A power system energy storage expansion planning strategy acquisition method, device and control system

Abstract

A power system energy storage expansion planning strategy acquisition method, device and control system belong to the technical field of power system planning, including determining the wind power abandonment power and load shedding power based on the explicit boundary of the wind power robust consumption domain under the typical scene; constructing the annual random consumption rate index and the annual expected load shedding amount calculation formula; using the wind power random scene approximation to calculate the discrete value of the annual random consumption rate index and the discrete value of the annual expected load shedding amount; constructing a maximum annual consumption level evaluation model, and obtaining the upper limit value of the wind power consumption level by solving the evaluation model; constructing a power system energy storage expansion planning model containing the annual random consumption rate constraint, and obtaining the energy storage expansion planning scheme by solving the model. The application can realize the evaluation of the maximum annual consumption level of the wind power of the power system, set the promotion target of the consumption level according to the evaluation, guarantee the achievement of the target consumption level through the explicit quantitative constraint, and optimize the economy of the energy storage expansion planning scheme on this basis.

Description

Technical Field

[0001] This invention belongs to the field of power system planning technology, and more specifically, relates to a method, device and control system for obtaining power system energy storage expansion planning strategies. Background Technology

[0002] With the continuous increase in the penetration rate of new energy sources, the power system's absorption capacity under the existing resource allocation is becoming increasingly insufficient. It is urgent to improve the system's absorption capacity through the expansion planning of flexible resources such as energy storage, so as to ensure the level of new energy absorption.

[0003] Compared to operation and scheduling, planning problems have a longer decision-making cycle. To effectively characterize the temporal characteristics of renewable energy output and load and improve the effectiveness of planning schemes, existing studies have used 8760 hours of time-series data throughout the year as input to fully reflect the intermittency, volatility, and load variation patterns of wind and solar power output. However, the actual power grid structure is complex and the number of generating units is large. Planning models built based on annual time-series scenarios contain massive variables and constraints, making them extremely difficult to solve. To balance model accuracy and solution efficiency, scenario reduction techniques based on algorithms such as K-means clustering and synchronous back-substitution elimination have been widely used. These methods can overcome the limitations of strong subjectivity caused by manually selecting typical daily scenarios while preserving the core characteristics of source-load time series.

[0004] Based on the reasonable setting of planning scenarios, some studies coordinate the economy and energy supply reliability of planning schemes by iteratively embedding key scenarios, but ignore the guarantee of the renewable energy absorption level; other studies construct planning models based on a single typical daily scenario and introduce absorption rate constraints to guarantee the renewable energy absorption level under the planning scheme, but it is difficult to fully consider the long-term (such as cross-seasonal) fluctuation characteristics of renewable energy output and load demand.

[0005] To address the randomness and volatility of renewable energy output, existing research has yielded significant results in robust programming methods and hybrid robust-stochastic programming methods. However, there is still a lack of efficient quantitative assessment and assurance mechanisms for the annual absorption capacity of a system under stochastic renewable energy access. This makes it difficult to directly and accurately balance economic efficiency and absorption capacity in planning decisions, thus hindering the development of planning schemes with optimal overall performance in the long term.

[0006] In summary, how to achieve a quantitative assessment of the renewable energy absorption level while fully considering the long-term fluctuation characteristics of the source-load relationship, and how to coordinate and optimize the renewable energy absorption level with the economic efficiency of system planning and operation, are urgent problems to be solved. Summary of the Invention

[0007] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method, device and control system for obtaining energy storage expansion planning strategies for power systems, thereby solving the technical problem of how to achieve quantitative assessment of the renewable energy absorption level and coordinate and optimize the renewable energy absorption level with the economic efficiency of system planning and operation, based on fully considering the long-term fluctuation characteristics of renewable energy and load.

[0008] To achieve the above objectives, according to one aspect of the present invention, a method for obtaining a power system energy storage expansion planning strategy is provided, comprising: S1. Based on the upper and lower boundary values ​​of the wind power robust absorption domain and the wind power output value in typical scenarios, formulate explicit wind curtailment and load shedding strategies. S2, Based on the explicit wind curtailment and load shedding strategies, construct the annual stochastic absorption rate index calculation formula with the upper boundary of the wind power robust absorption domain as the variable and the annual expected load shedding amount calculation formula with the lower boundary of the wind power robust absorption domain as the variable. S3. Using the random scenario of wind power, the discrete values ​​of the annual random absorption rate index corresponding to the upper boundary value of different wind power robust absorption domains are approximately calculated to establish the first discrete mapping relationship between the upper boundary variable and the annual random absorption rate index, and the discrete values ​​of the annual expected load shedding amount corresponding to the lower boundary value of different wind power robust absorption domains are calculated to establish the second discrete mapping relationship between the lower boundary variable and the annual expected load shedding amount. S4. Construct a maximum annual absorption level assessment model. The assessment model aims to maximize the annual random absorption rate index, considers power supply reliability constraints, and is linearly expressed based on the first and second discrete mapping relationships. By solving the assessment model, the upper limit of the wind power absorption level under the given energy storage expansion planning capacity limit is obtained. S5. Construct a power system energy storage expansion planning model that includes annual stochastic absorption rate constraints. The constant term in the annual stochastic absorption rate constraints is the target value for improving the wind power absorption level. The target value for improvement is not higher than the upper limit of the wind power absorption level. By solving the power system energy storage expansion planning model, an energy storage expansion planning scheme that can guarantee the set absorption level target and optimize the overall investment and operating costs is obtained.

[0009] Preferably, in S1, a robust elimination domain is utilized. Characterizes the range of power system capacity to absorb stochastic wind power output under various typical scenarios; Among them, subscript , and These are respectively the node number, time period number, and typical scenario number; For random wind power output; and These are the upper and lower boundary variables of the robust elimination domain, respectively.

[0010] Preferably, in step S1, the wind curtailment power is explicitly determined based on the upper and lower boundary values ​​of the wind power robust absorption domain and the wind power output value. and load shedding power The specific determination method is as follows:

[0011] in, and These represent the number of wind farms and load nodes, respectively. For random wind power output With the lower boundary of the robust absorption domain The power deficit between; This represents the load power.

[0012] Preferably, in step S2, the annual stochastic absorption rate index is constructed using the explicit wind curtailment and load shedding strategies. and annual expected load shedding The calculation formula is as follows:

[0013]

[0014] in, For expectation calculation; and These represent the number and probability of typical scenarios, respectively. The number of time periods in each typical scenario; The number of days in a year; The length of each time period; and Represent the upper boundary of the robust elimination domain, respectively. and lower boundary The function.

[0015] Preferably, in step S3, a random wind power scenario is utilized. Approximate calculations correspond to different upper boundary values ​​of the wind power robust absorption domain. Discrete value of annual random absorption rate index Corresponding to different lower boundary values ​​of the wind power robust absorption domain Discrete values ​​of annual expected load shedding The details are as follows:

[0016] in, Randomly assign numbers to wind power scenarios; Number the discrete values; The number of random wind power scenarios; , and These are the node number, time period number, and typical scenario number, respectively.

[0017] Preferably, in S4, the following is utilized: Let represent the objective function of the maximum annual absorption capacity assessment model, and utilize constraints. To ensure power supply reliability, among which, This is the limit for the annual expected load shedding, and and The calculated discrete values ​​of the annual random absorption rate index and the annual expected load shedding can be linearized, as follows:

[0018]

[0019] in, For linearized , and These are the number and sequence number of the linearized segments, respectively. and For the first The coefficients of the linearized segment expression; For linearized , These represent the number of linearized segments. and For the first The coefficients of the linearized segment expression.

[0020] Preferably, in S5, the following is utilized: Express the objective function of the power system energy storage expansion planning model, where, and These are the annualized investment cost and operating cost, respectively, and the specific calculation formulas are as follows:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] in, The investment recovery factor for energy storage, For the discount rate, For energy storage life, and These represent the existing energy storage capacity and the planned energy storage capacity in the system. and These refer to the energy storage construction power and capacity, respectively. and The cost of energy storage investment per unit power and capacity; , and These are the operating costs of thermal power units, the operation and maintenance costs of energy storage, and the cost of load shedding. This represents the number of thermal power units. Costs associated with starting and stopping thermal power units. and These are the 0-1 variables representing the start-stop status and start-stop action of the thermal power unit, respectively. To provide power to thermal power units, For the fuel cost function of thermal power units, The minimum fuel cost for thermal power unit operation. and These are the segment numbers and number of segments in the linearization of the fuel cost function for thermal power units. and These represent the portion of the thermal power unit's output in the nth linearized segment and the corresponding cost coefficient, respectively. and These are the upper and lower limits of the output of thermal power units, respectively. For the existing energy storage capacity in the system, and These are the annual operation and maintenance costs per unit capacity of existing energy storage and energy storage to be planned in the system, respectively. This is the load shedding cost coefficient.

[0030] Preferably, in S5, the annual random absorption rate constraint is used. To ensure that the wind power absorption rate can be increased to the target value, among which, This represents the target value for wind power absorption.

[0031] According to another aspect of the present invention, a device for obtaining power system energy storage expansion planning strategies is provided, comprising: The strategy formulation module is used to formulate explicit wind curtailment and load shedding strategies based on the upper and lower boundary values ​​of the wind power robust absorption domain and the wind power output value in typical scenarios. The module is used to construct, based on the explicit wind curtailment and load shedding strategies, the annual stochastic absorption rate index calculation formula with the upper boundary of the wind power robust absorption domain as the variable and the annual expected load shedding amount calculation formula with the lower boundary of the wind power robust absorption domain as the variable. The calculation module is used to approximate the discrete values ​​of the annual stochastic absorption rate index corresponding to different upper boundary values ​​of the wind power robust absorption domain using the wind power stochastic scenario, so as to establish the first discrete mapping relationship between the upper boundary variable and the annual stochastic absorption rate index and the discrete values ​​of the annual expected load shedding corresponding to different lower boundary values ​​of the wind power robust absorption domain, so as to establish the second discrete mapping relationship between the lower boundary variable and the annual expected load shedding. The evaluation module is used to construct an evaluation model for the maximum annual absorption level. The evaluation model aims to maximize the annual random absorption rate index, takes into account power supply reliability constraints, and is linearly expressed based on the first and second discrete mapping relationships. By solving the evaluation model, the upper limit of the wind power absorption level under the given energy storage expansion planning capacity limit can be obtained. The solution module is used to construct a power system energy storage expansion planning model that includes annual stochastic absorption rate constraints. The constant term in the annual stochastic absorption rate constraints is the target value for improving the wind power absorption level. The target value for improvement is not higher than the upper limit of the wind power absorption level. By solving the power system energy storage expansion planning model, an energy storage expansion planning scheme that can guarantee the set absorption level target and optimize the overall investment and operating costs is obtained.

[0032] According to another aspect of the present invention, a control system for a power system is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of any of the methods described above.

[0033] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The proposed method for obtaining power system energy storage expansion planning strategies first introduces the concept of a robust absorption domain and uses its boundary as an adjustable variable to flexibly define the power system's absorption range for wind power output. Based on this, wind curtailment and load shedding strategies are explicitly formulated according to the actual wind power output and the boundary of the robust absorption domain. This provides clear and operable countermeasures for the system when facing the randomness and volatility of wind power, serving as the foundation for subsequent quantitative assessment and planning. After formulating explicit wind curtailment and load shedding strategies, this invention further constructs calculation formulas for the annual random absorption rate index and the annual expected load shedding amount. These formulas transform the system's wind power absorption capacity and power supply reliability into quantifiable indicators. To more realistically reflect the randomness of wind power, this invention utilizes numerous wind power random scenarios to approximate... By calculating the discrete values ​​of these indicators, the impact of uncertainty on system performance can be comprehensively characterized, achieving a precise quantitative representation of wind power absorption levels and power supply reliability. After establishing a quantitative representation of system performance, this invention constructs a maximum annual absorption level assessment model. This model aims to maximize the annual stochastic absorption rate and, considering the capacity limitations of energy storage expansion planning, solves for the theoretically achievable upper limit of wind power absorption, avoiding blindly setting absorption targets and ensuring the feasibility of the plan. Finally, based on the upper limit of absorption obtained from the above assessment, this invention sets specific target values ​​for improving wind power absorption levels and embeds them as constraints into the power system energy storage expansion planning model. When seeking the optimal energy storage expansion scheme, this model must meet this set absorption target. In this way, this invention not only optimizes the investment and operating costs of energy storage, but more importantly, it clearly guarantees that the wind power absorption level can be improved and reach the preset target, thus achieving the best balance between economic efficiency and new energy absorption levels.

[0034] 2. The method for obtaining power system energy storage expansion planning strategies proposed in this invention utilizes robust absorption domains. It characterizes the range of power system's ability to absorb stochastic wind power output under various typical scenarios; the upper and lower boundaries of the robust absorption domain are both decision variables, which improves the flexibility of model solution and avoids the problem that the uncertainty set in traditional robust optimization methods is too conservative and may lead to no solution for the model.

[0035] 3. The method for obtaining the power system energy storage expansion planning strategy proposed in this invention determines the wind curtailment power based on the upper and lower boundary values ​​of the robust absorption domain and the wind power output value. and load shedding power It enables the formulation of explicit wind curtailment and load shedding strategies, providing clear decision-making guidance for dispatchers.

[0036] 4. The method for obtaining power system energy storage expansion planning strategies proposed in this invention utilizes the explicit wind curtailment and load shedding strategies to construct an annual stochastic absorption rate index. and annual expected load shedding The calculation formula; through the derived formula, it is possible to calculate during the planning and decision-making process. and This enables a quantitative characterization of absorption capacity and power supply reliability.

[0037] 5. The method for obtaining power system energy storage expansion planning strategies proposed in this invention utilizes wind power stochastic scenarios. Approximate calculations correspond to different upper boundary values ​​of the wind power robust absorption domain. Discrete value of annual random absorption rate index Corresponding to different lower boundary values ​​of the wind power robust absorption domain Discrete values ​​of annual expected load shedding The random wind power scenario is generated based on historical operating data, which avoids the problem of difficulty in accurately obtaining probability distribution information, and also enables... and Approximate calculation.

[0038] 6. The method for obtaining power system energy storage expansion planning strategies proposed in this invention utilizes... Let represent the objective function of the maximum annual absorption capacity assessment model, and utilize constraints. To ensure power supply reliability, among which, This is the limit for the annual expected load shedding, and and Linearization can be performed using the calculated discrete values ​​of the annual stochastic absorption rate index and the annual expected load shedding; As the objective function, and by setting constraints This allows for the assessment of the maximum annual absorption capacity while ensuring power supply reliability.

[0039] 7. The method for obtaining power system energy storage expansion planning strategies proposed in this invention utilizes... Express the objective function of the power system energy storage expansion planning model, where, and These are the annualized investment cost and operating cost, respectively. By minimizing the total annualized investment and operating cost as the objective function, the economic efficiency of the planning scheme can be optimized. Furthermore, the objective function no longer includes a wind curtailment penalty term, thus avoiding the impact of the penalty coefficient value on the planning scheme.

[0040] 8. The method for obtaining power system energy storage expansion planning strategies proposed in this invention utilizes annual stochastic absorption rate constraints. To ensure that the wind power absorption rate can be increased to the target value, among which, The target value for wind power absorption capacity; through setting clear constraints. This can effectively ensure that the planning schemes adopted in the decision-making process improve the absorption capacity. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method for obtaining the power system energy storage expansion planning strategy provided in Embodiment 1 of the present invention.

[0042] Figure 2 This is the topology diagram of the computational verification system provided in Embodiment 1 of the present invention.

[0043] Figure 3 This is a typical wind power output curve provided in Embodiment 1 of the present invention.

[0044] Figure 4 This is a typical scenario power load curve provided in Embodiment 1 of the present invention.

[0045] Figure 5 This is a box plot of the annual absorption rate of each method provided in Embodiment 1 of the present invention under random scenarios.

[0046] Figure 6 This is a box plot of the annual operating costs of the various methods provided in Embodiment 1 of the present invention under random scenarios. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0048] Example 1 like Figure 1 As shown in the figure, this embodiment provides a method for obtaining a power system energy storage expansion planning strategy, specifically including: S1: Based on the upper and lower boundary values ​​of the wind power robust absorption domain and the wind power output value under typical scenarios, formulate explicit wind curtailment and load shedding strategies.

[0049] S2: Based on the explicit wind curtailment and load shedding strategies, construct the annual stochastic absorption rate index calculation formula with the upper boundary of the wind power robust absorption domain as the variable and the annual expected load shedding amount calculation formula with the lower boundary of the wind power robust absorption domain as the variable.

[0050] S3: Using the wind power stochastic scenario, the discrete values ​​of the annual stochastic absorption rate index corresponding to the upper boundary value of different wind power robust absorption domains are approximated to establish the first discrete mapping relationship between the upper boundary variable and the annual stochastic absorption rate index, and the discrete values ​​of the annual expected load shedding amount corresponding to the lower boundary value of different wind power robust absorption domains are used to establish the second discrete mapping relationship between the lower boundary variable and the annual expected load shedding amount.

[0051] S4: Construct a maximum annual absorption level assessment model. The assessment model aims to maximize the annual random absorption rate index, considers power supply reliability constraints, and is linearly expressed based on the first and second discrete mapping relationships. By solving the assessment model, the upper limit of the wind power absorption level under the given energy storage expansion planning capacity limit is obtained.

[0052] S5: Construct a power system energy storage expansion planning model that includes annual stochastic absorption rate constraints. The constant term in the annual stochastic absorption rate constraints is the target value for improving the wind power absorption level. The target value for improvement is not higher than the upper limit of the wind power absorption level. By solving the power system energy storage expansion planning model, an energy storage expansion planning scheme that can guarantee the set absorption level target and optimize the overall investment and operating costs is obtained.

[0053] To further explain, in order to construct the power system energy storage expansion planning model designed in this application, firstly, power system parameters, typical scenario data, and energy storage parameters to be expanded are collected, including: A. The topology, line parameters, and equipment technical parameters of the power system; B. Wind power output and load curves under various typical scenarios, and the probability of each scenario; C. The upper and lower limits of power and capacity, lifespan, and investment cost coefficient of the planned energy storage to be expanded.

[0054] Secondly, the robust absorption domain (1) is used to characterize the range of power system's ability to absorb random wind power output under various typical scenarios; (1) Among them, subscript , and These are respectively the node number, time period number, and typical scenario number; For random wind power output; and These are the upper and lower boundary variables of the robust elimination domain, respectively.

[0055] Then, the wind curtailment power is determined based on the upper and lower boundary values ​​of the robust absorption domain and the wind power output value. and load shedding power The specific determination method is as follows: (2) in, and These represent the number of wind farms and load nodes, respectively. For random wind power output With the lower boundary of the robust absorption domain The power deficit between; This represents the load power.

[0056] Next, the annual stochastic absorption rate index is constructed using the explicit wind curtailment and load shedding strategies. and annual expected load shedding The calculation formula is as follows: (3) (4) in, For expectation calculation; and These represent the number and probability of typical scenarios, respectively. The number of time periods in each typical scenario; The number of days in a year; The length of each time period; and Represent the upper boundary of the robust elimination domain, respectively. and lower boundary The function.

[0057] To further illustrate, utilizing wind power random scenarios Approximate calculations correspond to different upper boundary values ​​of the wind power robust absorption domain. Discrete value of annual random absorption rate index Corresponding to different lower boundary values ​​of the wind power robust absorption domain Discrete values ​​of annual expected load shedding The details are as follows: (5) in, Randomly assign numbers to wind power scenarios; Number the discrete values; The number of random wind power scenarios; , and These are the node number, time period number, and typical scenario number, respectively.

[0058] To assess the upper limit of wind power absorption capacity under the constraints of energy storage expansion planning, this application designs a maximum annual absorption capacity assessment model, as follows: (6) (7) （8） （9） （10） （11） （12） （13） （14） （15） （16） （17） （18） （19） （20） （21） （22） （23） （24） （25） （26） （27） （28） （29） （30） （31） （32） （33） （34） （35） （36） （37） （38） （39） （40） (41) Wherein, equation (9) represents the power supply reliability constraint. This is the limit for the annual expected load shedding. For linearized , and These are the number and sequence number of the linearized segments, respectively. and For the first The coefficients of the linearized segment expression; For linearized , These represent the number of linearized segments. and For the first The coefficients of the linearized segment expression; Equation (10) represents the power and capacity constraints for energy storage construction, where, This is a state variable for energy storage construction; a value of 1 indicates construction, and a value of 0 indicates no construction. and These refer to the energy storage deployment power and capacity, respectively. and These are the minimum and maximum power requirements for energy storage construction, respectively. and These represent the minimum and maximum capacities for energy storage construction, respectively. and These are the minimum and maximum charging and discharging times for the energy storage system under construction, respectively; Equation (11) represents the minimum start-up and shutdown time constraints and start-up and shutdown state and start-up and shutdown action constraints for the thermal power unit, where, This is a 0-1 variable representing the start-up and shutdown status of the thermal power unit (1 indicates that the unit is in operation, and 0 indicates that the unit is in shutdown). For the start-stop action of thermal power units, there are 0-1 variables (1 indicates that a start-stop action is performed, and 0 indicates that a start-stop action is not performed). and These are the minimum start-up and shutdown times, respectively; Equation (12) represents the upper and lower limits of the thermal power unit's output, and Equations (13)-(14) represent the thermal power unit's ramp-up constraints, where, and These represent the minimum and maximum output of thermal power units, respectively. and These are the lower and upper limits of the adjustable power range for thermal power units, respectively. and Equation (15) represents the maximum uphill and downhill ramp rates of the thermal power unit; Equation (16) represents the charging and discharging power constraint of the existing energy storage in the system; Equations (16) and (17) represent the upper and lower limits of the state of charge constraint of the existing energy storage in the system, respectively. The superscript "ex" indicates the variable corresponding to the existing energy storage in the system. , and These represent the rated power, upper limit of discharge power, and upper limit of rechargeable power of the existing energy storage in the system, respectively. and These are the upper and lower limits of the stored energy in the system, respectively. and These represent the charging efficiency and discharging efficiency of the existing energy storage in the system, respectively. and These represent the upper and lower limits of the state of charge of existing energy storage in the system, respectively. and These are the rated capacity and stored energy variables of the existing energy storage in the system, respectively; similarly, equation (18) is the charging and discharging power constraint of the energy storage to be planned, and equations (19) and (20) are the upper and lower limits of the state of charge constraint of the energy storage to be planned, respectively, where the superscript "inv" indicates the variable corresponding to the energy storage to be planned. and These represent the energy storage power and capacity, respectively; Equation (21) is the boundary value range constraint of the robust absorption domain, where, For wind power installations; Equations (22)-(23) are power adequacy constraints within the robust absorption domain; where, , , , and These refer to the number of thermal power units, wind farms, loads, existing energy storage in the system, and the number of energy storage units to be planned. Let be the load power; Equation (24) represents the output constraint of the thermal power unit under the predicted scenario, where The output variables of thermal power units under the prediction scenario are: Equations (25) and (26) are the energy storage power constraints under the prediction scenario, respectively. and Let be the power variables of the existing energy storage and the energy storage to be planned in the system under the prediction scenario, respectively; Equation (27) is the power balance constraint under the prediction scenario, where, , and These represent the wind power, wind curtailment power, and load shedding power under the predicted scenario, respectively; Equation (28) represents the line capacity constraint under the predicted scenario, where, This is the upper limit of line capacity. The line power transmission distribution factor, subscript The line number is given; equations (29)-(30) represent the constraints on wind curtailment power and load shedding power under the predicted scenario, where, The corresponding variables in the prediction scenario are represented; Equations (33)-(41) are the operational constraints within the robust absorption domain. Specifically, Equation (33) indicates that wind curtailment and load shedding are not allowed within the robust absorption domain; Equation (34) is the wind power output constraint; Equation (35) is the wind curtailment power constraint; Equation (36) is the load shedding power constraint; Equation (37) is the thermal power unit output constraint; Equations (38) and (39) are the power constraints of existing energy storage and planned energy storage in the system, respectively; Equation (40) is the power balance constraint; and Equation (41) is the line capacity constraint. Describes the corresponding variable in a random scenario within the robust elimination domain. , .

[0059] The constraints (33) in the above model contain a max-min structure. The model is solved using a column and constraint generation algorithm, and the result is denoted as... .

[0060] Based on this, this application designs a robust extended programming model for power system energy storage, with the objective function being: (42) in, and The annualized investment and operating costs are calculated using the following formula.

[0061] (43) (44) (45) (46) (47) (48) (49) (50) in, The investment recovery factor for energy storage, For the discount rate, For energy storage life, and The cost of energy storage investment per unit power and capacity; , and These are the operating costs of thermal power units, the operation and maintenance costs of energy storage, and the cost of load shedding. Costs associated with starting and stopping thermal power units. Given the fuel cost function of thermal power units, the original function (47) can be linearized according to equation (48), where, The minimum fuel cost for thermal power unit operation. and These are the segment numbers and number of segments in the linearization of the fuel cost function for thermal power units. and These represent the portion of the thermal power unit's output in the nth linearized segment and the corresponding cost coefficient, respectively. and These are the upper and lower limits of the output of thermal power units, respectively. and These are the annual operation and maintenance costs per unit capacity of existing energy storage and energy storage to be planned in the system, respectively. This is the load shedding cost coefficient.

[0062] In addition, the constraints include equations (7)-(8), (10)-(41), and the annual random consumption rate constraint (51).

[0063] (51) in, The target value for wind power absorption level, satisfying .

[0064] The simulation data described herein is for illustrative purposes only and is not intended to limit the scope of the invention.

[0065] consider Figure 2 The verification system in the table shows the parameters of three thermal power units (TU1, TU2, and TU3). The wind power installed capacity is 300 MW. The existing energy storage rated power and capacity in the system are 40 MW and 160 MWh, respectively, with a charge and discharge efficiency of 95%. The initial, minimum, and maximum SOC are 60%, 20%, and 100%, respectively. The load shedding cost is set at $1000 / MWh, and the wind curtailment penalty cost is set at $100 / MWh. The system peak load is 240 MW. The wind power and load curves under various typical scenarios are shown in Table 1. Figure 3 and Figure 4 As shown in Table 2, the scenario probabilities are as follows. Taking the location of the wind farm as the candidate location for energy storage construction, the upper limit of the expanded energy storage power and capacity is 150 MW and 600 MWh, respectively. The investment cost per unit power is 145 k$ / MW, the investment cost per unit capacity is 300 k$ / MWh, the energy storage life is 10 years, the discount rate is 5%, and the minimum and maximum charge and discharge times are 1 h and 4 h, respectively.

[0066] To illustrate the effectiveness of the method proposed in this invention, it is compared with existing methods. The method proposed in this invention and the comparison method are described below.

[0067] The method proposed in this invention is a robust extended planning method for energy storage that considers the constraint of annual stochastic absorption rate.

[0068] Comparison Method 1: Deterministic Energy Storage Expansion Planning Method in Typical Scenarios; Comparison Method 2: Stochastic Energy Storage Expansion Planning Method Considering 10 Sets of Random Scenarios.

[0069] Table 1 Parameters of Thermal Power Units

[0070] Table 2 Probabilities of each typical scenario

[0071] First, to analyze the necessity of carrying out energy storage expansion planning under the background of increasing new energy penetration, the maximum annual absorption level assessment model designed in this application was used to evaluate the maximum annual random absorption rate of the system under the existing energy storage configuration and after expansion planning based on the maximum deployable energy storage capacity. The evaluation results are shown in Table 3. It can be seen that the increase in the proportion of new energy has exacerbated the absorption pressure of the system. Under the existing energy storage configuration, the maximum annual random absorption rate is only 89.25%, and the system's absorption capacity is significantly insufficient. However, through the expansion planning of battery energy storage, the maximum annual random absorption rate of the system has been greatly improved, reaching 98.15%, effectively breaking through the absorption bottleneck and fully releasing the potential for new energy access.

[0072] Table 3. Assessment Results of Maximum Absorption Level Before and After Planning

[0073] Based on the evaluation results of the maximum absorption level in Table 3, the absorption level target in the proposed method is set at 95%. This absorption level cannot be achieved under the existing energy storage configuration of the system, but it can be achieved by expanding the energy storage capacity. The overall results of the proposed method and the comparative methods are shown in Table 4. The average operating cost and average absorption rate are the average operating cost and absorption rate under 1000 random scenarios, respectively. The distribution of absorption rate and operating cost for each method under random scenarios is shown in Table 4. Figure 5 and Figure 6 As shown.

[0074] The results show that Method 1's decision result is that no additional energy storage is needed. Although the planning cost of this scheme is 0, due to the lack of consideration for uncertainty in the decision-making process, the amount of wind curtailment is the highest in all random scenarios, and the average absorption rate is only 88.44%, the lowest among all methods. Compared with Method 1, Method 2 further considers the random scenarios of wind power output. By adding 19.07 MW / 19.07 MWh of energy storage, it effectively reduces the amount of wind curtailment during operation, increases the average absorption rate to 91.45%, and has the best economic efficiency. However, it cannot guarantee the level of new energy absorption, mainly for two reasons: First, the random scenarios considered ignore low-probability, high-risk scenarios, making it difficult to fully represent the random characteristics of new energy output; second, the planning scheme decision is related to the setting of the penalty coefficient, and there is no clear correspondence between the value of the penalty coefficient and the absorption level. Compared to methods 1 and 2, the proposed method requires a higher planned capacity for energy storage expansion to ensure the absorption rate, which increases the planning cost accordingly. However, the increased scale of energy storage configuration further reduces the system operating cost and increases the system's average annual absorption rate to 95.23%, achieving the target level.

[0075] Table 4 Overall results of each method

[0076] Example 2 This embodiment provides a device for obtaining power system energy storage expansion planning strategies, including: a strategy formulation module, a construction module, a calculation module, an evaluation module, and a solution module.

[0077] The strategy formulation module is used to formulate explicit wind curtailment and load shedding strategies based on the upper and lower boundary values ​​of the wind power robust absorption domain and the wind power output value in typical scenarios. The module is used to construct, based on the explicit wind curtailment and load shedding strategies, the annual stochastic absorption rate index calculation formula with the upper boundary of the wind power robust absorption domain as the variable and the annual expected load shedding amount calculation formula with the lower boundary of the wind power robust absorption domain as the variable. The calculation module is used to approximate the discrete values ​​of the annual stochastic absorption rate index corresponding to different upper boundary values ​​of the wind power robust absorption domain using the wind power stochastic scenario, so as to establish the first discrete mapping relationship between the upper boundary variable and the annual stochastic absorption rate index and the discrete values ​​of the annual expected load shedding corresponding to different lower boundary values ​​of the wind power robust absorption domain, so as to establish the second discrete mapping relationship between the lower boundary variable and the annual expected load shedding. The evaluation module is used to construct an evaluation model for the maximum annual absorption level. The evaluation model aims to maximize the annual random absorption rate index, takes into account power supply reliability constraints, and is linearly expressed based on the first and second discrete mapping relationships. By solving the evaluation model, the upper limit of the wind power absorption level under the given energy storage expansion planning capacity limit can be obtained. The solution module is used to construct a power system energy storage expansion planning model that includes annual stochastic absorption rate constraints. The constant term in the annual stochastic absorption rate constraints is the target value for improving the wind power absorption level. The target value for improvement is not higher than the upper limit of the wind power absorption level. By solving the power system energy storage expansion planning model, an energy storage expansion planning scheme that can guarantee the set absorption level target and optimize the overall investment and operating costs is obtained.

[0078] Example 3 This embodiment provides a control system for a power system, including a memory and a processor. The memory stores a computer program, and the processor executes the steps of a method implemented by the computer program.

[0079] Example 4 This embodiment provides a computer-readable storage medium storing a computer program thereon, the steps of a method implemented when the computer program is executed by a processor.

[0080] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for obtaining a power system energy storage expansion planning strategy, characterized in that, include: S1. Based on the upper and lower boundary values ​​of the wind power robust absorption domain and the wind power output value in typical scenarios, formulate explicit wind curtailment and load shedding strategies. S2, Based on the explicit wind curtailment and load shedding strategies, construct the annual stochastic absorption rate index calculation formula with the upper boundary of the wind power robust absorption domain as the variable and the annual expected load shedding amount calculation formula with the lower boundary of the wind power robust absorption domain as the variable. S3. Using the random scenario of wind power, the discrete values ​​of the annual random absorption rate index corresponding to the upper boundary value of different wind power robust absorption domains are approximately calculated to establish the first discrete mapping relationship between the upper boundary variable and the annual random absorption rate index, and the discrete values ​​of the annual expected load shedding amount corresponding to the lower boundary value of different wind power robust absorption domains are calculated to establish the second discrete mapping relationship between the lower boundary variable and the annual expected load shedding amount. S4. Construct a maximum annual absorption level assessment model. The assessment model aims to maximize the annual random absorption rate index, considers power supply reliability constraints, and is linearly expressed based on the first and second discrete mapping relationships. By solving the assessment model, the upper limit of the wind power absorption level under the given energy storage expansion planning capacity limit is obtained. S5. Construct a power system energy storage expansion planning model that includes annual stochastic absorption rate constraints. The constant term in the annual stochastic absorption rate constraints is the target value for improving the wind power absorption level. The target value for improvement is not higher than the upper limit of the wind power absorption level. By solving the power system energy storage expansion planning model, an energy storage expansion planning scheme that can guarantee the set absorption level target and optimize the overall investment and operating costs is obtained.

2. The method for obtaining a power system energy storage expansion planning strategy according to claim 1, characterized in that, S1 utilizes a robust elimination domain Characterizes the range of power system capacity to absorb stochastic wind power output under various typical scenarios; Among them, subscript , and These are respectively the node number, time period number, and typical scenario number; For random wind power output; and These are the upper and lower boundary variables of the robust elimination domain, respectively.

3. The method for obtaining a power system energy storage expansion planning strategy according to claim 1, characterized in that, In S1, the wind curtailment power is explicitly determined based on the upper and lower boundary values ​​of the wind power robust absorption domain and the wind power output value. and load shedding power The specific determination method is as follows: in, and These represent the number of wind farms and load nodes, respectively. For random wind power output With the lower boundary of the robust absorption domain The power deficit between; This represents the load power.

4. The method for obtaining a power system energy storage expansion planning strategy according to claim 1, characterized in that, In S2, the annual stochastic absorption rate index is constructed using the explicit wind curtailment and load shedding strategies. and annual expected load shedding The calculation formula is as follows: in, For expectation calculation; and These represent the number and probability of typical scenarios, respectively. The number of time periods in each typical scenario; The number of days in a year; The length of each time period; and Represent the upper boundary of the robust elimination domain, respectively. and lower boundary The function.

5. The method for obtaining a power system energy storage expansion planning strategy according to claim 1, characterized in that, The S3 utilizes a random wind power scenario. Approximate calculations correspond to different upper boundary values ​​of the wind power robust absorption domain. Discrete value of annual random absorption rate index Corresponding to different lower boundary values ​​of the wind power robust absorption domain Discrete values ​​of annual expected load shedding The details are as follows: in, Randomly assign numbers to wind power scenarios; Number the discrete values; The number of random wind power scenarios; , and These are the node number, time period number, and typical scenario number, respectively.

6. The method for obtaining a power system energy storage expansion planning strategy according to claim 1, characterized in that, S4 utilizes Let represent the objective function of the maximum annual absorption capacity assessment model, and utilize constraints. To ensure power supply reliability, among which, This is the limit for the annual expected load shedding, and and The calculated discrete values ​​of the annual random absorption rate index and the annual expected load shedding can be linearized, as follows: in, For linearized , and These are the number and sequence number of the linearized segments, respectively. and For the first The coefficients of the linearized segment expression; For linearized , These represent the number of linearized segments. and For the first The coefficients of the linearized segment expression.

7. The method for obtaining a power system energy storage expansion planning strategy according to claim 1, characterized in that, S5 utilizes Express the objective function of the power system energy storage expansion planning model, where, and These are the annualized investment cost and operating cost, respectively, and the specific calculation formulas are as follows: in, The investment recovery factor for energy storage, For the discount rate, For energy storage life, and These represent the existing energy storage capacity and the planned energy storage capacity in the system. and These refer to the energy storage construction power and capacity, respectively. and The cost of energy storage investment per unit power and capacity; , and These are the operating costs of thermal power units, the operation and maintenance costs of energy storage, and the cost of load shedding. This represents the number of thermal power units. Costs associated with starting and stopping thermal power units. and These are the 0-1 variables representing the start-stop status and start-stop action of the thermal power unit, respectively. To provide power to thermal power units, For the fuel cost function of thermal power units, The minimum fuel cost for thermal power unit operation. and These are the segment numbers and number of segments in the linearization of the fuel cost function for thermal power units. and These represent the portion of the thermal power unit's output in the nth linearized segment and the corresponding cost coefficient, respectively. and These are the upper and lower limits of the output of thermal power units, respectively. For the existing energy storage capacity in the system, and These are the annual operation and maintenance costs per unit capacity of existing energy storage and energy storage to be planned in the system, respectively. This is the load shedding cost coefficient.

8. The method for obtaining a power system energy storage expansion planning strategy according to claim 1, characterized in that, The S5 utilizes the annual random absorption rate constraint. To ensure that the wind power absorption rate can be increased to the target value, among which, This represents the target value for wind power absorption.

9. A device for obtaining energy storage expansion planning strategies for power systems, characterized in that, include: The strategy formulation module is used to formulate explicit wind curtailment and load shedding strategies based on the upper and lower boundary values ​​of the wind power robust absorption domain and the wind power output value in typical scenarios. The module is used to construct, based on the explicit wind curtailment and load shedding strategies, the annual stochastic absorption rate index calculation formula with the upper boundary of the wind power robust absorption domain as the variable and the annual expected load shedding amount calculation formula with the lower boundary of the wind power robust absorption domain as the variable. The calculation module is used to approximate the discrete values ​​of the annual stochastic absorption rate index corresponding to different upper boundary values ​​of the wind power robust absorption domain using the wind power stochastic scenario, so as to establish the first discrete mapping relationship between the upper boundary variable and the annual stochastic absorption rate index and the discrete values ​​of the annual expected load shedding corresponding to different lower boundary values ​​of the wind power robust absorption domain, so as to establish the second discrete mapping relationship between the lower boundary variable and the annual expected load shedding. The evaluation module is used to construct an evaluation model for the maximum annual absorption level. The evaluation model aims to maximize the annual random absorption rate index, takes into account power supply reliability constraints, and is linearly expressed based on the first and second discrete mapping relationships. By solving the evaluation model, the upper limit of the wind power absorption level under the given energy storage expansion planning capacity limit can be obtained. The solution module is used to construct a power system energy storage expansion planning model that includes annual stochastic absorption rate constraints. The constant term in the annual stochastic absorption rate constraints is the target value for improving the wind power absorption level. The target value for improvement is not higher than the upper limit of the wind power absorption level. By solving the power system energy storage expansion planning model, an energy storage expansion planning scheme that can guarantee the set absorption level target and optimize the overall investment and operating costs is obtained.

10. A control system for an electric power system, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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