Pumped storage coupled constant-pressure compressed air energy storage capacity configuration optimization method

Abstract

The invention discloses a pumped storage coupled constant-pressure compressed air energy storage capacity configuration optimization method, and aims to solve the problem that economy and adjusting performance of an existing single energy storage technology are difficult to consider at the same time. A multi-objective optimization model is constructed, the full life cycle cost and the adjustment performance loss are taken as double objectives, and dynamic balance of economical efficiency and technical performance is realized in combination with a weight coefficient. An improved particle swarm optimization algorithm is innovatively adopted, and the optimal combination of pumped storage rated power and compressed air energy storage capacity is optimized and solved through adaptive inertia weight, dynamic learning factors and mutation operation. According to the method, the limitation of traditional single energy storage configuration is broken through, the complementary characteristics of two energy storage technologies are utilized, the comprehensive efficiency of an energy storage system is remarkably improved on the premise of meeting power balance, climbing rate and capacity constraint of a power grid, and an efficient energy storage configuration scheme is provided for a high-proportion renewable energy access scene.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power system energy storage, in particular to a capacity configuration optimization method based on coupling of pumped hydro energy storage and constant-pressure compressed air energy storage. BACKGROUND

[0002] In recent years, with the acceleration of energy structure transformation, energy storage technology has become a key means to smooth power grid fluctuations and improve renewable energy consumption capacity. Pumped hydro energy storage (PHES) has the advantages of fast response speed and long cycle life, but its construction is limited by geographical conditions and the unit power cost is relatively high. Compressed air energy storage (CAES) has the characteristics of low cost and flexible scale, but the energy conversion efficiency is relatively low. The coupling of pumped hydro energy storage and compressed air energy storage can effectively realize technology complementation: in terms of response speed, the second-level regulation capacity of PHES can quickly smooth the high-frequency fluctuations of the power grid, and CAES provides hour-level continuous output; in terms of geographical adaptability, the use of underground caves by CAES can make up for the dependence of PHES on mountain reservoirs; in terms of economy, the low power cost of CAES and the high energy density of PHES form a combined advantage, which comprehensively reduces the unit energy storage cost. The present application proposes a capacity configuration method for coupling of PHES and CAES, which optimizes the power and capacity parameters of the two to comprehensively improve the economy and regulation performance. SUMMARY

[0003] In view of the above problems in the prior art, the application provides a pumped hydro energy storage coupled with constant-pressure compressed air energy storage capacity configuration optimization method.

[0004] In order to achieve the above-mentioned application purposes, the technical scheme adopted by the application is as follows: A pumped hydro energy storage coupled with constant-pressure compressed air energy storage capacity configuration optimization method, comprising the following steps: S1. Constructing a multi-objective model framework to establish a double-target collaborative optimization mechanism of economic target and technical target, wherein the economic target drives cost minimization and the technical target guarantees regulation performance; S2, quantifying the economic target function and the technical target function, and determining the target function of the multi-objective model according to the established multi-objective optimization model; S3, establishing operation constraint conditions to provide boundary limitations for the target function quantified in S2; S4, using an improved particle swarm optimization algorithm to solve the optimization problem, mapping the pumped hydro energy storage rated power and the compressed air energy storage capacity into the optimized parameters through particle coding, and performing adaptive iteration based on the constraint conditions of S3 to output the energy storage configuration scheme.

[0005] Further, the multi-objective model in S2 takes the minimum total life cycle cost and regulation performance loss as the objective function, denoted as:

[0006] wherein, and are weight coefficients, and , respectively represent the importance of the total life cycle cost and the regulation performance loss in the total objective; is the total life cycle cost, and:

[0007] wherein, is the investment cost, is the operation and maintenance cost, is the electricity price arbitrage income; is the regulation performance loss, and:

[0008] wherein, and are the maximum values of the system comprehensive efficiency and the regulation power, respectively, and are the actual values of the system comprehensive efficiency and the regulation power, respectively.

[0009] Further, the constraint conditions in S3 include: the power balance constraint, denoted as:

[0010] wherein, is the total output power of the energy storage system, is the power shortage of the power grid; the ramp rate constraint:

[0011] wherein, is the change rate of the total output power of the energy storage system, is the maximum output power of the energy storage system; the energy storage capacity constraint, denoted as:

[0012] wherein, and are the real-time capacities of the pumped storage and the constant pressure compressed air energy storage, respectively; and are their maximum capacities.

[0013] Further, the S4 specifically comprises the following steps: S41, taking the rated power of pumped storage and the rated capacity of constant pressure compressed air energy storage as a particle, denoted as:

[0014] Wherein, is the rated power of pumped storage, is the capacity of constant pressure compressed air energy storage.

[0015] S42, using the inertia weight to balance the global and local search ability of the particle, and using the adaptive inertia weight method for adaptive iteration; S43, using the method of dynamic learning factor to control the step length of the particle flying to the historical optimal position and the global optimal position respectively; S44, using mutation operation to replace the original value with a value selected at will within the set range, so that the particle jumps out of the local optimal area and continues to search for the optimal solution until the global optimal solution is obtained, and the energy storage configuration scheme meeting the economic and technical double targets is output.

[0016] Further, the inertia weight in the S42 is represented as:

[0017] In the formula, and are the maximum value and the minimum value of the inertia weight respectively, is the maximum iteration number, is the current iteration number.

[0018] Further, the learning factor in the S43 is represented as:

[0019]

[0020] In the formula, and are the factors controlling the step length of the particle flying to the historical optimal position and the global optimal position respectively, and the subscripts max and min are the upper and lower limit values respectively.

[0021] The present application has the following beneficial effects: 1) Quantify the performance loss by the normalized ratio of system comprehensive efficiency and regulation power. The model also contains power balance, climbing rate and energy storage capacity constraints to ensure that the system realizes optimal configuration under the premise of meeting the operation demand of power grid.

[0022] 2) Adopting dynamic adjustment inertia weight and learning factor to balance global search and local development ability; meanwhile, introducing 5% probability mutation operation to effectively avoid local optimum. Through the above mechanism, the algorithm can efficiently optimize to obtain an energy storage configuration scheme meeting the dual objectives of economy and technology. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 A flowchart of the method for optimizing the capacity configuration of pumped hydro energy storage coupled with constant-pressure compressed air energy storage according to the present application is shown.

[0024] Figure 2 A flowchart of the improved particle swarm optimization algorithm according to the embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] The specific embodiments of the present application are described below to facilitate understanding of the present application by those skilled in the art, but it should be clear that the present application is not limited in scope to the specific embodiments, and for those skilled in the art, any changes that are obvious within the spirit and scope of the present application as defined and determined by the appended claims are obvious, and all applications utilizing the concept of the present application are within the scope of protection.

[0026] A method for optimizing the capacity configuration of pumped hydro energy storage coupled with constant-pressure compressed air energy storage, as shown in Figure 1 includes the following steps: S1. Constructing a multi-objective model framework to establish a dual-objective collaborative optimization mechanism of economic and technical objectives, wherein the economic objective drives cost minimization and the technical objective guarantees regulation performance; In this embodiment, the constructed multi-objective capacity optimization model takes the collaborative configuration of pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the core. The model takes the minimization of the life cycle cost as the economic objective, covering the PHES power investment, CAES capacity investment, operation and maintenance cost, and electricity price arbitrage income; at the same time, the model takes the minimization of the regulation performance loss as the technical objective, quantifying the performance loss through the normalized ratio of system comprehensive efficiency and regulation power. The model also includes power balance, ramp rate, and energy storage capacity constraints to ensure optimal configuration of the system under the premise of meeting the demand of power grid operation S2, quantifying the economic and technical objective functions, and determining the objective function of the multi-objective model according to the established multi-objective optimization model; In this embodiment, the objective function is: The multi-objective optimization model of the present application takes the minimization of the life cycle cost and the regulation performance loss as the objective, and adopts the weighted summation method to combine the two objective functions into one total objective function: (1a) wherein, and It is a weighting coefficient, and These represent the relative importance of total lifecycle cost and adjustment performance loss within the overall objective, respectively. The weighting coefficients can be adjusted based on actual circumstances to balance the relationships between different objectives.

[0027] Total life cycle cost : (1b) Investment costs : (1c) in This is the unit power investment cost (RMB / kW) of pumped hydro storage (PHES). It is the rated power (kW) of pumped storage. It is the unit capacity investment cost (yuan / kWh) of constant pressure compressed air energy storage (CAES). It is the rated capacity (kWh) of constant pressure compressed air energy storage.

[0028] Operation and maintenance costs : Costs including equipment maintenance, repair, and upkeep can usually be estimated based on empirical formulas or historical data.

[0029] Electricity price arbitrage profits : (1d) and These are peak electricity price and off-peak electricity price (yuan / kWh), and the peak electricity price varies greatly in different regions and at different times. and They are in the time period The discharge and charging amounts (kWh) of the internal energy storage system. The energy storage system can charge during off-peak electricity price periods and discharge during peak electricity price periods, thereby obtaining arbitrage profits from electricity prices.

[0030] Adjustment performance loss : (1e) Overall system efficiency : (1f) and These are the efficiencies of pumped hydro storage and high-pressure compressed air storage, respectively. They represent the energy conversion efficiency of the energy storage system during the charging and discharging process. and are the real-time power (kW) of pumped hydro storage and constant pressure compressed air storage, respectively.

[0031] represents the regulation power that the energy storage system can provide, which reflects the regulation ability of the energy storage system to power fluctuations of the power system. and are the maximum and minimum values of the system comprehensive efficiency and regulation power, respectively, which can be obtained by theoretical analysis or actual test. Regulation performance loss is smaller, the better the regulation performance of the energy storage system.

[0032] S3, establish operation constraints to provide boundary conditions for the objective function quantified in S2; In this embodiment, in order to ensure the safe and stable operation of the energy storage system, the multi-objective optimization model needs to meet the following constraint conditions: Power balance constraint: (1g) is the total output power (kW) of the energy storage system, which is equal to the sum of the output power of pumped hydro storage and constant pressure compressed air storage, i.e. (1h) is the power shortage (kW) of the power grid.

[0033] Ramp rate constraint: (1i) is the change rate of the total output power of the energy storage system (kW / s), is the maximum output power (kW) of the energy storage system.

[0034] Energy storage capacity constraint: (1j) and are the real-time capacity (kWh) of pumped hydro storage and constant pressure compressed air storage, respectively; and are their maximum capacities (kWh), respectively.

[0035] S4, solve the optimization problem by using an improved particle swarm optimization algorithm, map the rated power of pumped hydro storage and the capacity of compressed air storage to the optimized parameters through particle coding, and perform adaptive iteration based on the constraints of S3 to output the energy storage configuration scheme.

[0036] As shown in Figure 2 , in the improved particle swarm optimization algorithm, each particle represents a set of rated power of pumped hydro storage combined with constant pressure compressed air energy storage capacity For example, a particle can be represented as where and are the rated power of pumped hydro energy storage and the rated capacity of constant pressure compressed air energy storage, respectively. By updating and optimizing the position of the particle, the optimal energy storage capacity configuration scheme can be found.

[0037] Adaptive inertia weight: Inertia weight in particle swarm optimization algorithm for balancing the global search ability and local search ability of particles. In the improved particle swarm optimization algorithm, the method of adaptive inertia weight is adopted, that is, the inertia weight decreases linearly with the increase of the iteration number: (2a) where, and are the maximum value and the minimum value of the inertia weight, respectively, and in the present invention , . is the maximum iteration number, is the current iteration number. In the early stage of iteration, the inertia weight is large, and the particle has strong global search ability and can search for the optimal solution in a larger search space; with the increase of the iteration number, the inertia weight gradually decreases, and the local search ability of the particle is enhanced, which can search the area near the optimal solution more finely.

[0038] Dynamic learning factor: Learning factor and respectively control the step length of the particle flying to its historical optimal position and the global optimal position. In the improved particle swarm optimization algorithm, the method of dynamic learning factor is adopted, that is, the learning factor changes linearly with the increase of the iteration number: (2b) (2c) where, , , , In the early stage of iteration, is large, is small, and the particle tends to fly to its historical optimal position, enhancing the individual exploration ability of the particle; with the increase of the iteration number, gradually decreases, gradually increases, and the particle tends to fly to the global optimal position, enhancing the group cooperation ability of the particle.

[0039] Mutation operation: In order to avoid the particle swarm optimization algorithm falling into local optimum, the mutation operation is introduced in the improved particle swarm optimization algorithm. The position of the particle is randomly disturbed with a probability of 5%, that is, the value of a certain dimension (such as or ) of the particle is randomly modified. For example, a value can be randomly selected within a certain range to replace the original value, so that the particle jumps out of the local optimal region and continues to search for better solutions.

[0040] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flow Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0041] These computer program instructions can also be stored in a computer-readable memory that can cause the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the functions specified in the flow Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0042] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flow Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0043] The principles and implementation methods of the present application are described in the specific embodiments, and the above embodiment descriptions are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation methods and application scope will be changed, and the above description should not be understood as a limitation of the present application.

[0044] Those skilled in the art will appreciate that the embodiments described herein are presented for purposes of illustration and that the inventive principles are not limited to these particular embodiments. Other variations and modifications can be made to the embodiments without departing from the spirit and scope of the inventive principles.

Claims

1. A method for optimizing the capacity configuration of pumped hydro storage coupled with constant-pressure compressed air energy storage, characterized in that, Includes the following steps: S1. Construct a multi-objective model framework and establish a dual-objective collaborative optimization mechanism for economic and technical objectives, where the economic objective drives cost minimization while the technical objective ensures adjustment performance; S2. Quantify the economic objective function and the technical objective function, and determine the objective function of the multi-objective model based on the established multi-objective optimization model; S3. Establish operational constraints to provide boundary limitations for the objective function quantified in S2; S4. An improved particle swarm optimization algorithm is used to solve the optimization problem. The rated power of pumped hydro storage and the compressed air energy storage capacity are mapped to optimizable parameters through particle encoding. Based on the constraints in S3, adaptive iteration is performed to output the energy storage configuration scheme.

2. The method for optimizing the capacity configuration of pumped hydro storage coupled with constant-pressure compressed air energy storage according to claim 1, characterized in that, The multi-objective model in S2 uses the minimum total lifecycle cost and the adjustment performance loss as its objective functions, expressed as: in, and It is a weighting coefficient, and , respectively, represent the importance of total life cycle cost and adjustment performance loss in the overall objective; The total lifecycle cost is: In the formula, For investment costs, For maintenance costs, For electricity price arbitrage profits; To adjust for performance loss, and: In the formula, and These are the maximum values ​​of the system's overall efficiency and the regulating power, respectively. and These are the actual values ​​of the system's overall efficiency and regulation power, respectively.

3. The method for optimizing the capacity configuration of pumped hydro storage coupled with constant-pressure compressed air energy storage according to claim 1, characterized in that, The constraints in S3 include: Power balance constraints are expressed as: In the formula, It is the total output power of the energy storage system. It is a power deficit in the power grid; Climbing speed constraint: In the formula, It is the rate of change of the total output power of the energy storage system. This is the maximum output power of the energy storage system; Energy storage capacity constraints are expressed as: In the formula, and These are the real-time capacities of pumped hydro storage and constant-pressure compressed air storage, respectively. and These are their maximum capacities.

4. The method for optimizing the capacity configuration of pumped hydro storage coupled with constant-pressure compressed air energy storage according to claim 1, characterized in that, S4 specifically includes the following steps: S41. Taking the rated power of pumped hydro storage and the rated capacity of constant-pressure compressed air storage as a particle, it is represented as: in, This is the rated power of pumped storage. It is the energy storage capacity of constant pressure compressed air. S42. Utilize the inertial weight to balance the global and local search capabilities of particles, and use the adaptive inertial weight method for adaptive iteration; S43. Using a dynamic learning factor, the step size of the particle's flight towards its own historical best position and global best position are controlled respectively. S44. Using mutation operations, arbitrarily select a value of a particle within a set range to replace the original value, so that the particle jumps out of the local optimal region and continues to search for the optimal solution until the global optimal solution is obtained, and outputs an energy storage configuration scheme that meets both economic and technical objectives.

5. The method for optimizing the capacity configuration of pumped hydro storage coupled with constant-pressure compressed air energy storage according to claim 4, characterized in that, The inertial weight in S42 is represented as follows: In the formula, and These are the maximum and minimum values ​​of the inertia weight, respectively. It is the maximum number of iterations. This is the current iteration number.

6. The method for optimizing the capacity configuration of pumped hydro storage coupled with constant-pressure compressed air energy storage according to claim 4, characterized in that, The learning factor in S43 is represented as follows: In the formula, and These are the factors that control the step size of the particle as it flies toward its historical best position and global best position, respectively, with the subscripts max and min being its corresponding upper and lower limits, respectively.

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