodiment of the present invention;

[0187] Figure 5 This is a schematic diagram comparing the existing and optimized curves of DC channel interconnection lines in different seasons according to an embodiment of the present invention, which is a method for multi-stage wind and solar energy absorption in DC channels.

[0188] Figure 6 This is a schematic diagram comparing the minimum overall cost obtained by two methods in three sets of experiments for a DC channel multi-stage wind and solar energy absorption method according to an embodiment of the present invention.

[0189] Figure 7 This is a schematic diagram of the rescheduled power and safety range of energy storage resources under key scenarios, obtained by using implicit decision-making method in a multi-stage wind and solar power consumption method for DC channels according to an embodiment of the present invention.

[0190] Figure 8 This is a schematic diagram of the rescheduled power and safety range of energy storage resources under key scenarios, obtained using a proxy affine approximation algorithm, according to an embodiment of the present invention, for a multi-stage wind and solar power integration method for DC channels.

[0191] The present invention will be further explained below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0192] The following will refer to the appendix. Figures 1 to 8 Specific embodiments of the invention will be described in more detail below. While specific embodiments of the invention are shown in the accompanying drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0193] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions are preferred embodiments for carrying out the invention; however, these descriptions are for the purpose of understanding the general principles of the specification and are not intended to limit the scope of the invention. The scope of protection of this invention is determined by the appended claims.

[0194] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. The accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0195] In one embodiment, the present invention discloses a multi-stage wind-solar energy integration method for DC channels, comprising the following steps:

[0196] Acquire data from the DC channel and acquire characteristic data of the power systems at both the sending and receiving ends, wherein the DC channel is connected to the sending power grid at one end and the receiving power grid at the other end;

[0197] Based on the data from the DC channel and the characteristic data of the sending and receiving power systems, models of the DC channel, the sending-end power grid, and the receiving-end power grid are established. The model of the sending-end power grid includes aggregated thermal power units, aggregated new energy units, and aggregated energy storage. The model of the receiving-end power grid includes aggregated energy storage and demand-side response.

[0198] A new energy consumption model is established based on the DC channel model, the sending-end power grid model, and the receiving-end power grid model.

[0199] A decision-making model for DC channel renewable energy consumption is established based on the renewable energy consumption model.

[0200] The decision model for renewable energy consumption in the DC channel is solved, and the results are used to guide the planning and operation of power generation in the sending and receiving power grids, thereby realizing multi-stage wind and solar energy consumption in the DC channel.

[0201] Among them, multi-stage means that the optimal scheduling of power systems containing renewable energy can be transformed into a typical multi-stage planning and decision problem. Multi-stage decision-making involves multiple time periods, and each time period involves random factors, which refer to when the wind force is what it is and when the sunlight is what it is.

[0202] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0203] The data of the DC channel includes at least one of the following: DC channel transmission power limit, daily transmission power, maximum number of daily adjustments, and minimum adjustment time;

[0204] The power system characteristic data at the sending and receiving ends include wind and solar power output forecasts, output forecast confidence intervals, load forecasts, declared supply from power suppliers, declared demand from power demanders, the proportion of load that can be reduced, the ramp-up capability of thermal power units, peak-shaving depth, energy storage discharge depth, and charge / discharge power limits.

[0205] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0206] The decision-making model for renewable energy consumption in DC transmission channels is solved based on surrogate affine approximation, where...

[0207] First, the decision model for renewable energy consumption in the DC channel is rewritten as follows regarding problem P1:

[0208]

[0209] stAx+Eu≤b,

[0210]

[0211]

[0212] in,

[0213] x represents a vector containing DC transmission power, thermal power unit output, capacity, new energy unit output, net energy storage discharge power, electricity seller transaction volume, load transaction volume, and load shedding optimization variables;

[0214] ∈ indicates the uncertainty of new energy output;

[0215] u represents the optimal range for absorbing the uncertainties of new energy sources;

[0216] G represents a multi-stage affine strategy;

[0217] c represents the vector of relevant parameters in the objective function;

[0218] A, E, b represent relevant constraint parameters that do not contain ∈;

[0219] K, L, M, d represent the parameters related to the inequality constraints containing ∈ during the rescheduling of traditional controllable energy after the generation of uncertainty in new energy sources.

[0220] F, H, J, h represent the equation constraint parameters containing ∈ during the rescheduling of traditional controllable energy after the generation of uncertainty in new energy sources;

[0221] Furthermore, for the aforementioned DC channel renewable energy consumption decision model, proxy variables and proxy functions are introduced:

[0222] 0≤δ LB ≤1, 0≤δ UB ≤1,

[0223] s(U LB U UB )=U UB δ UB -U LB δ LB ,

[0224] in,

[0225] δ LB δ UB This indicates the introduced proxy variable;

[0226] U LB =diag(u low ), U UB =diag(u up ), where diag represents a diagonal matrix with the corresponding elements as diagonal elements;

[0227] s represents the corresponding proxy function constructed;

[0228] u up u low These represent the upper and lower limits of the optimal absorption range for new energy uncertainties, respectively.

[0229] Furthermore, the decision-making model for renewable energy consumption in the DC channel is processed using strong duality and a proxy affine strategy, wherein...

[0230] Based on strong duality, we obtain:

[0231] Kx+π·1≤d,

[0232]

[0233] π≥0,

[0234] in,

[0235] π represents the matrix formed by nonnegative dual multipliers;

[0236] The new surrogate strategy, in which the rescheduling process of traditional controllable energy sources after the introduction of uncertainty in new energy sources is represented as:

[0237]

[0238] right All of them are:

[0239] Fx = h,

[0240]

[0241] Therefore, the following surrogate affine approximation model for problem P2 is established:

[0242]

[0243] stAx+Eu≤b

[0244] Kx+π·1≤d

[0245]

[0246] π≥0

[0247] Fx = h

[0248]

[0249] Furthermore, the surrogate affine approximation model for problem P2 is solved to achieve a simplified solution for the DC channel renewable energy consumption decision model for problem P1.

[0250] Then, based on the solution results, the planning and operation of the power results in the sending-end grid and receiving-end grid are guided, realizing the multi-stage wind and solar power consumption of the DC channel.

[0251] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0252] The aforementioned multi-stage wind and solar power integration method for DC channels is designed to integrate renewable energy sources within the confidence interval of the output of photovoltaic and wind power generation units as defined below:

[0253]

[0254]

[0255] in,

[0256] This represents a set of time periods, where t represents the corresponding number of periods;

[0257] These represent the actual output of photovoltaic and wind power, respectively.

[0258] These represent the projected expected output of photovoltaic and wind power, respectively.

[0259] C pv C wThese represent the installed capacity of photovoltaic and wind power, respectively;

[0260] These represent the upper limits of the confidence intervals for the predicted output deviations of photovoltaic and wind power, respectively.

[0261] These represent the lower bounds of the confidence intervals for the predicted output deviations of photovoltaic and wind power, respectively.

[0262] Furthermore, a set is defined by a set of variable uncertainties. The range within which the uncertainty of new energy sources can be best absorbed during the mid-t period is as follows:

[0263]

[0264] in,

[0265] These represent the lower and upper limits of the uncertainty interval, respectively.

[0266] Represents a set The uncertainty of new energy in the middle t period is most easily absorbed, and it changes with the installed capacity of new energy.

[0267] Furthermore, in order to ensure that fluctuations in new energy output can be mitigated... The following restrictions are included:

[0268]

[0269]

[0270]

[0271]

[0272] in,

[0273] These represent the upper and lower limits of the uncertainty range for photovoltaic power output, respectively.

[0274] These represent the upper and lower limits of the uncertainty range of wind power output, respectively.

[0275] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0276] To simultaneously address the objective functions of maximizing new energy installed capacity and minimizing overall cost, the following multi-objective function is constructed:

[0277] ,

[0278] in,

[0279] ω1 and ω2 are weighting coefficients, and ω1 + ω2 = 1;

[0280] F g F ls F n F lt Let these represent the cost functions for thermal power transactions, load shedding, electricity seller transactions, and load transaction costs, respectively.

[0281] F pv F w F sto These represent the construction costs of photovoltaic units, wind turbine units, and energy storage, respectively.

[0282] C pv C w These represent the installed capacity of photovoltaic and wind power, respectively;

[0283] C sto Indicates the installed capacity of energy storage at both the sending and receiving ends;

[0284] This represents the thermal power supply of the power supplier in the kth segment of the power supply period t, where k represents the kth supply interval of the power supplier.

[0285] and These represent the load shedding response during time period t, the electricity demand of the demand side in the m-th segment, and the electricity supply of the supplier in the V-th segment at the receiving end, respectively.

[0286] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0287] The transmission power of the DC channel remains stable at least during the minimum adjustment time of the DC channel;

[0288] The model constraints of the DC channel include DC channel adjustment state ramp-up constraints, DC channel transmission power upper and lower limit constraints, DC channel minimum adjustment time constraints, DC channel maximum adjustment times constraints, and DC channel daily transmission power constraints.

[0289] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0290] On the one hand, the model constraints of the sending-end power grid include:

[0291] First, the power balance constraint at the sending end:

[0292] This formula indicates that the power transmitted through the DC channel should be balanced with the output of thermal power plants, the output of new energy units, and the net discharge power of energy storage.

[0293] in,

[0294] P t pv P t w , and P t g The subscript 's' indicates the planned output of photovoltaic units, wind turbine units, net discharge power of energy storage, and thermal power at the sending end during time period 't', with the subscript 's' indicating the sending end.

[0295] P t dc Power is supplied to the DC channel during time period t;

[0296] Secondly, the constraints of aggregated energy storage at the sending end:

[0297] These respectively represent the changes in the amount of energy stored at the sending end and the limiting conditions;

[0298] It represents the net discharge power limit condition for energy storage at the sending end;

[0299] E 0，s =E T，s , which represents the energy storage power dispatch cycle constraint condition;

[0300] in,

[0301] E t，s E t+1，s These represent the electricity generated by the energy storage resources at the sending end during time periods t and t+1, respectively.

[0302] η s α s These represent the capacity of the energy storage resources at the sending end, the upper limit coefficient of the net discharge power, and the depth of discharge, respectively.

[0303] E 0，s E T，s These represent the power of the energy storage resources at the beginning and end of the multi-stage scheduling cycle, respectively.

[0304] Δt represents the stable time period of the transmitted power from time period t to time period t+1;

[0305] Third, constraints on fusion-powered thermal power units:

[0306] These respectively represent the output of the thermal power unit and the climbing limit conditions;

[0307] It means that the output of thermal power is equal to the sum of the power supply of each segment;

[0308] It indicates the lower and upper limits of the power supply for each segment;

[0309] in,

[0310] Let k represent the set of segments of the power supply, where k represents the corresponding segment number.

[0311] C g ,β,R g , and These represent the thermal power unit capacity, peak shaving depth, maximum ramp rate, power supply of the k-th segment, and upper limit of power supply of the k-th segment, respectively.

[0312] P t g , These represent the thermal power output during time periods t and t-1, respectively.

[0313] Δt represents the stabilization time stage of the power output of the thermal power unit in this section;

[0314] Fourth, the relevant constraints of the integrated new energy unit:

[0315] These represent the planned output of photovoltaic units and wind turbine units, respectively.

[0316] These represent the predicted output of photovoltaic units and wind turbine units per unit installed capacity during time period t, respectively.

[0317] C pv C w These represent the installed capacity of photovoltaic and wind power, respectively;

[0318] On the other hand, the constraints of the receiving-end power grid include:

[0319] First, the power balance constraint at the receiving end:

[0320] This formula shows that the load demand at the receiving end plus the power demand at the demand side minus the load shedding at the demand side equals the sum of the net discharge power of the energy storage at the receiving end, the power transmitted through the DC channel, and the power supply at the supply side on the left side of the equation. The subscript r indicates the receiving end.

[0321] in,

[0322] P t l , and These represent the load demand at the receiving end during time period t, the load shedding amount in the demand-side response, the electricity demand of the demand side in the m-th segment, the net discharge power of the energy storage at the receiving end, and the electricity supply of the supplier at the receiving end in the v-th segment, respectively.

[0323] M represents the set of demand-side load demand segments, and m represents the corresponding segment number;

[0324] V represents the set of power segments of the receiving end supplier, and v represents the corresponding segment number;

[0325] Secondly, the relevant constraints of aggregated energy storage at the receiving end:

[0326] These represent the changes in the energy storage capacity at the receiving end and the limiting conditions, respectively.

[0327] It represents the net discharge power limitation of the energy storage at the receiving end;

[0328] E 0，r =E T，r This represents the constraint condition of the energy storage power dispatch cycle at the receiving end;

[0329] in,

[0330] E t，r E t+1，r These represent the energy stored at the receiving end during periods t and t+1, respectively.

[0331] η r α r These represent the receiving-end energy storage capacity, the upper limit coefficient of net discharge power, and the depth of discharge, respectively.

[0332] Δt represents the stable time period of the transmitted power from time period t to time period t+1;

[0333] E 0，r E T，r These represent the receiving-end energy storage capacity during the initial and final periods of the scheduling cycle, respectively.

[0334] T represents the total number of time periods in the scheduling cycle;

[0335] Third, demand-side response constraints:

[0336] in,

[0337] γ represents the upper limit coefficient for load shedding; this formula indicates the lower and upper limits of load shedding in the demand-side response at the receiving end.

[0338] Fourth, the constraints on the power demand at the receiving end:

[0339] This formula indicates the electricity demand of the m-th segment at the receiving end. The lower and upper limits;

[0340] in, This represents the upper limit of the power demand for the m-th segment;

[0341] Fifth, the constraints on the power supply at the receiving end:

[0342] in, This indicates the amount of electricity supplied by the supplier at the receiving end, segment v. The upper limit.

[0343] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0344] The constraints of the objective function in the new energy consumption decision model are as follows:

[0345]

[0346]

[0347]

[0348]

[0349]

[0350]

[0351]

[0352]

[0353]

[0354]

[0355]

[0356]

[0357]

[0358]

[0359]

[0360]

[0361]

[0362]

[0363]

[0364]

[0365]

[0366]

[0367]

[0368]

[0369]

[0370] in,

[0371] v t This is a 0-1 variable representing whether the DC channel's transmission power is adjusted during time period t; it is set to 1 if adjustment is performed and 0 if no adjustment is performed.

[0372] R dc This indicates the maximum ramp conversion rate for adjusting the power delivery of the DC channel;

[0373] T m Indicates the minimum interval time for DC channel power adjustment;

[0374] X represents the maximum number of times the DC channel is allowed to adjust its power in a day;

[0375] These represent the decision values ​​for thermal power units, sending-end energy storage, receiving-end energy storage, DC transmission power, and load shedding power during the rescheduling process of traditional controllable energy after the emergence of uncertainties in new energy sources.

[0376] τ represents the corresponding optimization time stage;

[0377] G t，τ This represents an affine strategy that maps uncertainty to the values ​​of rescheduling decisions;

[0378] ∈ t This indicates uncertainty regarding new energy sources;

[0379] Q represents the daily power transmission volume pre-planned by the power supply schedule for that day;

[0380] Δt represents the stabilization time phase of the transmitted power in this segment;

[0381] R g This indicates the maximum ramp rate of the thermal power unit;

[0382] P tl P t ls , and These represent the load demand at the receiving end during time period t, the load shedding amount in the demand-side response, the electricity demand of the demand side in the m-th segment, the net discharge power of the energy storage at the receiving end, and the electricity supply of the supplier at the receiving end in the v-th segment, respectively.

[0383] M represents the set of demand-side load demand segments, and m represents the corresponding segment number;

[0384] V represents the set of power segments of the receiving end supplier, and v represents the corresponding segment number;

[0385] γ represents the upper limit coefficient for load shedding;

[0386] This represents the electrical energy demand of segment m. The upper limit;

[0387] This indicates the amount of electricity supplied by the supplier at the receiving end, segment v. The upper limit;

[0388] η r α r These represent the receiving-end energy storage capacity, the upper limit coefficient of net discharge power, and the depth of discharge, respectively.

[0389] Let k represent the set of segments of the power supply, where k represents the corresponding segment number.

[0390] C g ,β,R g , and These represent the thermal power unit capacity, peak shaving depth, maximum ramp rate, power supply of the k-th segment, and upper limit of power supply of the k-th segment, respectively.

[0391] C pv C w These represent the installed capacity of photovoltaic and wind power, respectively;

[0392] η s α s These represent the capacity of the energy storage resources at the sending end, the upper limit coefficient of the net discharge power, and the depth of discharge, respectively.

[0393] E 0，s This indicates the power level of the energy storage resources at the sending end at the start of the scheduling cycle.

[0394] and The subscript 's' indicates the planned net discharge power of energy storage and thermal power output at the sending end during the 0-period period;

[0395] P t dc Power is supplied to the DC channel during time period t;

[0396] C sto Indicates the installed capacity of energy storage at both the sending and receiving ends;

[0397] This represents the thermal power supply from the power supplier in the kth segment of time period t.

[0398] These represent the upper and lower limits of the uncertainty range for photovoltaic power output, respectively.

[0399] These represent the upper and lower limits of the uncertainty range of wind power output, respectively. The upper and lower limits of the confidence interval for the prediction deviation of power output from photovoltaic and wind power, two new energy sources;

[0400] These represent the uncertain output of photovoltaic and wind power, respectively.

[0401] This indicates the optimal range for absorbing uncertainties in new energy sources.

[0402] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0403] A Gaussian distribution is introduced to model the uncertainty of the prediction bias of new energy power output. The actual power output of photovoltaic units and wind turbine units is expressed as follows:

[0404]

[0405]

[0406] in, These represent the actual output, predicted expected output, and predicted deviation of the photovoltaic unit during time period t, respectively. Let represent the actual output, predicted expected output, and predicted deviation of the wind turbine at time t, respectively. Let N represent a normal distribution conforming to the corresponding parameters, and σ1 2 , σ2 2 These represent the variance of the uncertainty;

[0407] When renewable energy sources fluctuate, the renewable energy consumption model is as follows:

[0408]

[0409] in, G represents the decision values ​​of thermal power units, sending-end energy storage, receiving-end energy storage, DC transmission power, and load shedding power during the rescheduling process of traditional controllable energy sources after the emergence of uncertainties in new energy sources. τ represents the corresponding scheduling time stage. t，τ This represents the affine policy that maps uncertainty to the rescheduling decision value, ∈ t This indicates uncertainty regarding new energy sources.

[0410] In another embodiment, in the described multi-stage wind and solar energy absorption method for DC channels,

[0411] The objective function for modeling the decision-making model for renewable energy consumption in DC channels is modeled as follows:

[0412] max∑(C pv +C w ),

[0413]

[0414] in,

[0415] The objective function for minimizing overall costs includes the transaction costs of thermal power plants at the sending end, load shedding compensation, power purchase costs at the receiving end, load transaction costs, and the construction costs of wind and solar power units and energy storage.

[0416] F g F ls F n F lt Let these represent the cost functions for thermal power transactions, load shedding, electricity seller transactions, and load transaction costs, respectively.

[0417] F pv F w F sto These represent the construction costs of photovoltaic units, wind turbine units, and energy storage, respectively.

[0418] C sto Indicates the installed capacity of energy storage at both the sending and receiving ends;

[0419] This represents the thermal power supply of the power supplier in the kth segment of the power supply period t, where k represents the kth supply interval of the power supplier.

[0420] P t ls , and These represent the load shedding response during time period t, the electricity demand of the demand side in the m-th segment, and the electricity supply of the supplier in the v-th segment, respectively.

[0421] Furthermore, in one embodiment, the present invention also discloses a DC channel multi-stage wind and solar energy absorption device, comprising,

[0422] The acquisition unit acquires data from the DC channel and acquires characteristic data of the power systems at both the sending and receiving ends. The DC channel is connected to the sending power grid at one end and the receiving power grid at the other end.

[0423] The modeling unit establishes models of the DC channel, the sending-end power grid, and the receiving-end power grid based on the data from the DC channel and the characteristic data of the sending and receiving power systems. The model of the sending-end power grid includes aggregated thermal power units, aggregated new energy units, and aggregated energy storage. The model of the receiving-end power grid includes aggregated energy storage and demand-side response.

[0424] A renewable energy consumption model is established based on the DC channel model, the sending-end power grid model, and the receiving-end power grid model; and,

[0425] A decision-making model for DC channel renewable energy consumption is established based on the renewable energy consumption model.

[0426] The execution unit solves the decision model for renewable energy consumption in the DC channel and guides the planning and operation of power results in the sending-end and receiving-end power grids based on the solution, thereby realizing multi-stage wind and solar power consumption in the DC channel.

[0427] Among them, multi-stage means that the optimal scheduling of power systems containing renewable energy can be transformed into a typical multi-stage planning and decision problem. Multi-stage decision-making involves multiple time periods, and each time period involves random factors, which refer to when the wind force is what it is and when the sunlight is what it is.

[0428] It is understood that the modeling unit in the aforementioned DC channel multi-stage wind and solar energy absorption device can further implement the specific data processing of each embodiment of the method described above.

[0429] Furthermore, the present invention discloses a computer-readable storage medium configured to perform the methods described above.

[0430] To better understand, such as Figures 1 to 8 As shown, a multi-stage wind-solar energy integration method for DC channels includes,

[0431] Step 1: Obtain the DC channel transmission power limit, daily transaction volume, maximum daily adjustment times, minimum stabilization time after adjustment, wind and solar power output forecast, confidence interval, investment cost, load forecast, electricity seller transaction declaration volume and price, electricity buyer transaction declaration volume and price, load reduction ratio and compensation price, thermal power unit ramping capacity, peak shaving depth and investment cost, energy storage discharge depth, charging and discharging power limit and investment cost.

[0432] Step 2: Based on the data obtained in Step 1, establish physical models of the DC transmission line, the wind and solar turbines of the sending and receiving power grids, and various flexible resources; the schematic diagram is shown below. Figure 2 As shown, the constraints of the established model include three types: DC channel-related physical constraints, sending-end grid constraints, and receiving-end grid constraints. First, the DC channel-related physical constraints are introduced. The DC channel transmission power cannot be continuously adjusted for a short period; that is, the transmission power needs to remain stable for at least the minimum adjustment time. Furthermore, frequent adjustments to the transmission power will reduce the lifespan of the sending and receiving end inverters; therefore, the maximum number of adjustments per day also needs to be limited. The specific introduction of the DC channel-related physical constraints is as follows:

[0433] DC channel regulation state ramp-up constraint conditions:

[0434]

[0435] in, For the set of all optimization time periods, P t dc For the DC channel power supplied during time period t, v t R is a 0-1 variable indicating whether the DC channel's transmission power is adjusted during time period t; it is set to 1 if adjustment is performed and 0 if no adjustment is performed. dc This represents the maximum ramp rate of change of power for DC channel transmission, expressed by the 0-1 variable v. t The value of indicates and records whether the DC channel transmission power during time period t needs to be adjusted.

[0436] DC channel power transmission upper and lower limit constraints:

[0437]

[0438] Among them, P dc , These represent the upper and lower limits of the DC channel's transmission power, respectively. This formula indicates that the DC channel's transmission power cannot exceed these limits.

[0439] Minimum settling time constraint for DC channel:

[0440]

[0441] Among them, T m This represents the minimum interval time for DC channel power adjustment. This formula indicates that at least a minimum interval time is required between two power adjustments of the DC channel.

[0442] Maximum number of adjustments for DC channel:

[0443]

[0444] Where X is the maximum number of times the DC channel is allowed to adjust its power in a day, this formula indicates that the DC channel can only adjust its transmission power at most X times a day.

[0445] DC channel daily trading volume constraints:

[0446]

[0447] Where Q represents the daily trading volume pre-planned by the trading plan. This formula indicates that the daily transmission mode must meet the daily trading volume requirements stipulated in the trading plan.

[0448] Secondly, the constraints of wind and solar turbines and various flexibility resources in the sending-end power grid are introduced. The sending-end power grid comprises four components: integrated thermal power units, integrated wind power units, integrated photovoltaic units, and integrated energy storage, each with its own relevant physical constraints. Simultaneously, the power balance conditions at the sending end must also be considered. The specific constraints of the sending-end power grid are introduced as follows:

[0449] Sending-end power balance constraints:

[0450]

[0451] Among them, P t pv P t w , and P t g These represent the planned output of photovoltaic units, wind turbine units, net discharge power of energy storage, and thermal power generation during time period t, respectively. This formula indicates that the power transmitted through the DC transmission channel must be balanced with the output of thermal power units, the output of new energy units, and the net discharge power of energy storage.

[0452] Constraints related to energy storage at the sending end:

[0453]

[0454]

[0455]

[0456] E 0，s =E T，s (10)

[0457] Among them, E t，s , η s α sEquations (7) and (8) represent the power, capacity, net discharge power upper limit coefficient, and discharge depth of the energy storage resources at the sending end, respectively. Equation (9) represents the net discharge power limit of the energy storage at the sending end. Equation (10) represents the periodic constraint condition of the energy storage power.

[0458] Relevant constraints of thermal power units:

[0459]

[0460]

[0461]

[0462]

[0463] Among them, C g ,β,R g , and Let (11) and (12) represent the thermal power unit capacity, peak shaving depth, maximum ramp rate, transaction volume and upper limit of the seller in the kth segment, respectively. Equations (13) and (14) represent the thermal power unit output and ramping limit conditions, respectively. Equation (15) indicates that the thermal power output is equal to the sum of the transaction volume in each segment. Equation (16) indicates the upper limit of the transaction volume in each segment.

[0464] Relevant constraints for new energy power units:

[0465]

[0466]

[0467] in, C represents the predicted output of photovoltaic and wind turbine units per unit installed capacity during time period t. pv C w Let represent the installed capacity of photovoltaic and wind power, respectively. Equations (15) and (16) represent the planned output of photovoltaic units and wind turbine units, respectively.

[0468] Finally, the load and various flexibility resource constraints of the receiving-end grid are introduced. Receiving-end flexibility resources include aggregated energy storage and demand-side response, each with its own associated physical constraints. Simultaneously, the power balance conditions at the receiving end must also be considered. The specific constraints of the receiving-end grid are introduced as follows.

[0469] Introduce receiving-end power balance constraints:

[0470]

[0471] Among them, P t l Pt ls , and Let represent the receiving-end load demand, load shedding, buyer transaction volume in segment m, net discharge power of receiving-end energy storage, and seller transaction volume in segment v, respectively. This formula shows that the receiving-end load demand plus buyer transaction volume minus demand-side response load shedding equals the sum of receiving-end energy storage net discharge power, DC transmission power, and seller transaction volume.

[0472] Introduce relevant constraints for receiving-end energy storage.

[0473]

[0474]

[0475]

[0476] E 0，r =E T，r (twenty one)

[0477] Among them, E t，r , η r α r Equations (18) and (19) represent the energy storage capacity, net discharge power limit coefficient, and depth of discharge at the receiving end, respectively. Equation (20) represents the net discharge power limit for energy storage at the receiving end. Equation (21) represents the periodic constraint on the energy storage capacity.

[0478] Introduce demand-side response-related constraints:

[0479]

[0480] This formula indicates the limit of load shedding on the demand side of the receiving end.

[0481] Introduce load trading related constraints:

[0482]

[0483] in, This represents the upper limit of the load transaction volume for the m-th segment. This formula indicates the limit of the transaction load at the receiving end.

[0484] Introduce relevant constraints for the receiving end electricity seller:

[0485]

[0486] in, This represents the upper limit of the transaction volume for the electricity seller in segment v.

[0487] Step 3: Based on the physical model established in Step 2, establish a multi-stage decision-making model for flexible resources to mitigate the uncertainties of wind and solar power. In this invention, the deviation in the predicted output of new energy sources will be modeled as the uncertainty of the model to better adapt to the real-world application scenarios. It should be noted that if load fluctuations are also modeled as uncertainties, the processing methods described below are equally applicable, since loads can also be modeled using a similar Gaussian distribution and treated as uncertainties using a subordinate multi-stage affine strategy. Therefore, this invention can be easily extended to application scenarios that consider uncertain loads.

[0488] By introducing a Gaussian distribution to model the uncertainty of predicted new energy output, the actual output of photovoltaic and wind turbine units can be expressed as:

[0489]

[0490]

[0491] in, These represent the actual output, predicted expected output, and predicted deviation of the photovoltaic unit during time period t, respectively. σ1 represents the actual output, predicted expected output, and predicted deviation of the wind turbine during time period t, respectively. 2 , σ2 2 These represent the variance of the uncertainty.

[0492] make These are the upper and lower limits of the confidence interval for predicting the output of new energy sources. Based on the linear relationship between new energy output and its capacity, The confidence intervals can be represented as follows: Therefore, the confidence interval for the output of the new energy generating units can be further rewritten as follows:

[0493]

[0494]

[0495] The above equation shows that the boundary of the uncertainty set of new energy sources changes with the installed capacity, therefore the uncertainty set is variable. Furthermore, this variable uncertainty set can be defined as:

[0496]

[0497] in, These represent the upper and lower limits of the uncertainty interval, respectively. This is referred to as the optimal absorption range for uncertainty, which varies with the installed capacity of new energy sources. To ensure that fluctuations in new energy output are included within the optimal absorption range, the following constraints are introduced:

[0498]

[0499]

[0500]

[0501]

[0502] When renewable energy sources fluctuate, flexible resources need to be rescheduled to absorb these fluctuations. This invention uses a multi-stage affine strategy as the rescheduling strategy for flexible resources. The rescheduling decisions related to thermal power, DC transmission lines, and energy storage depend on the total renewable energy output realized in the current time period, i.e., on the uncertain renewable energy output available up to the current time period. Using a multi-stage decision model to reschedule relevant flexible resources ensures the unexpectedness and robustness of the solution. Based on the linear affine strategy, the multi-stage decision model for flexible resources is as follows:

[0503]

[0504] in, These represent the decision values ​​for thermal power units, energy storage at the sending and receiving ends, power transmitted through the DC transmission channel, and load shedding power during the rescheduling process. They need to meet the physical constraints of the DC transmission channel interconnection line, new energy sources in the sending and receiving end power grids, and various flexible resources (1)-(24). G t，τ This represents an affine policy that maps uncertainty to the values ​​of rescheduling decisions. t This indicates uncertainty regarding new energy sources.

[0505] In this invention, the rescheduling decision for time period t depends on the uncertain available output of new energy up to time period t. The rescheduling modeling differs from two-stage robust optimization and scenario-based optimization models. It guarantees the unexpectedness and robustness of the solution; that is, the model proposed in this invention is a multi-stage model. Such a multi-stage model can consider the relationship between uncertainties at different time stages. Step 4: Based on the multi-stage decision model established in Step 3, with the optimization objective of minimizing new energy installed capacity and overall cost, establish a multi-stage decision optimization model for DC channel new energy consumption that considers market transactions. The objective function of the optimization model is...

[0506] max∑(C pv +C w (32)

[0507]

[0508] F g F ls F n , Let F represent the cost functions for thermal power bidding, load shedding, intra-provincial power purchase at the receiving end, and load transaction costs, respectively. pv F w F sto These represent the construction costs of photovoltaic units, wind turbine units, and energy storage, respectively.

[0509] To address both objective functions simultaneously, a multi-objective function is constructed as follows:

[0510]

[0511] Where ω1 and ω2 are weighting coefficients, and ω1+ω2=1.

[0512] Therefore, the constraints are determined as follows:

[0513]

[0514]

[0515]

[0516]

[0517]

[0518]

[0519]

[0520]

[0521]

[0522]

[0523]

[0524]

[0525]

[0526]

[0527]

[0528]

[0529]

[0530]

[0531]

[0532]

[0533]

[0534]

[0535]

[0536]

[0537]

[0538] In summary, the multi-stage decision optimization model for DC channel renewable energy consumption considering market transactions has been completed.

[0539] Step 5: Use the surrogate affine approximation technique to solve the optimization model established in Step 4, and output the two-region planning, trading, power generation and transmission dispatch schemes.

[0540] The multi-stage optimization model established in step 4 cannot be directly solved due to the infinite number of constraints in its rescheduling process. Furthermore, The changing installed capacity of new energy sources introduces more nonlinear factors into the model. Therefore, conventional affine strategies cannot directly solve the optimization problem in this invention. We will consider introducing surrogate affine techniques to solve the model.

[0541] First, rewrite the model form. To facilitate subsequent discussion and the development of related algorithms, the multi-stage decision model in step 4 can be rewritten as the following model (P1).

[0542]

[0543] stAx+Eu≤b (36)

[0544]

[0545]

[0546] Where x represents a vector containing DC transmission power, thermal power unit output, capacity, new energy unit output, net energy storage discharge power, receiving end provincial electricity purchase, load trading volume, and load shedding optimization variables, u represents the optimal absorption range of new energy uncertainty, and G represents a multi-stage affine strategy.

[0547] Equation (35) represents the objective function (34) in step 4, Equation (36) represents the constraint conditions without ∈ in step 4, and Equations (37) and (38) represent the inequality constraints and equality constraints with ∈ in the rescheduling process, respectively.

[0548] To improve computational efficiency, we will use surrogate affine transformations to solve the model. We will introduce a set of surrogate variables and surrogate functions:

[0549] 0≤δ LB ≤1, 0≤δ UB ≤1 (39)

[0550] s(U LB U UB )=U UB δ UB -U LB δ LB (40)

[0551] Among them, U LB =diag(u low ), U UB =diag(u up ).

[0552] Next, the relevant constraints are rewritten. Through strong duality and the surrogate affine strategy, the constraints in P1 can be further rewritten to establish the final surrogate affine approximation model. According to strong duality, equation (37) can be rewritten as:

[0553] Kx+π·1≤d (41)

[0554]

[0555] π≥0 (43)

[0556] Here, π is the matrix formed by nonnegative dual multipliers. The new affine proxy policy, in which redistribution can be represented as:

[0557]

[0558] Therefore, according to the above formula, formula (38) represents the following formula, that is, for All of them are:

[0559] Fx=h (45)

[0560]

[0561] Finally, a complete surrogate affine estimation model is established. Based on the above rewriting of the relevant constraints, the final surrogate affine approximation model is modeled as follows:

[0562]

[0563] st(36), (41)-(43), (45)-(46) (48)

[0564] At this point, the surrogate affine estimation model has been established, and problem (P2) is a mixed integer programming problem that can be solved efficiently using existing solvers.

[0565] The invention will be described in more detail below through further embodiments:

[0566] In one embodiment, to enable those skilled in the art to better understand the present invention, this embodiment uses relevant data from the Longdong-Shandong UHVDC project for experimental purposes. This embodiment considers a ten-year scheduling cycle, with a discount rate set at 3%. New energy output and load vary seasonally, and typical load and new energy data for different seasons are derived from real data from Gansu and Shandong provinces. Load and new energy forecast data and uncertainty confidence intervals are taken from historical data at 2-hour intervals. The test environment for this embodiment is: a computer with an Intel(R) Core(TM) i7-10875H CPU, 2.30GHz, 16GB of memory; programming software: MATLAB R2021b; solver: Gurobi 9.5.

[0567] Figure 3 Typical load forecast data for the receiving-end power grid in different seasons are presented. The peak load during winter is set at 10GW. Figure 4 This displays the predicted output and confidence intervals for new energy sources. For each season, peak photovoltaic output occurs at 12:00. Peak wind power output occurs at 15:00 in spring. The confidence interval for photovoltaic output is significantly narrower than that for wind power output, but the utilization period for wind power is longer.

[0568] Figure 5 The dashed lines represent the existing planned curves of the DC transmission line interconnection in different seasons. The traded electricity volumes from spring to winter are 112.8 GWh, 150.6 GWh, 112.8 GWh, and 126.6 GWh, respectively. Other technical and economic parameters regarding the sending and receiving ends and the DC transmission line, as well as the application settings for each trading entity, are shown in Tables 1 and 2 in this embodiment.

[0569] Table 1. Technical and economic parameters for the embodiments.

[0570]

[0571] Table 2. Reporting data of each transaction entity in the example.

[0572]

[0573]

[0574] To verify the effectiveness of the proposed method, this embodiment compares the experimental results of the proposed algorithm with those of another method for solving related problems, the implicit decision-making method. The implicit decision-making method ensures the robustness and unpredictability of the solution by selecting vertex scenarios and pre-specifying constraints. The embodiment conducted the following three sets of experimental cases and tested the weight values ​​of one set of objective functions.

[0575] Case 1: The power transmission of the DC channel tie line is determined by the existing planning curve, and there is no rescheduling strategy.

[0576] Case 2: The power transmitted by the DC channel tie line is determined by the optimization algorithm, without a rescheduling strategy.

[0577] Case 3: The power transmitted by the DC channel tie line is determined by an optimization algorithm, and a rescheduling strategy is introduced.

[0578] Taking ω2 = 1 as the weight of the objective function as an example, Figure 6 The figure shows the minimum overall cost obtained by two methods in three case studies. As can be seen from the figure, the cost of Case 3 is significantly lower than the other experiments. This is because Case 3 has higher system flexibility than the other groups. The receiving-end flexibility resources, namely energy storage and demand-side response load shedding, can also play a role in absorbing the fluctuations of renewable energy at the sending end during rescheduling, improving the renewable energy absorption capacity at the sending end, thereby reducing the overall system cost. In Case 2, due to the lack of such flexibility resources, the required overall cost is higher than in Case 3. Compared with Case 1, Case 2 introduces DC channel transmission power optimization. The DC channel transmission power can be adjusted at different times to adapt to the fluctuations of renewable energy at the sending end, reducing the curtailment of wind and solar power, and lowering the total operating cost of the power system. Figure 5 As shown, the optimized curve, compared to the existing curve, delivers higher power during midday in spring, autumn, and winter, enabling the transmission of more photovoltaic power. This increases the renewable energy consumption boundary at the sending end, thereby reducing overall costs. The above results and analysis demonstrate that the flexibility of resource allocation and DC transmission power adjustment contributes to renewable energy consumption, reduces overall costs, and improves economic efficiency.

[0579] Furthermore, the results show that the surrogate affine approximation algorithm used in this invention is superior to the implicit decision-making method. For example, in Case 3, the results corresponding to the method of this patent and the implicit decision-making method are $44.81 billion and $50.02 billion, respectively. This is because the surrogate affine approximation algorithm provides a larger safety margin for flexibility resources, thereby reducing the overall system cost. Figure 7 , Figure 8The images respectively illustrate the energy storage reschedulable capacity and its safety boundary under the implicit decision-making method and the method proposed in this patent, under the same critical scenario. The dashed line represents the reschedulable capacity, which is confined to a safe region by the safety boundary. As can be seen from the images, the safe region corresponding to the implicit decision-making method is significantly smaller than that of the surrogate affine approximation algorithm used in this invention. This is mainly because the implicit decision-making method pre-defined constraints limit the feasible region of rescheduling to ensure robustness and unpredictability. Conversely, the method proposed in this invention uses a surrogate affine strategy as the rescheduling strategy, without any pre-specified constraints or limits on the rescheduling process. In summary, a tighter safe region affects the feasible region of the problem; therefore, the surrogate affine strategy used in this invention is a better solution method.

[0580] A DC channel multi-stage wind and solar energy absorption device includes,

[0581] A DC transmission line, with one end connected to the sending-end power grid and the other end connected to the receiving-end power grid.

[0582] The data acquisition unit acquires data from the DC channel and power system characteristic data from both the sending and receiving ends.

[0583] A modeling unit, connected to the DC channel and the acquisition unit, establishes models of the DC channel, the sending-end power grid, and the receiving-end power grid based on the data from the DC channel and the characteristic data of the sending and receiving power systems. The transmitted power of the DC channel must remain stable at least during the minimum adjustment time of the DC channel. The constraints of the DC channel are as follows:

[0584] DC channel regulation state ramp-up constraint conditions:

[0585]

[0586] Where t is the optimization period. For the set of all optimized time periods, P t dc For the DC channel power transmitted during time period t-1, v t R is a 0-1 variable indicating whether the DC channel's transmission power is adjusted during time period t; it is set to 1 if adjustment is performed and 0 if no adjustment is performed. dc This represents the maximum ramp rate of the DC channel power transmission adjustment, expressed by the 0-1 variable v. t The value of represents and records whether the DC channel transmission power during time period t needs to be adjusted;

[0587] DC channel power transmission upper and lower limit constraints:

[0588]

[0589] in, P dc , These represent the upper and lower limits of the DC channel's transmission power, respectively. This formula indicates that the DC channel's transmission power cannot exceed these limits.

[0590] Minimum settling time constraint for DC channel:

[0591]

[0592] Among them, T m This represents the minimum interval time for DC channel power adjustment. The formula indicates that a minimum interval time is required between two DC channel power adjustments.

[0593] Maximum number of adjustments for DC channel:

[0594]

[0595] Where X is the maximum number of times the DC channel is allowed to adjust its power in a day, this formula indicates that the DC channel can only perform a maximum of X power adjustments per day.

[0596] Daily power transmission constraints for DC channels:

[0597]

[0598] Where Q is the daily transmission power planned in advance by the power supply plan, and Δt represents the stable time period of the transmission power. This formula shows that the transmission mode on that day needs to meet the daily transmission power in the transaction plan.

[0599] The model of the sending-end power grid includes aggregated thermal power units, aggregated new energy units, and aggregated energy storage. The model constraints of the sending-end power grid are as follows:

[0600] Sending-end power balance constraints:

[0601]

[0602] Among them, P t pv P t w , and P t g Let t represent the planned output of photovoltaic units, wind turbine units, net discharge power of energy storage, and thermal power at the sending end during time period t. This formula indicates that the DC transmission power should be balanced with the output of thermal power, new energy units, and net discharge power of energy storage.

[0603] Constraints of aggregated energy storage at the sending end:

[0604] These represent the changes in the energy storage capacity at the sending end and the limiting conditions, respectively.

[0605] It represents the net discharge power limit condition for energy storage at the sending end;

[0606] E 0，s =E T，s , which represents the energy storage power dispatch cycle constraint condition;

[0607] Among them, E t，s E t+1，s , η s α s E represents the energy, capacity, net discharge power upper limit coefficient, and discharge depth of the energy storage resources at the sending end during time periods t and t+1, respectively. 0，s E T，s These represent the amount of energy stored at the sending end at the beginning and end of the scheduling cycle, respectively, and Δt represents the net discharge power of the energy storage during the stable period of the transmission power in this segment.

[0608] Constraints of polymer thermal power units:

[0609] These represent the output of the thermal power unit and the climbing limit conditions, respectively.

[0610] It means that the output of thermal power is equal to the sum of the power supply of each segment;

[0611] It indicates the upper limit of the power supply for each segment;

[0612] Among them, C g ,β,R g , and P represents the thermal power unit capacity, peak shaving depth, maximum ramp rate, power supply and upper limit of the k-th segment, respectively. t g , Δt represents the thermal power output during time periods t and t-1, and the stable time stage of the thermal power unit output during that period.

[0613] Relevant constraints of the integrated new energy unit:

[0614] These represent the planned output of photovoltaic units and wind turbine units, respectively. C represents the predicted output of photovoltaic units and wind turbine units per unit installed capacity during time period t, respectively. pv C w These represent the installed capacity of photovoltaic and wind power, respectively;

[0615] The receiving-end power grid model includes aggregated energy storage and demand-side response. The constraints of the receiving-end power grid are as follows:

[0616] Receiving-end power balance constraints:

[0617]

[0618] Among them, P t l P t ls , and Let M and V represent the load demand at the receiving end, the load shedding amount, the electricity demand of the demand side in the mth segment, the net discharge power of the energy storage at the receiving end, and the electricity supply of the supplier in the vth segment at the receiving end, respectively. M represents the set of load demand segments at the demand side, m represents the corresponding segment number, and V is the set of electricity supply segments at the receiving end, v also represents its segment number.

[0619] This formula shows that the sum of the receiving end load demand, the demand side power demand, and the demand side load shedding is equal to the sum of the receiving end energy storage net discharge power, the DC channel transmission power, and the supplier power supply.

[0620] Introducing relevant constraints for aggregated energy storage at the receiving end:

[0621] These represent the changes in the energy storage capacity at the receiving end and the limiting conditions, respectively.

[0622] The net discharge power limitation for receiving-end energy storage;

[0623] E 0，r =E T，r This indicates the constraints of the energy storage power dispatch cycle;

[0624] Among them, E t，r E t+1，r , η r α r E represents the energy, capacity, upper limit coefficient of net discharge power, and depth of discharge of the receiving-end energy storage during periods t and t+1, respectively; Δt represents the net discharge power of the energy storage during the steady-state period of the transmitted power in this segment. 0，r E T，r These represent the receiving-end energy storage capacity during the initial and final optimization periods, respectively, and T represents the total number of optimization periods.

[0625] Demand-side response constraints:

[0626] Where γ represents the upper limit coefficient for load shedding; this formula indicates the limit of load shedding on the demand side at the receiving end.

[0627] Receiving-end power demand constraints:

[0628]

[0629] in, This represents the upper limit of the electricity demand in the m-th segment. This formula indicates the limit of the electricity demand of the receiving end as the electricity demander.

[0630] Introduce receiving-end power supply constraints:

[0631] in, This indicates the upper limit of the power supply for segment v;

[0632] The new energy consumption model is established through the following sub-steps:

[0633] A Gaussian distribution is introduced to model the uncertainty of the prediction bias of new energy power output. The actual power output of photovoltaic units and wind turbine units is expressed as follows:

[0634]

[0635]

[0636] in, These represent the actual output, predicted expected output, and predicted deviation of the photovoltaic unit during time period t, respectively. Let represent the actual output, predicted expected output, and predicted deviation of the wind turbine at time t, respectively. Let N represent a normal distribution conforming to the corresponding parameters, and σ1 2 , σ2 2 These represent the variance of the uncertainty;

[0637] When renewable energy sources fluctuate, the renewable energy consumption model is as follows:

[0638]

[0639] in, These represent the decision values ​​of thermal power units, sending-end energy storage, receiving-end energy storage, DC transmission power, and load shedding power during the rescheduling process, respectively. τ represents the corresponding optimization time stage, and G... t，τ This represents the affine policy that maps uncertainty to the rescheduling decision value, ∈ t This indicates uncertainty regarding new energy sources;

[0640] The modeling process for the DC channel renewable energy consumption decision model is as follows:

[0641] The objective function is determined as follows:

[0642] max∑(C pv +C w ),

[0643]

[0644] These represent maximizing new energy installed capacity and minimizing overall cost, which includes sending-end thermal power transaction fees, load shedding compensation, receiving-end electricity purchase costs, load transaction fees, and the construction costs of wind and solar turbines and energy storage. F g F ls F n F lt Let F represent the cost functions for thermal power transactions, load shedding, electricity seller transactions, and load transactions, respectively. pv F w F sto C represents the construction costs of photovoltaic units, wind turbine units, and energy storage, respectively. sto Indicates the installed capacity of energy storage at both the sending and receiving ends.

[0645] The constraints are determined as follows:

[0646]

[0647]

[0648]

[0649]

[0650]

[0651]

[0652]

[0653]

[0654]

[0655]

[0656]

[0657]

[0658]

[0659]

[0660]

[0661]

[0662]

[0663]

[0664]

[0665]

[0666]

[0667]

[0668]

[0669]

[0670]

[0671] in These represent the upper and lower limits of the uncertainty range for photovoltaic power output, respectively. These represent the upper and lower limits of the uncertainty range of wind power output, respectively. The upper and lower limits of the confidence interval for the prediction deviation of power output from photovoltaic and wind power, two new energy sources; These represent the uncertain output of photovoltaic and wind power, respectively. These represent the projected expected output of photovoltaic and wind power, respectively. This indicates the optimal range for absorbing uncertainty.

[0672] In a preferred embodiment of the DC channel multi-stage wind and solar power integration device, the DC channel data includes the DC channel transmission power limit, daily transmission power, daily maximum adjustment frequency, and minimum adjustment time. The power system characteristic data at the sending and receiving ends includes wind and solar power output forecast, output forecast confidence interval, load forecast, power supply reported by power suppliers, power demand reported by power demanders, load reduction ratio, thermal power unit ramping capacity, peak shaving depth, energy storage discharge depth, and charge / discharge power limit.

[0673] In a preferred embodiment of the DC-channel multi-stage wind and solar energy integration device, the decision model for DC-channel renewable energy integration is solved based on surrogate affine approximation, wherein...

[0674] The decision model is rewritten as model P1 as follows:

[0675]

[0676] stAx+Eu≤b,

[0677]

[0678]

[0679] Where x represents optimization variables including DC transmission power, thermal power unit output, capacity, new energy unit output, net energy storage discharge power, electricity seller transaction volume, load transaction volume, and load shedding, ∈ represents the uncertainty of new energy output, u represents the optimal absorption range of new energy uncertainty, and G represents the multi-stage affine strategy; where c represents the relevant parameters in the objective function, A, E, b represent the relevant constraint parameters without ∈ in step 4, K, L, M, d represent the relevant parameters of inequality constraints with ∈ in the rescheduling process, and F, H, J, h represent the relevant parameters of equality constraints with ∈ in the rescheduling process;

[0680] Introducing proxy variables and proxy functions:

[0681] 0≤δ LB ≤1, 0≤δ UB ≤1,

[0682] s(U LB U UB )=U UB δ UB -U LB δ LB ,

[0683] Where, δ LB δ UB For the introduced proxy variable, U LB =diag(u low ), U UB =diag(u up ), diag represents a diagonal matrix with the corresponding elements as diagonal elements, s represents the constructed corresponding surrogate function, u up u low These represent the upper and lower limits of the uncertainty range for new energy sources, respectively.

[0684] Using strong duality and the surrogate affine strategy, we obtain the following based on strong duality:

[0685] Kx+π·1≤d,

[0686]

[0687] π≥0,

[0688] Where π is the matrix formed by nonnegative dual multipliers. The new affine proxy policy is represented as follows:

[0689]

[0690] That is to All of them are:

[0691] Fx = h,

[0692]

[0693] Establish a proxy affine approximation model:

[0694]

[0695] stAx+Eu≤b

[0696] Kx+π·1≤d

[0697]

[0698] π≥0

[0699] Fx = h

[0700]

[0701] At this point, the surrogate affine estimation model has been established, and the solver solves problem P2.

[0702] In a preferred embodiment of the DC-channel multi-stage wind and solar power integration device, based on the linear relationship between the output of new energy sources and their capacity, The confidence intervals are respectively represented as The confidence interval for the output of the new energy generating units has been further rewritten as follows:

[0703]

[0704]

[0705] in, These represent the uncertain output of wind power and solar power, respectively. Let these represent the expected output forecasts for wind power and solar power, respectively. The set of variable uncertainties is defined as follows:

[0706]

[0707] in, These represent the upper and lower limits of the uncertainty interval, respectively. This is referred to as the optimal absorption range for uncertainty, which varies with the installed capacity of new energy sources. To ensure that fluctuations in new energy output are included within this optimal absorption range, the following constraints are introduced.

[0708]

[0709]

[0710]

[0711]

[0712] Among them, P t pv P t w This indicates the actual output of photovoltaic units and wind turbine units. These represent the upper and lower limits of the uncertainty range for photovoltaic power output, respectively. These represent the upper and lower limits of the uncertainty range of wind power output, respectively.

[0713] In a preferred embodiment of the DC channel multi-stage wind-solar energy absorption device, in order to simultaneously process the objective function, the following multi-objective function is constructed:

[0714] ,

[0715] Where ω1 and ω2 are weighting coefficients, and ω1+ω2=1.

[0716] Furthermore, the present invention discloses a computer-readable storage medium configured to perform the methods described in the preceding embodiments.

[0717] Furthermore, the present invention also discloses an electronic device comprising:

[0718] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0719] When the processor executes the program, it implements the methods described in the preceding embodiments.

[0720] 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 implemented 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. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0721] It should be understood that each block of a flowchart and / or block diagram, and combinations of blocks in a flowchart and / or block diagram, 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 device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 The 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 operate 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 functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus 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.

[0722] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A multi-stage wind-solar energy consumption method for DC channels, characterized in that, include: S100: Acquire data from the DC channel and acquire characteristic data of the power system at the sending and receiving ends, wherein one end of the DC channel is connected to the sending power grid and the other end is connected to the receiving power grid; S200: Based on the data from the DC channel and the characteristic data of the sending and receiving power systems, establish models for the DC channel, the sending-end power grid, and the receiving-end power grid; S300: Establish a decision model for the consumption of new energy in DC channels based on the models of DC channels, sending-end power grids, and receiving-end power grids; S400: Solve the decision model for renewable energy consumption in the DC channel, and determine the planned operation of the power results in the sending-end grid and receiving-end grid based on the solution results, so as to realize the multi-stage wind and solar power consumption in the DC channel; The decision model for renewable energy consumption in the DC channel is rewritten as follows regarding problem P1: ， ， ， ， in, This represents a vector containing DC transmission power, thermal power unit output and capacity, new energy unit output, net energy storage discharge power, electricity seller transaction volume, load transaction volume, and load shedding optimization variables. This indicates the uncertainty of new energy output; The optimal absorption range representing the uncertainty of new energy sources; This indicates a multi-stage affine strategy; A vector representing the relevant parameters in the objective function; Indicates that it does not contain Relevant constraint parameters; This indicates the redistribution process of traditional controllable energy sources after the emergence of uncertainties in new energy sources. The parameters related to the inequality constraints, This indicates the redistribution process of traditional controllable energy sources after the emergence of uncertainties in new energy sources. The parameters related to the equality constraints; Solving the decision model for renewable energy consumption in the DC channel includes: For the aforementioned decision-making model for renewable energy consumption in the DC channel, proxy variables and proxy functions are introduced: ， ， in, , This indicates the introduced proxy variable. , , This represents a diagonal matrix with the corresponding elements as diagonal elements. This represents the corresponding proxy function that is constructed. , These represent the upper and lower limits of the optimal absorption range for new energy uncertainties, respectively. The decision model for renewable energy consumption in the DC channel is processed using strong duality and a proxy affine strategy, wherein: Based on strong duality, we obtain: ， ， ， in, Let represent the matrix formed by nonnegative dual multipliers. The new surrogate strategy, in which the rescheduling process of traditional controllable energy sources after the introduction of uncertainty in new energy sources is represented as: ， right All of them have: ， ， Establish the following surrogate affine approximation model for problem P2: ， ， The surrogate affine approximation model for problem P2 is solved to achieve a simplified solution for the DC channel renewable energy consumption decision model for problem P1.

2. The multi-stage wind and solar energy absorption method for DC channels according to claim 1, wherein: The aforementioned multi-stage wind and solar power integration method for DC channels is designed to integrate renewable energy sources within the confidence interval of the output of photovoltaic and wind power generation units as defined below: ， ， in, Represents a set of time periods. Indicates the corresponding segment number, , These represent the actual output of photovoltaic and wind power, respectively. , These represent the projected expected output of photovoltaic and wind power, respectively. , These represent the installed capacity of photovoltaic and wind power, respectively. , These represent the upper limits of the confidence intervals for the predicted output deviation of photovoltaic and wind power, respectively. , These represent the lower bounds of the confidence intervals for the predicted output deviations of photovoltaic and wind power, respectively. Define a set by a set of variable uncertainties. The optimal absorption range of new energy uncertainty during the mid-t period is as follows: ， in, , These represent the lower and upper limits of the uncertainty interval, respectively. Represents a set The optimal absorption range of new energy uncertainties during the mid-t period, which varies with the installed capacity of new energy. This indicates uncertainty regarding new energy sources; The following constraints are introduced to ensure that Including fluctuations in the output of all new energy sources: ， ， ， ； in, These represent the upper and lower limits of the uncertainty range for photovoltaic power output, respectively. These represent the upper and lower limits of the uncertainty range of wind power output, respectively.

3. The multi-stage wind and solar energy absorption method for DC channels according to claim 1, wherein: The following multi-objective function is constructed to introduce an optimization objective into the decision model for renewable energy consumption in DC channels, so as to maximize the installed capacity of renewable energy and minimize the overall cost: ， in, These are the weighting coefficients. ; , , , Let these represent the cost functions for thermal power transactions, load shedding, electricity seller transactions, and load transaction costs, respectively. , , These represent the construction costs of photovoltaic units, wind turbine units, and energy storage, respectively. These represent the installed capacity of photovoltaic and wind power, respectively; Indicates the installed capacity of energy storage at both the sending and receiving ends; express Time period, power supplier The thermal power supply of the section, Indicates the electricity supplier's Supply range; , and They represent Demand-side response load shedding during the period, the first Demand side electricity demand and receiving side The amount of electricity supplied by the supplier in this section.

4. The multi-stage wind and solar energy absorption method for DC channels according to claim 1, wherein: In the decision-making model for renewable energy consumption in DC channels, the transmission power of DC channels remains stable at least during the minimum adjustment time of DC channels.

5. The multi-stage wind and solar energy absorption method for DC channels according to claim 1, wherein: In the decision-making model for renewable energy consumption in DC transmission channels, the constraints at the sending and receiving ends include: Power balance constraints at the sending end, energy storage constraints at the sending end, constraints on thermal power units, and related constraints on new energy units; The constraints include: power balance at the receiving end, energy storage-related constraints at the receiving end, demand-side response constraints, power demand constraints at the receiving end, and power supply constraints at the receiving end.

6. The multi-stage wind and solar energy absorption method for DC channels according to claim 1, wherein: The constraints of the DC channel renewable energy consumption decision model are: ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ； in: Indicates in The 0-1 variable indicating whether the DC channel during a time period needs to adjust its transmission power is set to 1 if adjustment is needed and 0 if no adjustment is needed. This indicates the maximum ramp conversion rate for adjusting the power delivery of the DC channel; Represents a set of time periods. Indicates the corresponding segment number; Indicates the minimum interval time for DC channel power adjustment; This represents the maximum number of times the DC channel is allowed to adjust its power in a single day. , , , , These represent the decision values ​​for thermal power units, sending-end energy storage, receiving-end energy storage, DC transmission power, and load shedding power during the rescheduling process of traditional controllable energy after the emergence of uncertainties in new energy sources. and These represent the lower and upper limits of the DC channel's transmission power, respectively. Indicates the corresponding scheduling time phase; This represents an affine strategy that maps uncertainty to the values ​​of rescheduling decisions; This indicates uncertainty regarding new energy sources; This refers to the daily power transmission volume pre-planned by the power supply schedule for that day; This indicates the stabilization time phase of the transmitted power in this segment; This indicates the maximum ramp rate of the thermal power unit; , , , and They represent Time period load demand, demand-side response load shedding, and the first Demand side electricity demand, receiving end net discharge power of energy storage and receiving end first The power supply from the supplier in this section; This represents the set of demand-side load demand segments. Indicates the corresponding segment number; This represents the set of power segments from the receiving end of the supplier. Indicates the corresponding segment number; This represents the upper limit coefficient for load shedding; Indicates the first Section of electricity demand The upper limit; Indicates the receiving end Section supplier's power supply The upper limit; , , These represent the receiving-end energy storage capacity, the upper limit coefficient of net discharge power, and the depth of discharge, respectively. This represents a set of segments of electricity suppliers. Indicates the corresponding segment number; , and These represent the capacity of the thermal power unit, the peak-shaving depth, and the... The upper limit of the power supply from the power supplier in this section; These represent the installed capacity of photovoltaic and wind power, respectively; , , These represent the capacity of the energy storage resources at the sending end, the upper limit coefficient of the net discharge power, and the depth of discharge, respectively. This indicates the power level of the energy storage resources at the sending end at the start of the scheduling cycle. and They represent The planned net discharge power of energy storage and thermal power output at the sending end of the time period are specified, with the subscript 's' indicating the sending end. for DC channel power transmission during specific time periods; This represents the thermal power supply from the power supplier in the kth segment of time period t. These represent the upper and lower limits of the uncertainty range for photovoltaic power output, respectively. These represent the upper and lower limits of the uncertainty range of wind power output, respectively. The upper and lower limits of the confidence interval for the prediction deviation of power output from photovoltaic and wind power, two new energy sources; These represent the uncertain output of photovoltaic and wind power, respectively. Represents a set The optimal absorption range of new energy uncertainties during the mid-t period.

7. The multi-stage wind and solar energy absorption method for DC channels according to claim 1, wherein, Step S300 includes: S301: Establish a new energy consumption model based on the DC channel model, the sending-end power grid model, and the receiving-end power grid model; among them, When renewable energy sources fluctuate, the renewable energy consumption model is as follows: ， in, , , , , These represent the decision values ​​for thermal power units, sending-end energy storage, receiving-end energy storage, DC transmission power, and load shedding power during the rescheduling process of traditional controllable energy sources after the emergence of uncertainties in new energy sources. This indicates the corresponding scheduling time phase. This represents an affine policy that maps uncertainty to the values ​​of rescheduling decisions. This indicates uncertainty regarding new energy sources; S302: Establishing a decision-making model for DC channel renewable energy consumption based on the renewable energy consumption model; where; In the decision-making model for renewable energy consumption in DC channels, a Gaussian distribution is introduced to model the uncertainty of renewable energy output prediction deviation.

8. The multi-stage wind and solar energy absorption method for DC channels according to claim 1, wherein, The objective function for modeling the decision-making model for renewable energy consumption in DC channels is modeled as follows: , , in, The objective function for minimizing overall costs includes the transaction costs of thermal power plants at the sending end, load shedding compensation, power purchase costs at the receiving end, load transaction costs, and the construction costs of wind and solar power units and energy storage. These represent the installed capacity of photovoltaic and wind power, respectively; , , , Let these represent the cost functions for thermal power transactions, load shedding, electricity seller transactions, and load transaction costs, respectively. , , These represent the construction costs of photovoltaic units, wind turbine units, and energy storage, respectively. Indicates the installed capacity of energy storage at both the sending and receiving ends; express Time period, power supplier The thermal power supply of the section, Indicates the electricity supplier's Supply range; , and They represent Demand-side response load shedding during the period, the first Demand side electricity demand and receiving side The amount of electricity supplied by the supplier in this section.

9. A DC-channel multi-stage wind and solar energy absorption device, characterized in that, include: The acquisition unit is used to acquire data from the DC channel and acquire characteristic data of the power systems at the sending and receiving ends, wherein one end of the DC channel is connected to the sending-end power grid and the other end is connected to the receiving-end power grid; The power grid model building unit is used to build models of the DC channel, the sending-end power grid, and the receiving-end power grid based on the data of the DC channel and the characteristic data of the sending and receiving end power systems. The DC channel renewable energy consumption decision model establishment unit is used to establish a DC channel renewable energy consumption decision model based on the DC channel model, the sending-end grid model, and the receiving-end grid model. The execution unit is used to solve the decision model for renewable energy consumption in the DC channel, and to determine the planned operation of the power results in the sending-end grid and the receiving-end grid based on the solution results, so as to realize the multi-stage wind and solar power consumption in the DC channel. The decision model for renewable energy consumption in the DC channel is rewritten as follows regarding problem P1: ， ， ， ， in, This represents a vector containing DC transmission power, thermal power unit output and capacity, new energy unit output, net energy storage discharge power, electricity seller transaction volume, load transaction volume, and load shedding optimization variables. This indicates the uncertainty of new energy output; The optimal absorption range representing the uncertainty of new energy sources; This indicates a multi-stage affine strategy; A vector representing the relevant parameters in the objective function; Indicates that it does not contain Relevant constraint parameters; This indicates the redistribution process of traditional controllable energy sources after the emergence of uncertainties in new energy sources. The parameters related to the inequality constraints, This indicates the redistribution process of traditional controllable energy sources after the emergence of uncertainties in new energy sources. The parameters related to the equality constraints; Solving the decision model for renewable energy consumption in the DC channel includes: For the aforementioned decision-making model for renewable energy consumption in the DC channel, proxy variables and proxy functions are introduced: ， ， in, , This indicates the introduced proxy variable. , , This represents a diagonal matrix with the corresponding elements as diagonal elements. This represents the corresponding proxy function that is constructed. , These represent the upper and lower limits of the optimal absorption range for new energy uncertainties, respectively. The decision model for renewable energy consumption in the DC channel is processed using strong duality and a proxy affine strategy, wherein: Based on strong duality, we obtain: ， ， ， in, Let represent the matrix formed by nonnegative dual multipliers. The new surrogate strategy, in which the rescheduling process of traditional controllable energy sources after the introduction of uncertainty in new energy sources is represented as: ， right All of them have: ， ， Establish the following surrogate affine approximation model for problem P2: ， ， The surrogate affine approximation model for problem P2 is solved to achieve a simplified solution for the DC channel renewable energy consumption decision model for problem P1.

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