A power transmission network expansion planning method considering hydro-thermal rich area flood and drought characteristics

Abstract

The application discloses a power transmission network expansion planning method considering the flood and drought characteristics of hydropower-rich areas, relates to the field of power system planning, and performs minimum analysis on the target cost of a power generator, and combines a first multi-constraint and an equal annual value method to construct an upper line planning model; minimum analysis is performed on the target cost of a power grid company, and a second multi-constraint and a fuzzy C-means algorithm are combined to construct a lower market clearing model; the upper line planning model and the lower market clearing model are coupled and calculated through KKT conditions, and a double-layer planning model is obtained. The planning model proposed in the analysis of network congestion surplus and other transaction signals can accurately locate the congestion section, and evaluate the quantitative influence of line selection on congestion and even future power consumption cost, so that the planning is more promising and operable compared with traditional market planning.

Description

Technical Field

[0001] This invention relates to the field of power system planning, specifically a method for expanding transmission network planning that takes into account the characteristics of abundant and scarce hydropower in areas with high hydropower availability. Background Technology

[0002] In power grids located in hydropower-rich areas, electricity prices vary depending on the cost of power generation during the dry and wet seasons, thus leveraging the economic effect of price. By implementing expansion plans for power grids with a high proportion of hydropower, it is possible to reduce hydropower waste during floods, absorb excess hydropower output during floods, optimize the annual distribution of electricity, increase electricity consumption during high-water seasons and decrease consumption during low-water seasons, effectively balancing peak and dry season loads and alleviating the seasonal load regulation pressure on the power grid. Furthermore, the grid can provide a good incentive mechanism to reduce electricity prices and mitigate market operational risks.

[0003] In conclusion, researching extended planning that takes into account the characteristics of the electricity market's abundance and scarcity in light of market price signals, and promoting the utilization of hydropower resources throughout the region, is an important task with both theoretical and practical significance. Summary of the Invention

[0004] In view of the above-mentioned technical shortcomings, the present invention provides a transmission network expansion planning method that takes into account the characteristics of abundant and scarce hydropower in areas.

[0005] To address the problems raised in the background art, the technical solution of the present invention is as follows: A method for planning the expansion of a power transmission network that takes into account the characteristics of abundant and scarce hydropower in areas with high hydropower availability includes the following steps: S1, minimize the target cost of the power generator, and construct the upper-level line planning model by combining the first multivariate constraint and the equivalent annual value method; among which, the first multivariate constraint includes the upper and lower limits of the equivalent hydropower station's energy storage capacity, the constraint of the number of new lines, the constraint of the hydropower unit's processing characteristics, and the upper and lower limits of the reservoir's outflow. S2 performs a minimization analysis of the target cost of the power grid company and constructs a lower-level market clearing model by combining the second multivariate constraints and the fuzzy C-means algorithm. The second multivariate constraints include node power balance constraints, line transmission constraints, generator output constraints, initial period unit ramp-up constraints, non-initial period unit ramp-up constraints, and power generation rights trading volume constraints. S3 uses KKT conditions to couple the upper-level route planning model and the lower-level market clearing model to obtain a two-level planning model.

[0006] As a preferred embodiment, the mathematical expression of the fuzzy C-means algorithm described in step S2 is as follows: in, This is the cluster center matrix; It is a weighted index; Indicates sample k To the i Distance between cluster centers.

[0007] According to the Lagrange multiplier method, the sample can be obtained. k Belongs to the i Membership of each cluster center and the i Cluster center of class They are: In the formula: The number of clusters; Indicates sample k To the i Distance between cluster centers; It is a weighted index; n The total number of samples; for No. k One sample.

[0008] As a preferred embodiment, the isochronous value method described in step S1 has the following mathematical expression: The equivalent annual value method is a method that converts all cash flows or net present value into equivalent annual cash flows or net equivalent annual values ​​that occur on average each year over the entire lifespan, based on the required rate of return on the investment, and then analyzes and evaluates investment projects accordingly.

[0009] in i This indicates the interest rate; n This refers to the expected service life.

[0010] As a preferred embodiment, the target cost of the power generator mentioned in step S1 is expressed mathematically as follows: Maintenance costs + operating costs - power generation revenue - power generation rights trading revenue.

[0011] As a preferred embodiment, the mathematical expression for the upper and lower limits of the equivalent hydropower station energy storage capacity constraints mentioned in step S1 is as follows: in, , These are the upper and lower limits of the energy storage capacity of the equivalent hydropower station, respectively. The mathematical expression for the constraint on the number of newly built lines is as follows: in, This represents the maximum investment budget. The mathematical expressions for the upper and lower limits of the reservoir outflow are as follows: in, Let k be the minimum warning capacity of reservoir k; This represents the maximum warning capacity of reservoir k.

[0012] As a preferred embodiment, the objective function expression of the upper-level route planning model described in step S1 is as follows: in, and They are nodes i and j Between l 0-1 binary integer decision variables for each route and their equivalent annual investment cost; and They are nodes i No. k generator set 0-1 Binary integer investment decision variables and their equivalent annual investment costs; and These are the sets of candidate routes and the sets of candidate generator sets, respectively. and They are nodes i No. k The power output of the generator set in scenario s and its operating cost per unit output.

[0013] As a preferred embodiment, the nodal power balance constraint described in step S2 has the following mathematical expression: in, Indicates that it is located at node j A collection of generator sets, Indicates generator set g exist t Time-slot scheduling output For transmission lines l The sensitivity value, express t Time period nodes i The load, , Representing nodes respectively i and nodes j exist t Phase angle value for the time period; The mathematical expression for the line transmission constraint is as follows: in, This represents the maximum transmission capacity of the transmission line connecting node i and node j; The mathematical expression for the generator set output constraint is as follows: in, Indicates generator set g The minimum generating capacity declared in the electricity market Indicates generator set g The maximum generating capacity declared in the electricity market; The mathematical expression for the initial period unit ramp-up constraint is as follows: in, Indicates generator set g downhill slope rate For generator sets g Power generation output in the initial period, Indicates generator set g The rate of ascent; The mathematical expression for the non-initial period unit ramp-up constraint is as follows: in, Indicates generator set g exist t Dispatch output during the -1 time period.

[0014] As a preferred embodiment, the objective function expression of the lower-level market clearing model described in step S1 is as follows: in, t This is the clearing period for the electricity market. and These represent the set of nodes and the set of clearing periods, respectively. This represents the quadratic coefficient, linear coefficient, and constant coefficient of the declared curve of generator set g.

[0015] The beneficial effects of this invention are: (1) Compared with the traditional planning scheme, the line planning is no longer confined to the perspective of operation mode and safety standards. Instead, it takes into account the fluctuations of market price signals during the wet and dry seasons. Under the background of "new power reform", it truly uses market-oriented transaction signals as a guide to incentivize the optimal allocation of power grid hydropower resources. It reflects the quantitative impact of the overall supply and demand situation under the competitive market model on the investment returns, market participants' willingness, and social benefits of hydropower-rich power grids in the future. (2) Traditional extended planning methods have very limited effect on improving the hydropower absorption capacity of hydropower-rich areas. The planning scheme proposed in this invention takes into account the price elasticity of nodes more, which can effectively alleviate network congestion, promote the absorption of hydropower resources, and stimulate the growth of load demand, thereby greatly improving the hydropower absorption capacity. (3) In the analysis of transaction signals such as network congestion surplus, the planning model proposed in this invention can accurately locate the congestion section and evaluate the quantitative impact of line selection on congestion and even future electricity costs. Compared with traditional market planning, it is more promising and operable. Attached Figure Description

[0016] Figure 1 A schematic diagram of the model provided for this invention. Detailed Implementation

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] In a market like the Sichuan power grid, which contains a high proportion of hydropower, the complex hydraulic coupling relationships between multiple hydropower stations mean that the power generation capacity of downstream stations is constrained by upstream power. When multiple hydropower stations belong to different investment entities and cannot coordinate their bids, the mismatch between the winning bids and the independent participation in the electricity spot market makes it impossible to dispatch power according to the transaction results, leading to water wastage or difficulties in executing the transaction results. Furthermore, the day-ahead and real-time electricity spot market planning based on competitive bidding for hydropower stations / units cannot simultaneously consider the long-term optimal allocation of power resources for multi-year regulating hydropower stations, making it difficult to fully leverage the hydropower resource advantages of hydropower-rich areas.

[0019] Existing power grid expansion planning methods mostly focus on improving grid transmission capacity and stability from the perspectives of operation modes and safety standards to meet basic load demands. They rarely address the impact of the wet and dry seasons on electricity market clearing prices, making it difficult to comprehensively reflect the quantitative impact of the overall supply and demand situation under the current competitive market model on grid investment efficiency, market participants' willingness to participate, and social marginal benefits. Therefore, considering market signals in expanding hydropower-rich power grids is of great significance.

[0020] like Figure 1As shown, the constraints of the upper-level model aim to minimize the total cost in the planning year, taking into account constraints such as the upper and lower limits of the equivalent hydropower station's energy storage capacity, cost restrictions on line investment, transmission capacity constraints of key sections within the province, the processing characteristics of hydropower units, and the upper and lower limits of reservoir outflow.

[0021] Considering the upper and lower limits of the equivalent hydropower station's energy storage capacity: , These represent the upper and lower limits of the energy storage capacity of the equivalent hydropower station.

[0022] Investment constraints of the line: in, This represents the maximum investment budget.

[0023] Transmission capacity constraints at key sections within the province in This represents the maximum amount of electricity that can be transmitted.

[0024] Hydropower unit processing characteristics (Benders cut) For simplex multipliers, and t+1 Stage equivalent hydropower station k The shadow price corresponds to the energy storage balance constraint, representing the time period. t For every additional unit of stored energy, The increase in the optimal value of the comprehensive electricity purchase cost in the province during the period (yuan / MWh).

[0025] Upper and lower limits of reservoir outflow constraints in, Let k be the minimum warning capacity of reservoir k; This represents the maximum warning capacity of reservoir k.

[0026] Since the route planning layer is set as the upper layer of the model, the objective is to minimize the total cost of the planned route in the current year. This cost includes construction costs, labor costs, etc. The objective function expression is: in, and They are nodes i and j Between l 0-1 binary integer decision variables for each route and their equivalent annual investment cost; and They are nodes i No. k generator set 0-1 Binary integer investment decision variables and their equivalent annual investment costs; and These are the sets of candidate routes and the sets of candidate generator sets, respectively. and They are nodes i No. k The power output of the generator set in scenario s and its operating cost per unit output.

[0027] The lower-level clearing model assumes that all generating units in the power system are in operation. The objective function of the lower-level power market economic dispatch clearing model is: The clearing model for power grid expansion planning that considers both wet and dry seasons mainly includes nodal power supply and demand balance constraints and line transmission constraints. This paper will use the DCOPF model to simulate the power market clearing process, with the specific formula as follows: Node power balance constraints: Line transmission constraints: Generator output constraints: Initial ramp-up constraints for the generator: Unit ramp-up constraints outside the initial period: In the formula, Indicates that it is located at node j A collection of generator sets, For transmission lines l The sensitivity value, express t Time period nodes i The load. Indicates the connection node i and nodes j The maximum transmission capacity of the transmission line, Let g be the power output of generator set g in the initial period. The symbols on the right-hand side of each constraint condition represent the corresponding Lagrange multipliers in each formula.

[0028] Solving the model: The lower-level model in this invention contains many variables and formulas, but its mathematical essence is still a linear programming problem involving equality and inequality. In order to more intuitively illustrate the process of KKT conditional equivalence substitution, a simple linear programming problem involving equality and inequality will be used as an example below.

[0029] Where x is a continuous variable.

[0030] The Lagrange function corresponding to this problem is: in, These are the Lagrange multipliers corresponding to the equality constraints; These are the Lagrange multipliers corresponding to the inequality constraints.

[0031] The KKT conditions for this problem include, The above equation contains nonlinear complementary constraints, which can be linearized using the Big M method. By introducing 0-1 variables and sufficiently large numbers, the nonlinear term can be transformed into: in, For the introduced 0-1 variables, It is a sufficiently large number.

[0032] The constraints of the current optimization model itself and its KKT conditions are used as constraints to couple with the upper planning layer model into a single-layer mixed integer programming model, which will be named the new planning layer. After market clearing, the total investment return is returned to the new planning layer to further revise the upper-level decision. The number of iterations is set to 50, and the fitness function of the upper-level model is calculated after each iteration. The loop exits when the requirements are met.

[0033] This invention proposes a power grid expansion planning method for hydropower-rich areas, taking into account the abundant and scarce characteristics of hydropower. By expanding the power transmission lines, the method can help to clear blockages in hydropower transmission and promote the absorption of hydropower resources, thereby transforming resource advantages into economic advantages.

[0034] This invention uses a C-means algorithm to cluster and generate typical days for power grids that take into account wet and dry seasons, based on seasonal changes and load size. The results represent most scenarios of hydropower operation under wet and dry seasons, and can quickly solve related planning difficulties.

[0035] This invention employs a two-layer programming model. The upper layer is the planning layer, where the net present value of the investment cost is calculated using the equal annual value method based on the planning period. The lower layer is the market clearing layer, where C-means clustering is used to obtain the typical annual operation mode of the high-proportion hydropower network, and the KKT conditions are used to perform dimensionality reduction and stability solving of the two-layer model.

[0036] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for planning the expansion of a power transmission network considering the characteristics of abundant and scarce hydropower in areas with high hydropower availability, characterized in that, Includes the following steps: S1, minimize the target cost of the power generator, and construct the upper-level line planning model by combining the first multivariate constraint and the equivalent annual value method; among which, the first multivariate constraint includes the upper and lower limits of the equivalent hydropower station's energy storage capacity, the constraint of the number of new lines, the constraint of the hydropower unit's processing characteristics, and the upper and lower limits of the reservoir's outflow. S2 performs a minimization analysis of the target cost of the power grid company and constructs a lower-level market clearing model by combining the second multivariate constraints and the fuzzy C-means algorithm. The second multivariate constraints include node power balance constraints, line transmission constraints, generator output constraints, initial period unit ramp-up constraints, non-initial period unit ramp-up constraints, and power generation rights trading volume constraints. S3, by coupling the upper-level route planning model and the lower-level market clearing model through KKT conditions, a two-level planning model is obtained; The mathematical expressions for the upper and lower limits of the equivalent hydropower station's energy storage capacity constraints mentioned in step S1 are as follows: in, , These are the upper and lower limits of the energy storage capacity of the equivalent hydropower station, respectively. The mathematical expression for the constraint on the number of newly built lines is as follows: in, This represents the maximum investment budget. The mathematical expressions for the upper and lower limits of the reservoir outflow are as follows: in, Let k be the minimum warning capacity of reservoir k; For reservoir k Maximum warning capacity; The objective function expression of the upper-level route planning model described in step S1 is as follows: in, and They are nodes i and j Between l 0-1 binary integer decision variables for each route and their equivalent annual investment cost; and They are nodes i No. k 0-1 binary integer investment decision variables for generator sets and their equivalent annual investment costs; and These are the sets of candidate routes and the sets of candidate generator sets, respectively. and They are nodes i No. k The power output of the generator set in scenario s and its unit output operating cost; The nodal power balance constraint mentioned in step S2 has the following mathematical expression: in, Indicates that it is located at node j A collection of generator sets, Indicates generator set g exist t Time-slot scheduling output For transmission lines l The sensitivity value, express t Time period nodes i The load, , Representing nodes respectively i and nodes j exist t Phase angle value for the time period; The mathematical expression for the line transmission constraint is as follows: in, This represents the maximum transmission capacity of the transmission line connecting node i and node j; The mathematical expression for the generator set output constraint is as follows: in, Indicates generator set g The minimum power generation output declared in the electricity market Indicates generator set g The maximum generating capacity declared in the electricity market; The mathematical expression for the initial period unit ramp-up constraint is as follows: in, Indicates generator set g downhill slope rate For generator sets g Power generation output in the initial period, Indicates generator set g The rate of ascent; The mathematical expression for the non-initial period unit ramp-up constraint is as follows: in, Indicates generator set g exist t Dispatch output during the -1 time period; The objective function expression of the lower-level market clearing model described in step S1 is as follows: in, t This is the clearing period for the electricity market. and These represent the set of nodes and the set of clearing periods, respectively. The coefficients of the quadratic term, the linear term, and the constant term of the application curve for generator set g are represented. Indicates generator set g exist t Output during specific time periods.

2. The method for power grid expansion planning considering the abundant and scarce characteristics of hydropower-rich areas according to claim 1, characterized in that, The mathematical expression of the fuzzy C-means algorithm described in step S2 is as follows: in, This is the cluster center matrix; It is a weighted index; Indicates sample k To the i Distance between cluster centers; n The total number of samples; This represents the number of clusters.

3. The method for power grid expansion planning considering the abundant and scarce characteristics of hydropower-rich areas according to claim 1, characterized in that, The isochronous value method described in step S1 has the following mathematical expression: ； in, i This indicates the interest rate; n This refers to the expected service life.

4. The method for power grid expansion planning considering the abundant and scarce characteristics of hydropower-rich areas according to claim 1, characterized in that, The target cost of the power generator mentioned in step S1 is expressed mathematically as follows: Maintenance costs + operating costs - power generation revenue - power generation rights trading revenue.

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