A micro-grid operation optimization and energy efficiency management method

Abstract

The application discloses a kind of based on micro-grid operation optimization and energy efficiency management method, it is related to micro-grid control and integrated energy management field.The method comprises: real-time acquisition micro-grid multi-source operation data;Distributed power output prediction model is constructed;Establish the state model of energy storage considering dynamic degradation cost to evaluate the evolution of state of charge and life-shortening cost;Establish load demand response model based on price elasticity;Establish ladder type carbon trading cost evaluation model;Based on model predictive control, the above-mentioned model is integrated into discrete state space equation, and multi-objective optimization problem is solved by rolling and output closed-loop optimal scheduling instruction.The application explicitly constrains energy storage dynamic degradation and carbon trading mechanism, effectively suppresses renewable energy fluctuation, realizes multi-objective global optimization of extending equipment life, reducing comprehensive cost and reducing carbon emission, and improves system disturbance robustness.

Description

Technical Field

[0001] This invention relates to microgrid management, and more particularly to a method based on microgrid operation optimization and energy efficiency management. Background Technology

[0002] In the context of the accelerated transformation of the global energy structure and the response to climate change, building a new power system with new energy sources as the mainstay has become a core driving force for achieving the "dual carbon" goal. With the maturity of distributed generation technology, the form of the power system is undergoing a historic leap from traditional centralized to flattened and decentralized.

[0003] In this process, microgrids, as regional autonomous systems that highly integrate distributed power sources, energy storage devices, energy conversion units, and diverse loads, have become a key physical carrier for improving power supply reliability and promoting the local consumption of clean energy, thanks to their flexible grid connection and islanding capabilities. The efficient operation of microgrids heavily relies on their internal energy management system (EMS) to coordinate energy flow. However, the strong randomness and volatility of renewable energy output such as wind and solar power, coupled with the complex nonlinear physical constraints in the multi-energy coupling scenario of microgrids, pose significant challenges to the real-time power balance of the system. Meanwhile, with the large-scale application of electrochemical energy storage and the full launch of the national carbon emission trading market, the operational goals of modern microgrids are no longer limited to simple power matching, but have evolved into a complex global optimization problem that considers equipment lifespan degradation, carbon emission footprint, and multi-dimensional economic indicators.

[0004] Existing technologies generally have the following limitations: First, existing scheduling strategies generally set the charging and discharging costs of energy storage systems as fixed static constants, failing to quantitatively consider the dynamic electrochemical degradation caused by different depths of discharge (DOD) and charge / discharge rates on battery life. This open-loop instruction, which ignores hidden physical losses, is very likely to lead to high-frequency deep cycling of batteries in actual operation, thereby causing premature scrapping of energy storage assets and extremely poor economic efficiency throughout their entire life cycle.

[0005] Secondly, traditional optimization often focuses solely on minimizing grid purchase costs, completely disregarding the system's carbon footprint constraints. Under the current increasingly stringent carbon quota and carbon trading market mechanisms, existing systems cannot quantify the dynamic carbon penalties or benefits during microgrid interactions, making it difficult to meet the dual requirements of modern microgrids for green premiums.

[0006] Third, most existing technologies rely heavily on the absolute accuracy of the day-ahead short-term forecast curves, lack comprehensive system-level state-space modeling, and do not introduce intraday and real-time closed-loop rolling correction mechanisms. When faced with complex weather changes or sudden load surges and other strong disturbances, they can easily lead to system-level real-time power imbalances or node voltage exceeding limits. Summary of the Invention

[0007] To address the shortcomings of existing energy management systems, such as lack of global mathematical modeling, poor disturbance resistance, and limited cost assessment, this invention provides a microgrid operation optimization and energy efficiency management method. It constructs a system-level state-space equation encompassing distributed generation, energy storage SOC and degradation, price-based demand response, and carbon trading mechanisms. Using MPC as the core, it reduces the dimensionality of all nonlinear components to explicit mathematical constraints, achieving smooth tracking and economic optimization of the global operating trajectory of the microgrid system under complex operating conditions. The specific technical solution is as follows: A method for microgrid operation optimization and energy efficiency management includes the following steps: S1: Real-time acquisition of meteorological environmental parameters of distributed power sources, electrochemical status of energy storage systems, and price demand information on the load side; S2: Construct a mathematical prediction model for the output of distributed power sources, establish equivalent output equations for the physical characteristics of photovoltaic and wind power generation respectively, and obtain the predicted output of distributed power sources. S3: Establish a state model for electrochemical energy storage systems that takes into account dynamic degradation costs, and comprehensively evaluate the time evolution of the energy storage state of charge and the equivalent lifetime cost. S4: Establish a load demand response management model based on price elasticity and calculate the actual flexible load after demand response optimization; S5: Establish a cost assessment model for carbon quotas and tiered carbon trading to quantify the dynamic carbon penalties or benefits in the microgrid interaction process; S6: Construct a multi-objective rolling optimization decision model based on model predictive control, integrate the dynamic characteristics of steps S2 to S5 into discrete state-space equations, and solve the optimization problem containing multiple economic and low-carbon objectives in the prediction time domain, outputting the closed-loop optimal scheduling command.

[0008] Preferably, step S1 includes: acquiring light and wind speed parameters through a high-precision meteorological sensor equipped with a distributed power supply array; monitoring the cell status of the electrochemical energy storage unit in real time through a battery management system; receiving time-of-use electricity price signals from the distribution network through a flexible load control terminal; and aggregating the above multi-source data and performing multi-dimensional optimization decisions through a microgrid central controller.

[0009] Preferably, the mathematical prediction model for distributed power output in step S2 specifically includes: The output power P of the photovoltaic array PV The expression for (t) is:

[0010] In the formula, P STCS is the rated power under standard test conditions, and S(t) is the actual illuminance. STC Where β is the standard illumination intensity, and T is the power temperature coefficient. c (t) represents the actual operating temperature, T STC For reference temperature; Wind turbine output power P WT (t) is based on the piecewise function of wind speed:

[0011] In the formula, v(t) is the wind speed at the hub, v ci ,v r ,v co These are the cut-in, rated, and cut-out wind velocities, respectively, P r This is the rated power.

[0012] Preferably, the establishment of the electrochemical energy storage system state model taking into account dynamic degradation costs in step S3 specifically includes: The discrete-time evolution equation of the energy storage state of charge, considering charge and discharge efficiency, is established as follows:

[0013] In the formula, P c (t) and P d (t) represents the charging and discharging power; η c With η d E represents charge / discharge efficiency. cap Rated capacity; Establish an equivalent dynamic degradation cost C for energy storage based on rainflow counting method. deg (t) model:

[0014] In the formula, C bat For investment costs, N cycle (DOD) is the equivalent cycle lifetime function at the corresponding depth of discharge.

[0015] Preferably, step S4, establishing a load demand response management model based on price elasticity, specifically includes: Introducing the self-elasticity and cross-elasticity coefficient matrix E, where the elements quantify the impact of electricity price fluctuation ρj in time period j on load Li in time period i;

[0016] In the formula, E ij ΔL is an element in the load price elasticity matrix, used to characterize the sensitivity of the relative change in electricity price in time period j to the relative change in load in time period i; i L represents the load adjustment amount for time period i; i0The original baseline load for time period i; Δρ j ρ represents the electricity price adjustment for time period j. j0 is the original benchmark electricity price for time period j; i and j represent the load response period and the electricity price change period, respectively.

[0017] Based on this, the actual flexible load L after demand response optimization DR The (t) model is calculated as follows:

[0018] In the formula, L DR L(t) represents the actual load during period t after demand response; L0(t) represents the baseline load during period t before demand response; E tj ρ(j) represents the elasticity coefficients between time period t and time period j in the price elasticity matrix; ρ(j) represents the actual time-of-use electricity price for time period j; ρ0(j) represents the benchmark time-of-use electricity price for time period j. This indicates that the impact on electricity prices is accumulated across all 24 dispatch periods throughout the day.

[0019] Guide users to voluntarily reduce peak loads and lower valleys, thereby reducing the system's peak backup pressure.

[0020] Preferably, the step S5 of establishing a carbon quota and tiered carbon trading cost assessment model specifically includes: Calculate the actual carbon emissions of the microgrid system in time period t and the system's baseline carbon allowance; Construct a tiered carbon trading cost function CCO2(t): When the actual carbon emissions exceed the benchmark carbon quota, calculate the increasing carbon trading cost based on the tiered range in which the excess amount is located, using the base carbon price cc, the tiered range length l, and the tiered price growth rate α, thereby restricting high carbon emission behavior.

[0021] Preferably, step S6, which involves constructing a multi-objective rolling optimization decision model based on model predictive control, specifically includes: The dynamic characteristics of steps S2 to S5 are integrated into discrete state-space equations.

[0022] The system must satisfy strict power balance constraints during control period k:

[0023] Define MPC in the prediction time domain N p The multi-objective objective function J is defined and solved using quadratic programming.

[0024] In the formula, C op (k) represents the direct operating economic cost, ω n These are the weighting coefficients; The system only sends the control sequence for the first step in the prediction time domain to the physical execution layer, and then collects the state error at the next moment for rolling correction to achieve disturbance-resistant closed loop.

[0025] An electronic device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program, when executed by the processor, implements the method described above.

[0026] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0027] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a microgrid operation optimization and energy efficiency management method that deeply integrates multi-dimensional physical field coupling, dynamic degradation assessment of energy storage, and a tiered carbon trading mechanism to establish an efficient microgrid operation optimization and energy efficiency management system. An optimized state-space equation and multi-objective rolling decision-making architecture are formulated, achieving more precise power smoothing and higher life-cycle economic efficiency. This provides a green and reliable energy dispatch solution for new power systems and smart city construction, significantly improving the system's disturbance resistance robustness and the comprehensive utilization rate of clean energy. Attached Figure Description

[0028] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0029] The present invention will now be further described with reference to the accompanying drawings.

[0030] This invention constructs a high-dimensional mathematical model that incorporates wind and solar theoretical output prediction, dynamic degradation assessment of energy storage charging and discharging, price-based demand response, and a tiered carbon trading mechanism. This model transforms complex multidimensional physical parameters into explicit constraints. Based on this, a closed-loop rolling decision-making system based on Model Predictive Control (MPC) state-space equations is designed. This method not only mitigates random fluctuations in renewable energy and ensures real-time power balance, but also achieves multi-objective global optimization by extending equipment lifespan, reducing overall costs, and decreasing carbon emissions, providing a highly robust solution for the construction of new power systems.

[0031] like Figure 1 As shown, a method for microgrid operation optimization and energy efficiency management includes the following steps: S1: Real-time acquisition of meteorological environmental parameters of distributed power sources, electrochemical status of energy storage systems, and price demand information on the load side; Light and wind speed parameters are obtained through high-precision meteorological sensors equipped with a distributed power supply array. The battery management system monitors the cell status of the electrochemical energy storage unit in real time. Receive time-of-use electricity price signals from the distribution network through a flexible load control terminal; The microgrid central controller aggregates the aforementioned multi-source data and performs multi-dimensional optimization decisions.

[0032] S2: Construct a mathematical prediction model for the output of distributed power sources, establish equivalent output equations for the physical characteristics of photovoltaic and wind power generation respectively, and obtain the predicted output of distributed power sources. S21: The output power of a photovoltaic array is affected by the combined effects of light intensity and temperature; its output power P PV The expression for (t) is:

[0033] In the formula, P STC S is the rated power under standard test conditions, and S(t) is the actual illuminance. STC Where β is the standard illumination intensity, and T is the power temperature coefficient. c (t) represents the actual operating temperature, T STC This is a reference temperature.

[0034] S22: Wind turbine output power P WT (t) is based on the piecewise function of wind speed:

[0035] In the formula, v(t) is the wind speed at the hub, v ci ,v r ,v co These are the cut-in, rated, and cut-out wind velocities, respectively, P r This is the rated power.

[0036] S3: Establish a state model for the electrochemical energy storage system (ESS) that takes into account dynamic degradation costs. To accurately assess the impact of microgrid dispatch on battery life, the system comprehensively considers the time evolution of the state of charge and the equivalent life loss cost.

[0037] S31: The discrete-time evolution equation of the energy storage state of charge (SOC) considering charge and discharge efficiency is as follows:

[0038] In the formula, P c (t) and P d (t) represents the charging and discharging power; η c With η d E represents charge / discharge efficiency. cap This is the rated capacity.

[0039] S32: Equivalent dynamic degradation cost of energy storage based on rainflow counting method Cdeg The (t) model makes the lifetime reduction caused by the depth of discharge explicit:

[0040] In the formula, C bat For investment costs, N cycle (DOD) is the equivalent cycle lifetime function at the corresponding depth of discharge.

[0041] S4: Establish a load demand response (DR) management model based on price elasticity. The system introduces an autoelasticity and cross-elasticity coefficient matrix E, where elements...

[0042] In the formula, E ij ΔL is the element in the i-th row and j-th column of the price elasticity coefficient matrix E, representing the degree of influence of electricity price changes in time period j on load changes in time period i; i L represents the load change during time period i; i0 The baseline load for time period i; Δρ j ρ represents the change in electricity price during time period j; j0 Let i be the base electricity price for time period j; i and j are the dispatch time period numbers. The electricity price ρ for time period j was quantified. j Fluctuations in load L during period i i The impact of this. Based on this, the actual flexible load L after demand response optimization. DR The (t) model is calculated as follows:

[0043] In the formula, L DR (t) represents the actual flexible load after demand response optimization for time period t; L0(t) represents the baseline load before demand response for time period t; E tj ρ(j) is the elasticity coefficient of the change in electricity price during time period j on the load during time period t; ρ(j) is the actual electricity price during time period j; ρ0(j) is the benchmark electricity price during time period j; j is the time period number affected by the electricity price, with a value range from 1 to 24; This mechanism can guide users to voluntarily reduce peak loads and lower valleys, thereby reducing the system's peak reserve pressure.

[0044] S5: Establish a cost assessment model for carbon quotas and tiered carbon trading. Actual carbon emissions of a microgrid system in time period t:

[0045] System baseline carbon quota:

[0046] To limit high-carbon-emission behaviors, a tiered carbon trading cost function C is constructed. CO2 (t):

[0047] In the formula, c c Let l be the base carbon price, l be the length of the tiered price interval, and α be the tiered price growth rate.

[0048] S6: Construct a multi-objective rolling optimization decision model based on model predictive control (MPC) and output the closed-loop optimal scheduling instruction.

[0049] S61: Integrate the dynamic characteristics of S2 to S5 into discrete state-space equations:

[0050] The system must satisfy strict power balance constraints during control period k:

[0051] S62: Define MPC in the prediction time domain N p The multi-objective objective function J is defined and solved using quadratic programming.

[0052] In the formula, C op (k) represents the direct operating economic cost, ω n The weighting coefficients are used. The system only sends the control sequence for the first step in the prediction time domain to the physical execution layer, and then collects the state error at the next time step for rolling correction to achieve an anti-disturbance closed loop.

[0053] A microgrid operation optimization and energy efficiency management system comprises a distributed power supply array, an electrochemical energy storage unit, a flexible load control terminal, and a microgrid central controller (MGCC) as its underlying hardware topology and data acquisition architecture. The distributed power supply array is equipped with high-precision meteorological sensors to acquire sunlight and wind speed parameters; the electrochemical energy storage unit monitors the cell status in real time through a battery management system (BMS); the flexible load control terminal receives time-of-use electricity price signals from the distribution network; and the MGCC, as the central system, is responsible for aggregating multi-source data and executing multi-dimensional optimization decisions.

[0054] Advantages of this application: First, by explicitly embedding the dynamic electrochemical degradation cost of the energy storage system into the objective function, excessive use of equipment is avoided, and the lifespan of the core assets of the microgrid is significantly extended. Second, it seamlessly couples the tiered carbon trading mechanism with the price-based demand response model, quantifying the economic penalties for carbon emissions while achieving peak shaving and valley filling, thus ensuring green and low-carbon operation. Third, the state-space equation closed-loop rolling optimization framework based on MPC is more robust to meteorological changes and load fluctuations than the traditional day-ahead open-loop scheduling, ensuring the absolute balance of microgrid power and system stability.

[0055] An electronic device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program, when executed by the processor, implements the method described above.

[0056] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0057] The technical principles of the present invention have been described above with reference to specific embodiments. These descriptions are merely for explaining the principles of the invention and should not be construed as limiting the scope of protection of the invention in any way. Based on this explanation, those skilled in the art can readily conceive of other specific embodiments of the invention without inventive effort, and these embodiments will all fall within the scope of protection of the claims of the present invention.

Claims

1. A method for microgrid operation optimization and energy efficiency management, characterized in that, Includes the following steps: S1: Real-time acquisition of meteorological environmental parameters of distributed power sources, electrochemical status of energy storage systems, and price demand information on the load side; S2: Construct a mathematical prediction model for the output of distributed power sources, establish equivalent output equations for the physical characteristics of photovoltaic and wind power generation respectively, and obtain the predicted output of distributed power sources. S3: Establish a state model for electrochemical energy storage systems that takes into account dynamic degradation costs, and comprehensively evaluate the time evolution of the energy storage state of charge and the equivalent lifetime cost. S4: Establish a load demand response management model based on price elasticity and calculate the actual flexible load after demand response optimization; S5: Establish a cost assessment model for carbon quotas and tiered carbon trading to quantify the dynamic carbon penalties or benefits in the microgrid interaction process; S6: Construct a multi-objective rolling optimization decision model based on model predictive control, integrate the dynamic characteristics of steps S2 to S5 into discrete state-space equations, and solve the optimization problem containing multiple economic and low-carbon objectives in the prediction time domain, outputting the closed-loop optimal scheduling command.

2. The method for microgrid operation optimization and energy efficiency management according to claim 1, characterized in that, Step S1 includes: Light and wind speed parameters are obtained through high-precision meteorological sensors equipped with a distributed power supply array. The battery management system monitors the cell status of the electrochemical energy storage unit in real time. Receive time-of-use electricity price signals from the distribution network through a flexible load control terminal; The microgrid central controller aggregates the aforementioned multi-source data and performs multi-dimensional optimization decisions.

3. The method for microgrid operation optimization and energy efficiency management according to claim 1, characterized in that, The specific steps in step S2 of constructing the mathematical prediction model for distributed power output include: The output power P of the photovoltaic array PV The expression for (t) is: ； In the formula, P STC S is the rated power under standard test conditions, and S(t) is the actual illuminance. STC Where β is the standard illumination intensity, and T is the power temperature coefficient. c (t) represents the actual operating temperature, T STC For reference temperature; Wind turbine output power P WT (t) is based on the piecewise function of wind speed: ； In the formula, v(t) is the wind speed at the hub, v ci ,v r ,v co These are the cut-in, rated, and cut-out wind velocities, respectively, P r This is the rated power.

4. The method for microgrid operation optimization and energy efficiency management according to claim 1, characterized in that, The establishment of the state model of the electrochemical energy storage system that takes into account the dynamic degradation cost in step S3 specifically includes: The discrete-time evolution equation of the energy storage state of charge, considering charge and discharge efficiency, is established as follows: ； In the formula, P c (t) and P d (t) represents the charging and discharging power; η c With η d E represents charge / discharge efficiency. cap Rated capacity; Establish an equivalent dynamic degradation cost C for energy storage based on rainflow counting method. deg (t) model: ； In the formula, C bat For investment costs, N cycle (DOD) is the equivalent cycle lifetime function at the corresponding depth of discharge.

5. The method for microgrid operation optimization and energy efficiency management according to claim 1, characterized in that, The specific steps in step S4 of establishing a load demand response management model based on price elasticity include: Introducing the self-elasticity and cross-elasticity coefficient matrix E, where the elements quantify the electricity price ρ in time period j. j Fluctuations in load L during period i i The impact; ； In the formula, E ij ΔL is the element in the i-th row and j-th column of the price elasticity coefficient matrix E, representing the degree of influence of electricity price changes in time period j on load changes in time period i; i L represents the load change during time period i; i0 The baseline load for time period i; Δρ j ρ represents the change in electricity price during time period j; j0 Let i be the base electricity price for time period j; i and j are the dispatch time period numbers. Based on this, the actual flexible load L after demand response optimization DR The (t) model is calculated as follows: ； In the formula, L DR (t) represents the actual flexible load after demand response optimization for time period t; L0(t) represents the baseline load before demand response for time period t; E tj ρ(j) is the elasticity coefficient of the change in electricity price during time period j on the load during time period t; ρ(j) is the actual electricity price during time period j; ρ0(j) is the benchmark electricity price during time period j; j is the time period number affected by the electricity price, with a value range from 1 to 24; Guide users to voluntarily reduce peak loads and lower valleys, thereby reducing the system's peak backup pressure.

6. The method for microgrid operation optimization and energy efficiency management according to claim 1, characterized in that, The specific steps in step S5 of establishing the carbon quota and tiered carbon trading cost assessment model include: Calculate the actual carbon emissions of the microgrid system in time period t and the system's baseline carbon allowance; Construct a tiered carbon trading cost function CCO2(t): When the actual carbon emissions exceed the benchmark carbon quota, calculate the increasing carbon trading cost based on the tiered range in which the excess amount is located, using the base carbon price cc, the tiered range length l, and the tiered price growth rate α, thereby restricting high carbon emission behavior.

7. The method for microgrid operation optimization and energy efficiency management according to claim 1, characterized in that, The construction of the multi-objective rolling optimization decision model based on model predictive control in step S6 specifically includes: The dynamic characteristics of steps S2 to S5 are integrated into discrete state-space equations. ； The system must satisfy strict power balance constraints during control period k: ； Define MPC in the prediction time domain N p The multi-objective objective function J is defined and solved using quadratic programming. ； In the formula, C op (k) represents the direct operating economic cost, ω n These are the weighting coefficients; The system only sends the control sequence for the first step in the prediction time domain to the physical execution layer, and then collects the state error at the next moment for rolling correction to achieve disturbance-resistant closed loop.

8. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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