A coordinated expansion planning method for source, grid and load considering the optimization of DC transmission from hydropower, wind and solar power plants

Abstract

The present invention relates to a source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind-solar and direct current transmission, belonging to the technical field of integrated energy system planning and operation. The method carries out detailed modeling of cascade hydropower stations and demand response, and takes into account the operational constraints of direct current transmission. The method utilizes demand response resources to alleviate the intermittent output of wind and solar power stations on the power supply side, proposes a load-side demand response service planning model to reduce the system's long-term planning costs, and establishes a source-grid-load coordinated planning model. The method establishes an opportunity constraint model to ensure the system's operational safety and the effective absorption of wind and solar power output, and utilizes the strong extension formula of the sample average approximation method to transform the opportunity constraint model into a mixed integer linear programming problem for solution.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system planning and operation, and specifically relates to a source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission. Background Art

[0002] Renewable energy sources like wind and solar power have poor controllability, and their intermittent and uncertain output poses significant challenges to the safe and economical operation of power grids. Cascade hydropower stations, with their flexible regulation capabilities, can effectively mitigate the uncertainty of wind and solar power station output. By leveraging the complementary nature of cascade hydropower and wind and solar power stations, combined with the coordinated optimization of DC transmission, they can achieve peak load regulation and peak-load shifting.

[0003] The complementarity of hydropower, wind, and solar power resources manifests itself on various timescales, including medium- and long-term energy resource complementarity, short-term scheduling complementarity, and real-time control complementarity for rapid power fluctuations. Current research on hydropower, wind, and solar power systems primarily focuses on short-term optimal operation. Considerable research has been conducted both domestically and internationally on power system planning for renewable energy sources such as wind and solar. Many studies model expansion planning for the generation and transmission systems separately, reflecting the varying impacts of expansion on the overall power system. However, from a holistic perspective, optimizing the combined expansion of the generation and transmission systems can yield a more economically optimal solution. Existing research suffers from two key issues: 1) Limited research on medium- and long-term planning encompassing cascade hydropower stations, wind and solar power plants, and DC transmission; and 2) insufficiently leveraging the advantages of load-side demand response to foster complementary source-load resources.

[0004] Therefore, at this stage, it is necessary to design a source-grid-load coordinated expansion planning method that takes into account the optimization of combined hydropower, wind power and solar power DC transmission to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a source-grid-load coordinated expansion planning method that takes into account the optimization of combined direct current transmission of water, wind and solar power, which is used to solve the technical problems existing in the above-mentioned existing technologies, promote the consumption of water, wind and solar power resources, and reduce the medium- and long-term planning costs of the system. It is of great significance to the study of coordinated planning and operation of multi-source power systems.

[0006] To achieve the above object, the technical solution of the present invention is:

[0007] The source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission includes the following steps:

[0008] Step 1: Establish an equipment investment model and a system operation model for the expansion plan, including the investment and construction of generators, transmission lines, and load demand response services, as well as the operating constraints of the power system, including generators, transmission lines, and busbars;

[0009] Step 2: Establish hydropower station and wind / solar power station models, including the hydropower conversion relationship, upper and lower power generation constraints, ramp constraints, power generation flow, reservoir capacity balance, and wind / solar output constraints;

[0010] Step 3: Establish a load-side demand response service model to constrain load reduction or load transfer;

[0011] Step 4: Establish a DC transmission model, including upper and lower power limit constraints, transmission power adjustment ramp constraints, power cannot be reversed in adjacent time periods, limits on the number of upward and downward transmission power adjustments, and contract transmission power constraints;

[0012] Step 5: Construct a power system source-grid-load coordinated optimization expansion model with minimizing the total system cost as the objective function, including the cost of power system investment and construction facilities, the operating cost of thermal power units, and the load loss penalty cost;

[0013] Step 6: Considering the consumption of new energy, impose opportunity constraints on wind and solar power output, use the Latin hypercube method to sample the wind and solar power output forecast error, and use the sample average approximation method to transform the opportunity constraint model into a mixed integer linear programming problem for solution;

[0014] Step 7: Input the multi-source power system data, equipment parameters, and operating parameters, and use the commercial solver Gurobi to solve the source-grid-load coordinated expansion planning model to obtain the planning operation results.

[0015] Furthermore, the expansion planning equipment investment and system operation model described in step 1 are as follows:

[0016] (1) Expansion planning equipment investment model:

[0017] y i(t-1) ≤y it ,i∈CG

[0018] y l(t-1) ≤y lt ,l∈CL

[0019] y d(t-1) ≤y dt ,d∈CD

[0020] t≥T i retire , i∈EG

[0021] Where t is the index of the year; i, l and d are the indices of the generator, transmission line and load respectively; CG, CL and CD are the candidate investment facilities for thermal power generation, transmission line and load demand response services respectively; y it ,y ltand y dt Refers to the construction status of candidate investment facilities for power generation units, transmission lines and load demand response services respectively; and T i retire They are the operating status and retirement time of the existing units;

[0022] (2) Expansion planning system operation model:

[0023]

[0024]

[0025] P lat ·X l =(θ s(l)at -θ r(l)at ),l∈EL

[0026] -(1-y lt )·M≤P lat ·X l -(θ s(l)at -θ r(l)at )≤(1-y lt )·M,l∈CL

[0027] -P l max ≤P lat ≤P l max ,l∈EL

[0028] P l max ·y lt ≤P lat ≤P l max ·y lt ,l∈CL

[0029]

[0030] i∈EG

[0031] 0≤P iat ≤P i max ·y it ,i∈CG

[0032] -Δ i ≤P iat -P i,a-1,t ≤Δ i ,i∈EG∪CG

[0033] Where: P hatis the dispatching value of hydropower station h; P wat is the dispatch value of wind farm w; P sat is the dispatch value of photovoltaic power station s; v dat is the load loss of power load d; P dat is the planned load after considering the load d demand response; is the DC c external power; P lat and θ bat are the power flow of transmission line l and the phase angle of bus b respectively; s(l) and r(l) refer to the sending end bus and the receiving end bus of transmission line l respectively; X l is the reactance of transmission line l; M is a sufficiently large number; N(b) is a set of equipment connected to bus b; EL and EG are the sets of existing transmission lines and thermal power units, respectively.

[0034] Furthermore, the hydropower station and wind-solar power station models described in step 2 are as follows:

[0035] (1) Hydropower station model:

[0036] P hat =g·η h Q hat ·H hat

[0037]

[0038] -Δ h ≤P hat -P h,a-1,t ≤Δ h

[0039]

[0040]

[0041] V h,0,t =v h,0,t ,V h,NT,t =v h,NT,t

[0042]

[0043] H ht =h 0,h +α h ·V ht

[0044] Where: g is the hydropower conversion coefficient; η h h is the power generation efficiency of the hydropower station; Q hat is the power generation flow of the hydropower station; H hat is the water head of the hydropower station; Δ his the climbing power limit of the hydropower station h; V hat is the storage capacity of the hydropower station; NT is the final time of the typical day's optimized operation; v h,0,t and v h,NT,t are constants, representing the initial storage capacity and final storage capacity of the hydropower station on a typical day h; τ h is the water flow time lag of the hydropower station h; For the upper hydropower station h-1 at a-τ h The power generation flow at the moment; h 0,h and α h is a constant, determined by the size of the hydropower station h;

[0045] (2) Wind and solar power station model:

[0046]

[0047]

[0048] Where: and are the predicted output values of wind farms and photovoltaic power stations respectively.

[0049] Furthermore, the load-side demand response service model described in step 3 is specifically as follows:

[0050] For loads where demand response devices have been installed, a certain degree of load reduction or load shifting is implemented. The total demand response load on a typical day must be greater than or equal to 0 and cannot exceed the upper limit allowed by the system.

[0051]

[0052]

[0053]

[0054]

[0055] Where: and They represent the forecast load and demand response load of load d respectively; Indicates the maximum load value of the allowed load d; β dat is a number in the interval [0,1], which is used to represent the demand response participation of load d; It represents the typical daily demand response reduction limit of load d. If it is set to 0, it means that load reduction is not allowed, that is, the load reduction amount of load d will be completely transferred to other time periods.

[0056] Furthermore, the DC transmission model described in step 4 is specifically as follows:

[0057] The constraints include upper and lower limits of power operation, power adjustment ramp constraints, no reverse adjustment in adjacent periods, limit on the number of upward and downward adjustments of the external power, contracted external power constraints, and tie-line adjustment interval constraints, i.e.

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] Where: and They are respectively the upward and downward ramp power limits of DC transmission power; and is a 0-1 variable, indicating the upward and downward adjustment states of the DC transmission power; Q ct is the daily contracted transmission capacity of DC transmission channel c; ΔT dc It is the minimum stable period interval after the DC tie line adjusts the power.

[0065] Furthermore, the objectives of the source-grid-load coordinated optimization expansion model described in step 5 are as follows:

[0066] Objective function: The power system source-grid-load coordinated expansion planning model aims to minimize the total system cost, including the cost of power system investment and construction facilities, the operating cost of thermal power units, the penalty cost for load loss, and the penalty cost for wind power curtailment.

[0067]

[0068] in:

[0069]

[0070]

[0071] κ t =1 / (1+d dr ) t-1

[0072] Where: t and a are the indexes of year and typical day respectively; IC and OC refer to the investment and construction cost and operation cost respectively; C I is the power loss load cost; ΔD t is the total amount of load loss in year t; κt and d dr are the current market value coefficient and discount rate respectively; i, l and d are the indexes of power generation units, transmission lines and loads respectively; CG, CL and CD refer to the candidate investment facilities set of thermal power units, transmission lines and load demand response services respectively; C inv The cost of investing in new facilities in the power system; it ,y lt and y dt They refer to the construction status of candidate investment facilities for generators, transmission lines and load demand response services respectively; DT is the duration of the load block; P iat is the output of generator set i; F i (·) is the cost curve of generator set i.

[0073] Furthermore, the method for processing the power system source-grid-load coordinated expansion plan considering uncertainty described in step 6 is specifically as follows:

[0074] (1) Linearization of hydropower conversion function: Substitute the relationship between water head and reservoir capacity into the hydropower conversion function to obtain the relationship between the generated power of the cascade hydropower station and the generated flow and reservoir capacity:

[0075] P hat =g·η h Q hat ·(h 0,h +α h ·V hat )

[0076] In the formula, since the power generation of cascade hydropower stations is a nonlinear function, auxiliary continuous variables and 0-1 variables are introduced to process it, and the power generation calculation of cascade hydropower stations is converted into a mixed integer linear programming problem;

[0077] (2) Wind and solar power output opportunity constraint: To ensure the absorption of new energy sources such as wind and solar power, add wind and solar power absorption opportunity constraint, satisfy the confidence level of 1-ε, and ensure that the absorption ratio reaches α:

[0078]

[0079]

[0080]

[0081]

[0082] Where: Pr{·} represents the probability; α is the predicted absorption ratio of wind and solar power; ε is the significance level;

[0083] (3) Wind and solar output scenario generation: To represent the uncertainty of wind and solar output, we first assume that the error of wind and solar output satisfies the normal distribution N(μ,σ 2 ), where the mean of the wind and solar power forecast error is μ and the variance is σ 2 ,Based on the analysis of historical data, the variance of uncertainty and volatility of wind power and photovoltaic,was statistically analyzed, and the Latin hypercube method was used to sample the,wind and solar output forecast errors;

[0084] (4) Strong extension formula of opportunity constraint SAA: The sample average approximation method is used to transform the opportunity constraint, and the strong extension formula is used to solve the problem that the constraint formula increases with the number of scenarios:

[0085]

[0086] r t,k -r t,k+1 ≥0

[0087] δ t,e(k) -r k ≥0

[0088]

[0089] γ wat,k -γ wat,k+1 ≥0

[0090] δ t,e(k) -γ wt,k ≥0

[0091]

[0092] Where: is the predicted wind power output of scenario ρ; δ t,ρ is a 0-1 variable; NS is the number of scenes; e(k)

[0093] is the element index of the descending sequence; wat,k and r k It is a 0-1 variable.

[0094] Furthermore, the multi-source power system data in step 7 includes the system topology and transmission line parameters, the equipment parameters include the number, capacity, and output upper and lower limits of thermal power units, hydropower units, wind farms, and photovoltaic farms, and the operating parameters include the fuel consumption and fuel price of thermal power units, load loss cost coefficient, various operating parameters of equipment, load-side demand response limits, and load and wind power and photovoltaic output forecast data.

[0095] Compared with the prior art, the present invention has the following beneficial effects:

[0096] One of the beneficial effects of this solution is that it covers the medium- and long-term planning of cascade hydropower stations and wind and solar power stations, takes into account the complementary characteristics of water, wind, and solar power and the DC transmission model, and proposes a source-grid-load coordinated expansion planning model that takes into account the optimization of combined water, wind, and solar power DC transmission. By establishing a load-side demand response service model, the system's medium- and long-term planning costs are reduced, the advantages of load-side demand response are fully utilized, and the advantages of source-load resource complementarity are formed. In the case of uncertain output of wind and solar power stations, the strong expansion formula of the sample average approximation method is used to transform the opportunity constraint model into a mixed integer linear programming problem for solution. The use of the strong expansion formula can greatly reduce the amount of calculation and improve the calculation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a flow chart of the steps of the method of the present invention.

[0098] Figure 2 It refers to the hydropower, wind and solar power output and DC transmission power during the normal water period.

[0099] Figure 3 It is the water, wind and solar power output and DC transmission power during the dry season.

[0100] Figure 4 This is a comparison chart of sensitivity analysis of different parameters under chance constraints. DETAILED DESCRIPTION

[0101] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for the purpose of explaining the present invention and are not intended to limit the present invention. That is, the embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and illustrated in the drawings herein can be arranged and designed in various different configurations.

[0102] 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 merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work shall fall within the scope of protection of the present invention. It should be noted that relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0103] Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0104] The present invention discloses a source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission. The specific implementation steps are as follows: Figure 1 As shown, the technical solution of the present invention includes the following steps:

[0105] Step 1: Establish an expansion planning equipment investment model and a system operation model, which mainly includes the investment and construction of generator sets, transmission lines, and load demand response services, as well as the operation constraints of the power system such as generator sets, transmission lines, and busbars.

[0106] (1.1) Expansion planning equipment investment model: When a facility is put into construction, its construction status will be fixed at 1 for the rest of the time; when the existing thermal power unit is retired, its operating status will be adjusted to 0.

[0107] y i(t-1) ≤y it ,i∈CG

[0108] y l(t-1) ≤y lt ,l∈CL

[0109] y d(t-1) ≤y dt ,d∈CD

[0110] t≥T i retire , i∈EG

[0111] Where t is the index of the year; i, l and d are the indices of the generator, transmission line and load respectively; CG, CL and CD are the candidate investment facilities for thermal power generation, transmission line and load demand response services respectively; y it ,y lt and y dt Refers to the construction status of candidate investment facilities for power generation units, transmission lines and load demand response services respectively; and T i retire They are the operating status and retirement time of the existing units.

[0112] (1.2) Expansion planning system operation constraints: including the operation of generator sets, transmission lines, and busbars, namely, power balance of power system nodes, annual load loss calculation, power flow calculation of transmission lines, upper and lower limit constraints of power flow, upper and lower limit constraints of busbar phase angle, capacity constraints of thermal power units, and unit ramping constraints.

[0113]

[0114]

[0115] P lat ·X l =(θ s(l)at -θ r(l)at ),l∈EL

[0116] -(1-ylt)·M≤P lat ·X l -(θ s(l)at -θ r(l)at )≤(1-ylt)·M,l∈CL

[0117] -P l max ≤P lat ≤P l max ,l∈EL

[0118] P l max ·y lt ≤P lat ≤P l max ·y lt ,l∈CL

[0119]

[0120] i∈EG

[0121] 0≤P iat ≤P i max ·y it ,i∈CG

[0122] -Δ i ≤P iat -P i,a-1,t ≤Δ i ,i∈EG∪CG

[0123] Where: P hat is the dispatching value of hydropower station h; P wat is the dispatch value of wind farm w; P sat is the dispatch value of photovoltaic power station s; v datis the load loss of power load d; P dat is the planned load after considering the load d demand response; is the DC c external power; P lat and θ bat are the power flow of transmission line l and the phase angle of bus b respectively; s(l) and r(l) refer to the sending end bus and the receiving end bus of transmission line l respectively; X l is the reactance of transmission line l; M is a sufficiently large number; N(b) is a set of equipment connected to bus b; EL and EG are the sets of existing transmission lines and thermal power units, respectively.

[0124] Step 2: Establish models of hydropower stations and wind and solar power stations, including constraints such as hydropower conversion relationship, upper and lower limits of power generation, ramp constraints, power generation flow, reservoir capacity balance, and wind and solar output.

[0125] (2.1) Hydropower Station Model: This includes the hydropower transfer function, upper and lower constraints on the hydropower generation capacity, ramping constraints, power generation flow inequality constraints, reservoir capacity inequality constraints, initial and final reservoir capacity constraints, reservoir capacity balance constraints, and head variation. The inflow to the current cascade hydropower station includes both the natural inflow and the power generation flow from the previous station. The reservoir capacity balance constraint also considers the time lag between the current and previous stations. The head of a cascade hydropower station changes with the reservoir capacity; that is, the head is a linear function of the reservoir capacity.

[0126] P hat =g·η h Q hat ·H hat

[0127]

[0128] -Δ h ≤P hat -P h,a-1,t ≤Δ h

[0129]

[0130]

[0131] V h,0,t =v h,0,t ,V h,NT,t =v h,NT,t

[0132]

[0133] H ht =h 0,h +α h ·Vht

[0134] Where: g is the hydropower conversion coefficient, generally taken as 9.81; η h h is the power generation efficiency of the hydropower station; Q hat is the power generation flow of the hydropower station; H hat is the water head of the hydropower station; Δ h is the climbing power limit of the hydropower station h; V hat is the storage capacity of the hydropower station; NT is the final time of the typical day's optimized operation; v h,0,t and v h,NT,t are constants, representing the initial storage capacity and final storage capacity of the hydropower station on a typical day h; τ h is the water flow time lag of the hydropower station h; For the upper hydropower station h-1 at a-τ h The power generation flow at the moment; h 0,h and α h is a constant and is determined by the size of the hydropower station h.

[0135] (2.2) Wind and photovoltaic power station model: The output of wind farms and photovoltaic power stations can be dispatched to a certain extent, that is, optimized peak regulation can be achieved by appropriately curtailing wind and solar power.

[0136]

[0137]

[0138] Where: and are the predicted output values of wind farms and photovoltaic power stations respectively.

[0139] Step 3: Establish a load-side demand response service model. The loads for which demand response devices have been invested and built can be subjected to a certain degree of load reduction or load transfer. The sum of demand response loads on a typical day must be greater than or equal to 0 and cannot exceed the upper limit allowed by the system.

[0140]

[0141]

[0142]

[0143]

[0144] Where: and They represent the forecast load and demand response load of load d respectively; Indicates the maximum load value of the allowed load d; β datis a number in the interval [0,1], which is used to represent the demand response participation of load d; It represents the typical daily demand response reduction limit of load d. If it is set to 0, it means that load reduction is not allowed, that is, the load reduction amount of load d will be completely transferred to other time periods.

[0145] Step 4: Establish a DC transmission model. In the present invention, the DC transmission power cannot be adjusted frequently. The main constraints include power operation upper and lower limit constraints, power adjustment ramp constraints, no reverse adjustment in adjacent time periods, a limit on the number of upward and downward adjustments of the transmission power, and contract transmission power constraints.

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152] Where: and They are respectively the upward and downward ramp power limits of DC transmission power; and is a 0-1 variable, indicating the upward and downward adjustment states of the DC transmission power; Q ct is the daily contracted transmission capacity of DC transmission channel c; ΔT dc It is the minimum stable period interval after the DC tie line adjusts the power.

[0153] Step 5: Construct a power system source-grid-load coordinated optimization expansion model with the objective function of minimizing the total system cost, including the cost of power system investment and construction facilities, the operating cost of thermal power units, the penalty cost for load loss, and the penalty cost for wind power curtailment.

[0154] Objective function:

[0155] in:

[0156]

[0157]

[0158] κ t =1 / (1+d dr ) t-1

[0159] Where: t and a are the indexes of year and typical day respectively; IC and OC refer to the investment and construction cost and operation cost respectively; C I is the power loss load cost; ΔD t is the total amount of load loss in year t; κ t and d dr are the current market value coefficient and discount rate respectively; i, l and d are the indexes of power generation units, transmission lines and loads respectively; CG, CL and CD refer to the candidate investment facilities set of thermal power units, transmission lines and load demand response services respectively; C inv The cost of investing in new facilities in the power system; it ,y lt and y dt They refer to the construction status of candidate investment facilities for generators, transmission lines and load demand response services respectively; DT is the duration of the load block; P iat is the output of generator set i; F i (·) is the cost curve of generator set i.

[0160] Step 6: Considering the uncertainty of the source-grid-load coordinated expansion planning model, the hydropower transfer function is linearized and the wind and solar power output is opportunity constrained. The Latin hypercube method is used to sample the wind and solar power output prediction error, and the sample average approximation (SAA) is used to transform the opportunity constraint model into a mixed integer linear programming problem for solution.

[0161] (6.1) Linearization of the hydropower transfer function: Substituting the relationship between water head and reservoir capacity into the hydropower transfer function, we obtain the relationship between the generated power of the cascade hydropower station and the generated flow and reservoir capacity:

[0162] P hat =g·η h Q hat ·(h 0,h +α h ·V hat )

[0163] In the formula, since the power generation of cascade hydropower stations is a nonlinear function, it can be processed by introducing auxiliary continuous variables and 0-1 variables, and the power generation calculation of cascade hydropower stations is converted into a mixed integer linear programming problem.

[0164] (6.2) Wind and solar power output opportunity constraint: To ensure the absorption of new energy sources such as wind and solar power, add wind and solar power absorption opportunity constraint, satisfy the confidence level of 1-ε, and ensure that the absorption ratio reaches α:

[0165]

[0166]

[0167]

[0168]

[0169] Where: Pr{·} represents the probability; α is the predicted absorption ratio of wind and solar power; ε is the significance level.

[0170] (6.3) Wind and solar output scenario generation: In order to express the uncertainty of wind and solar output, we first assume that the error of wind and solar output satisfies the normal distribution N(μ,σ 2 ), where the mean of the wind and solar power forecast error is μ and the variance is σ 2 ,Based on the analysis of historical data, the variance of uncertainty and volatility of wind power and photovoltaic ,power was statistically analyzed, and the Latin hypercube method was used to ,sample the wind and solar output forecast errors.

[0171] (6.4) Strong extension formula of chance-constrained SAA: The chance constraint is transformed using the sample average approximation method, and a strong extension formula is used to solve the problem that the constraint formula increases with the number of scenarios.

[0172]

[0173] r t,k -r t,k+1 ≥0

[0174] δ t,e(k) -r k ≥0

[0175]

[0176] γ wat,k -γ wat,k+1 ≥0

[0177] δ t,e(k) -γ wt,k ≥0

[0178]

[0179] Where: is the predicted wind power output of scenario ρ; δ t,ρ is a 0-1 variable; NS is the number of scenes; e(k)

[0180] is the element index of the descending sequence; γ wat,k and r k It is a 0-1 variable.

[0181] Step 7: Input multi-source power system data, equipment parameters, operating parameters, etc., and use the commercial solver Gurobi to solve the source-grid-load coordinated expansion planning model, obtain the planning operation results, and test the effectiveness of the proposed method.

[0182] The effects of the present invention are described in detail below through specific embodiments.

[0183] (1) Introduction to the example.

[0184] The modified 24-bus IEEE reliability test system was used for verification, which includes 26 traditional thermal power units, 38 transmission lines and 17 power loads. Three wind farms are located at nodes 1, 2 and 22, two photovoltaic power stations are located at nodes 16 and 22, and three hydropower stations are located at nodes 1, 7 and 21. Two of the hydropower stations are cascade hydropower stations, and the water inflow is considered according to the normal water period. The DC transmission is located at node 22. In addition, 18 candidate investment generators, 16 candidate investment transmission lines, and 19 candidate investment demand response services are considered. The expansion period of the study is 5 years. In the first year, the power load of wind power generation and photovoltaic power generation are 2850MW, 720MW and 500MW respectively, with annual growth rates of 3%, 8% and 8% respectively. The minimum and maximum power of DC transmission are 100MW and 500MW respectively, and the daily transmission power is 6000MWh. The typical daily power load, wind and solar output curve is as follows Figure 2 The test tool uses Matlab 2018a programming software and GUROBI 8.1 commercial solver.

[0185] (2) Description of the implementation scenario.

[0186] To study the proposed source-grid-load coordinated expansion planning method for combined hydropower, wind-solar and DC transmission optimization, highlighting the complementary coordination advantages of combined hydropower, wind-solar and DC transmission, the impact of load-side demand response on renewable energy consumption, and the impact of output uncertainty of renewable energy such as wind and solar on planning results, the following examples 1-5 are set.

[0187] Example 1: Power supply planning is performed first, followed by transmission network planning.

[0188] Example 2: Power system source-grid coordination planning considering combined hydropower, wind power, and solar power DC transmission;

[0189] Example 3: Power system source-grid-load coordination planning considering demand response services combined with DC transmission;

[0190] Example 4: Based on Example 2, consider the opportunity constraint;

[0191] Example 5: Based on Example 3, consider the opportunity constraint.

[0192] Table 1 Simulation results of examples 1-5

[0193]

[0194] (3) Analysis of the results of the embodiment.

[0195] Table 1 presents the planning results for Examples 1-5, where G, L, and D represent generators, transmission lines, and demand response services, respectively. In Example 1, seven thermal power units with a total capacity of 669 MW were scheduled to retire after the expansion planning period. To meet the increased load, five relatively inexpensive candidate generators were commissioned. To prevent line congestion, the transmission network was reinforced, and five transmission lines were constructed. Notably, due to the lack of consideration for source-grid coordination, four new transmission lines were constructed in the fifth year to meet load requirements. In Example 2, due to consideration for source-grid coordination, candidate unit 6 was constructed in the first year, and candidate unit 4 was replaced by candidate unit 11. This avoided the need for extensive transmission line construction in the fifth year. Therefore, in Example 2, only candidate line 12 was required for construction in the fifth year, reducing the total cost of Example 2. In Example 3, the planning of load-side demand response services is further considered, that is, the source-grid-load coordination optimization is considered in the planning model, which further reduces the total planning cost of Example 3 compared with Example 2. Taking the source-grid-load coordination planning results of Example 3 as an example, the characteristics of hydropower, wind, and solar power stations and DC transmission are further analyzed. Figure 2 It can be seen that due to the limitation that the DC transmission power can only be adjusted up and down once a day, the DC transmission power basically reaches the maximum value when the wind power is large and the load is small at night, and the DC transmission power is the minimum value when the wind power is low and the load is large during the day. Since the water inflow of the hydropower station is relatively sufficient, it basically maintains a relatively stable output. When the photovoltaic power station output is at its maximum, the output is appropriately reduced to reduce the system's abandoned light. In order to further explore the peak regulation of the hydropower station and the coordinated and complementary characteristics of water, wind and solar power, the water inflow of the hydropower station is reduced to study the relevant characteristics of the water, wind and solar power stations and DC transmission during the dry season, such as Figure 3 As shown in Figure 1, wind and solar power plants have a certain degree of complementarity on a daily timescale, while cascade hydropower stations offer good peak-shaving performance, adjusting according to the size and fluctuation of wind and solar power output, more economically meeting the requirements of DC transmission and source-load balance. Comparing the analysis of Examples 1-3, it can be seen that medium- and long-term coordinated planning of power system sources, grids, and loads can maximize resource utilization and reduce unnecessary investment.

[0196] When considering the uncertainty of wind and solar power station output, chance constraints are used to ensure the system's ability to absorb wind and solar power. In Example 4, the renewable energy absorption ratio α is set to 80%, the number of renewable energy scenarios is set to 5,000, and the significance level ε is set to 0.1. Due to the need to ensure the safety of power system operation and the absorption of renewable energy sources such as wind and solar power, the total planning cost increases by 7.5% compared to Example 2. In this case, a new candidate generator unit 3 is added to ensure system operational safety. Furthermore, candidate units 1 and 11 are also commissioned one year in advance. The uncertainty of wind and solar power output has no significant impact on transmission line planning. In Example 5, due to the consideration of the impact of load-side demand response services, the investment in candidate generator units is reduced compared to Example 4. The construction years of some candidate units are slightly different, which reduces the total planning cost.

[0197] Table 2 shows the planning results under different wind and solar power output forecast errors. From this, we can conclude that as the wind and solar power output forecast error continues to increase, more and more generators, transmission lines and demand response services are invested and built, and the total planning cost is also increasing.

[0198] Table 2 Planning results of wind and solar power output prediction error

[0199]

[0200] Figure 4 A sensitivity analysis is presented for different wind and solar power output absorption ratios α and significance levels ε to further investigate the impact of opportunity constraint parameters on the power system source-grid-load coordination planning results. It is found that as the absorption ratio α increases or the significance level ε decreases, the total system planning cost shows a monotonically increasing trend. It can be seen that the significance level ε has a more significant impact on the total planning cost. To achieve better economic efficiency, the system should maximize the utilization of renewable energy sources such as wind and solar power, which means a higher absorption ratio α, which has a smaller impact on the total planning cost. This sensitivity study can guide power system planners in determining appropriate power system planning decisions under different wind and solar power output absorption ratios to ensure system operation security and the absorption of renewable energy sources such as wind and solar power.

[0201] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions and effects do not exceed the scope of the technical solution of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission, characterized by: The following steps are involved: Step 1: Establish an equipment investment model and a system operation model for the expansion plan, including the investment and construction of generators, transmission lines, and load demand response services, as well as the operating constraints of the power system, including generators, transmission lines, and busbars; Step 2: Establish hydropower station and wind / solar power station models, including the hydropower conversion relationship, upper and lower power generation constraints, ramp constraints, power generation flow, reservoir capacity balance, and wind / solar output constraints; Step 3: Establish a load-side demand response service model to constrain load reduction or load transfer; Step 4: Establish a DC transmission model, including upper and lower power limit constraints, transmission power adjustment ramp constraints, power cannot be reversed in adjacent time periods, limits on the number of upward and downward transmission power adjustments, and contract transmission power constraints; Step 5: Construct a power system source-grid-load coordinated optimization expansion model with minimizing the total system cost as the objective function, including the cost of power system investment and construction facilities, the operating cost of thermal power units, and the load loss penalty cost; Step 6: Considering the consumption of new energy, impose opportunity constraints on wind and solar power output, use the Latin hypercube method to sample the wind and solar power output forecast error, and use the sample average approximation method to transform the opportunity constraint model into a mixed integer linear programming problem for solution; Step 7: Input the multi-source power system data, equipment parameters, and operating parameters, and use the commercial solver Gurobi to solve the source-grid-load coordinated expansion planning model to obtain the planning operation results.

2. The source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission according to claim 1 is characterized in that: The expansion planning equipment investment and system operation model described in step 1 are as follows: (1) Expansion planning equipment investment model: and i(t-1) ≤y it ,i∈CG and l(t-1) ≤y lt ,l∈CL and d(t-1) ≤y dt ,d∈CD Where t is the index of the year; i, l and d are the indices of the generator, transmission line and load respectively; CG, CL and CD are the candidate investment facilities for thermal power generation, transmission line and load demand response services respectively; y it ,y lt and y dt Refers to the construction status of candidate investment facilities for power generation units, transmission lines and load demand response services respectively; and T i retire They are the operating status and retirement time of the existing units; (2) Expansion planning system operation model: P lat ·X l =(θ s(l)at -θ r(l)at ),l∈EL -(1-y lt )·M≤P lat ·X l -(θ s(l)at -θ r(l)at )≤(1-y lt )·M,l∈CL -P l max ≤P lat ≤P l max ,l∈EL P l max ·and lt ≤P lat ≤P l max ·and lt ,l∈CL 0≤P iat ≤P i max ·and it ,i∈CG -D i ≤P iat -P i,a-1,t ≤Δ i ,i∈EG∪CG Where: P hat is the dispatching value of hydropower station h; P wat is the dispatch value of wind farm w; P sat is the dispatch value of photovoltaic power station s; v dat is the load loss of power load d; P dat is the planned load after considering the load d demand response; P is the DC power output; lat and θ bat are the power flow of transmission line l and the phase angle of bus b respectively; s(l) and r(l) refer to the sending end bus and the receiving end bus of transmission line l respectively; X l is the reactance of transmission line l; M is a sufficiently large number; N(b) is a set of devices connected to bus b; EL and EG are the sets of existing transmission lines and thermal power units, respectively; DT is the duration of the load block; P iat is the output of generator set i.

3. The source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission according to claim 2 is characterized in that: The hydropower station and wind power station models described in step 2 are as follows: (1) Hydropower station model: P.S hat G·η h ·Q hat ·H hat -D h ≤P hat -P h,a-1,t ≤Δ h V h,0,t =v h,0,t ,V h,NT,t =v h,NT,t V hat =V h,a-1,t +R hat +Q h-1,a-τh,t -Q hat H ht =h 0,h +α h ·V ht Where: g is the hydropower conversion coefficient; η h h is the power generation efficiency of the hydropower station; Q hat is the power generation flow of the hydropower station; H hat is the water head of the hydropower station; Δ h is the climbing power limit of the hydropower station h; V hat is the storage capacity of the hydropower station; NT is the final time of the typical day's optimized operation; v h,0,t and v h,NT,t are constants, representing the initial storage capacity and final storage capacity of the hydropower station on a typical day h; τ h is the water flow time lag of the hydropower station h; For the upper hydropower station h-1 at a-τ h The power generation flow at the moment; h 0,h and α h is a constant, determined by the size of the hydropower station h; (2) Wind and solar power station model: Where: and are the predicted output values of wind farms and photovoltaic power stations respectively.

4. The source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission according to claim 3 is characterized in that: The load-side demand response service model described in step 3 is as follows: For loads where demand response devices have been installed, a certain degree of load reduction or load shifting is implemented. The total demand response load on a typical day must be greater than or equal to 0 and cannot exceed the upper limit allowed by the system. Where: and They represent the forecast load and demand response load of load d respectively; Indicates the maximum load value of the allowed load d; β dat is a number in the interval [0,1], which is used to represent the demand response participation of load d; It represents the typical daily demand response reduction limit of load d. If it is set to 0, it means that load reduction is not allowed, that is, the load reduction amount of load d will be completely transferred to other time periods.

5. The source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission according to claim 4 is characterized in that: The DC transmission model described in step 4 is as follows: The constraints include upper and lower limits of power operation, power adjustment ramp constraints, no reverse adjustment in adjacent periods, limit on the number of upward and downward adjustments of the external power, contracted external power constraints, and tie-line adjustment interval constraints, i.e. Where: and They are respectively the upward and downward ramp power limits of DC transmission power; and is a 0-1 variable, indicating the upward and downward adjustment states of the DC transmission power; Q ct is the daily contracted transmission capacity of DC transmission channel c; ΔT dc It is the minimum stable period interval after the DC tie line adjusts the power.

6. The source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission according to claim 5 is characterized in that: The specific objectives of the source-grid-load coordinated optimization expansion model described in step 5 are as follows: Objective function: The power system source-grid-load coordinated expansion planning model aims to minimize the total system cost, including the cost of power system investment and construction facilities, the operating cost of thermal power units, the penalty cost for load loss, and the penalty cost for wind power curtailment. in: k t =1 / (1+d dr ) t-1 Where: t and a are the indexes of year and typical day respectively; IC and OC refer to the investment and construction cost and operation cost respectively; C I is the power loss load cost; ΔD t is the total amount of load loss in year t; κ t and d dr are the current market value coefficient and discount rate respectively; i, l and d are the indexes of power generation units, transmission lines and loads respectively; CG, CL and CD refer to the candidate investment facilities set of thermal power units, transmission lines and load demand response services respectively; C inv The cost of investing in new facilities in the power system; it ,y lt and y dt They refer to the construction status of candidate investment facilities for generators, transmission lines and load demand response services respectively; DT is the duration of the load block; P iat is the output of generator set i; F i (·) is the cost curve of generator set i.

7. The source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission according to claim 6 is characterized in that: Step 6: The specific processing method for the power system source-grid-load coordinated expansion planning considering uncertainty is as follows: (1) Linearization of hydropower conversion function: Substitute the relationship between water head and reservoir capacity into the hydropower conversion function to obtain the relationship between the generated power of the cascade hydropower station and the generated flow and reservoir capacity: P hat =g·h h ·Q hat ·(h 0,h +a h ·V hat ) In the formula, since the power generation of cascade hydropower stations is a nonlinear function, auxiliary continuous variables and 0-1 variables are introduced to process it, and the power generation calculation of cascade hydropower stations is converted into a mixed integer linear programming problem; (2) Wind and solar power output opportunity constraint: To ensure the absorption of wind and solar energy, add wind and solar absorption opportunity constraint, satisfy the confidence level of 1-ε, and ensure that the absorption ratio reaches α: Where: Pr{·} represents the probability; α is the predicted absorption ratio of wind and solar power; ε is the significance level; (3) Wind and solar output scenario generation: To represent the uncertainty of wind and solar output, we first assume that the error of wind and solar output satisfies the normal distribution N(μ,σ 2 ), where the mean of the wind and solar power forecast error is μ and the variance is σ 2 ,Based on the analysis of historical data, the variance of uncertainty and volatility of wind power and photovoltaic,was statistically analyzed, and the Latin hypercube method was used to sample the,wind and solar output forecast errors; (4) Strong extension formula of opportunity constraint SAA: The sample average approximation method is used to transform the opportunity constraint, and the strong extension formula is used to solve the problem that the constraint formula increases with the number of scenarios: r t,k -r t,k+1 ≥0 d t,e(k) -r k ≥0 c wat,k -c wat,k+1 ≥0 d t,e(k) -c wt,k ≥0 Where: is the predicted wind power output of scenario ρ; δ t,ρ is a 0-1 variable; NS is the number of scenes; e(k) is the element index of the descending sequence; γ wat,k and r k It is a 0-1 variable.

8. The source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind power and solar power DC transmission according to claim 1 is characterized in that: The multi-source power system data described in step 7 includes the system topology and transmission line parameters. The equipment parameters include the number, capacity, and output upper and lower limits of thermal power units, hydropower units, wind farms, and photovoltaic power plants. The operating parameters include the fuel consumption and fuel price of thermal power units, load loss cost coefficient, various equipment operating parameters, load-side demand response limits, and load and wind power and photovoltaic output forecast data.

Images