Energy storage configuration method for improving new energy bearing capacity based on collaborative optimization

Abstract

The invention discloses an energy storage configuration method for improving new energy bearing capacity based on collaborative optimization, which relates to the technical field of new energy power systems, and optimizes a load curve and solves initial energy storage configuration based on a Logistic function by constructing an upper-layer constant volume model; constructing a middle-layer simulation operation model, and calculating a bearing capacity upper limit by taking minimum system operation cost and maximum new energy bearing capacity as double targets; constructing a lower-layer optimization model, optimizing the configuration of the network construction type power supply and the phase modifier, and completing security constraint verification; and iteratively solving the three-layer model by adopting a generalized Benders decomposition algorithm, and outputting a global optimal energy storage configuration and new energy bearing capacity improvement result. According to the method, the load side, power side and system side strategies are optimized in a layered and collaborative manner, economy, reliability and safety are considered, and the new energy bearing and absorption capacity of the power grid is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of new energy power system technology, and more specifically to an energy storage configuration method based on collaborative optimization to improve the carrying capacity of new energy. Background Technology

[0002] Currently, addressing the grid's renewable energy carrying capacity under the constraints of safety, stability, and power supply guarantees is crucial. Researching the upper limit of the grid's renewable energy carrying capacity under these constraints is key, while simultaneously resolving the increased uncertainty and more complex multi-factor coupling issues related to "abundance" and "security." This research aims to clarify the reasonable operating scale and comprehensive utilization rate of renewable energy in the grid, propose development recommendations for renewable energy under the conditions of ensuring supply, consumption, and security, and provide guidance for the safe and stable operation and planning of the power system during the renewable energy transition. This will support the sustainable development of the new power system and solidify the foundation for a safe and reliable power supply and efficient renewable energy consumption.

[0003] With the large-scale development of new energy power generation and the annual construction of DC transmission projects, especially given the current rapid growth of new energy development far exceeding expectations, significant challenges have arisen for power system planning, design, and operation control. On the one hand, new energy power generation output is random and volatile, making the "duck-shaped" characteristic of the grid's net load curve increasingly prominent. Midday peak-shaving curtailment of new energy and evening peak-shaving power shortages will coexist for a long time, resulting in extremely uneven spatial and temporal distribution of power, with both abundance and scarcity presenting challenges to power sufficiency. Furthermore, constrained by the volatility of new energy output and grid security and stability, the problem of new energy absorption is severe, with significant risks of wind and solar curtailment. On the one hand, new energy power generation equipment has low immunity and weak support. While new energy power generation is replacing conventional units on a large scale, the effective power supply capacity has not increased significantly with the scale of new energy installations. Reliable power supply still requires regulating power sources such as coal-fired and hydropower units to play a backup role, posing security challenges. The material and technological foundations of the new power system, with its gradually increasing proportion of new energy, are constantly changing. New energy's support capacity for peak load power balance and system safety operation is limited. Power shortages, new energy absorption, and security and stability issues will frequently occur, which are problems that urgently need to be addressed in the development of the new power system.

[0004] Therefore, there is an urgent need for a hierarchical, collaborative, and multi-objective optimization method for energy storage configuration, which comprehensively coordinates the equipment control and configuration strategies of the load side, power supply side, and system side, takes into account the economy, reliability, and security of the power grid, and effectively improves the carrying capacity of new energy sources. Summary of the Invention

[0005] In view of this, the present invention provides an energy storage configuration method and system for improving the carrying capacity of new energy sources based on collaborative optimization, taking into account load demand response under time-of-use pricing, thereby achieving the effect of smoothing load. Furthermore, an energy storage capacity configuration optimization model for improving the carrying capacity of new energy sources is established to calculate the maximum carrying capacity of new energy sources; simultaneously, the system's new energy absorption is improved through collaborative optimization of grid-connected power sources and synchronous condensers, thereby achieving the effect of improving the carrying capacity of new energy sources by configuring energy storage capacity.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Energy storage configuration methods for improving the carrying capacity of new energy sources based on collaborative optimization include: S1. Construct an upper-level fixed-capacity model, build a load demand response model based on the Logistic function, calculate the load transfer rate and optimize the load curve through time-of-use pricing, and comprehensively consider the constraints of new energy output, hydropower operation and energy storage system operation. With the goal of minimizing the investment cost and operation and maintenance cost of energy storage capacity, solve for the initial energy storage configuration scheme and the smoothed net load curve. S2. Construct a mid-level simulation operation model, receive the initial energy storage configuration scheme and net load curve output by the upper-level fixed capacity model, take the minimum total system operating cost and the maximum new energy carrying capacity as dual objectives, obtain the comprehensive objective function through normalization and weighting, combine power balance constraints and wind and solar curtailment rate constraints to complete the system simulation operation, calculate the upper limit of the grid new energy carrying capacity that meets the load failure probability reliability standard, and output the system operation status results; S3. Construct a lower-level optimization model. Based on the system operation status results of the middle-level simulation operation model, optimize the configuration capacity of grid-type power sources and synchronous condensers. With the goal of minimizing the investment and total operating cost of grid-type power sources and synchronous condensers, combine short-circuit ratio constraints, node voltage constraints, line power flow constraints, and branch power constraints to complete the system safety constraint verification and feed the safety verification results back to the middle-level simulation operation model. S4. The generalized Benders decomposition algorithm is used to iteratively solve the three-layer collaborative optimization model until the safe and stable convergence conditions are met, and the globally optimal energy storage configuration result and the result of the improvement of new energy carrying capacity are output.

[0007] Optionally, the objective function F of the upper-level sizing model is:

[0008] In the formula: ΔC ESS,p Indicates the configuration cost of energy storage power; ΔC ESS This indicates the cost of configuring energy storage capacity; ΔC ESS,w This indicates the maintenance cost of energy storage.

[0009] Optionally, the objective function of the intermediate-level simulation model is: The goal is to minimize the total operating cost of the system.

[0010] In the formula: f 1 represents the total operating cost of the system; C coal Operating costs of thermal power units; C carbon Cost of carbon emissions; C h The operating cost of the hydroelectric power unit; C WT For wind power operating costs; C PV For photovoltaic operating costs; C ESS For energy storage operating costs; The maximum expression for the carrying capacity of new energy sources is:

[0011] In the formula: f 2 represents the maximum carrying capacity of new energy sources; P load,max This represents the system's maximum load. P line This represents the maximum switching power to the external network. P T,min For the minimum technical output of conventional units within the system; k This represents the rate of curtailment of renewable energy in the system operation; ∑ C ess Total installed capacity of energy storage units; The two objective functions, total system operating cost and new energy carrying capacity, are normalized and weighted before being combined into a comprehensive objective function F:

[0012] In the formula, It is the objective function f The weight is 1, and its value ranges from (0,1). f 1,max , f 1,min It is the objective function f The maximum and minimum values ​​of 1; f 2,max , f 2,min It is the objective function f The maximum and minimum values ​​of 2.

[0013] Optionally, the lower-level optimization model constructs an objective function that minimizes the sum of investment and operating costs of grid-connected renewable energy sources and synchronous condensers:

[0014] In the formula, C Total configuration cost; C n,in , C n,m These are the investment cost and operation and maintenance cost of grid-connected power sources, respectively. C syn,in , C syn,m These are the investment cost and maintenance cost of the synchronous condenser; c n,in It is the investment cost coefficient for grid-type power sources per unit capacity; c n,m It is the unit capacity grid-type power supply operation and maintenance cost coefficient; c syn1,in , c syn2,in These are the investment cost coefficients for distributed and centralized synchronous condensers per unit capacity, respectively. c syn1,m , c syn2,m These are the operation and maintenance cost coefficients for distributed and centralized synchronous condensers per unit capacity, respectively. S n It is the grid-connected capacity of a single grid-connected power supply; Q syn1 This refers to the grid connection capacity of a single distributed synchronous condenser. Q syn2,i for i Node configuration centralized synchronous condenser capacity; , These represent the number of grid-type power supplies and distributed synchronous condensers configured at node i, respectively.

[0015] Optionally, the specific process of iteratively solving the three-layer collaborative optimization model using the generalized Benders decomposition algorithm is as follows: The intermediate-level simulation operation model is set as the master problem, responsible for handling discrete decision variables, with the goal of minimizing the total system operating cost. The upper-level capacitive model and the lower-level optimization model are subproblems, respectively handling continuous variables. The intermediate-level model, as the master problem, passes the solution containing discrete decision variables to the upper-level model subproblem. After solving based on this solution, the upper-level model generates feasible cuts and feeds them back to the intermediate-level model master problem to guide the master problem in adjusting the solution in the next iteration. After solving, the lower-level model generates feasible cuts for safety constraints, which are passed to the intermediate-level model in the form of linear constraints. The intermediate-level model adjusts the operation plan accordingly to ensure the coordination of safety and economy. Through iterative solution, the upper and lower bounds are gradually tightened until the safety and stability conditions are met, and finally, the globally optimal energy storage configuration result and the result of improving the carrying capacity of new energy are output.

[0016] Optionally, the system safety constraint verification uses the node voltage exceedance probability as a quantitative indicator:

[0017] In the formula, Y i ( u ) represents the node voltage exceeding the limit at that node; G i ( u ) represents the over-limit rate of the node voltage at that node; S ev ( w i The number represents the severity of the node voltage exceeding the limit at that node.

[0018] In the formula, V i This is the per-unit value of the node voltage; w i This is the voltage limit exceeded by this node; V max and V min This indicates the maximum and minimum values ​​of the node voltage.

[0019] As can be seen from the above technical solution, compared with the prior art, this invention discloses an energy storage configuration method for improving the carrying capacity of new energy sources based on collaborative optimization. The core is a three-layer collaborative optimization model, which addresses the energy storage configuration and carrying capacity improvement problem by layering the solutions to consider economy, reliability, and safety. In the upper-layer model, load shifting during peak, flat, and valley periods is optimized to effectively smooth fluctuations in the net load curve. Based on this, the upper-layer model takes minimizing the investment and operation and maintenance costs of the energy storage system as its core objective. Under the premise of satisfying constraints on photovoltaic and wind power output, hydropower unit operation, and the energy storage system's own charging, discharging, and state of charge constraints, it optimizes and solves for the most economically efficient initial energy storage configuration scheme. The middle-level model receives the smoothed load curve and initial energy storage configuration plan from the upper-level model, with minimizing the total system operating cost as its primary objective. This objective function encompasses the coal consumption and carbon emission costs of thermal power units, the operating costs of hydropower units, the operating and curtailment penalty costs of wind and solar power, and the operating costs of energy storage systems. It introduces the probability of load shedding as a key indicator of system reliability and uses a carrying capacity calculation formula to link multiple factors such as the system's maximum load, external grid exchange power, minimum technical output of conventional units, curtailment rate, and energy storage configuration capacity. This allows for the scientific calculation of the upper limit of the grid's renewable energy carrying capacity under certain reliability standards. The lower-level model focuses on the safety and stability of the power system after a high proportion of renewable energy is integrated. Grid-connected power sources provide inertia and voltage support, replacing the "grid-connecting" function of conventional units and enhancing the system's anti-disturbance capability. Synchronous condensers suppress voltage fluctuations and frequency deviations through reactive power compensation and inertial response. By combining the operating status obtained from the mid-level model with grid-connected power sources and synchronous condensers, system stability is improved and the probability of voltage exceeding limits is reduced. By optimizing the configuration capacity of grid-connected power sources and synchronous condensers, safety constraints such as short-circuit ratio and node voltage are met, thereby enhancing the grid's support capacity for renewable energy consumption. Finally, the generalized Benders decomposition algorithm is used to coordinate the optimization solution of the three-layer complex model. The mid-level model is set as the main problem, responsible for handling discrete decision variables (unit operating status, etc.) with the objective of minimizing the total system operating cost; the upper-level model (optimizing energy storage capacity investment and operation and maintenance costs) and the lower-level model (safety and stability constraints) are sub-problems, respectively handling continuous variables. Through iterative solutions until the safety and stability conditions are met, the globally optimal energy storage configuration result and renewable energy carrying capacity improvement result are finally output. It is evident that this invention improves renewable energy consumption capacity by starting from demand response, grid-connected power sources, synchronous condensers, and energy storage support and regulation equipment. Through optimizing the control and regulation strategies of various types of equipment, the improvement effect on renewable energy consumption capacity is achieved, thereby enhancing the grid's renewable energy consumption capacity and safety and stability level. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0021] Figure 1 This is a schematic diagram illustrating the principle of the method provided by the present invention; Figure 2 The demand response diagram based on the Logistic function provided by this invention; Figure 3 A schematic diagram illustrating the solution process of the three-layer optimization model based on the Benders algorithm provided by this invention; Figure 4 The voltage amplitude change diagram before and after optimization is provided for this invention; Figure 5 The present invention provides a distribution diagram of voltage stability index before and after the configuration of the synchronous condenser; wherein (a) is a distribution diagram of voltage stability index before the configuration of the synchronous condenser, and (b) is a distribution diagram of voltage stability index after the configuration of the synchronous condenser. Figure 6 The power timing diagram of the energy storage configuration before collaborative optimization provided by this invention; Figure 7 The power timing diagram of the collaboratively optimized energy storage configuration provided by this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention discloses an energy storage configuration method for improving the carrying capacity of new energy sources based on collaborative optimization, such as... Figure 1 As shown, it includes: S1. Construct an upper-level fixed-capacity model. Based on the Logistic function, build a load demand response model. Calculate the load transfer rate and optimize the load curve using time-of-use pricing. Simultaneously, comprehensively consider the constraints of new energy output, hydropower operation, and energy storage system operation. With the goal of minimizing the investment cost and operation and maintenance cost of energy storage capacity, solve for the initial energy storage configuration scheme and the smoothed net load curve. The upper-level model adopts a price-based demand response for the load side. By solving for the load curve with the minimum volatility, it is passed to the upper-level optimization model. The upper-level optimization model is based on the output of wind, solar, hydro, and thermal power and the obtained load demand response curves. It considers the output constraints of wind, solar, hydro, and thermal power generation and the relevant constraints of energy storage system operation. It optimizes the output of traditional units at each time period and the charging and discharging power of energy storage at each time period. It establishes an upper-level optimization scheduling model with the goal of minimizing energy storage capacity investment and operation and maintenance costs. It obtains the net load curve of the system and the energy storage capacity, and then passes them to the middle-level optimization model.

[0024] S2. Construct a mid-level simulation operation model, receive the initial energy storage configuration scheme and net load curve output by the upper-level fixed capacity model, take the minimum total system operating cost and the maximum new energy carrying capacity as dual objectives, obtain the comprehensive objective function through normalization and weighting, combine power balance constraints and wind and solar curtailment rate constraints to complete the system simulation operation, calculate the upper limit of the grid new energy carrying capacity that meets the load failure probability reliability standard, and output the system operation status results; The intermediate-level operation model is based on the data and curves obtained from the upper-level model. It comprehensively considers the operating costs of wind, solar, hydro and thermal power generation, the operating costs of energy storage systems, the carbon emission costs of thermal power units, as well as the operating constraints of wind, solar, hydro and thermal power units, external transmission capacity constraints, power flow security constraints, etc., and establishes an intermediate-level model with the goal of minimizing the operating cost of the power system and maximizing the carrying capacity of new energy sources, and obtains various operating results of the power system.

[0025] S3. Construct a lower-level optimization model. Based on the system operation status results of the middle-level simulation operation model, optimize the configuration capacity of grid-type power sources and synchronous condensers. With the goal of minimizing the investment and total operating cost of grid-type power sources and synchronous condensers, combine short-circuit ratio constraints, node voltage constraints, line power flow constraints, and branch power constraints to complete the system safety constraint verification and feed the safety verification results back to the middle-level simulation operation model. The lower-level optimization model optimizes the middle-level model. Grid-connected power sources provide inertia and voltage support, enhancing the system's anti-disturbance capability. Synchronous condensers suppress voltage fluctuations and frequency deviations through reactive power compensation and inertial response. By combining the operating status obtained from running the middle-level model with the grid-connected power sources and synchronous condensers, system stability is improved, the probability of voltage exceeding limits is reduced, and wind and solar curtailment due to safety constraints is reduced, thereby indirectly increasing the space for renewable energy consumption. Considering factors such as short-circuit ratio and voltage stability, a lower-level optimization model is established based on voltage stability and frequency control optimization, and the safety constraint verification results (short-circuit ratio, voltage amplitude) are fed back to the middle-level model.

[0026] S4. The generalized Benders decomposition algorithm is used to iteratively solve the three-layer collaborative optimization model until the safe and stable convergence conditions are met, and the globally optimal energy storage configuration result and the result of the improvement of new energy carrying capacity are output.

[0027] The specific principle is explained as follows: 1. Upper-level constant volume model The upper-level model optimizes the load curve through price-based demand response and initially determines the energy storage configuration scheme with the goal of minimizing energy storage investment and operation and maintenance costs.

[0028] (1) Objective function

[0029] To reduce the peak-shaving difficulty of thermal power units, the upper-level model considers introducing a price-based demand response, with the goal of minimizing energy storage capacity. The corresponding objective function is as follows: (1) In the formula: ΔC ESS,p Indicates the configuration cost of energy storage power; ΔC ESS This indicates the cost of configuring energy storage capacity; ΔC ESS,w This indicates the maintenance cost of energy storage.

[0030] (3) (4) In the formula: E ESS ( t ) represents the energy charged and discharged by the energy storage device, and represents the value of the energy storage at a certain moment. P r ( t (This refers to the real-time grid electricity price.) P max This refers to the rated capacity of the energy storage device. The cost per unit of lithium battery energy storage power is mainly the unit purchase cost of the power converter; C pess For energy storage device capacity; P p,cap Cost per unit of energy storage capacity.

[0031] (2) Constraints

[0032] 1) New energy output constraints include photovoltaic and wind power output constraints.

[0033] In high-proportion renewable energy systems, the constraints for handling photovoltaic and wind power should meet the following conditions: (5) (6) In the formula, Indicates wind power output. Indicates the maximum output of wind power; Indicates photovoltaic power output. This indicates the maximum output of photovoltaic power.

[0034] 2) Hydropower operation constraints, i.e., hydropower unit output constraints, should meet the following constraints: (7) (8) (9) In the formula: P h ( t () represents the output of the hydroelectric generator unit at that moment; P h,min and P h,max These are the upper and lower limits of the active power output of the hydropower unit; , This indicates the upper and lower standby capacities of the hydropower unit at that moment; α h,t This represents the 0-1 variable of the hydropower unit at that moment; P h,up and P h,down These represent the unit's uphill and downhill ramp rates, respectively.

[0035] 3) Energy storage system operation constraints

[0036] Expressions for upper and lower limits of energy storage state of charge constraints: (10) In the formula: SOC(t) is the state of charge of the stored energy at that moment, and the maximum state of charge of the stored energy is set. and minimum state of charge The values ​​are 0.95 and 0.05, respectively.

[0037] The energy storage charge and discharge constraint expressions are as follows: (11) In the formula: E c_max ( t )and E f_max (t) These are the upper limits for charging power and discharging power of energy storage systems, respectively. , These are the charging power and discharging power of the energy storage system, respectively. The charge-discharge cycle constraints of the lithium battery energy storage system need to be considered, and the following conditions must be met: (12) In the formula: DOD represents the depth of discharge of the stored energy, which is taken as 0.1. For energy storage efficiency.

[0038] 4) Load demand response constraints

[0039] like Figure 2 As shown, the load shifting rate model based on the Logistic function is used to characterize the willingness and extent to which electricity users adjust their electricity consumption patterns when faced with different electricity price signals.

[0040] a. Demand Response Model Construction

[0041] To more accurately simulate the impact of time-of-use pricing on user electricity consumption behavior, internal adjustable parameters are adjusted to expand the response range of load transfer rate as a function of price differences. The functional expression is as follows: (13) in: Δp For electricity price difference; λ This represents the load transfer rate; x, z, and μ are fixed values ​​in this function; y represents an adjustable parameter.

[0042] (14) (15) In the formula: This represents the actual load transfer rate; and These represent the load transfer rates for optimistic and pessimistic demand responses, respectively. x pv and y pv The dividing point between the two dividing zones; m This represents the membership degree of an optimistic response.

[0043] Using the above method, the actual load transfer rate of electricity consumption shifted from peak hours to off-peak hours and from off-peak hours to valley hours can be further calculated. This objective function considers both the load transfer amount and the load value after demand response. The expression is as follows: (16) (17) In the formula: T p , T f , T v These represent three time periods in Fengping Valley; , , These represent the average load before demand response; D t This indicates the electricity consumption that changes in response to demand. ；L t0 and Lt These represent the electricity consumption at that specific time during peak and off-peak periods; , These represent the load transfer rates from peak to flat and flat to valley, respectively.

[0044] b. Time-of-use pricing optimization model

[0045] Time-of-use pricing is an important mechanism for guiding electricity users to smooth peak and valley loads, ensuring the safe, stable, and economical operation of the power system. By coordinating the interests of power grid companies and users, it guides load shifting from peak to off-peak periods, optimizes the load curve, and ultimately minimizes system load fluctuations while protecting the interests of all parties. The relationship is as follows: (18) In the formula: N T This indicates the optimization cycle.

[0046] (19) (20) (twenty one) In the formula: U 1 and U 2 represents electricity satisfaction and cost-effectiveness, respectively; This represents the absolute value of the difference in electricity consumption before and after optimization; c t0 and c t Indicates the time-of-use electricity price before and after optimization; when f The closer 2 is to 1, the better the user satisfaction and cost-effectiveness.

[0047] User-side constraints aim to ensure both the satisfaction of load transfer and that consumers can also obtain certain economic benefits: (twenty two) For power grid companies, it is necessary to ensure that their profits are not lower than before optimization. (twenty three) In the formula: c 0 This represents the electricity purchase price, which is a known quantity.

[0048] Before and after demand response, it is necessary to ensure that load demand fluctuates within a reasonable range of the original total demand. (twenty four) In the formula: μ This represents the load fluctuation, which is a known quantity.

[0049] 2. Mid-level simulation operation model

[0050] Based on the initial results of the upper-level operation, the simulation operation is carried out with the primary goal of minimizing the total operating cost of the system, and the upper limit of the new energy carrying capacity under the reliability standard is calculated.

[0051] (1) Objective function

[0052] When considering a combined wind, solar, thermal, and energy storage system, the objective should be to minimize the total operating cost of the system. The corresponding relationship expression is: (25) In the formula: f 1 represents the total operating cost of the system; C coal Operating costs of thermal power units; C carbon Cost of carbon emissions; C h The operating cost of the hydroelectric power unit; C WT For wind power operating costs; C PV For photovoltaic operating costs; C ESS This refers to the operating costs of energy storage.

[0053] The maximum expression for the carrying capacity of new energy sources is: (26) In the formula: f 2 represents the maximum carrying capacity of new energy sources; P load,max This represents the system's maximum load. P line This represents the maximum switching power to the external network. P T,min For the minimum technical output of conventional units within the system; k This represents the rate of curtailment of renewable energy in the system operation; ∑ C ess Total installed capacity of energy storage units.

[0054] The two objective functions, economic cost and new energy carrying capacity, are normalized and weighted before being merged into a comprehensive objective function F to facilitate collaborative optimization.

[0055] (27)

[0056] In the formula, It is the objective function f The weight is 1, and its value ranges from (0,1). f 1,max , f 1,min It is the objective functionf The maximum and minimum values ​​of 1; f 2,max , f 2,min It is the objective function f The maximum and minimum values ​​of 2.

[0057] Operating costs of thermal power units

[0058] To improve the solution efficiency of the optimization model, the quadratic cost function in the coal consumption characteristics of thermal power units is approximated as a piecewise linear function, thereby transforming the original nonlinear optimization problem into an easily solvable linear programming problem. The piecewise linearized expression for the coal consumption of thermal power units is as follows: (28) (29) (30) In the formula: a i For the first i The slope of the segment; F i For the first i Maximum coal consumption of the section; N The number of segments; P i (t) This indicates the output power of the thermal power unit at that moment. P i Output power of thermal power units.

[0059] Operating costs of thermal power units for: (31) In the formula: a t , b t ,c t This represents the secondary energy consumption cost coefficient for thermal power units. S t For unit start-up and shutdown costs, u t Start-stop state variables ∈{0,1} (0 for stop, 1 for run). P t Let t be the output of the thermal power unit.

[0060] The carbon emission cost of thermal power units is: (32) In the formula: tan c For carbon capture costs; Cc Indicates the carbon emission coefficient of thermal power units; P t This indicates the output of the thermal power unit at that moment.

[0061] Hydropower operating costs (33) In the formula: C h The operating cost of the hydroelectric power unit; c i For the first i Unit investment cost of a hydropower station; α i Considering the difficulties and related costs of reservoir construction; N i This refers to the installed capacity of the hydropower unit.

[0062] Wind power operating costs (34) (35) (36) In the formula: C W,op , C W,cur These are wind power operating costs and wind curtailment costs, respectively. C W,om , r W,cur The unit operation and maintenance cost of the wind turbine and the unit wind curtailment penalty cost are known quantities. This represents the power generation of the wind turbine at any given time. P W (t) This represents the actual power consumption of the wind turbine during a given time period.

[0063] Photovoltaic operating costs (37) (38) (39) In the formula: C P,op , C P,cur These are the operating costs of photovoltaic units and the cost of curtailment of solar power. C P,om , r P,cur The unit operation and maintenance cost of the photovoltaic unit and the unit curtailment penalty cost are known quantities; The amount of electricity generated by the photovoltaic unit at any given moment; PP (t) represents the actual power used by the photovoltaic unit during the time period.

[0064] (2) Constraints

[0065] 1) Power balance constraints

[0066] For the overall power system output, the output of thermal power units, wind power, photovoltaic power, and energy storage discharge power need to match the demand power and energy storage charging power: (40) In the formula: P g (t) Indicates the output of the thermal power unit; P PV (t) Indicates photovoltaic power output; P WT (t) Indicates wind power output; P load (t) Indicates load demand; E c (t) and E f ( t () indicates the energy storage charging and discharging power.

[0067] 2) Constraints on wind and solar curtailment rates

[0068] Considering the power surplus caused by the uneven spatial and temporal distribution of new energy power generation, energy storage devices can be used to balance the fluctuations in wind and solar power with demand-side loads, thereby improving the efficiency of new energy consumption.

[0069] (41)

[0070] In the formula: R represents the renewable energy consumption rate.

[0071] Reliability index calculation

[0072] From a reliability perspective, the performance evaluation of power grid operation uses the probability of load shedding as a reliability index, namely: (42) In the formula, T represents time; T L For all periods when the system power is low; T j The duration of the time period state.

[0073] The expected indicators for power shortages are as follows: (43) In the formula, EENS represents the expected value of power shortage; P load ( t )for t Total system load demand during the time period; P available (t) The available system capacity for time period t; The duration of each time period; The function ensures that only the portion of the load exceeding the available capacity is calculated.

[0074] 3. Lower-level optimization model

[0075] With the goal of ensuring system safety and stability, the configuration of grid-connected power sources and synchronous condensers is optimized to meet safety constraints such as short-circuit ratio and voltage stability, thereby enhancing the grid's ability to support new energy carrying capacity.

[0076] (1) Objective function

[0077] The objective function is constructed to minimize the sum of investment and operating costs of grid-connected renewable energy sources and synchronous condensers. The objective function is as follows: (44) (45) In the formula, C Total configuration cost; C n,in , C n,m These are the investment cost and operation and maintenance cost of grid-connected power sources, respectively. C syn,in , C syn,m These are the investment cost and maintenance cost of the synchronous condenser; c n,in It is the investment cost coefficient for grid-type power sources per unit capacity; c n,m It is the unit capacity grid-type power supply operation and maintenance cost coefficient; c syn1,in , c syn2,in These are the investment cost coefficients for distributed and centralized synchronous condensers per unit capacity, respectively. c syn1,m , c syn2,m These are the operation and maintenance cost coefficients for distributed and centralized synchronous condensers per unit capacity, respectively. S n It is the grid-connected capacity of a single grid-connected power supply; Q syn1 This refers to the grid connection capacity of a single distributed synchronous condenser. Q syn2,ifor i Node configuration centralizes the capacity of the synchronous condenser.

[0078] (2) Constraints

[0079] 1) Grid-type power supply and synchronous condenser i Node count constraint (46) In the formula, N n,i , N syn,i These are nodes i The upper limit on the number of grid-type power supplies and distributed synchronous condensers that can be configured.

[0080] Grid-type power supply and synchronous condenser capacity constraints

[0081] 2) The capacity constraints for each device are as follows: (47) (48) In the formula, S n,max , S n,min These are the upper and lower limits of the total capacity of a grid-connected power supply configuration, respectively. S sys It is the total system capacity. η It is the percentage of grid-type power supply equipment capacity that meets the minimum inertia requirements of the system. Q syn,max This is the upper limit of the total capacity of the camera configuration; Q syn2,max It is the upper limit of the configuration capacity of a single centralized synchronous condenser.

[0082] 3) Short-circuit ratio constraint

[0083] When the short-circuit ratio at the new energy access point is between 1.1 and 1.8, a critical instability may occur. Combining equations (44), (45), (46), and (47), the short-circuit ratio constraint can be obtained as follows: (49) In the formula, U syn This refers to adjusting the camera's rated voltage amplitude; U N It is the system's nominal voltage; X syn1,j yes i Equivalent internal impedance of node-distributed synchronous condensers; X syn2,j yes i Equivalent internal impedance of a node-centralized synchronous condenser; It is inversely proportional to the capacity of the camera. S syn2 It is the unit capacity susceptance of a centralized synchronous condenser; Take 0, These represent the baseline capacity of the node, respectively. Represents a node i New energy injection power, Represents a node j New energy injection power, Represents a node i Self-impedance, This represents the mutual impedance between nodes.

[0084] 4) Power flow constraints of the line (50) (51) In the formula: P i and Q i These represent the active and reactive power of the node, respectively. U i and U j These represent the node voltages at the nodes; and These represent the phase angles at the nodes; G ij and B ij These represent the real and imaginary parts of the row and column of the node admittance matrix, respectively; N represents the total number of nodes in the network.

[0085] 5) Branch power constraints (52) In the formula: for Flowing through the side road The active power, in the direction of the node. i Flow to Node j ; and Branch roads ij Upper and lower limits of circulating active power.

[0086] 6) Node voltage constraints

[0087] To ensure the safe and stable operation of the regional power grid, after adding energy storage, it is necessary to ensure that the voltage of each node is kept within a reasonable and safe range. The relevant expressions are as follows.

[0088] (53) (54) In the formula, V i The node voltage amplitude of the system; V n This refers to the voltage amplitude at the substation node. N bus The number of nodes in the regional power grid system; U max and U min These represent the maximum and minimum values ​​of the node voltage amplitude, respectively.

[0089] 7) Voltage over-limit indicators

[0090] Using the probability of node voltage exceeding limits as a key quantitative indicator for power grid security assessment: (55) In the formula, Y i (u) This is the node voltage limit value at that node; G i (u) This represents the rate of voltage exceedance at that node. S ev (w i ) This indicates the severity of the node voltage exceeding the limit at that node.

[0091] (56) (57) In the formula, V i This is the per-unit value of the node voltage; w i This is the voltage limit exceeded by this node; V max and V min This indicates the maximum and minimum values ​​of the node voltage.

[0092] 4. Solution Algorithm

[0093] The generalized Benders algorithm is an effective method for solving complex optimization problems, especially mixed-integer nonlinear programming problems. It decomposes the original problem into a main problem and subproblems through a decomposition and coordination strategy, and approximates the optimal solution through iteration.

[0094] The original problem is decomposed into a main problem and subproblems, the mathematical forms of which are as follows: Subproblems (58) In the formula, The objective function for state estimation; Represents equality constraints; These represent the real and imaginary parts of the node voltage, respectively.

[0095] For the original problem of equation (58), by fixing the switching variable Then we can obtain the subproblems. When the subproblems are feasible, the Lagrangian dual problem and the constraints of the feasible solution are shown in (59)-(60): (59) (60) Feasibility subproblems (61) In the formula, This indicates the voltage limit exceeded at that node. When the subproblem is infeasible, the Lagrange dual problem constructed and the constraints of the feasible solution are shown in equations (62)-(63): (62) (63) Relaxation main problem (64) In the formula, Let represent a slack variable, and its feasible and infeasible solution constraints are shown in equations (60) and (63).

[0096] The flowchart for solving the three-layer optimization model based on the Benders algorithm is as follows: Figure 3 As shown.

[0097] This invention employs the generalized Benders decomposition algorithm to solve the three-layer collaborative optimization model, and its core process is as follows: Figure 3As shown, the algorithm sets the middle-level model as the master problem, responsible for handling discrete decision variables (such as unit operating status), with the objective of minimizing the total system operating cost. The upper-level model (optimizing energy storage capacity investment and operation and maintenance costs) and the lower-level model (safety and stability constraints) are treated as sub-problems, respectively handling continuous variables. The middle-level model, as the master problem, passes the solution containing discrete decision variables (unit operating status) to the upper-level model sub-problem. After solving this solution, the upper-level model generates feasible cuts and feeds them back to the middle-level model master problem to guide the master problem in adjusting the solution in the next iteration. After solving, the lower-level model generates feasible cuts for safety constraints, which are passed to the middle-level model in the form of linear constraints. The middle-level model adjusts its operating plan accordingly to ensure a balance between safety and economy. Through iterative solving, the upper and lower bounds are gradually tightened until the safety and stability conditions are met. Finally, the globally optimal energy storage configuration result and the result of improving the new energy carrying capacity are output, thereby improving the new energy carrying capacity while ensuring system safety and stability.

[0098] In one specific embodiment, the voltage amplitude change before and after optimization is shown in the figure below. Figure 4 As shown in the figure, there are two core curves, which respectively show the changes in the voltage amplitude of the grid nodes before and after the collaborative optimization of energy storage configuration: before optimization, the voltage amplitude fluctuates greatly and frequently exceeds the safe operating range, resulting in poor voltage stability; after optimization, the voltage amplitude is controlled in a stable range, the fluctuation is significantly narrowed, and it is always maintained within the safe threshold, which intuitively reflects the effect of this method on improving the voltage stability of the grid.

[0099] The voltage stability index distribution before and after adjusting the camera configuration is shown in the figure below. Figure 5 As shown, the voltage stability indices of each node in the power grid are presented in the form of discrete points or bar charts: (e.g.) Figure 5 As shown in (a), before the synchronous condenser was configured, the voltage stability indicators of each node were scattered and the values ​​were low, with many nodes in a state of weak voltage stability or instability; for example Figure 5 As shown in (b), after configuring the synchronous condenser, the voltage stability index of each node shifted upward and became more concentrated, and the voltage stability level was greatly improved, which verifies the optimization effect of the synchronous condenser on the voltage support and stability of the system.

[0100] The power timing diagram of the energy storage configuration before collaborative optimization is as follows: Figure 6 As shown in the figure, before collaborative optimization, the output curves, load demand curves, and total power generation curves of thermal power units, wind power units, photovoltaic units, hydropower units, and energy storage units are displayed. The energy storage output is irregular, and the matching degree with the output of new energy sources and load demand is extremely poor. The total power generation deviates greatly from the load demand, the new energy curtailment rate is high, the system peak-shaving pressure is significant, and the energy storage has not played an efficient regulatory role.

[0101] The power timing diagram of the energy storage configuration after collaborative optimization is as follows: Figure 7As shown, after the three-layer collaborative optimization of this invention, the output of the energy storage unit is precisely matched with the output of wind and solar new energy and the load demand, realizing peak shaving and valley filling; the combined output of thermal / hydro / wind / solar / energy storage is smooth, the total power generation is highly consistent with the load demand, the wind curtailment rate is reduced, the system power balance and peak shaving capability are significantly improved, and the operation effect after the energy storage configuration optimization is clearly presented.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0103] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for improving the carrying capacity of new energy sources based on collaborative optimization, characterized in that, include: S1. Construct an upper-level fixed-capacity model, build a load demand response model based on the Logistic function, calculate the load transfer rate and optimize the load curve through time-of-use pricing, and comprehensively consider the constraints of new energy output, hydropower operation and energy storage system operation. With the goal of minimizing the investment cost and operation and maintenance cost of energy storage capacity, solve for the initial energy storage configuration scheme and the smoothed net load curve. S2. Construct a mid-level simulation operation model, receive the initial energy storage configuration scheme and net load curve output by the upper-level fixed capacity model, take the minimum total system operating cost and the maximum new energy carrying capacity as dual objectives, obtain the comprehensive objective function through normalization and weighting, combine power balance constraints and wind and solar curtailment rate constraints to complete the system simulation operation, calculate the upper limit of the grid new energy carrying capacity that meets the load failure probability reliability standard, and output the system operation status results; S3. Construct a lower-level optimization model. Based on the system operation status results of the middle-level simulation operation model, optimize the configuration capacity of grid-type power sources and synchronous condensers. With the goal of minimizing the investment and total operating cost of grid-type power sources and synchronous condensers, combine short-circuit ratio constraints, node voltage constraints, line power flow constraints, and branch power constraints to complete the system safety constraint verification and feed the safety verification results back to the middle-level simulation operation model in the form of linear constraints. S4. The generalized Benders decomposition algorithm is used to iteratively solve the three-layer collaborative optimization model until the safe and stable convergence conditions are met, and the globally optimal energy storage configuration result and the result of the improvement of new energy carrying capacity are output.

2. The energy storage configuration method for improving the carrying capacity of new energy sources based on collaborative optimization according to claim 1, characterized in that, The objective function F of the upper-level capacitive model is: In the formula: ΔC ESS,p Indicates the configuration cost of energy storage power; ΔC ESS This indicates the cost of configuring energy storage capacity; ΔC ESS,w This indicates the maintenance cost of energy storage.

3. The energy storage configuration method for improving the carrying capacity of new energy sources based on collaborative optimization according to claim 1, characterized in that, The objective function of the intermediate-level simulation model is: The goal is to minimize the total operating cost of the system. In the formula: f 1 represents the total operating cost of the system; C coal Operating costs of thermal power units; C carbon Cost of carbon emissions; C h The operating cost of the hydroelectric power unit; C WT For wind power operating costs; C PV For photovoltaic operating costs; C ESS For energy storage operating costs; The maximum expression for the carrying capacity of new energy sources is: In the formula: f 2 represents the maximum carrying capacity of new energy sources; P load,max This represents the system's maximum load. P line This represents the maximum switching power to the external network. P T,min For the minimum technical output of conventional units within the system; k This represents the rate of curtailment of renewable energy in the system operation; ∑ C ess Total installed capacity of energy storage units; The two objective functions, total system operating cost and new energy carrying capacity, are normalized and weighted before being combined into a comprehensive objective function F: In the formula, It is the objective function f The weight is 1, and its value ranges from (0,1). f 1,max , f 1,min It is the objective function f The maximum and minimum values ​​of 1; f 2,max , f 2,min It is the objective function f The maximum and minimum values ​​of 2.

4. The energy storage configuration method for improving the carrying capacity of new energy sources based on collaborative optimization according to claim 1, characterized in that, The lower-level optimization model constructs an objective function by minimizing the sum of investment and operating costs of grid-connected renewable energy sources and synchronous condensers: In the formula, C Total configuration cost; C n,in , C n,m These are the investment cost and operation and maintenance cost of grid-connected power sources, respectively. C syn,in , C syn,m These are the investment cost and maintenance cost of the synchronous condenser; c n,in It is the investment cost coefficient for grid-type power sources per unit capacity; c n,m It is the unit capacity grid-type power supply operation and maintenance cost coefficient; c syn1,in , c syn2,in These are the investment cost coefficients for distributed and centralized synchronous condensers per unit capacity, respectively. c syn1,m , c syn2,m These are the operation and maintenance cost coefficients for distributed and centralized synchronous condensers per unit capacity, respectively. S n It is the grid-connected capacity of a single grid-connected power supply; Q syn1 This refers to the grid connection capacity of a single distributed synchronous condenser. Q syn2,i for i Node configuration centralized synchronous condenser capacity; , They are nodes i Number of grid-type power supplies and distributed synchronous condensers configured.

5. The energy storage configuration method for improving the carrying capacity of new energy sources based on collaborative optimization according to claim 1, characterized in that, The specific process of iteratively solving the three-layer collaborative optimization model using the generalized Benders decomposition algorithm is as follows: The intermediate-level simulation operation model is set as the master problem, responsible for handling discrete decision variables, with the goal of minimizing the total system operating cost. The upper-level capacitive model and the lower-level optimization model are subproblems, respectively handling continuous variables. The intermediate-level model, as the master problem, passes the solution containing discrete decision variables to the upper-level model subproblem. After solving based on this solution, the upper-level model generates feasible cuts and feeds them back to the intermediate-level model master problem to guide the master problem in adjusting the solution in the next iteration. After solving, the lower-level model generates feasible cuts for safety constraints, which are passed to the intermediate-level model in the form of linear constraints. The intermediate-level model adjusts the operation plan accordingly to ensure the coordination of safety and economy. Through iterative solution, the upper and lower bounds are gradually tightened until the safety and stability conditions are met, and finally, the globally optimal energy storage configuration result and the result of improving the carrying capacity of new energy are output.

6. The energy storage configuration method for improving the carrying capacity of new energy sources based on collaborative optimization according to claim 1, characterized in that, The system safety constraint verification uses the node voltage exceedance probability as a quantitative indicator: In the formula, Y i ( u ) represents the node voltage exceeding the limit at that node; G i ( u ) represents the over-limit rate of the node voltage at that node; S ev ( w i The degree of the node voltage exceeding the limit at that node is denoted as . In the formula, V i This is the per-unit value of the node voltage; w i This is the voltage limit exceeded by this node; V max and V min This indicates the maximum and minimum values ​​of the node voltage.

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