Energy storage double-layer optimization configuration method and system for novel electric power

Abstract

The invention discloses a novel electric power-oriented energy storage double-layer optimization configuration method and system, and the method comprises the steps: S1, constructing an energy storage system model, defining the static parameters of energy storage, including rated capacity, rated power, charge and discharge efficiency, initial charge state and the like, and further quantifying the supporting capability of energy storage for the dynamic stability of the system, therefore, the enhancement effect of energy storage on frequency stability and small interference stability is directly reflected. According to the method, the system dynamic stability index, the green energy consumption index and the market economic index are uniformly incorporated into an energy storage optimization configuration framework, so that the limitation of single target of a traditional method is overcome, and a key technical support is provided for constructing a safe, green and economic novel power system; secondly, market price and risk constraints based on probability prediction are introduced into a lower-layer operation model, so that an energy storage operation strategy obtained through optimization can effectively cope with market fluctuation, the stability of earnings is guaranteed while high earnings are pursued, and the anti-risk capability of investment decision making is enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system planning and operation technology, and more specifically, to a two-layer optimized configuration method and system for energy storage for new types of power. Background Technology

[0002] With the increasing penetration of new energy sources such as wind power and photovoltaics into the power system, the power system is undergoing profound changes. The randomness, volatility, and low inertia of new energy power generation pose unprecedented challenges to the safe and stable operation of the power grid. On the one hand, the system inertia and damping level have decreased significantly, and frequency stability issues have become increasingly prominent; on the other hand, the centralized grid connection of new energy sources has exacerbated the curtailment of wind and solar power in some areas. Energy storage systems, as a flexible regulatory resource, are considered one of the key means to solve these problems.

[0003] Currently, research on the optimal configuration of energy storage has made some progress. For example, the invention patent with publication number "CN120320384A" proposes a two-layer optimal configuration method for energy storage systems to improve the voltage quality of distribution networks. This method aims to reduce network losses and improve voltage quality by optimizing site selection and capacity at the upper layer and optimizing operation strategies at the lower layer. However, this method mainly focuses on the static power quality issues at the distribution network level and does not address the dynamic stability issues at the system level, such as inertia support and damping control. In addition, its lower-layer operation model is relatively simple and does not fully consider the issues of maximizing returns and controlling risks in multiple market scenarios such as energy storage participation in the electricity spot market and frequency regulation ancillary service market. The coupling between planning and operation is insufficient, making it difficult to adapt to the needs of new power systems for the comprehensive value mining of energy storage.

[0004] Another published document, “CN202510627188.0”, focuses on the ex-post evaluation of the energy storage operation effect and lacks forward-looking and proactive optimization guidance on energy storage configuration and operation strategies.

[0005] Therefore, existing technologies have the following shortcomings: First, the energy storage configuration model fails to effectively incorporate system dynamic stability indicators (such as virtual inertia and damping); second, it fails to fully consider the uncertainty and risks of returns under the electricity market environment; and third, the planning and operation layer models are loosely coupled, lacking an effective closed-loop feedback mechanism, which may cause the optimization results to deviate from the actual operational optimum. There is an urgent need for an integrated energy storage optimization configuration method that can simultaneously consider system dynamic stability, renewable energy consumption, and market economic benefits. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a two-layer optimized configuration method and system for energy storage for new types of electricity, so as to solve the technical problem of how to ensure the dynamic security of the power grid, promote the consumption of green energy and achieve the stability of market returns in the context of high proportion of new energy access.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a two-layer optimized configuration method for energy storage that balances system dynamic stability and market risk control, comprising the following steps: S1. Construct an energy storage system model: Define the static parameters of energy storage, quantify its virtual inertia contribution and damping contribution to characterize its support capability for the dynamic stability of the system, and calculate its local green electricity consumption gain at the access node. S2. Establish a two-layer optimization model: Construct a two-layer optimization framework comprising an upper-layer planning model and a lower-layer operation model; the upper-layer planning model aims to maximize the system's dynamic performance and green electricity absorption capacity, with decision variables including energy storage capacity and power; the lower-layer operation model, under the constraints of the upper-layer output, aims to maximize the expected revenue of the electricity market and minimize the revenue risk, with decision variables including the charging and discharging power of energy storage and frequency regulation output; the lower-layer operation model feeds back the optimized revenue information and operation strategy to the upper-layer planning model, forming a closed-loop iterative mechanism. S3. Collaborative Solution: Solve the two-layer optimization model and use the closed-loop iteration mechanism to make the upper and lower layer models converge collaboratively to obtain the optimal configuration and operation strategy. S4. Output Configuration Scheme: Outputs the final energy storage capacity, power, virtual inertia contribution, damping contribution, green electricity consumption gain, and market return risk assessment results.

[0008] Preferably, the quantification of virtual inertia contribution and damping contribution in step S1 is to treat the energy storage as an equivalent element with inertia and damping characteristics, and add its contribution value to the total inertia and total damping of the system, which is directly used to evaluate and optimize the frequency stability and small disturbance stability of the system.

[0009] As a preferred embodiment, the objective function of the lower-level operating model in step S2 introduces market price and return variance terms based on probability prediction. By modeling the uncertainty of market prices, while pursuing high expected returns, it actively controls the volatility risk of returns, making the optimization results more robust.

[0010] Preferably, the closed-loop iterative mechanism achieves deep coupling between planning and operation. The upper-level planning sets the physical boundaries for the lower-level operation, while the lower-level operation feeds back market value signals to the upper level, guiding adjustments to planning decisions, thereby ensuring that the final configuration scheme meets both the system's dynamic security requirements and has good economic feasibility.

[0011] Secondly, the present invention provides a two-layer energy storage optimization configuration system for implementing the above method, including a model building module, an optimization modeling module, a solution module and an output module.

[0012] Thirdly, the present invention provides an electronic device including a memory, a processor, and a computer program stored thereon, wherein the processor executes the program to implement the steps of the above-described method.

[0013] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0014] The technical effects and advantages of this invention are as follows: 1. Achieved synergistic optimization of multi-dimensional objectives: For the first time, this invention integrates system dynamic stability indicators (virtual inertia, damping), green energy consumption indicators, and market economic indicators into the energy storage optimization configuration framework, overcoming the limitation of single objectives in traditional methods, and providing key technical support for building a safe, green, and economical new power system.

[0015] 2. Improved decision-making robustness in market environments: By introducing market prices and risk constraints based on probability prediction into the lower-level operating model, the optimized energy storage operation strategy can effectively cope with market fluctuations, ensuring the stability of returns while pursuing high returns, and enhancing the risk resistance of investment decisions.

[0016] 3. A closed-loop optimization mechanism for planning and operation was constructed: Through iterative feedback between upper and lower level models, the barrier between planning and operation was broken down, enabling energy storage configuration schemes to fully reflect their operational value in the real market environment, avoiding the problem of "planning" and "operation" being disconnected, and significantly improving the practicality and economy of the optimization results.

[0017] 4. Comprehensive and highly instructive output results: The final configuration scheme not only includes conventional capacity and power, but also clarifies the dynamic "responsibilities" of energy storage such as virtual inertia and damping, as well as its contribution to green electricity consumption, and includes a risk assessment, providing a comprehensive and quantitative basis for energy storage investment planning, scheduling operation and performance evaluation. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a dual-layer energy storage optimization configuration method that balances system dynamic stability and market risk control, as provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the closed-loop iterative solution process of the two-layer optimization model in an embodiment of the present invention.

[0020] Figure 3 This is a structural block diagram of a dual-layer optimized configuration system for energy storage provided in an embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0022] The attached diagram is labeled as follows: 301, Model building module; 302, Optimization modeling module; 303, Solver module; 304, Output module; 401, Memory; 402, Processor; 403, Communication interface; 404, Bus. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1: This embodiment details a two-layer energy storage optimization configuration method that balances system dynamic stability and market risk control. The overall process is as follows: Figure 1 As shown, the specific steps include: As attached Figures 1 to 4 As shown, S101: Constructing an energy storage system model This step aims to comprehensively describe the static characteristics, dynamic capabilities, and role of energy storage in the absorption of new energy sources.

[0025] First, define the static parameters of energy storage, including rated capacity E_ESS, rated power P_ESS_max, charge / discharge efficiency η_c, η_d, and initial state of charge SOC_0. These parameters constitute the physical basis of energy storage regulation capability.

[0026] Secondly, the ability of energy storage to support the dynamic stability of the system is quantified by defining the virtual inertia contribution H_ESS and damping contribution D_ESS of energy storage, using the formula "maxF_upper=w1". (H_ESS / H_system)+w2 (D_ESS / D_system)+w3 \overline{ΔG_{ESS,n}}-w4 "Cost_inv" adds the contribution of energy storage to the total system inertia and total damping, which allows the optimization model to directly evaluate the impact of different configuration schemes on the system frequency stability and small disturbance stability.

[0027] Finally, the gain of energy storage in local green electricity consumption at specific access nodes is calculated as ΔG_{ESS,n}}. A large number of typical renewable energy output scenarios P_{RE,n}^{(s)} are generated using the Markov Chain Monte Carlo (MCMC) method. In each scenario, the amount of green electricity consumed by the node before and after energy storage regulation is simulated, and its relative increase percentage ΔG_{ESS,n}^{(s)} is calculated. Finally, the gains of all scenarios are probability-weighted averaged to obtain the expected average node consumption gain. This indicator quantifies the value of energy storage in promoting local consumption of new energy.

[0028] S102: Establish a two-level optimization model and a closed-loop iterative mechanism This step constructs as follows: Figure 2 The two-layer optimization framework and its collaborative mechanism are shown.

[0029] The upper-level planning model aims to optimize system-level performance. Its objective function, F_upper, aims to maximize the overall benefits of energy storage. maxF_upper=w1 (H_ESS / H_system)+w2 (D_ESS / D_system)+w3 \overline{ΔG_{ESS,n}}-w4 Cost_inv” Among them, w1 to w4 are weights determined by data-driven methods (such as entropy weight method), which are used to balance multiple objectives such as inertia support, damping enhancement, green electricity consumption and investment cost. The decision variables are the energy storage configuration capacity C_i and power P_i_max.

[0030] The lower-level operating model optimizes the short-term market behavior of energy storage within given capacity and power boundaries. Its objective function, F_lower, focuses on economic efficiency. maxF_lower=E[π_spot p_i(t)+π_freq f_i(t)]-λ Var(π_spot p_i(t)+π_freq f_i(t))” In this model, π_spot and π_freq are the spot and frequency regulation prices obtained based on MCMC probability prediction, E[·] represents the expected return, Var(·) represents the return variance, and λ is the risk adjustment coefficient. The model manages the risk of return volatility by controlling the variance. The decision variables are the detailed charging and discharging power p_i(t) and the frequency regulation output f_i(t).

[0031] The key innovation lies in the closed-loop iterative mechanism. The upper layer outputs a set of configuration schemes (particles) to the lower layer; the lower layer optimizes each scheme and feeds back the calculated benefits and other information to the upper layer; the upper layer evaluates the fitness of the configuration scheme based on the feedback information and adjusts the search direction accordingly to generate a new generation of configuration schemes. This process is repeated until convergence.

[0032] S103: Collaborative Solving A hybrid solution approach is employed, combining intelligent optimization algorithms (such as Particle Swarm Optimization (PSO)) with mathematical programming solvers (such as Gurobi). The upper-level PSO layer generates configuration schemes, while the lower-level Gurobi layer performs rapid simulations and profit calculations for each scheme. Figure 2 The iterative process shown ultimately yields an energy storage configuration scheme that achieves an optimal balance between dynamic performance, absorption capacity, and economic risk.

[0033] S104: Output comprehensive optimized configuration scheme After the solution is completed, the final optimization results are output. These results include not only the rated capacity C_i and power P_i_max of the energy storage, but also its promised virtual inertia H_ESS, damping D_ESS, and the expected green energy absorption gain ΔG_{ESS,n}}. A market return expectation and risk (variance) assessment report based on probabilistic scenarios is also included, providing comprehensive information support for decision-makers.

[0034] Example 2: This embodiment provides an energy storage two-layer optimized configuration system for implementing the above method, the structure of which is as follows: Figure 3 As shown, it includes: The model building module 301 is used to execute step S101 in Example 1, and is responsible for defining the static parameters of energy storage and quantifying the dynamic indicators and green electricity consumption gains.

[0035] The optimization modeling module 302 is used to execute step S102 and is responsible for building the upper-level planning model, the lower-level running model, and the closed-loop iterative logic between the two.

[0036] The solver module 303 is used to execute step S103, and is responsible for calling the optimization algorithm and solver to perform the cooperative iterative solution of the two-layer model.

[0037] Output module 304 is used to execute step S104, and is responsible for organizing and outputting the final energy storage optimization configuration scheme and risk assessment report.

[0038] Example 3: Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Figure 4As shown, the electronic device includes a processor 401, a memory 402, a communication interface 403, and a bus 404. The processor 401, memory 402, and communication interface 403 communicate with each other via the bus 404.

[0039] Memory 402 is used to store computer programs; The processor 401 is used to execute the computer program stored in the memory 402 to implement the steps of the energy storage dual-layer optimized configuration method described in Embodiment 1 above.

[0040] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0041] This invention provides a two-layer optimized configuration method and system for energy storage in the context of new types of electricity, aiming to address multiple challenges faced by the power system under high-proportion renewable energy integration, including dynamic stability, green electricity consumption, and market returns. The specific implementation principle is as follows: First, in step S1, an energy storage system model is constructed, defining the static parameters of the energy storage, including rated capacity, rated power, charge / discharge efficiency, and initial state of charge. Further, the supporting capability of energy storage for the dynamic stability of the system is quantified, specifically by calculating its virtual inertia contribution H_ESS and damping contribution D_ESS, and then superimposing them onto the total system inertia H_system and total damping D_system, respectively. This directly reflects the enhancing effect of energy storage on frequency stability and small-disturbance stability. Simultaneously, a set of typical renewable energy output scenarios is generated using the Markov chain Monte Carlo method, and based on this, the local consumption of green electricity brought by energy storage at the access node is calculated. Gain is calculated by weighted averaging across scenarios to obtain the expected gain value, thereby quantifying the benefits of energy storage in promoting local consumption of new energy. Subsequently, in step S2, a two-layer optimization model is established and a closed-loop iterative mechanism is formed. This model includes an upper-layer planning model and a lower-layer operation model. The upper-layer planning model aims to maximize the system's dynamic performance and green electricity consumption capacity. Its objective function comprehensively considers the proportion of virtual inertia contribution, damping contribution, average green electricity consumption gain, and energy storage investment cost. The decision variables are the configured capacity and power of energy storage. The lower-layer operation model operates under the capacity and power boundary constraints given by the upper layer, aiming to maximize the expected revenue of the electricity market and minimize the revenue risk. The objective function of the energy storage system incorporates spot market prices and frequency regulation market prices based on probability prediction, and includes a revenue variance term to control risk. The decision variables are the real-time charging and discharging power of the energy storage and the frequency regulation output. Crucially, the lower-level operating model feeds back the optimized revenue information and operating strategy to the upper-level planning model. The upper-level model updates the weights or constraints in its objective function accordingly and generates a new energy storage configuration boundary. This closed-loop iterative mechanism achieves deep coupling and collaborative optimization between planning and operation. Then, in step S3, the two-layer optimization model is solved collaboratively. This process, through the aforementioned closed-loop iterative mechanism, enables the upper and lower-level models to converge collaboratively. Specifically, intelligent [mechanical system / technology] can be used. The optimization algorithm is combined with a mathematical programming solver for hybrid solution. The upper layer generates configuration schemes, and the lower layer performs operation simulation and benefit calculation for each scheme. Through multiple iterations and feedback, the energy storage configuration and operation strategy that achieves the optimal balance between dynamic stability, green electricity consumption, and market benefit risk is finally obtained. Finally, in step S4, a comprehensive optimized configuration scheme is output. This scheme not only includes the final energy storage capacity and power configuration, but also clearly gives its virtual inertia contribution, damping contribution, green electricity consumption gain, and market benefit expectation and risk assessment results based on probability scenarios. This provides a comprehensive and quantitative decision-making basis for energy storage investment planning, system scheduling, and performance evaluation. The entire operation process, through model building, two-layer optimization, closed-loop iteration and collaborative solution, achieves overall optimization of energy storage configuration in multiple dimensions, such as ensuring the dynamic security of the power grid, improving the level of green electricity consumption and ensuring the stability of market returns.

[0042] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change. Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A two-layer optimized configuration method for energy storage in the context of new types of electricity, characterized in that, Includes the following steps: S1. Construct an energy storage system model: Define the static parameters of energy storage, quantify its virtual inertia contribution and damping contribution to characterize its support capability for the dynamic stability of the system, and calculate its local green electricity consumption gain at the access node. S2. Establish a two-layer optimization model: Construct a two-layer optimization framework comprising an upper-layer planning model and a lower-layer operation model; the upper-layer planning model aims to maximize the system's dynamic performance and green electricity absorption capacity, with decision variables including energy storage capacity and power; the lower-layer operation model, under the constraints of the upper-layer output, aims to maximize the expected revenue of the electricity market and minimize the revenue risk, with decision variables including the charging and discharging power of energy storage and frequency regulation output; the lower-layer operation model feeds back the optimized revenue information and operation strategy to the upper-layer planning model, forming a closed-loop iterative mechanism. S3. Collaborative Solution: Solve the two-layer optimization model and use the closed-loop iteration mechanism to make the upper and lower layer models converge collaboratively to obtain the optimal configuration and operation strategy. S4. Output Configuration Scheme: Outputs the final energy storage capacity, power, virtual inertia contribution, damping contribution, green electricity consumption gain, and market return risk assessment results.

2. The method according to claim 1, characterized in that, In step S1, the quantification of its virtual inertia contribution and damping contribution specifically involves: Calculate the virtual inertia contribution H_ESS and damping contribution D_ESS provided by energy storage, and add them to the total system inertia H_system and total damping D_system: H_system = H_SG + H_ESS D_system=D_SG+D_ESS H_SG and D_SG are the inertia and damping provided by the synchronizing machine, respectively.

3. The method according to claim 1, characterized in that, In step S1, the calculation of the local green electricity absorption gain at the access node specifically involves: The Markov Chain Monte Carlo (MCMC) method is used to generate a set of typical renewable energy scenarios P_{RE,n}^{(s)} within the node; Calculate the local absorption gain ΔG_{ESS,n}^{(s)} brought about by energy storage regulation in each scenario, and calculate the expected value of all scenarios with weights to obtain the node average local absorption gain \overline{ΔG_{ESS,n}}.

4. The method according to claim 1, characterized in that, In step S2, the objective function of the upper-level planning model is: maxF_upper=w1 (H_ESS / H_system)+w2 (D_ESS / D_system)+w3 \overline{ΔG_{ESS,n}}-w4 Cost_inv Where w1, w2, w3, and w4 are weighting coefficients determined by a data-driven method, and Cost_inv is the energy storage investment cost.

5. The method according to claim 1, characterized in that, In step S2, the objective function of the lower-level running model is: maxF_lower=E[π_spot p_i(t)+π_freq f_i(t)]-λ Var(π_spot p_i(t)+π_freq f_i(t)) Where E[·] represents expected return, π_spot and π_freq are the spot market price and frequency modulation market price based on probability prediction, respectively, Var(·) represents the variance of return, and λ is the risk adjustment coefficient.

6. The method according to claim 5, characterized in that, The spot market price and frequency regulation market price based on probability prediction are obtained by using the Markov chain Monte Carlo (MCMC) method to perform joint probabilistic modeling and random sampling of historical and predicted electricity price data to obtain the electricity price boundary distribution.

7. The method according to claim 1, characterized in that, The closed-loop iteration mechanism is as follows: The upper-level planning model outputs a set of energy storage capacity and power boundaries to the lower-level operation model; The lower-level operation model performs optimization based on this boundary and feeds back the calculated optimal scheduling strategy and corresponding revenue information to the upper-level planning model. The upper-level planning model updates the weights or constraints in its objective function based on feedback information and generates new energy storage configuration boundaries; Repeat the above process until the iterative convergence condition is met.

8. A two-layer optimized configuration system for energy storage implementing the method as described in any one of claims 1-7, characterized in that, include: The model building module (301) is used to execute step S1 and build the energy storage system model; The optimization modeling module (302) is used to execute step S2 and establish the two-layer optimization model and closed-loop iteration mechanism; The solver module (303) is used to execute step S3 to solve the two-layer optimization model collaboratively; The output module (304) is used to execute step S4 and output the comprehensive optimization configuration scheme.

9. An electronic device comprising a memory (401), a processor (402), and a computer program stored in the memory (401) and executable on the processor (402), characterized in that, When the processor (402) executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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