A dynamic strategy configuration method for energy storage power stations based on confidence theory

Abstract

The present invention discloses a dynamic strategy configuration method for energy storage power stations based on confidence theory, which relates to the technical field of power system planning, constructs a power balance model, calculates the maximum power that wind and solar power generation can stably supply to the power grid under a specific confidence level, uses the capacity coefficient of a specific time period and the equivalent reliable unit capacity as indicators, evaluates the capacity confidence of the wind and solar new energy system, formulates the energy storage power station configuration strategy under a high proportion of new energy access, and dynamically adjusts the charging and discharging strategy of the energy storage system according to the confidence capacity of wind and solar output and the demand of the power grid. The present invention considers the balance relationship between power generation and load demand and network loss, and on this basis considers wind and solar new energy, introduces the capacity confidence index specific time period capacity coefficient and equivalent reliable unit capacity, calculates its confidence capacity under the net load key scenario, obtains the power balance equation constraint considering the confidence capacity theory, and effectively improves the capacity confidence of the new energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a dynamic strategy configuration method for an energy storage power station based on confidence theory. Background Art

[0002] As renewable energy continues to grow in the global energy mix, power system planning and operation face unprecedented challenges. The intermittent and uncertain nature of renewable resources introduces new complexities to traditional transmission networks. Furthermore, energy storage power stations, a key technology for balancing fluctuations in power supply and demand, are increasingly being used in modern transmission networks. However, efficient transmission network planning and capacity optimization in uncertain environments remain a pressing challenge.

[0003] The existing dynamic strategy configuration method for energy storage power stations has the following defects:

[0004] 1. Patent document CN116345507B primarily implements multi-objective optimization of energy storage power station capacity with variable energy storage cycles. This overcomes the limitations of existing energy storage power station capacity optimization methods, which are only applicable to specific energy storage durations or specific energy storage cycles, and fail to consider how to efficiently plan and optimize transmission networks in uncertain environments.

[0005] 2. Application document CN116706949A primarily considers energy storage capacity configuration solutions for different application scenarios requiring different power supply reliability and guaranteed power supply, but fails to consider the volatility and uncertainty of power generation in new energy power station capacity planning.

[0006] 3. Application document CN114336748A primarily considers how to achieve the optimal energy storage capacity configuration, without taking into account the impact of various factors such as investment costs, operating costs, and grid losses on power planning;

[0007] 4. In patent document CN115001037B, the main consideration is how to perform multi-objective optimization of energy storage operation while ensuring the economy of the system and improve the utilization efficiency of the energy storage system, but it does not take into account the high cost of energy storage operation. Summary of the Invention

[0008] The purpose of the present invention is to provide a dynamic strategy configuration method for an energy storage power station based on confidence theory to solve the problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solutions: a method for configuring a dynamic strategy for an energy storage power station based on confidence theory. The method for configuring a dynamic strategy for an energy storage power station is as follows:

[0010] S1. Introduction of confidence theory: Apply confidence theory to power system planning;

[0011] S2. Data Collection and Analysis: Collect historical data of wind, solar, and energy storage systems, and analyze their output characteristics and volatility;

[0012] S3. Power balance model: construct a power balance model;

[0013] S4. Wind and solar power output confidence capacity calculation: Calculate the maximum power that wind and solar power generation can stably supply to the grid under a specific confidence level;

[0014] S5. Capacity confidence assessment: Use the capacity factor of a specific period and the equivalent reliable unit capacity as indicators to assess the capacity confidence of the wind and solar energy system;

[0015] S6. Energy storage system output strategy: Develop energy storage power station configuration strategies for high-proportion renewable energy access, and dynamically adjust the energy storage system's charging and discharging strategies based on the confidence level of wind and solar power output and grid demand.

[0016] S7. Robust optimization model: Construct a robust optimization model that considers the uncertainty of renewable energy output;

[0017] S8, Two-stage robust optimization: A two-stage robust optimization method is used. The first stage determines the output of renewable energy, and the second stage minimizes the total cost based on the given renewable energy output.

[0018] S9. Model Solving: Use column and constraint generation algorithms or other optimization algorithms to solve the robust optimization model and find the optimal energy storage power station configuration and charging and discharging strategy;

[0019] S10, Dynamic Adjustment: Dynamically adjust the energy storage power station strategy based on the real-time operating status of the power grid and changes in renewable energy output, and continuously monitor the operating status of the power grid;

[0020] S11. Feedback and optimization: Provide feedback and further optimize the strategy based on implementation results and monitoring data.

[0021] Preferably, in S4, the method further includes:

[0022] S4-1. In the grid planning model, a power balance constraint is added. In the absence of wind and solar power, the power balance constraint equation is as follows:

[0023]

[0024] Where t is time, P G，i (t) is the power generation of the i-th traditional generator in time period t, P L，j (t) is the power of the jth load, Ploss (t) is the network loss, P ESS (t) is the energy storage power, a positive value indicates discharge, and a negative value indicates charge;

[0025] S4-2. When the power grid is connected to a high proportion of renewable energy, the confidence capacity of wind and solar power generation is calculated based on the power balance of S4-1.

[0026] Preferably, in S5, the method further includes:

[0027] S5-1. The capacity factor for a specific period refers to the period with higher load or higher probability of load loss selected in proportion. The new energy load rate corresponding to this period can be used as a good approximation of the new energy confidence capacity. The calculation formula for the capacity factor λ for a specific period is:

[0028]

[0029] Where: C LDC，t Indicates the load duration curve value at time t, C NLDC，t is the net load continuity curve value after the new energy to be evaluated is connected at time t, T p is the peak period of the selected load duration curve, C res Installed capacity for new energy sources;

[0030] S5-2. The proportion of equivalent reliable unit capacity refers to the proportion of capacity that can be substituted for completely reliable conventional units by the new energy system. That is, after removing the resources to be evaluated and adding conventional units with a certain capacity and no outage rate, the reliability level of the new energy system is consistent with the reliability level of the new energy system including the resources to be evaluated. At this time, the proportion of equivalent conventional unit capacity to new energy installed capacity is the new energy capacity credibility, which is calculated as follows:

[0031] R{(C g +C res );D}=R{(C EFC +C res );D}

[0032] χ=C EFC / C res

[0033] Where: R is the reliability index, C g and C res They represent the installed capacity of conventional units and new energy units respectively, D represents the load sequence, C EFC represents the installed capacity of conventional units added to achieve the same system reliability level, and χ is the capacity confidence of the corresponding assessed resource.

[0034] Preferably, in S6, the energy storage power station configuration strategy under high proportion of new energy access is as follows:

[0035] Set the power balance index θ

[0036] θ=P DG -P Load

[0037] Among them, P DG Contribute to new energy, P Load is the load,

[0038] When θ>0, the energy storage is charged to absorb the excess renewable energy output; when θ<0, the energy storage is discharged to meet the load demand;

[0039] When θ<0, the power balance index θ must be within the following range: θ≤0.2*(P DG -P Load ).

[0040] Preferably, in S7, a robust optimization model is proposed to optimize the objective function f of the problem, hoping to minimize the cost:

[0041] f total =min(f i +f o +f c )

[0042] where f i is the investment cost, f o is the operating cost, f c The cost of curtailing wind and solar power.

[0043] Preferably, in S8, due to the strong uncertainty of the output of new energy, an intermediate variable is introduced to transform the robust optimization model into a two-stage robust optimization model;

[0044] Incorporating power balancing costs into traditional grid planning models:

[0045] f p =β(P DG -P Load )

[0046] Among them, β is the power balance penalty coefficient, P DG Contribute to new energy, P Load For load;

[0047] The first stage of the two-stage robust optimization model uses renewable energy output as the decision variable and the minimum cost of power supply balance under a certain confidence level as the objective function. The second stage uses the minimum total cost as the objective function. The model is as follows:

[0048]

[0049] Where g(x) is the relevant constraint condition in the first stage, and h(x) is the relevant constraint condition in the second stage.

[0050] Preferably, the two-stage robust optimization model is a min-max-min three-layer optimization model, which can be solved using a column and constraint generation algorithm.

[0051] Preferably, in S9, the robust optimization model solution is as follows:

[0052] S9-1. Set the slack η and the initial solution. Under the given η, determine the new energy output in the first stage and substitute it into the second stage.

[0053] S9-2, the second stage model is transformed into a solvable single-level linear programming model using the column and constraint generation algorithm to solve the problem and obtain the minimum total cost, which is then substituted back into the first stage;

[0054] S9-3. Repeat multiple times until the optimal solution is found.

[0055] Preferably, in S4-2, the confidence capacity calculation steps of wind and solar power generation are as follows:

[0056] (1) Obtain historical data on wind, solar, and energy storage;

[0057] (2) At time t, the wind and solar power output P RE and energy storage system P ESS The output sum of the statistical output is greater than or equal to P;

[0058] (3) Under the condition of probability Pr(P RE (t)+P ESS Under the condition of (t)≥P)≥α, find the maximum value P REα ;

[0059] P REα =max{P|Pr(P RE (t)+P ESS (t)≥P)≥α}

[0060] Among them, α is the confidence probability, P REα It indicates the maximum power that can be stably supplied by wind and solar power under a certain confidence level, P RE P is the power transmitted from the wind and solar energy system to the grid. ESS is the power delivered by the energy storage system to the grid, and P is a fixed power value determined in advance;

[0061] Then P REα is the stable output that the wind, solar and energy storage systems can provide to the power grid system under the confidence probability α;

[0062] Therefore, the power balance constraint equation in S4-1 becomes:

[0063]

[0064] In the power balance model, generator constraints, energy storage constraints and network security constraints are considered:

[0065]

[0066] SOC min ≤SOC≤SOC max

[0067] in: and are the upper and lower limits of the i-th generator power, and is the upper and lower limits of energy storage power, V min With V max is the upper and lower limits of the power network voltage, V n (t) represents the voltage of the nth node, SOC is the state of charge of the energy storage, SOC min With SOC max is the upper and lower limits of the energy storage charge state, fl min with fl max is the upper and lower limits of the power network flow, fl n (t) represents the power flow of the nth line.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1. The present invention considers the balance relationship between power generation, load demand and network loss, and on this basis, considers a high proportion of wind and solar renewable energy, introduces the capacity confidence index, the capacity coefficient for a specific period of time and the equivalent reliable unit capacity, calculates its confidence capacity under the net load key scenario, obtains the power balance equation constraint considering the confidence capacity theory, and effectively improves the capacity confidence of the new energy system.

[0070] 2. The present invention determines its capacity and charge and discharge rate based on the load demand and the energy storage demand of the system, so that it can improve the flexibility and stability of the power grid while ensuring that the energy storage system effectively regulates power supply and demand. At the same time, when considering the capacity planning of new energy power stations, it is taken into account that power generation is affected by factors such as weather, and its power generation has certain volatility and uncertainty, so as to meet the reliability and stability requirements of the power grid.

[0071] 3. The present invention comprehensively considers factors such as investment cost, operating cost and grid loss, and comprehensively considers and weighs these factors to find the most cost-effective solution, providing effective support for the planning and operation of the power system.

[0072] 4. When formulating the charging and discharging strategy for the energy storage power station, the present invention needs to reduce the loss of energy storage equipment by reducing the number of energy storage charging and discharging cycles, thereby reducing the energy storage operating cost and achieving the optimal system cost, while meeting the load demand. At the same time, it is necessary to consider the volatility and uncertainty brought to the power grid by the access of a high proportion of new energy. It is not allowed to blindly increase the capacity of new energy and reduce the number of energy storage discharges, nor is it allowed to install too few new energy sources, resulting in insufficient energy storage discharge to meet normal load demand. DETAILED DESCRIPTION

[0073] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0074] An embodiment provided by the present invention is a method for configuring a dynamic strategy for an energy storage power station based on confidence theory. The method for configuring a dynamic strategy for an energy storage power station is as follows:

[0075] S1. Introduction of confidence theory: Apply confidence theory to power system planning;

[0076] S2. Data Collection and Analysis: Collect historical data of wind, solar, and energy storage systems, and analyze their output characteristics and volatility;

[0077] S3. Power balance model: construct a power balance model;

[0078] S4. Wind and solar power output confidence capacity calculation: Calculate the maximum power that wind and solar power generation can stably supply to the grid under a specific confidence level;

[0079] S5. Capacity confidence assessment: Use the capacity factor of a specific period and the equivalent reliable unit capacity as indicators to assess the capacity confidence of the wind and solar energy system;

[0080] S6. Energy storage system output strategy: Develop energy storage power station configuration strategies for high-proportion renewable energy access, and dynamically adjust the energy storage system's charging and discharging strategies based on the confidence level of wind and solar power output and grid demand.

[0081] S7. Robust optimization model: Construct a robust optimization model that considers the uncertainty of renewable energy output;

[0082] S8, Two-stage robust optimization: A two-stage robust optimization method is used. The first stage determines the output of renewable energy, and the second stage minimizes the total cost based on the given renewable energy output.

[0083] S9. Model Solving: Use column and constraint generation algorithms or other optimization algorithms to solve the robust optimization model and find the optimal energy storage power station configuration and charging and discharging strategy;

[0084] S10, Dynamic Adjustment: Dynamically adjust the energy storage power station strategy based on the real-time operating status of the power grid and changes in renewable energy output, and continuously monitor the operating status of the power grid;

[0085] S11. Feedback and optimization: Provide feedback and further optimize the strategy based on implementation results and monitoring data.

[0086] Furthermore, by setting different confidence levels to evaluate the reliable power supply capabilities of renewable energy power generation under different probabilities, we first comprehensively collect historical operating data of new energy systems such as wind power, photovoltaics, and energy storage systems, analyze these data, and understand their output characteristics, such as daily changes, seasonal changes, volatility, and mutual correlation. Based on the load demand of the power grid, the power generation capacity of traditional energy, and the forecast of new energy power generation, we construct a power balance model. Under a specific confidence level, we calculate the maximum power that wind and solar power generation can stably supply to the power grid, and use the capacity factor and equivalent reliable unit capacity of a specific period as evaluation indicators. This allows us to quantitatively evaluate the capacity confidence of the new energy system and understand its reliability under different conditions. Then, based on the confidence capacity of the new energy output and the demand of the power grid, we formulate the configuration strategy of the energy storage power station and dynamically adjust the charging and discharging strategy of the energy storage system. In order to balance the volatility of renewable energy power generation and the stability of the power grid, taking into account the uncertainty of renewable energy output, a robust optimization model is constructed. Under uncertain conditions, the operation strategy of the power grid is optimized to ensure the economy and reliability of the system. Then a two-stage robust optimization method is adopted. The first stage determines the optimal output plan of renewable energy. The second stage minimizes the total operating cost of the power grid based on the given renewable energy output. The robust optimization model is solved using column and constraint generation algorithms, mixed integer linear programming or other optimization algorithms to find the optimal energy storage power station configuration and charging and discharging strategy to provide guidance for the actual operation of the power grid, and continuously monitor the real-time operating status of the power grid and changes in renewable energy output, so as to facilitate dynamic adjustment of the energy storage power station strategy according to the monitoring data to cope with the uncertainty of renewable energy output and emergencies in the power grid, thereby improving the stability and economy of the power grid.

[0087] An embodiment provided by the present invention is a method for configuring a dynamic strategy for an energy storage power station based on confidence theory, which, in S4, further includes:

[0088] S4-1. In the grid planning model, a power balance constraint is added. In the absence of wind and solar power, the power balance constraint equation is as follows:

[0089]

[0090] Where t is time, P G，i (t) is the power generation of the i-th traditional generator in time period t, P L，j (t) is the power of the jth load, P loss (t) is the network loss, P ESS (t) is the energy storage power, a positive value indicates discharge, and a negative value indicates charge;

[0091] S4-2. When the power grid is connected to a high proportion of renewable energy, the confidence capacity of wind and solar power generation is calculated based on the power balance of S4-1.

[0092] In S4-2, the confidence capacity calculation steps for wind and solar power generation are as follows:

[0093] (1) Obtain historical data on wind, solar, and energy storage;

[0094] (2) At time t, the wind and solar power output P RE and energy storage system P ESS The output sum of the statistical output is greater than or equal to P;

[0095] (3) Under the condition of probability Pr(P RE (t)+P ESS Under the condition of (t)≥P)≥α, find the maximum value P REα ;

[0096] P REα =max{P|Pr(P RE (t)+P ESS (t)≥P)≥α}

[0097] Among them, α is the confidence probability, P REα It indicates the maximum power that can be stably supplied by wind and solar power under a certain confidence level, P RE P is the power transmitted from the wind and solar energy system to the grid. ESS is the power delivered by the energy storage system to the grid, and P is a fixed power value determined in advance;

[0098] Then P REα is the stable output that the wind, solar and energy storage systems can provide to the power grid system under the confidence probability α;

[0099] Therefore, the power balance constraint equation in S4-1 becomes:

[0100]

[0101] In the power balance model, generator constraints, energy storage constraints and network security constraints are considered:

[0102]

[0103] SOC min ≤SOC≤SOC max

[0104] in: and are the upper and lower limits of the i-th generator power, and is the upper and lower limits of energy storage power, V min With V max is the upper and lower limits of the power network voltage, V n (t) represents the voltage of the nth node, SOC is the state of charge of the energy storage, SOC min With SOC max is the upper and lower limits of the energy storage charge state, fl min with fl max is the upper and lower limits of the power network flow, fl n (t) represents the power flow of the nth line.

[0105] Furthermore, in the power grid planning model, power balance is a core constraint that ensures the balance of supply and demand in the power grid at different time periods. When a high proportion of renewable energy (such as wind power and photovoltaic power) is connected to the power grid, this balance becomes more complicated because the output of renewable energy is uncertain. In order to deal with this uncertainty, the concept of confidence capacity is introduced and integrated into the power balance constraint. By considering the balance relationship between power generation, load demand and network loss, and on this basis, considering a high proportion of wind and solar renewable energy, the capacity confidence index is introduced, the capacity factor in a specific time period and the equivalent reliable unit capacity are introduced, and its confidence capacity is calculated under the net load critical scenario. The power balance equation constraint considering the confidence capacity theory is obtained, which effectively improves the capacity confidence of the renewable energy system.

[0106] When a high proportion of renewable energy is connected to the power grid, the confidence capacity of wind and solar power generation needs to be considered on the basis of this power balance. This refers to the power or load that the wind farm and energy storage system can stably supply to the power grid under a certain confidence level. The output power of wind power and photovoltaic power generation is random and uncertain. By rationally configuring energy storage equipment, the power output of the wind farm can be smoothed, and its thermal power replacement capacity and load supply capacity can be improved.

[0107] An embodiment of the present invention provides a method for configuring a dynamic strategy for an energy storage power station based on confidence theory, which, in S5, further includes:

[0108] S5-1. The capacity factor for a specific period refers to the period with higher load or higher probability of load loss selected in proportion. The new energy load rate corresponding to this period can be used as a good approximation of the new energy confidence capacity. The calculation formula for the capacity factor λ for a specific period is:

[0109]

[0110] Where: C LDC，t Indicates the load duration curve value at time t, C NLDC，t is the net load continuity curve value after the new energy to be evaluated is connected at time t, T p is the peak period of the selected load duration curve, C res Installed capacity for new energy sources;

[0111] S5-2. The proportion of equivalent reliable unit capacity refers to the proportion of capacity that can be substituted for completely reliable conventional units by the new energy system. That is, after removing the resources to be evaluated and adding conventional units with a certain capacity and no outage rate, the reliability level of the new energy system is consistent with the reliability level of the new energy system including the resources to be evaluated. At this time, the proportion of equivalent conventional unit capacity to new energy installed capacity is the new energy capacity credibility, which is calculated as follows:

[0112] R{(C g +C res );D}=R{(C EFC +C res );D}

[0113] χ=C EFC / C res

[0114] Where: R is the reliability index, C g and C res They represent the installed capacity of conventional units and new energy units respectively, D represents the load sequence, C EFC represents the installed capacity of conventional units added to achieve the same system reliability level, and χ is the capacity confidence of the corresponding assessed resource.

[0115] Furthermore, in grid planning and new energy access assessment, the capacity factor for a specific period and the proportion of equivalent reliable unit capacity are two important concepts, which are used to evaluate the performance and reliability of new energy systems under different conditions, and determine their capacity and charge and discharge rates based on load demand and the system's energy storage requirements, so that they can improve the flexibility and stability of the grid while ensuring that the energy storage system effectively regulates power supply and demand. At the same time, when considering the capacity planning of new energy power stations, it is taken into account that power generation is affected by factors such as weather, and its power generation has certain volatility and uncertainty, so as to meet the reliability and stability requirements of the grid.

[0116] An embodiment of the present invention provides a method for configuring a dynamic strategy for an energy storage power station based on confidence theory. In S6, the energy storage power station configuration strategy under a high proportion of new energy access is as follows:

[0117] Set the power balance index θ

[0118] θ=PDG -P Load

[0119] Among them, P DG Contribute to new energy, P Load is the load,

[0120] When θ>0, the energy storage is charged to absorb the excess renewable energy output; when θ<0, the energy storage is discharged to meet the load demand;

[0121] When θ<0, the power balance index θ must be within the following range: θ≤0.2*(P DG -P Load ).

[0122] Furthermore, based on the historical output data of renewable energy and load forecasts, the total capacity of the energy storage power station is reasonably determined to ensure that there is sufficient energy storage capacity to smooth out fluctuations when the output of renewable energy fluctuates greatly. The power configuration of the energy storage power station is determined based on the charging and discharging rate requirements of the energy storage power station, that is, the amount of electricity that can be charged or discharged per unit time, to meet the grid's demand for rapid response capabilities. A detailed control strategy is formulated, including the charging and discharging logic of the energy storage power station, charging and discharging priorities, and smooth adjustment of charging and discharging power, to ensure that the energy storage power station can operate efficiently and stably and work in coordination with other equipment in the grid. The power balance index θ is also calculated and set. When θ>0, the energy storage is charged to absorb excess renewable energy output. When θ<0, the energy storage is discharged to meet load demand.

[0123] When formulating the charging and discharging strategy for energy storage power stations, on the premise of meeting load demand, it is necessary to reduce the loss of energy storage equipment by reducing the number of energy storage charging and discharging cycles, thereby reducing the energy storage operating costs to achieve optimal system costs. At the same time, it is necessary to consider the volatility and uncertainty brought to the power grid by the access of a high proportion of renewable energy. It is not possible to blindly increase the capacity of renewable energy and reduce the number of energy storage discharges, nor is it possible to install too few renewable energy sources, resulting in insufficient energy storage discharge to meet normal load demand.

[0124] An embodiment provided by the present invention is a method for configuring a dynamic strategy for an energy storage power station based on confidence theory. In step S7, a robustness optimization model is proposed to optimize the objective function f of the problem, hoping to minimize the cost:

[0125] f total =min(f i +f o +f c )

[0126] where f i is the investment cost, f o is the operating cost, f c The cost of curtailing wind and solar power.

[0127] Furthermore, an optimization model is proposed to solve the site selection, capacity and installation time of distributed generation (DG) after a high proportion of new energy access. The objective function f of the optimization problem contains f i Investment cost, f o Running costs and f c The cost of curtailing wind and solar power is expected to be minimized;

[0128] The investment cost and operating cost are as follows:

[0129]

[0130]

[0131] f c =αP c

[0132] Where: i and j represent the serial number of the busbar, m and n represent the serial number of another group of buses, ψ B , ψ T , ψ L and ψ CB represents the set of bus, year, load level and connected network buses, t and l represent the planned years and load level index respectively, r represents the reference bus, IC i With CC i They represent the capacity and investment cost of each DG unit installed on busbar i, dr represents the discount rate, α it With α i(t-1) denote the total number of DGs installed on bus i as of year t and year t-1, CR ij represents the reinforcement cost of the feeder between busbars i and j, β ijt and β ij(t-1) denotes the reinforcement status of the feeder between busbars i and j in year t and t-1 respectively, β ijt =1 means reinforcement, β ijt =0 means no reinforcement, TD l Indicates the power grid loss, EP tl It represents the electricity price predicted for load level l in year t, GP rtl With GP itl They represent the active power generated by the DG on bus i and the active power of the network on the reference bus at load level l in year t, CO itl represents the DG operating cost of bus i at load level l in year t, VLL t Indicates the loss load value in year t, LS itl It represents the load loss of bus i at load level l in year t, V itl It represents the voltage of bus i at load level l in year t, Vj+i represents the sum of the voltages of busbars i and j, β mnt Indicates the reinforcement status of the feeder between busbars m and n in year t, Y ij represents the node admittance between i and j, θ itl and θ jtl They represent the phase angles of busbars i and j at load level l in year t, Represents the phase angle between busbars i and j, DP itl represents the active power demand of bus i at load level l in year t, PL tl represents the power loss of the distribution network at load level l in year t, α is the penalty coefficient for wind and solar power abandonment, P c It is the abandoned wind and solar power.

[0133] An embodiment provided by the present invention is a method for configuring a dynamic strategy for an energy storage power station based on confidence theory. In step S8, due to the strong uncertainty of the output of renewable energy, an intermediate variable is introduced to transform the robust optimization model into a two-stage robust optimization model.

[0134] Incorporating power balancing costs into traditional grid planning models:

[0135] f p =β(P DG -P Load )

[0136] Among them, β is the power balance penalty coefficient, P DG Contribute to new energy, P Load For load;

[0137] The first stage of the two-stage robust optimization model uses renewable energy output as the decision variable and the minimum cost of power supply balance under a certain confidence level as the objective function. The second stage uses the minimum total cost as the objective function. The model is as follows:

[0138]

[0139] Where g(x) is the relevant constraint condition in the first stage, and h(x) is the relevant constraint condition in the second stage.

[0140] The two-stage robust optimization model is a min-max-min three-level optimization model, which can be solved using the column and constraint generation algorithm.

[0141] In S9, the robust optimization model solution is as follows:

[0142] S9-1. Set the slack η and the initial solution. Under the given η, determine the new energy output in the first stage and substitute it into the second stage.

[0143] S9-2, the second stage model is transformed into a solvable single-level linear programming model using the column and constraint generation algorithm to solve the problem and obtain the minimum total cost, which is then substituted back into the first stage;

[0144] S9-3. Repeat multiple times until the optimal solution is found.

[0145] Furthermore, in order to deal with the uncertainty of renewable energy output, intermediate variables such as the forecast interval or scenario set of renewable energy output can be introduced to represent this uncertainty. These intermediate variables will be used as parameters or decision variables in the model to build a robust optimization model.

[0146] Phase 1: Using renewable energy output as the decision variable, the goal is to minimize the power supply balancing cost within a certain confidence level. Phase 2: Minimizing the total cost, including power balancing cost, energy storage cost, and grid operation cost, is the objective function. Considering how to adjust other system resources, such as conventional units and energy storage systems, based on the renewable energy output decided in Phase 1, to meet load demand and minimize total cost.

[0147] Set the slack η and the initial solution. Under the given η, solve the first-stage model to obtain the renewable energy output decision. Substitute the renewable energy output obtained in the first stage into the second-stage model and use the column and constraint generation algorithm to convert it into a solvable single-level linear programming model for solution calculation to obtain the DG location, capacity, installation time, and total cost. In each iteration, solve the second-stage model to obtain the current minimum total cost, and update the first-stage decision accordingly. Through multiple iterations, the first and second-stage models are solved alternately until the optimal solution that meets the convergence conditions is found.

[0148] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be embraced therein.

Claims

1. A dynamic strategy configuration method for energy storage power stations based on confidence theory, characterized in that: The dynamic strategy configuration method for energy storage power stations is as follows: S1. Introduction of confidence theory: Apply confidence theory to power system planning; S2. Data Collection and Analysis: Collect historical data of wind, solar, and energy storage systems, and analyze their output characteristics and volatility; S3. Power balance model: construct a power balance model; S4. Wind and solar power output confidence capacity calculation: Calculate the maximum power that wind and solar power generation can stably supply to the grid under a specific confidence level; In S4, it also includes: S4-1. In the grid planning model, a power balance constraint is added. In the absence of wind and solar power, the power balance constraint equation is as follows: Where t is time, P G，i (t) is the power generation of the i-th traditional generator in time period t, P L，j (t) is the power of the jth load, P loss (t) is the network loss, P ESS (t) is the energy storage power, a positive value indicates discharge, and a negative value indicates charge; S4-2. When a high proportion of renewable energy is connected to the power grid, the confidence capacity of wind and solar power generation is calculated based on the power balance in S4-1. In S4-2, the confidence capacity calculation steps for wind and solar power generation are as follows: (1) Obtain historical data on wind, solar, and energy storage; (2) At time t, the wind and solar power output P RE and energy storage system P ESS The output sum of the statistical output is greater than or equal to P; (3) Under the condition of probability Pr(P RE (t)+P ESS Under the condition of (t)≥P)≥α, find the maximum value P REα ; P REα =max{P|Pr(P RE (t)+P ESS (t)≥P)≥α} Among them, α is the confidence probability, P REα It indicates the maximum power that can be stably supplied by wind and solar power under a certain confidence level, P RE P is the power transmitted from the wind and solar energy system to the grid. ESS is the power delivered by the energy storage system to the grid, and P is a fixed power value determined in advance; Then P REα is the stable output that the wind, solar and energy storage systems can provide to the power grid system under the confidence probability α; Therefore, the power balance constraint equation in S4-1 becomes: In the power balance model, generator constraints, energy storage constraints and network security constraints are considered: SOC min ≤SOC≤SOC max in: and are the upper and lower limits of the i-th generator power, and is the upper and lower limits of energy storage power, V min With V max is the upper and lower limits of the power network voltage, V n (t) represents the voltage of the nth node, SOC is the state of charge of the energy storage, SOC min With SOC max is the upper and lower limits of the energy storage charge state, fl min with fl max is the upper and lower limits of the power network flow, fl n (t) represents the power flow of the nth line; S5. Capacity confidence assessment: Use the capacity factor of a specific period and the equivalent reliable unit capacity as indicators to assess the capacity confidence of the wind and solar energy system; In S5, also included: S5-1. The capacity factor for a specific period refers to the period with higher load or higher probability of load loss selected in proportion. The new energy load rate corresponding to this period can be used as a good approximation of the new energy confidence capacity. The calculation formula for the capacity factor λ for a specific period is: Where: C LDC，t Indicates the load duration curve value at time t, C NLDC，t is the net load continuity curve value after the new energy to be evaluated is connected at time t, T p is the peak period of the selected load duration curve, C res Installed capacity for new energy sources; S5-2. The proportion of equivalent reliable unit capacity refers to the proportion of capacity that can be substituted for completely reliable conventional units by the new energy system. That is, after removing the resources to be evaluated and adding conventional units with a certain capacity and no outage rate, the reliability level of the new energy system is consistent with the reliability level of the new energy system including the resources to be evaluated. At this time, the proportion of equivalent conventional unit capacity to new energy installed capacity is the new energy capacity credibility, which is calculated as follows: R{(C g +C res );D}=R{(C EFC +C res );D} x=C EFC / C res Where: R is the reliability index, C g and C res They represent the installed capacity of conventional units and new energy units respectively, D represents the load sequence, C EFC represents the installed capacity of conventional units added to achieve the same system reliability level, and χ is the capacity confidence of the corresponding assessed resource; S6. Energy storage system output strategy: Develop energy storage power station configuration strategies for high-proportion renewable energy access, and dynamically adjust the energy storage system's charging and discharging strategies based on the confidence level of wind and solar power output and grid demand. S7. Robust optimization model: Construct a robust optimization model that considers the uncertainty of renewable energy output; In S7, a robust optimization model is proposed to optimize the objective function f of the problem, hoping to minimize the cost: f total =min(f i +f o +f c ) where f i is the investment cost, f o is the operating cost, f c Cost of curtailing wind and solar power; S8, Two-stage robust optimization: A two-stage robust optimization method is used. The first stage determines the output of renewable energy, and the second stage minimizes the total cost based on the given renewable energy output. S9, Model Solving: Use the column and constraint generation algorithm to solve the robust optimization model and find the optimal energy storage power station configuration and charging and discharging strategy; S10, Dynamic Adjustment: Dynamically adjust the energy storage power station strategy based on the real-time operating status of the power grid and changes in renewable energy output, and continuously monitor the operating status of the power grid; S11. Feedback and optimization: Provide feedback and further optimize the strategy based on implementation results and monitoring data.

2. The method for configuring a dynamic strategy for an energy storage power station based on confidence theory according to claim 1, characterized in that: In S6, the energy storage power station configuration strategy for a high proportion of renewable energy access is as follows: Set the power balance index θ θ=P DG -P Load Among them, P DG Contribute to new energy, P Load is the load, When θ>0, the energy storage is charged to absorb the excess renewable energy output; when θ<0, the energy storage is discharged to meet the load demand; When θ<0, the power balance index θ must be within the following range: θ≤0.2*(P DG -P Load ).

3. The method for configuring a dynamic strategy for an energy storage power station based on confidence theory according to claim 1, characterized in that: In S8, due to the strong uncertainty of renewable energy output, intermediate variables are introduced to transform the robust optimization model into a two-stage robust optimization model; Incorporating power balancing costs into traditional grid planning models: f p =β(P DG -P Load ) Among them, β is the power balance penalty coefficient, P DG Contribute to new energy, P Load For load; The first stage of the two-stage robust optimization model uses renewable energy output as the decision variable and the minimum cost of power supply balance under a certain confidence level as the objective function. The second stage uses the minimum total cost as the objective function. The model is as follows: Where g(x) is the relevant constraint condition in the first stage, and h(x) is the relevant constraint condition in the second stage.

4. The method for configuring a dynamic strategy for an energy storage power station based on confidence theory according to claim 3 is characterized in that: The two-stage robust optimization model is a min-max-min three-level optimization model, which can be solved using the column and constraint generation algorithm.

5. The method for configuring a dynamic strategy for an energy storage power station based on confidence theory according to claim 3, characterized in that: In S9, the robust optimization model solution is as follows: S9-1. Set the slack η and the initial solution. Under the given η, determine the new energy output in the first stage and substitute it into the second stage. S9-2, the second stage model is transformed into a solvable single-level linear programming model using the column and constraint generation algorithm to solve the problem and obtain the minimum total cost, which is then substituted back into the first stage; S9-3. Repeat multiple times until the optimal solution is found.

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