Multi-element complementary base delivery consumption optimization scheduling method considering channel constraint

Abstract

The invention belongs to the technical field of optimal dispatching operation of a power system, and particularly relates to a multi-element complementary base delivery consumption optimal dispatching method considering channel constraints. According to the technical scheme, the multi-element complementary base delivery consumption optimization scheduling method considering the channel constraint comprises the following steps: S1, establishing a water-wind-light coordinated optimization scheduling strategy; s2, considering constraint conditions including channel power balance and safety, and performing power transmission capability optimization based on a power transmission channel feasible region; and S3, establishing a multivariate complementary base delivery consumption optimization model considering channel constraint conditions and power generation benefit maximization based on the analysis in the step S2. The invention provides a multi-element complementary base delivery consumption coordinated optimization scheduling method considering channel constraints and power generation benefits, so as to solve the problem of demand balance between a feasible region of a power transmission channel and new energy power generation, and enable the operation profit of the system to be the highest.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimized dispatching and operation of electric power systems, and in particular relates to a multi-complementary base transmission and consumption optimization dispatching method taking channel constraints into account. Background Art

[0002] With the introduction of the "dual carbon" goals, the demand for development and innovation in the energy structure is increasing. Clean energy consumption should be closely integrated with these goals, with the continuous development of new power sources to promote the transformation of the energy structure towards a green and low-carbon direction. From the perspective of power supply for West-to-East Power Transmission, clean energy sources such as water, gas, wind, and solar energy have complementary characteristics and are the sources of clean and stable electricity for power transmission projects. However, the lagging construction of transmission channels and the random volatility of new energy sources have increased the difficulty of transmission and consumption, resulting in the existing transmission channel capacity being insufficient to meet the demand for new energy transmission. There is an urgent need to find efficient transmission optimization methods to address the underutilization of transmission channels caused by random fluctuations in new energy sources and loads. On the other hand, leveraging the superior regulation capabilities of hydropower and gas-fired power can help balance the fluctuating characteristics of wind and photovoltaic power, enhancing the grid's ability to absorb these clean energy sources. Therefore, this paper proposes a coordinated optimization scheduling model for transmission and consumption based on the transmission capacity of multiple complementary bases. This model, which takes into account constraints such as channel power and safety, alleviates the insufficient transmission and consumption capacity of new energy sources and improves the economic efficiency and robustness of system operation.

[0003] Currently, most methods use transferable capacity and equivalent network constraints to describe the power feasible region of transmission channels. However, these methods do not fully consider the tight coupling between grid security constraints, resulting in insufficient calculation accuracy. Therefore, it is necessary to seek efficient and stable transmission optimization methods in the context of "dual carbon".

[0004] The random fluctuations exhibited by renewable energy sources cause their output to fluctuate between zero and rated output. When renewable energy generation is high, it cannot be fully absorbed due to channel limitations. When renewable energy generation is low, the transmission capacity of transmission channels is underutilized, limiting the ability to transmit and absorb renewable energy. Currently, there is little research on the balance between the feasible domain of transmission channels and renewable energy generation demand. Further exploration is needed to leverage the complementary advantages of multiple sources, coordinate hydropower operations, and achieve cross-provincial energy consumption. Summary of the Invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the purpose of the present invention is to provide a coordinated optimization scheduling method for transmission and consumption of multiple complementary bases taking into account channel constraints and power generation benefits, so as to solve the demand balance problem between the feasible domain of transmission channels and renewable energy power generation, so as to maximize the operating profit of the system.

[0006] The technical solution adopted in the present invention is:

[0007] A multi-complementary base transmission and consumption optimization scheduling method taking into account channel constraints includes the following steps:

[0008] S1: Establish a coordinated optimization scheduling strategy for water, wind and solar power;

[0009] S2: Considering the constraints including channel power balance and safety, optimize the transmission capacity based on the feasible region of the transmission channel;

[0010] S3: Based on the analysis of step S2, a multi-complementary base transmission and consumption optimization model is established that takes into account channel constraints and maximizes power generation benefits.

[0011] Based on my country's "West-to-East Gas Transmission" policy, this paper establishes an optimization model for the transmission and consumption of multiple complementary bases, taking into account channel constraints. First, the energy output characteristics of the multiple complementary bases are analyzed. To improve the transmission capacity of the transmission channel, a transmission capacity optimization method based on the feasible region of the transmission channel is proposed. A peak-valley segmented assessment strategy for residual load is proposed, taking into account the fluctuation of residual load throughout the day and the penalty for curtailed power. Finally, an optimization model for the transmission and consumption of multiple complementary bases is established, taking into account channel constraints and maximizing power generation benefits.

[0012] As a preferred solution of the present invention, in step S1, the water-wind-solar coordinated optimization scheduling strategy is:

[0013] By utilizing the daily and long-term regulation capabilities of the hydropower station, the water-wind-solar hybrid system compensates nearby photovoltaic and wind power stations in advance and fully utilizes the hydropower station's excess power generation capacity;

[0014] Utilize the reservoir's regulating capacity to adjust the hydropower output process, reducing hydropower output when photovoltaic and wind power output are high and the combined hydropower, wind, and solar power exceed the load; and increasing hydropower output when the combined photovoltaic and wind power output is low and electricity demand is high. If hydropower output has not yet reached balance at this time, it is necessary to continue to increase hydropower output during periods of low electricity demand until hydropower output reaches intraday balance;

[0015] When the total output of photovoltaic and wind power is large and electricity demand is high, in order to absorb more photovoltaic and wind power, hydropower output may be reduced to the lower limit of instantaneous output, and some wind and photovoltaic power will be abandoned to ensure that hydropower output does not fall below the lower limit of instantaneous output. When the total output of photovoltaic and wind power is small and the water volume on that day is large, after the peak of electricity demand ends, hydropower output will be increased to balance the hydropower production capacity on that day, but it will not exceed the expected output.

[0016] While ensuring that the daily hydropower generation benefits are not lost and the hydropower output does not exceed expectations, hydropower can increase its output capacity every day; during the period when the combined output of water, wind and solar energy exceeds the transmission channel, hydropower will reduce its output.

[0017] As a preferred solution of the present invention, in step S2, the optimization of the power transmission capacity based on the feasible region of the power transmission channel specifically includes the following steps:

[0018] S21: Method for determining the feasible region of transmission channels: When constructing the computational model of the feasible region of transmission channels in a sub-region, only the operating boundaries of multiple consecutive time periods are selected as key data to ensure that all considered constraints are part of the complete subset of scheduling plan constraints, thereby reducing modeling complexity.

[0019] S22: Analysis of transmission capacity for inter-regional transmission: The operation of the power grid needs to meet multiple types of constraints, so that the executable transmission power of different time periods and different transmission channels has a coupling relationship.

[0020] As a preferred embodiment of the present invention, step S21 specifically includes the following steps:

[0021] S211: Setting of time period subset:

[0022] Original H T The scheduling period is divided into H T-n There are n+1 time period subsets, each of which consists of n+1 consecutive time periods. Two adjacent time period subsets differ in only one time period, while the rest remain the same. Each subset contains n+1 time periods, of which n time periods overlap and only one time period differs.

[0023] S212: Construction of the feasible domain sub-model corresponding to the time period subset:

[0024] Considering the coupling constraints within the time period subset, the feasible region sub-model of the sub-regional power grid is constructed; when considering the time period coupling relationship contained in the ξ-1 time period subset to obtain the feasible region of time period i, the objective function can be expressed as:

[0025]

[0026] Where, P TL,i is the sequence matrix of the power received by the nodes of the transmission channel during period i;

[0027] column vector is the hyperplane coefficient of the feasible region boundary corresponding to the search of the v-th vertex in time period i; the power balance constraint can be expressed as:

[0028]

[0029] The maximum and minimum output constraints of the unit can be expressed as:

[0030] P G,min ≤P G,j ≤P G,max ;

[0031] Where, P G,max 、P G,min is the column vector of the maximum and minimum output of the units in the sub-region;

[0032] The climbing constraint can be expressed as:

[0033] -D G ≤P G,j+1 -P G,j ≤U G i≤j≤i+n-2;

[0034] Where U G is the column vector of the unit's ramp limit; D G is the column vector of the unit's down-slope limit;

[0035] The safety constraint can be expressed as:

[0036]

[0037] Where G G and G TL is the active sensitivity matrix of the nodes where the units, transmission channels and loads are located; P S,max is the column vector of safety constraint limits; e H is a column vector with the same dimension as the number of scheduling periods and all elements are 1;

[0038] S213: Solution of the feasible domain sub-model:

[0039] For the subset of the ξ-1 time period, n+1 vertex search models can be constructed. After the feasible regions of the n+1 time periods are obtained by the vertex search method, the feasible region of the transmission channel in time period i considering the coupling constraints contained in the subset of the ξ-1 time period is expressed as Similarly, the feasible region of the transmission channel in period i considering the coupling constraints contained in the subset of period ξ can be calculated:

[0040] S214: Synthesis of feasible regions for each time period:

[0041] The feasible regions of the transmission channels in the same period obtained under the coupling constraints of different period subsets are intersected to obtain the final feasible region of the transmission channel in the sub-region of period i, which is expressed as:

[0042]

[0043] As a preferred embodiment of the present invention, step S22 is specifically as follows:

[0044] The scheduling feasible region is expressed as:

[0045]

[0046] Where N G is the number of units; N TL is the number of transmission channels and time periods; N H is the number of time periods;

[0047] The feasible region of the transmission channel obtained by dimensionality reduction projection is expressed as:

[0048]

[0049] As a preferred solution of the present invention, in step S3, the multi-complementary base transmission and consumption optimization scheduling model taking into account channel constraints includes objective function 1 and objective function 2;

[0050] Objective function 1: Utilize the regulation performance of the multi-component base to smooth the residual load process of the power grid; by minimizing the residual load variance of the objective function, the residual load curve will become a horizontal line;

[0051] Objective function 2: Set the optimized objective function to maximize the benefit of external transmission and consumption, minimize the amount of abandoned water, abandoned solar power, and abandoned wind in the system's external transmission and consumption, and add a penalty for abandoned electricity to the benefit.

[0052] As a preferred embodiment of the present invention, the first objective function is:

[0053]

[0054] Where, L is the number of transmission channels; D k,t and D′ k,t is the system load and residual load of the k-th power grid in time period t; P l,k,t is the power transmitted by transmission channel l to grid k in time period t; F k is the average value of the residual load of the k-th grid from the average absolute value;

[0055] To simplify the solution, the multi-objective optimization problem is transformed into a single-objective problem, and the objective weighting method is adopted. At the same time, the regulated electricity will be sent to the receiving power grid to achieve the clean energy consumption goal:

[0056]

[0057] Where, ω k is the weight value of the k-th power grid; K is the total number of power grids;

[0058] When the power generation of the power station is significantly lower than the load demand, or when the power generation is only during peak hours, the residual load is smoothed during specific periods. According to the characteristics of the grid load, the time is divided into peak and valley periods, and corresponding evaluation functions are established for each period.

[0059] The peak segment objective function can be expressed as:

[0060]

[0061] The valley segment objective function can be expressed as:

[0062]

[0063] Where, k，1 , is the peak period collection of the k-grid; k，2 is the valley period set of the k-grid; w k,1 is the peak period target weight of the k-grid; w k,2 is the target weight of the off-peak period of the k-th power grid;

[0064] The target weights for peak and trough periods are determined by the following formula:

[0065]

[0066] Where, τ k is the grid weight value; r k is the artificial slack variable; η k is the ratio of the peak-to-valley difference of the k-grid to the sum of the peak-to-valley differences of all receiving-end grids.

[0067] As a preferred solution of the present invention, the second objective function is:

[0068]

[0069] Where B H,t is the water abandonment power at time t; P i,k,t The power that power station i sends to grid k; B W,t is the wind power abandoned at time t; B S,t is the abandoned optical power at time t; b H is the penalty coefficient for abandoning water per unit time; b W is the penalty coefficient for abandoning water per unit time; b S is the penalty coefficient for abandoning water per unit time; C ele For the on-grid electricity price.

[0070] As a preferred solution of the present invention, in step S3, the constraints include transmission channel constraints, power and electricity balance constraints, power station and transmission channel power constraints, power station power and electricity constraints, water balance constraints, hydropower station and unit outflow constraints and hydropower station head constraints.

[0071] As a preferred embodiment of the present invention, the constraints are specifically as follows:

[0072] Transmission channel constraints:

[0073] Transmission channel constraints generally refer to the restrictions on transmission lines or transmission channels in a power system, which are used to ensure safe, stable, and reliable operation during power transmission. Transmission channel constraints include the transmission capacity, voltage stability, and current load of the transmission line. Transmission channel constraints are considered in power system planning, operation, and scheduling to ensure the normal operation of the power grid and the reliability of power supply.

[0074] The specific constraints are expressed as follows, which limit the upper and lower limits of the transmission channel power:

[0075] P l，k，min P l，k，t P l，k，max ;

[0076] Where, P l,k,t is the transmission power of transmission channel l at time t; P l,min is the minimum value of the transmission power of transmission channel l; P l,max is the maximum value of the transmission power of transmission channel l;

[0077] The power fluctuation constraint of the transmission channel is usually used to limit the power fluctuation range of the transmission channel. The power fluctuation constraint of the transmission channel is expressed as:

[0078] |P l,t -P l,t-1 |,,ΔP l ;

[0079] Where, ΔP l is the maximum fluctuation amplitude of the transmission power of transmission channel l in adjacent time periods;

[0080] The minimum duration constraint of a transmission channel is usually used to ensure the stability and reliability of the transmission channel. The minimum duration constraint of a transmission channel is expressed as:

[0081] (P l,t-τ+1 -P l,t-τ )(P l,t -P l,t-1 )…0τ=1,2,…,ε l ;

[0082] Where: ε l is the minimum duration that the transmission channel l must operate stably after the power changes;

[0083] Power balance constraints:

[0084] When receiving power, each power grid needs to meet the power ratio constraint, which is specifically expressed as:

[0085]

[0086] Where, σ i,kis the ratio coefficient of power transmission from multi-complementary base i to grid k; P i,k,t The power transmitted from the multi-complementary base i to the k-grid; P l,k,t For the contribution of the diverse and complementary i-base;

[0087] Power plant and transmission channel constraints:

[0088] The power output of the power station and the transmission channel need to be balanced, which can be expressed as:

[0089]

[0090] Power plant power consumption constraints:

[0091] P i,min ≤P i,t ≤P i,max ;

[0092] Where, P i,t Contribute to the diverse and complementary i-base; i,min is the minimum output limit of the multi-complementary base i; P i,max It is the maximum output limit of the multi-complementary i-base;

[0093] Hydropower station output limits:

[0094] P i,H,min ≤P i,H,t ≤P i,H,max ;

[0095] Where, P i,,H,t Provide power for the multi-complementary i base hydropower station; i,H,min is the minimum output limit of the multi-complementary i-base hydropower station; P i,H,max It is the maximum output limit of the hydropower station in the multi-complementary i base;

[0096] Output limit of photovoltaic power station:

[0097] P i,S,min ≤P i,S,t ≤P i,S,max

[0098] Where, P i,S,t Producing power for the multi-complementary i-base photovoltaic power station; i,S,min is the minimum output limit of the multi-complementary photovoltaic power station in base i; P i,S,max It is the maximum output limit of the multi-complementary i-base photovoltaic power station;

[0099] Wind power station output limit:

[0100] P i,W,min ≤P i,W,t ≤P i,W,max

[0101] Where, P i,W,t Power generation for the multi-complementary i-base wind power station; P i,W,min is the minimum output limit of the wind power station in the multi-complementary i-base; P i,W,max It is the maximum output limit of the wind power station in the multi-complementary i-base;

[0102] Water balance constraints:

[0103]

[0104] Where V i,t is the storage capacity of multi-complementary i in period t; I i,t is the inflow flow, unit is m 3 / s; The nth direct upstream hydropower station of multi-complementary i at t-TC n,i Outbound flow rate during the period, unit: m 3 / s;TC n,i is the flow residence time, unit is h;

[0105] Constraints on outbound flow of hydropower stations and units:

[0106]

[0107] Where Q i,t is the outbound flow of the multi-complementary base i in period t, unit: m 3 / s;Q i,t,on is the power generation flow of multi-complementary base i in period t, unit: m 3 / s;q i,t is the water abandonment flow of multi-complementary base i in period t, unit: m 3 / s;Q i,min is the minimum outbound flow limit of the multi-complementary base i, unit is m 3 / s;Q i,max is the maximum outbound flow limit of the multi-complementary base i, unit is m 3 / s;

[0108] V i,min ≤V i,t ≤V i,max ;

[0109] Where V i,min is the minimum storage capacity limit of power station i, unit is m 3 ; V i,max is the maximum storage capacity limit of power station i, unit is m 3 ;

[0110] Head constraints of hydropower stations:

[0111]

[0112] Z tou,i,min ≤Z tou,i,t ≤Z tou,i,max ;

[0113] Where Z tou,i,min is the minimum head limit of power station i, unit is m; Z tou,i,max is the maximum head limit of power station i, in m; Z wei,i,t is the tailwater level of power station i at time t, in m; z i,t is the head loss of power station i at time t, in m.

[0114] This invention proposes a coordinated optimization scheduling strategy for water, wind and solar power, and analyzes the output characteristics of hydropower, wind power and photovoltaic power stations. The basin is rich in wind, light, water and natural gas resources. The annual output of wind power and photovoltaic power varies greatly in winter and spring, and decreases in summer and autumn. The output characteristics have natural complementary advantages in seasons. On this basis, a multi-complementary base of water, wind and solar power is constructed according to the energy characteristics.

[0115] This paper proposes a method and process for optimizing the transmission capacity of transmission channels. It analyzes cross-regional transmission capacity, conducting impact analyses for both single-channel and dual-grid transmission scenarios. It proposes a time-segment subset method and then uses a vertex search method to determine the feasible region of transmission channels. Based on this, it considers the impact of available channel capacity on market clearing outcomes under a path-reduction model, and proposes a method and process for optimizing the transmission capacity of transmission channels.

[0116] This paper proposes a dual-objective optimization model that takes into account the constraints of power transmission channels, with the goal of minimizing the variance of residual load and maximizing the efficiency of power stations based on multiple complementary bases. To ensure the economic and stable operation of the power grid, it is necessary to fully utilize the regulation performance of the multiple complementary bases to smooth the residual load process of the power grid as much as possible. The NSGA-II algorithm is proposed to introduce a new crowding distance formula that considers variance for improvement, and it is used to solve the problem.

[0117] The beneficial effects of the present invention are:

[0118] 1. The coordinated optimization scheduling strategy of water, wind and solar power proposed in this invention fully utilizes the output characteristics of various power sources to effectively reduce the volatility, randomness and intermittency of system output. It also uses the regulating capacity of reservoirs to adjust the hydropower output process, so that when the output of photovoltaic and wind power is large and the total output of water, wind and solar power exceeds the load, the hydropower output is reduced.

[0119] 2. The proposed clean energy curtailment estimation method ensures that hydropower generation benefits are not compromised while ensuring that hydropower output does not exceed expectations. In this scenario, hydropower can increase its output capacity daily to meet daily electricity demand.

[0120] 3. This paper analyzes the transmission capacity of the sub-regional transmission channels, and then considers the impact of multiple power grid interconnection on the determination of the feasible domain. It proposes a method for determining the feasible domain of the transmission channels in each time period from the perspective of power grid operation, so as to obtain the feasible domain of the transmission channels from the perspective of the entire network operation, and establishes a method for optimizing the transmission capacity of the transmission channels.

[0121] 4. The present invention can maximize the transmission and consumption benefits of the multi-complementary base. By adjusting the operation mode of water, wind and solar power generation equipment in different time periods, it aims to achieve the optimal operation of the base, make full use of renewable energy, and at the same time ensure the stability and reliability of the power system, and finely manage the energy output of the multi-complementary base of water, gas, wind and solar.

[0122] 5. This invention aims to minimize the variance of residual load and the amount of clean energy curtailment, taking into account constraints such as access, power, and safety, and constructs an optimization scheduling model. It divides time into peak and valley periods according to the characteristics of the grid load, and establishes corresponding evaluation functions for each period to consider the changes in grid load in more detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0123] Figure 1 It is the basic structural diagram of the multiple complementary base in the lower reaches of the river;

[0124] Figure 2 is the period subset setting diagram;

[0125] Figure 3 This is a schematic diagram of the multiple complementary bases in the lower reaches of the river sending water to Guangdong for consumption;

[0126] Figure 4 This is a schematic diagram of the multiple complementary bases in the lower reaches of the river sending water to Jiangsu for consumption.

[0127] Figure 5 Schematic diagram of the downstream river's multi-complementary base sending water to Zhejiang for consumption

[0128] Figure 6 Comparative diagram of the benefits of Wudongde, a diversified complementary base in the lower reaches of the river

[0129] Figure 7 Comparative diagram of the benefits of Luoxidu, a diversified complementary base in the lower reaches of the river

[0130] Figure 8 This is a comparison chart of the benefits of Baihetan, a diversified complementary base in the lower reaches of the river. DETAILED DESCRIPTION

[0131] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0132] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features therein may be combined with each other unless there is a conflict.

[0133] The multi-component base transmission and consumption optimization scheduling method considering channel constraints in this embodiment includes the following steps:

[0134] S1: Based on domestic and international research on multi-energy complementarity, establish a coordinated optimization scheduling strategy for water, wind and solar power;

[0135] S2: Considering the constraints of channel power balance, safety, etc., conduct transmission capacity analysis based on the feasible region of the transmission channel;

[0136] S3: Based on the above analysis, a multi-complementary base transmission and consumption optimization model is established that takes into account channel constraints and maximizes power generation benefits.

[0137] Coordinated optimization scheduling strategy for water, wind and solar power:

[0138] like Figure 1 As shown, by leveraging the daily and long-term regulation capabilities of hydropower stations, a hydropower-wind-solar hybrid system preemptively compensates nearby photovoltaic and wind power plants and fully utilizes the hydropower station's excess generating capacity, thereby reducing transmission investment costs. This approach aims to meet the grid's ultimate power demand while facilitating the absorption of less stable photovoltaic and wind power. By maximizing the benefits of power generation, hydropower-wind-solar hybridization provides a viable solution for the efficient utilization of clean energy. Since the optimized scheduling methods considered throughout this article are all intraday, this section only analyzes the intraday characteristics of hydropower-wind-solar hybridization.

[0139] Utilizing the reservoir's regulating capacity, hydropower output is adjusted to reduce when photovoltaic and wind power output is high and the combined hydropower, wind, and solar power systems exceed the load. At the same time, to achieve intraday hydropower balance, hydropower output is increased when the combined photovoltaic and wind power output is low and electricity demand is high. If hydropower output has not yet reached equilibrium at this time, it is necessary to continue increasing hydropower output during periods of low electricity demand until intraday hydropower output reaches equilibrium.

[0140] However, while both increasing and decreasing output are subject to certain limits—the upper limit of cascade output is the expected output, and the lower limit is the sum of the minimum outputs of each station—when the total output of photovoltaic and wind power is large and electricity demand is high, hydropower output may be reduced to the lower limit of instantaneous output to absorb more photovoltaic and wind power. This necessitates partially abandoning wind and photovoltaic power to ensure that hydropower output does not fall below the lower limit of instantaneous output, thereby safeguarding downstream ecological water flows. On the other hand, when the total output of photovoltaic and wind power is small and the daily water volume is high, hydropower output can be increased after the peak in electricity demand ends to balance the hydropower production capacity for the day, but it cannot exceed the expected output.

[0141] While ensuring that hydropower generation benefits are not lost and that hydropower output does not exceed expectations, hydropower can increase its daily output capacity. However, during periods when the combined output of hydropower, wind power, and solar power exceeds the transmission channel, hydropower needs to reduce its output to avoid idle photovoltaic and wind power.

[0142] Optimization of power transmission capacity based on the feasible region of power transmission channels:

[0143] By using the feasible region, the channel transmission capacity can be accurately assessed while avoiding conflicts between the power transmission and reception pre-planning and the sub-regional operation constraints. This is not intended to replace the detailed safety verification afterwards.

[0144] 1. Method for determining the feasible region of transmission channels:

[0145] When constructing the computational model for the feasible region of subregional transmission channels, only the operational boundaries of multiple consecutive time periods are selected as key data. This ensures that all considered constraints are part of a complete subset of the scheduling plan's constraints, thereby reducing modeling complexity. The specific steps for calculating the feasible region of subregional transmission channels are detailed below.

[0146] 1) Setting of time period subset:

[0147] Original H T The scheduling period is divided into H T-n There are n+1 consecutive time period subsets in each subset. Between two adjacent time period subsets, only one time period is different, and the rest of the time periods are the same. Figure 2As shown, the adjacent time period subsets ξ-1 and ξ each contain n+1 time periods, of which n time periods overlap and only one time period is different.

[0148] 2) Construction of the feasible domain sub-model corresponding to the time period subset:

[0149] Considering the coupling constraints within the time period subset, the feasible domain sub-model of the sub-regional power grid is constructed. Figure 2 When the time period subset reconstruction strategy is used to obtain the feasible region of time period i by considering the time period coupling relationship contained in the ξ-1 time period subsets, the objective function can be expressed as:

[0150]

[0151] Where, P TL,i is the sequence matrix of the power received by the nodes of the transmission channel in period i.

[0152] column vector The hyperplane coefficient of the feasible region boundary corresponding to the search for the vth vertex in time period i.

[0153] The power balance constraint can be expressed as

[0154]

[0155] The maximum and minimum output constraints of the unit can be expressed as:

[0156] P G,min ≤P G,j ≤P G,max ;

[0157] Where, P G,max 、P G,min is the column vector of the maximum and minimum output of the units in the sub-area.

[0158] The climbing constraint can be expressed as:

[0159] -D G ≤P G,j+1 -P G,j ≤U G i≤j≤i+n-2;

[0160] Where U G is the column vector of the unit's ramp limit; D G is the column vector of the unit's down-climbing limit.

[0161] The safety constraint can be expressed as:

[0162]

[0163] Where G G and G TLis the active sensitivity matrix of the nodes where the units, transmission channels and loads are located; P S,max is the column vector of safety constraint limits; e H is a column vector with the same dimension as the number of scheduling periods and all elements are 1.

[0164] 3) Solving the feasible domain sub-model:

[0165] For the subset of the ξ-1 time period, n+1 vertex search models can be constructed. After the feasible regions of the n+1 time periods are obtained using the vertex search method, the feasible region of the transmission channel in time period i considering the coupling constraints contained in the subset of the ξ-1 time period is denoted as Similarly, the feasible region of the transmission channel in period i considering the coupling constraints contained in the subset of period ξ can be calculated:

[0166] 4) Synthesis of feasible regions for each time period:

[0167] If only using To represent the feasible domain of the sub-regional power grid transmission channel in the scheduling period i, the feasible domain result will not reflect the influence of the coupling characteristics between the subsets of each period (for example, the coupling relationship between period i+n and period i+n-1 cannot be reflected by To this end, the feasible regions of the transmission channels of the same period obtained under the coupling constraints of different period subsets are intersected to obtain the final feasible region of the transmission channel of the sub-region of period i, which is expressed as follows:

[0168]

[0169] 2. Analysis of power transmission capacity for inter-regional transmission:

[0170] The operation of the power grid needs to meet multiple constraints, so that the executable transmission power of different transmission channels in different time periods has a coupling relationship. Based on this, the scheduling feasible domain can be expressed as:

[0171]

[0172] Where N G is the number of units; N TL is the number of transmission channels and time periods; N H The number of time periods

[0173] The feasible region of the transmission channel is obtained by dimensionality reduction projection, which is expressed as follows:

[0174]

[0175] Optimal dispatch model for transmission and consumption of multiple complementary bases taking into account channel constraints:

[0176] 1. Objective function 1:

[0177] To ensure the smooth and economical operation of the power grid, it is necessary to fully utilize the regulation performance of the multiple complementary bases to smooth the residual load process as much as possible. The objective function is to minimize the residual load variance. The goal is to minimize the peak load target of each power grid to zero, which means that the residual load curve will become a horizontal line.

[0178]

[0179] Where, L is the number of transmission channels; D k,t and D′ k,t is the system load and residual load of the k-th power grid in time period t; P l,k,t is the power transmitted by transmission channel l to grid k in time period t; F k is the average value of the residual load of grid k from the average absolute value.

[0180] The above equation represents a multi-objective optimization problem. To simplify the solution, we transform it into a single-objective problem using the objective weighting method. Simultaneously, the regulated electricity will be delivered to the receiving grid to achieve the clean energy consumption goal.

[0181]

[0182] Where, ω k is the weight value of the k-th power grid; K is the total number of power grids.

[0183] When a power plant's power generation significantly falls short of load demand, or when it generates power only during peak hours, achieving optimal peak load regulation becomes more difficult. To avoid significant fluctuations in the grid's residual load throughout the day, it's often necessary to minimize the residual load during specific periods (e.g., peak or off-peak hours). Based on the characteristics of the grid load, time is divided into peak and off-peak periods, and corresponding evaluation functions are developed for each to more fully account for grid load variations.

[0184] The peak segment objective function can be expressed as:

[0185]

[0186] The valley segment objective function can be expressed as:

[0187]

[0188] Where, k，1 , is the peak period collection of the k-grid; k，2 is the valley period set of the k-grid; w k,1 is the peak period target weight of the k-grid; wk,2 is the target weight of the off-peak period of the k-th power grid;

[0189] The target weights for peak and trough periods are determined by the following formula:

[0190]

[0191] Where, τ k is the grid weight value; r k is the artificial slack variable (reflecting the preference for artificial peak regulation, generally set to 1); η k is the ratio of the peak-to-valley difference of the k-grid to the sum of the peak-to-valley differences of all receiving-end grids.

[0192] 2. Objective Function 2

[0193] To minimize the amount of curtailed hydropower, solar power, and wind power, this chapter focuses on optimizing scheduling methods to fine-tune the energy output of a multi-component hydropower, wind, and solar power base. The optimization objective, function 2, is to maximize the benefits of external transmission and consumption, aiming to minimize the amount of curtailed hydropower, solar power, and wind power in the system's external transmission and consumption. Therefore, a curtailment penalty is incorporated into the benefits to improve overall energy efficiency. Objective function 2 is as follows. By adjusting the operating mode of hydropower, wind, and solar power generation equipment during different time periods, the goal is to achieve optimal base operation, fully utilize renewable energy, and ensure the stability and reliability of the power system.

[0194]

[0195] Where B H,t is the water abandonment power at time t; P i,k,t The power that power station i sends to grid k; B W,t is the wind power abandoned at time t; B S,t is the abandoned optical power at time t; b H is the penalty coefficient for abandoning water per unit time; b W is the penalty coefficient for abandoning water per unit time; b S is the penalty coefficient for abandoning water per unit time; C ele For the on-grid electricity price.

[0196] Constraints:

[0197] 1. Transmission channel constraints:

[0198] Transmission channel constraints typically refer to restrictions on transmission lines or channels within a power system, ensuring safe, stable, and reliable power transmission. These constraints may include limitations on transmission line capacity, voltage stability, and current load. These constraints must be considered during power system planning, operation, and scheduling to ensure the normal operation of the grid and the reliability of power supply.

[0199] The specific constraints can be shown in the following formula, which limits the upper and lower limits of the transmission channel power.

[0200] P l，k，min P l,k,t P l,k,max ;

[0201] Where, P l,k,t is the transmission power of transmission channel l at time t; P l,min is the minimum value of the transmission power of transmission channel l; P l,max is the maximum value of the transmission power of transmission channel l.

[0202] Transmission channel power fluctuation constraints are usually used to limit the power fluctuation range of the transmission channel to ensure the stable operation of the power grid. The transmission channel power fluctuation constraint is expressed as follows:

[0203] |P l,t -P l,t-1 |,,ΔP l ;

[0204] Where, ΔP l is the maximum fluctuation amplitude of the transmission power of transmission channel l in adjacent time periods.

[0205] The minimum duration constraint of a transmission channel is usually used to ensure the stability and reliability of the transmission channel. The minimum duration constraint of a transmission channel is expressed as follows:

[0206] (P l,t-τ+1 -P l,t-τ )(P l,t -P l,t-1 )…0τ=1,2,…,ε l ;

[0207] Where: ε l The minimum duration that the transmission channel l must operate stably after the power changes.

[0208] 2. Power balance constraints:

[0209] When receiving power, each power grid needs to meet the power ratio constraint, which is expressed as:

[0210]

[0211] Where, σ i,k is the ratio coefficient of power transmission from multi-complementary base i to grid k; P i,k,t The power transmitted from the multi-complementary base i to the k-grid; P l,k,t For the contribution of the diverse and complementary i-base;

[0212] 3. Power plant and transmission channel constraints:

[0213] The power output of the power station and the transmission channel need to be balanced, and the expression is:

[0214]

[0215] 4. Power generation constraints of power stations:

[0216] P i,min ≤P i,t ≤P i,max ;

[0217] Where, P i,t Contribute to the diverse and complementary i-base; i,min is the minimum output limit of the multi-complementary base i; P i,max It is the maximum output limit of the multi-complementary i-base.

[0218] Hydropower station output limit:

[0219] P i,H,min ≤P i,H,t ≤P i,H,max ;

[0220] Where, P i,,H,t Provide power for the multi-complementary i base hydropower station; i,H,min is the minimum output limit of the multi-complementary i-base hydropower station; P i,H,max It is the maximum output limit of the multi-complementary i-base hydropower station.

[0221] Output limit of photovoltaic power station:

[0222] P i,S,min ≤P i,S,t ≤P i,S,max ;

[0223] Where, P i,S,t Producing power for the multi-complementary i-base photovoltaic power station; i,S,min is the minimum output limit of the multi-complementary photovoltaic power station in base i; P i,S,max It is the maximum output limit of the multi-complementary i-base photovoltaic power station.

[0224] Wind power station output limit:

[0225] P i,W,min ≤P i,W,t ≤P i,W,max ;

[0226] Where, P i,W,t Power generation for the multi-complementary i-base wind power station; P i,W,min is the minimum output limit of the wind power station in the multi-complementary i-base; P i,W,max It is the maximum output limit of the multi-complementary i-base wind power station.

[0227] 5. Water balance constraints:

[0228]

[0229] Where V i,t is the storage capacity of multi-complementary i in period t; I i,t is the inflow flow, m 3 / s; The nth direct upstream hydropower station of multi-complementary i at t-TC n,i Outbound flow in the period, m 3 / s;TC n,i is the flow residence time, h.

[0230] 6. Constraints on outbound flow from hydropower stations and units:

[0231]

[0232] Where Q i,t is the outbound flow of the multi-complementary base i in period t, m 3 / s;Q i,t,on is the power generation flow of multi-complementary base i in period t, m 3 / s;q i,t is the water abandonment flow of multi-complementary base i in period t, m 3 / s;Q i,min is the minimum outbound flow limit of the multi-complementary base i, m 3 / s;Q i,max is the maximum outbound flow limit of the multi-complementary base i, m 3 / s.

[0233] V i,min ≤V i,t ≤V i,max ;

[0234] Where V i,min is the minimum storage capacity limit of power station i, m 3 ; V i,max is the maximum storage capacity limit of power station i, m 3 ;

[0235] 7. Hydropower station head constraints:

[0236]

[0237] Z tou,i,min ≤Z tou,i,t ≤Z tou,i,max ;

[0238] Where Z tou,i,min is the minimum head limit of power station i, m; Z tou,i,maxis the maximum head limit of power station i, m; Z wei,i,t is the tailwater level of power station i at time t, m; z i,t is the head loss of power station i at time t, m.

[0239] Case Analysis

[0240] A multi-complementary base transmission and consumption optimization model taking into account channel constraints, the method comprising:

[0241] Step 1: Based on domestic and international research on multi-energy complementarity, establish a coordinated optimization scheduling strategy for water, wind, and solar power;

[0242] Step 2: Considering the constraints of channel power balance, safety, etc., the transmission capacity analysis based on the feasible region of the transmission channel is carried out;

[0243] Step three: Based on the above analysis, a multi-complementary base transmission and consumption optimization model taking into account channel constraints is established.

[0244] Step 4: Implementation and analysis.

[0245] Analysis of an Implementation Example of a Multi-complementary Base Optimization Scheduling Model Considering Channel Constraints

[0246] To verify the feasibility and accuracy of the optimized model for transmission and consumption of multi-complementary bases established in this paper, we used the multi-complementary base in the lower reaches of the Jinsha River as the background, and used each hydropower station to bundle the surrounding wind and solar resources for transmission and consumption as the research object. We set up the following two strategies for comparison:

[0247] Scheme 1: The model constructed in this paper (Model B) is compared with the model without considering the peak-valley segmented assessment of residual load (Model C);

[0248] Option 2: The proposed model (Model B) is compared with the model with the maximum benefit and without considering the penalty cost of power curtailment (Model D).

[0249] Analysis of external transmission, consumption and base benefits under two schemes:

[0250] pass Figure 3 、 Figure 4 、 Figure 5 A comparison shows that, in Scheme 1, both methods, based on the overall peak-shaving performance, reduce peak loads and minimize peak-to-valley load variations across power grids to varying degrees. Specifically, Model C effectively coordinates peak-shaving and valley-filling across power grids, achieving relative stability across peak and valley load periods throughout the day. This is particularly evident in the Guangdong and Zhejiang power grids, while the Jiangsu power grid, which receives a smaller share of power, exhibits relatively poor peak-shaving effectiveness. Model B, on the other hand, better leverages the power station's load regulation capabilities during peak periods. By evaluating peak-shaving targets in segments, it can improve relative stability across peak and valley load periods to a certain extent.

[0251] Table 1 Benefits of each power station

[0252] power station Model B / 10,000 yuan D model / 10,000 yuan Wudongde 9409.7 8900.8 Baihetan 10363.2 9980.2 Xiluodu 9700.5 9085.9

[0253] Depend on Figure 6 、 Figure 7 、 Figure 8 The results in Table 1 show that the two models of the Wudongde Hydropower Station achieve the same total output over a 24-hour day, but differ significantly in their curtailment choices. Model B curtails hydropower at 8:00, 9:00, 12:00, 13:00, and 18:00, while curtailing wind power only at 9:00, 12:00, and 13:00. There is no curtailment of solar power throughout the day, and the amount of wind curtailment is very small. Model D curtails hydropower only at 9:00, but curtails wind and solar power more frequently at other times of the day, with the amount of curtailment significantly exceeding that of Model B. In terms of the benefits of power plant operation, the benefits after considering curtailment costs are significantly better than when not considering them. Model B achieves a typical daily operating benefit of 94.097 million yuan, 5.069 million yuan higher than that of Model D. Overall, Model B curtails more hydropower than Model D, but curtails wind and solar power much less than Model D, resulting in a higher benefit than Model D.

[0254] The present invention is not limited to the above-mentioned optional implementation modes. Anyone can derive other forms of products under the inspiration of the present invention. However, no matter what changes are made in the shape or structure, any technical solution that falls within the scope defined by the claims of the present invention falls within the scope of protection of the present invention.

Claims

1. A multi-component base transmission and consumption optimization scheduling method taking into account channel constraints, characterized by: The following steps are involved: S1: Establish a coordinated optimization scheduling strategy for water, wind and solar power; S2: Considering the constraints including channel power balance and safety, optimize the transmission capacity based on the feasible region of the transmission channel; S3: Based on the analysis of step S2, a multi-complementary base transmission and consumption optimization model is established that takes into account channel constraints and maximizes power generation benefits.

2. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 1, characterized in that: In step S1, the coordinated optimization scheduling strategy of water, wind and solar power is: By utilizing the daily and long-term regulation capabilities of the hydropower station, the water-wind-solar hybrid system compensates nearby photovoltaic and wind power stations in advance and fully utilizes the hydropower station's excess power generation capacity; Utilize the reservoir's regulating capacity to adjust the hydropower output process, reducing hydropower output when photovoltaic and wind power output are high and the combined hydropower, wind, and solar power exceed the load; and increasing hydropower output when the combined photovoltaic and wind power output is low and electricity demand is high. If hydropower output has not yet reached balance at this time, it is necessary to continue to increase hydropower output during periods of low electricity demand until hydropower output reaches intraday balance; When the total output of photovoltaic and wind power is large and electricity demand is high, in order to absorb more photovoltaic and wind power, hydropower output may be reduced to the lower limit of instantaneous output, and some wind and photovoltaic power will be abandoned to ensure that hydropower output does not fall below the lower limit of instantaneous output. When the total output of photovoltaic and wind power is small and the water volume on that day is large, after the peak of electricity demand ends, hydropower output will be increased to balance the hydropower production capacity on that day, but it will not exceed the expected output. While ensuring that the daily hydropower generation benefits are not lost and the hydropower output does not exceed expectations, hydropower can increase its output capacity every day; during the period when the combined output of water, wind and solar energy exceeds the transmission channel, hydropower will reduce its output.

3. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 1, characterized in that: In step S2, the optimization of the transmission capacity based on the feasible region of the transmission channel specifically includes the following steps: S21: Method for determining the feasible region of transmission channels: When constructing the computational model of the feasible region of transmission channels in a sub-region, only the operating boundaries of multiple consecutive time periods are selected as key data to ensure that all considered constraints are part of the complete subset of scheduling plan constraints, thereby reducing modeling complexity. S22: Analysis of transmission capacity for inter-regional transmission: The operation of the power grid needs to meet multiple types of constraints, so that the executable transmission power of different time periods and different transmission channels has a coupling relationship.

4. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 3 is characterized by: Step S21 specifically includes the following steps: S211: Setting of time period subset: Original H T The scheduling period is divided into H T-n There are n+1 time period subsets, each of which consists of n+1 consecutive time periods. Two adjacent time period subsets differ in only one time period, while the rest remain the same. Each subset contains n+1 time periods, of which n time periods overlap and only one time period differs. S212: Construction of the feasible domain sub-model corresponding to the time period subset: Considering the coupling constraints within the time period subset, the feasible region sub-model of the sub-regional power grid is constructed; when considering the time period coupling relationship contained in the ξ-1 time period subset to obtain the feasible region of time period i, the objective function can be expressed as: Where, P TL,i is the sequence matrix of the power received by the nodes of the transmission channel during period i; column vector is the hyperplane coefficient of the feasible region boundary corresponding to the search of the v-th vertex in time period i; the power balance constraint can be expressed as: The maximum and minimum output constraints of the unit can be expressed as: P G,min ≤P G,j ≤P G,max ; Where, P G,max 、P G,min is the column vector of the maximum and minimum output of the units in the sub-region; The climbing constraint can be expressed as: -D G ≤P G,j+1 -P G,j ≤U G i≤j≤i+n-2; Where U G is the column vector of the unit's ramp limit; D G is the column vector of the unit's down-slope limit; The safety constraint can be expressed as: Where G G and G TL is the active sensitivity matrix of the nodes where the units, transmission channels and loads are located; P S,max is the column vector of safety constraint limits; e H is a column vector with the same dimension as the number of scheduling periods and all elements are 1; S213: Solution of the feasible domain sub-model: For the subset of the ξ-1 time period, n+1 vertex search models can be constructed. After the feasible regions of the n+1 time periods are obtained by the vertex search method, the feasible region of the transmission channel in time period i considering the coupling constraints contained in the subset of the ξ-1 time period is expressed as Similarly, the feasible region of the transmission channel in period i considering the coupling constraints contained in the subset of period ξ can be calculated: S214: Synthesis of feasible regions for each time period: The feasible regions of the transmission channels in the same period obtained under the coupling constraints of different period subsets are intersected to obtain the final feasible region of the transmission channel in the sub-region of period i, which is expressed as:

5. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 3 is characterized by: Step S22 is specifically as follows: The scheduling feasible region is expressed as: Where N G is the number of units; N TL is the number of transmission channels and time periods; N H is the number of time periods; The feasible region of the transmission channel obtained by dimensionality reduction projection is expressed as:

6. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 1, characterized in that: In step S3, the multi-complementary base transmission and consumption optimization scheduling model taking into account channel constraints includes objective function 1 and objective function 2; Objective function 1: Utilize the regulation performance of the multi-component base to smooth the residual load process of the power grid; by minimizing the residual load variance of the objective function, the residual load curve will become a horizontal line; Objective function 2: Set the optimized objective function to maximize the benefit of external transmission and consumption, minimize the amount of abandoned water, abandoned solar power, and abandoned wind in the system's external transmission and consumption, and add a penalty for abandoned electricity to the benefit.

7. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 6, characterized in that: The objective function 1 is: Where, L is the number of transmission channels; D k,t and D′ k,t is the system load and residual load of the k-th power grid in time period t; P l,k,t is the power transmitted by transmission channel l to grid k in time period t; F k is the average value of the residual load of the k-th grid from the average absolute value; To simplify the solution, the multi-objective optimization problem is transformed into a single-objective problem, and the objective weighting method is adopted. At the same time, the regulated electricity will be sent to the receiving power grid to achieve the clean energy consumption goal: Where, ω k is the weight value of the k-th power grid; K is the total number of power grids; When the power generation of the power station is significantly lower than the load demand, or when the power generation is only during peak hours, the residual load is smoothed during specific periods. According to the characteristics of the grid load, the time is divided into peak and valley periods, and corresponding evaluation functions are established for each period. The peak segment objective function can be expressed as: The valley segment objective function can be expressed as: Where, k，1 , is the peak period collection of the k-grid; k，2 is the valley period set of the k-grid; w k,1 is the peak period target weight of the k-grid; w k,2 is the target weight of the off-peak period of the k-th power grid; The target weights for peak and trough periods are determined by the following formula: Where, τ k is the grid weight value; r k is the artificial slack variable; η k is the ratio of the peak-to-valley difference of the k-grid to the sum of the peak-to-valley differences of all receiving-end grids.

8. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 6, characterized in that: The second objective function is: Where B H,t is the water abandonment power at time t; P i,k,t The power that power station i sends to grid k; B W,t is the wind power abandoned at time t; B S,t is the abandoned optical power at time t; b H is the penalty coefficient for abandoning water per unit time; b W is the penalty coefficient for abandoning water per unit time; b S is the penalty coefficient for abandoning water per unit time; C ele For the on-grid electricity price.

9. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 1, characterized in that: In step S3, the constraints include transmission channel constraints, power balance constraints, power plant and transmission channel power constraints, power plant power constraints, water balance constraints, hydropower station and unit outflow constraints, and hydropower station head constraints.

10. The method for optimizing the dispatching of power transmission and consumption from multiple complementary bases taking into account channel constraints according to claim 9, characterized in that: The specific constraints are: Transmission channel constraints: Transmission channel constraints generally refer to the restrictions on transmission lines or transmission channels in a power system, which are used to ensure safe, stable, and reliable operation during power transmission. Transmission channel constraints include the transmission capacity, voltage stability, and current load of the transmission line. Transmission channel constraints are considered in power system planning, operation, and scheduling to ensure the normal operation of the power grid and the reliability of power supply. The specific constraints are expressed as follows, which limit the upper and lower limits of the transmission channel power: P l，k，min P l，k，t P l，k，max ; Where, P l,k,t is the transmission power of transmission channel l at time t; P l,min is the minimum value of the transmission power of transmission channel l; P l,max is the maximum value of the transmission power of transmission channel l; The power fluctuation constraint of the transmission channel is usually used to limit the power fluctuation range of the transmission channel. The power fluctuation constraint of the transmission channel is expressed as: |P l,t -P l,t-1 |,,ΔP l ; Where, ΔP l is the maximum fluctuation amplitude of the transmission power of transmission channel l in adjacent time periods; The minimum duration constraint of a transmission channel is usually used to ensure the stability and reliability of the transmission channel. The minimum duration constraint of a transmission channel is expressed as: (P l,t-τ+1 -P l,t-τ )(P l,t -P l,t-1 )…0τ=1,2,…,ε l ; Where: ε l is the minimum duration that the transmission channel l must operate stably after the power changes; Power balance constraints: When receiving power, each power grid needs to meet the power ratio constraint, which is specifically expressed as: Where, σ i,k is the ratio coefficient of power transmission from multi-complementary base i to grid k; P i,k,t The power transmitted from the multi-complementary base i to the k-grid; P l,k,t For the contribution of the diverse and complementary i-base; Power plant and transmission channel constraints: The power output of the power station and the transmission channel need to be balanced, which can be expressed as: Power plant power consumption constraints: P i,min ≤P i,t ≤P i,max ; Where, P i,t Contribute to the diverse and complementary i-base; i,min is the minimum output limit of the multi-complementary base i; P i,max It is the maximum output limit of the multi-complementary i-base; Hydropower station output limits: P i,H,min ≤P i,H,t ≤P i,H,max ; Where, P i,,H,t Provide power for the multi-complementary i base hydropower station; i,H,min is the minimum output limit of the multi-complementary i-base hydropower station; P i,H,max It is the maximum output limit of the hydropower station in the multi-complementary i base; Output limit of photovoltaic power station: P i,S,min ≤P i,S,t ≤P i,S,max Where, P i,S,t Producing power for the multi-complementary i-base photovoltaic power station; i,S,min is the minimum output limit of the multi-complementary photovoltaic power station in base i; P i,S,max It is the maximum output limit of the multi-complementary i-base photovoltaic power station; Wind power station output limit: P i,W,min ≤P i,W,t ≤P i,W,max Where, P i,W,t Power generation for the multi-complementary i-base wind power station; P i,W,min is the minimum output limit of the wind power station in the multi-complementary i-base; P i,W,max It is the maximum output limit of the wind power station in the multi-complementary i-base; Water balance constraints: Where V i,t is the storage capacity of multi-complementary i in period t; I i,t is the inflow flow, unit is m 3 / s; The nth direct upstream hydropower station of multi-complementary i at t-TC n, Outbound flow in period i, unit: m 3 / s;TC n, i is the flow residence time, unit is h; Constraints on outbound flow of hydropower stations and units: Where Q i,t is the outbound flow of the multi-complementary base i in period t, unit: m 3 / s;Q i,t,on is the power generation flow of multi-complementary base i in period t, unit: m 3 / s;q i,t is the water abandonment flow of multi-complementary base i in period t, unit: m 3 / s;Q i,min is the minimum outbound flow limit of the multi-complementary base i, unit is m 3 / s;Q i,max is the maximum outbound flow limit of the multi-complementary base i, unit is m 3 / s; In i,min ≤V i,t ≤V i,max ; Where V i,min is the minimum storage capacity limit of power station i, unit is m 3 ; V i,max is the maximum storage capacity limit of power station i, unit is m 3 ; Head constraints of hydropower stations: WITH tou,i,min ≤Z tou,i,t ≤Z tou,i,max ; Where Z tou,i,min is the minimum head limit of power station i, in m; Z tou,i,max is the maximum head limit of power station i, in m; Z wei,i,t is the tailwater level of power station i at time t, in m; z i,t is the head loss of power station i at time t, in m.

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