Multi-microgrid optimization method for compressed air energy storage and demand response

Abstract

The invention relates to a multi-micro-grid optimization method for compressed air energy storage and demand response, which comprises the following steps: constructing a multi-micro-grid network comprising a solar power generation system, a gas turbine system and a compressed air energy storage system, and storing excess power by using a compressed air energy storage unit when photovoltaic power generation is excess; compressed air is released to assist power generation during load peak or insufficient power supply, so that continuity and stability of power supply are ensured; meanwhile, in combination with a demand response mechanism, load balance and peak load reduction are realized by dynamically adjusting load demands, and the flexibility of the system is improved; besides, an artificial intelligence optimization technology is adopted, key operation parameters are dynamically adjusted, energy production and storage are further optimized, the energy utilization efficiency is improved, the problem of power instability caused by renewable energy power generation volatility is effectively solved, and the method has the advantages of being efficient, capable of saving energy, stable in power supply and high in economical efficiency; the method has the advantages that energy scheduling is optimized, and stability and economic benefits are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization, and in particular relates to a multi-microgrid optimization method for compressed air energy storage and demand response. Background Art

[0002] With the widespread application of renewable energy, especially solar energy and wind energy, multi-microgrid systems play an increasingly important role in power systems. However, due to the volatility and intermittency of renewable energy generation, this leads to unstable power supply, especially during peak load demand periods. In addition, the ever-changing power demand further increases the complexity of energy management in multi-microgrids. Traditional power management methods have many limitations in dealing with these uncertainties and cannot fully utilize renewable energy and ensure the stability of the power grid. At present, compressed air energy storage systems and demand response technologies have been widely used to alleviate the contradiction between power supply and demand. Compressed air energy storage systems can store energy when there is excess renewable energy generation and release energy during peak power demand periods, thereby balancing supply and demand. Demand response technology dynamically adjusts power demand, optimizes load curves, and reduces the peak load of the power grid. However, the independent application of these two technologies still has room for optimization when dealing with variable demands in complex power grid environments. Therefore, it is of great significance to propose a dynamic optimization method combining compressed air energy storage and demand response mechanism to further improve the operating efficiency and stability of multi-microgrids. Therefore, it is very necessary to provide a multi-microgrid optimization method of compressed air energy storage and demand response that adopts a dynamic optimization method combining multiple technologies, optimizes energy scheduling, and improves stability and economic benefits. Summary of the invention

[0003] 1. Technical issues

[0004] In view of the above-mentioned existing technical status, this application mainly addresses the following technical problems:

[0005] 1. Traditional power management methods have many limitations in dealing with these uncertainties and cannot fully utilize renewable energy and ensure the stability of the power grid;

[0006] 2. There is still room for optimization when using compressed air energy storage systems and demand response technologies independently to cope with changing demands in complex power grid environments.

[0007] (II) Technical solution

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a multi-microgrid optimization method for compressed air energy storage and demand response that adopts a dynamic optimization method combining multiple technologies, optimizes energy scheduling, and improves stability and economic benefits.

[0009] The object of the present invention is achieved by: a multi-microgrid optimization method for compressed air energy storage and demand response, the method comprising the following steps:

[0010] Step 1: Construction of a multi-microgrid system: By interconnecting multiple microgrids, each microgrid contains a solar photovoltaic power station, a gas turbine, and a compressed air energy storage unit, forming a multi-microgrid network with decentralized power management capabilities;

[0011] Step 2: Application of the unused generation capacity index: After the system is built, the unused generation capacity index is introduced to monitor the energy usage of each microgrid in real time and evaluate the generation and storage capacity of each microgrid;

[0012] Step 3: Introduction of Chance Constrained Programming Framework: The uncertainty in the network, especially the volatility of load demand and renewable energy generation, is handled by chance constrained programming method to ensure the optimal operation of the entire system with high confidence.

[0013] Step 4: Integration of compressed air energy storage system: Use compressed air energy storage units to store excess electricity when there is excess photovoltaic power generation, and release compressed air to assist gas turbines in generating electricity when photovoltaic power supply is insufficient or at peak load, thereby ensuring the stability of power supply, especially during periods when solar energy is unsustainable;

[0014] Step 5: Implementation of demand response: Combine the unused generation capacity index with the dispatch of compressed air energy storage to achieve load management through demand response procedures, smooth the power load curve, reduce peak load pressure, and improve system flexibility;

[0015] Step 6: Application of artificial intelligence optimization technology: Through artificial neural networks and optimization algorithms, key parameters in the multi-microgrid system are dynamically adjusted to optimize energy production and storage.

[0016] Furthermore, the unused generation capacity index in step 2 is calculated by dividing the net load by the maximum generation capacity of the microgrid: Where UPC m represents the unused generation capacity index of microgrid m; represents the net load of microgrid m at time t; It represents the maximum total power generation capacity of all power generation equipment in microgrid m within time t; the calculated unused power generation capacity value should meet the set target unused power generation capacity value: In the formula represents the target value of the unused power generation capacity of microgrid m.

[0017] Furthermore, the specific calculation method of the net load in step 2 is defined by the following formula based on the existing system components, which includes the scheduling of photovoltaic power generation, wind power generation, gas turbine power generation and battery energy storage unit: In the formula represents the net load demand of microgrid m at time t; represents the total load demand of microgrid m at time t; represents the load reduction of microgrid m at time t through the demand response plan; represents the local power generation of microgrid m at time t; represents the net power generated by the photovoltaic power generation equipment in the microgrid m at time t; represents the net power generated by the wind power generation equipment in the microgrid m at time t; represents the power released by the battery energy storage unit of microgrid m at time t; It represents the power consumed by microgrid m to charge the battery energy storage system at time t.

[0018] Furthermore, the net power generated by the photovoltaic power generation equipment in step 2 is for: In the formula represents the total photovoltaic power generation of microgrid m at time t; represents the photovoltaic power generation self-use power of microgrid m at time t; the net power generated by the wind power generation equipment for: In the formula represents the total wind power generation power of microgrid m at time t; It represents the power consumed by the wind power generation system of microgrid m within time t.

[0019] Furthermore, the step 3 deals with the uncertainty in the network by means of a chance constrained programming method. The basic steps and framework for dealing with uncertainty can be clearly expressed by formulas (6)-(8): minZ = f(x) (6), Where h k (x) represents the constraint function on the variable x; k∈Ω n The index k representing the constraint belongs to the set Ω n ; Pr{g j (x,ξ)≥0}≥α,j∈Ω j (8), where Pr represents the probability function; g j (x,ξ) represents the constraint function on the variable x and the random variable ξ; α is a confidence level or probability threshold; j∈Ω j The index j representing the constraint belongs to the constraint set Ω j .

[0020] Furthermore, in the energy management problem of multiple microgrids in step 3, the power generation of renewable energy and power demand are uncertain factors affecting the model. According to the formula In the formula For every j that belongs to the set Ω j All constraints g j (x,ξ), these conditions must be met at the same time; for each bus connected to the microgrid, its power balance constraint can be expressed in chance constrained programming as: In the formula It means to conditionally constrain the power generation of each microgrid in the multi-microgrid system at a specified time point to ensure that the power generation of each microgrid can meet its load demand; represents the total power output from microgrid m to other load nodes q; represents the net load demand of microgrid m at time t.

[0021] Furthermore, the power balance constraint formula represents the combined probability that the output power of the power generation side minus the net power flow should meet or exceed the power demand. The combined probability must reach or exceed the set confidence level. Assuming that these uncertain factors follow a Gaussian distribution, the net load can be expressed as follows: In the formula represents the actual net load demand of microgrid m at time t; represents the predicted net load demand of microgrid m at time t; Δe m,t represents the prediction error at time t.

[0022] Furthermore, in step 5, the unused generation capacity index is combined with the dispatch of compressed air energy storage, and only the transferable load is included in the demand response plan, with the purpose of transferring the load from the peak period to the valley period; the demand response plan is defined as: However, the transferred load should be compensated in other periods, subject to the following constraints: In the formula represents the power increased by the microgrid m in response to demand in time t; γ e It is a coefficient indicating the maximum proportion of load increase, usually a constant less than 1; represents the total load demand of microgrid m at time t; represents the power reduction of microgrid m in response to demand at time t; It represents the total amount of load increase of microgrid m within a period of time; It represents the total load reduction of microgrid m in the same time period; represents the actual load demand of microgrid m at time t; represents the total load demand of microgrid m at time t.

[0023] Furthermore, the artificial intelligence optimization technology in step 6 specifically includes: dynamically adjusting key parameters in the multi-microgrid system through a neural network model and an optimization algorithm to achieve optimization of energy production and storage;

[0024] Among them, the neural network model is used to establish the nonlinear relationship between the various parts of the multi-microgrid. The input includes variables such as solar power generation, wind power generation, load demand, ambient temperature, etc. The above key parameters are dynamically adjusted through optimization technology, thereby improving the thermodynamic efficiency of the system while reducing the total cost of system operation.

[0025] Furthermore, the key parameters include the number of solar panels, the pressure ratio of the compressed air energy storage system, the efficiency of the gas turbine and the efficiency of the compressor.

[0026] (III) Beneficial effects

[0027] 1. The present invention constructs a multi-microgrid network including solar power generation, gas turbines and compressed air energy storage systems, uses compressed air energy storage units to store excess electricity when photovoltaic power generation is in excess, and releases compressed air to assist in power generation when load peaks or power supply is insufficient, thereby ensuring the continuity and stability of power supply;

[0028] 2. The present invention combines the demand response mechanism to dynamically adjust the load demand, achieve load balancing and peak load reduction, and improve system flexibility;

[0029] 3. The present invention uses artificial intelligence optimization technology to dynamically adjust key operating parameters, further optimize energy production and storage, reduce operating costs, and improve energy utilization efficiency;

[0030] 4. The present invention effectively solves the problem of power instability caused by the volatility of renewable energy power generation, and has the characteristics of high efficiency, energy saving, stable power supply and strong economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of multi-microgrid optimization for dynamic regulation of compressed air energy storage and demand response according to the present invention. DETAILED DESCRIPTION

[0032] The present invention belongs to the technical field of power system optimization, and specifically relates to energy management and scheduling in a multi-microgrid system, and in particular to a multi-microgrid optimization method that achieves efficient energy scheduling by dynamically regulating a compressed air energy storage system and a demand response mechanism.

[0033] The purpose of the present invention is to provide a multi-microgrid optimization method for compressed air energy storage and demand response, which optimizes energy scheduling and improves the stability and economic benefits of the power system by integrating the compressed air energy storage system with the demand response mechanism.

[0034] The present invention is further described below in conjunction with embodiments and / or drawings.

[0035] Example 1

[0036] like Figure 1 As shown, a multi-microgrid optimization method for compressed air energy storage and demand response, the method comprising the following steps:

[0037] Step 1: Construction of a multi-microgrid system: By interconnecting multiple microgrids, each microgrid contains a solar photovoltaic power station, a gas turbine, and a compressed air energy storage unit, forming a multi-microgrid network with decentralized power management capabilities;

[0038] Step 2: Application of the unused power generation capacity index: After the system is built, the unused power generation capacity index is introduced to monitor the energy usage of each microgrid in real time, evaluate the power generation and energy storage capacity of each microgrid, help adjust power output and input, and achieve efficient allocation of resources;

[0039] In the present invention, a new index called unused power capacity is introduced to characterize the available energy level of each microgrid. The index clarifies the surplus or insufficient power in each microgrid that can be used for power exchange with the upstream power grid; the unused power generation capacity index is calculated by dividing the net load (i.e., the required power of the microgrid, calculated as the total power consumption of the microgrid minus its power generation) by the maximum power generation capacity of the microgrid (see formula 1); the calculated unused power generation capacity value should meet the set target unused power generation capacity value (see formula 2); the specific calculation method of the net load is defined by formulas 3 to 5 based on the existing system components, which includes the scheduling of photovoltaic power generation, wind power generation, gas turbine power generation and battery energy storage units.

[0040] Where UPC m represents the unused generation capacity index of microgrid m, which reflects the available generation capacity in the microgrid, specifically the ratio of the remaining generation capacity to the total load demand; represents the net load of microgrid m at time t, that is, the power demand of the microgrid, which is calculated by subtracting all available local generation from the total load of the microgrid; It represents the maximum total power generation capacity of all power generation equipment in microgrid m within time t, including renewable energy such as solar energy, wind energy, gas turbines and conventional energy.

[0041] In the formula Represents the target value of the unused generation capacity of microgrid m; this value is a preset standard used to ensure that the microgrid has sufficient generation capacity to meet load demand and cope with uncertain fluctuations.

[0042] ① In the formula represents the net load demand of microgrid m at time t; represents the total load demand of microgrid m at time t; represents the load reduction of microgrid m at time t through the demand response plan; represents the local power generation of microgrid m at time t; represents the net power generated by the photovoltaic power generation equipment in the microgrid m at time t; represents the net power generated by the wind power generation equipment in the microgrid m at time t; represents the power released by the battery energy storage unit of microgrid m at time t; It represents the power consumed by microgrid m to charge the battery energy storage system at time t.

[0043] ② In the formula represents the total photovoltaic power generation of microgrid m at time t, that is, the total power generated by the photovoltaic power generation system during this period; It represents the self-used power of photovoltaic power generation of microgrid m at time t. This part of electricity is the power consumption of the photovoltaic system for its own operation or the power consumption of the internal electrical equipment of the microgrid, and is not supplied to the outside.

[0044] ③ In the formula represents the total wind power generation power of microgrid m at time t, that is, the total power generation of the wind power generation system in this time period; It represents the power consumed by the wind power generation system of microgrid m within time t. This part of power is used for the operation of the wind power generation system itself or the power consumption of other equipment within the microgrid.

[0045] Step 3: Introduction of Chance Constrained Programming Framework: Chance constrained programming methods are used to handle uncertainties in the network, especially the volatility of load demand and renewable energy (such as solar and wind) power generation, to ensure that the entire system operates optimally with high confidence and improve the reliability of power supply;

[0046] In the present invention, the uncertainty in the network is handled by the chance constrained programming method. Chance constrained programming is a planning method used to handle uncertainty problems and optimize decision-making. It ensures that the system can maintain efficient and safe operation when facing various uncertain factors by managing the changes in input parameters in the model. This method can not only identify and respond to potential risks, but also take into account both safety and performance during the optimization process. It is particularly suitable for complex fields such as power systems. The basic steps and framework for handling uncertainty can be clearly expressed by formulas (6)-(8), ensuring that the system always achieves the best operating state under the premise of meeting a high confidence level.

[0047] Where h k (x) represents the constraint function about the variable x, k is the index or number of the specific constraint, and different constraint functions are defined; k∈Ω n The index k representing the constraint belongs to the set Ω n , where Ω n Represents the set of all constraints, that is, there are n constraints in the system, and each constraint needs to be met.

[0048] Pr{g j (x,ξ)≥0}≥α,j∈Ω j (8), where Pr represents the probability function, which indicates the probability of an event occurring; g j (x,ξ) represents the constraint function about the variable x and the random variable ξ; the value of this function is affected by the uncertainty ξ, which may be related to the volatility of renewable energy generation or the change of load demand; α is a confidence level or probability threshold, indicating that the probability that the constraint is satisfied is at least α; j∈Ω j The index j representing the constraint belongs to the constraint set Ω j , that is, there are multiple constraints in the system, each of which needs to satisfy a specified probability limit.

[0049] Equation (7) describes a model that involves a fixed limit for finding the optimal solution. This model sets the boundaries or restrictions that need to be followed during the system optimization process under given conditions; the constraint (8) ensures that the restrictions imposed on the variable variables have a high degree of reliability, that is, the probability of non-compliance with the restrictions does not exceed the parameter α; this constraint not only includes restrictions on the variable variables, but also takes into account the influence of a set of random variables, ensuring that the optimal solution of the system can cope with these uncertainties in a complex environment.

[0050] In the energy management problem of multiple microgrids, the power generation of renewable energy (such as solar and wind power) and the power demand are uncertain factors that affect the model. According to formula (9), for each bus connected to the microgrid, its power balance constraint can be expressed as formula (10) in chance constrained programming.

[0051] In the formula For every j that belongs to the set Ω j All constraints g j (x,ξ), these conditions must be met at the same time.

[0052] In the formula It means to conditionally constrain the power generation of each microgrid in the multi-microgrid system at a specified time point to ensure that the power generation of each microgrid can meet its load demand; It represents the total power output from microgrid m to other load nodes q, that is, the power transmission between microgrid and other nodes; It represents the net load demand of microgrid m at time t, that is, the actual power demand of the microgrid during that period, which is the load demand calculated after considering all local power generation, energy storage and demand response factors.

[0053] Formula (10) represents the combined probability that the output power of the power generation side minus the net power flow should meet or exceed the power demand, and this combined probability must reach or exceed the set confidence level. Various methods have been proposed in the literature to solve the complex constraint problem defined in formula (10). However, the conventional method is to model the forecast errors of wind power, solar power and load demand through a single probability function. In this study, it is assumed that these uncertainties follow a Gaussian distribution, so that the net load can be expressed as follows: In the formula represents the actual net load demand of microgrid m at time t; net load demand refers to the actual power demand that the microgrid needs to meet after taking into account local power generation, energy storage and other factors; represents the predicted net load demand of microgrid m at time t, that is, the estimated value of the net load obtained by some method (such as historical data, model prediction); Δe m,t represents the forecast error at time t, which is the difference between the actual net load and the predicted net load; this error may be caused by factors such as fluctuations in load demand or uncertainty in power generation.

[0054] Step 4: Integration of compressed air energy storage system: Use compressed air energy storage units to store excess electricity when there is excess photovoltaic power generation, and release compressed air to assist gas turbines in generating electricity when photovoltaic power supply is insufficient or at peak load, thereby ensuring the stability of power supply, especially during periods when solar energy is unsustainable;

[0055] Step 5: Implementation of demand response: Combine the unused generation capacity index with the dispatch of compressed air energy storage to achieve load management through demand response procedures, smooth the power load curve, reduce peak load pressure, and improve system flexibility;

[0056] Step 6: Application of artificial intelligence optimization technology: Through artificial neural networks and optimization algorithms, key parameters in the multi-microgrid system, such as the number of photovoltaic panels, the pressure ratio of compressed air energy storage, and the efficiency of gas turbines, are dynamically adjusted to optimize energy production and storage, and improve the exergy efficiency and economic benefits of the entire system.

[0057] The present invention is a multi-microgrid optimization method for compressed air energy storage and demand response. In use, the present invention aims to improve the energy management efficiency and stability of the multi-microgrid system. By constructing a multi-microgrid network including solar power generation, gas turbines and compressed air energy storage systems, the compressed air energy storage unit is used to store excess electricity when photovoltaic power generation is in excess, and compressed air is released to assist power generation when the load is peak or the power supply is insufficient, thereby ensuring the continuity and stability of power supply; at the same time, combined with the demand response mechanism, load balance and peak load reduction are achieved by dynamically adjusting the load demand, thereby improving the flexibility of the system; in addition, artificial intelligence optimization technology is used to dynamically adjust key operating parameters (such as the number of photovoltaic devices, the pressure ratio of the energy storage system and the efficiency of the gas turbine), further optimize energy production and storage, reduce operating costs, and improve energy utilization efficiency; the method of the present invention effectively solves the problem of power instability caused by the volatility of renewable energy power generation, and has the characteristics of high efficiency and energy saving, stable power supply and strong economy; the present invention has the advantages of adopting a dynamic optimization method combining multiple technologies, optimizing energy scheduling, and improving stability and economic benefits.

[0058] Example 2

[0059] like Figure 1 As shown, a multi-microgrid optimization method for compressed air energy storage and demand response, the method comprising the following steps:

[0060] Step 1: Construction of a multi-microgrid system: By interconnecting multiple microgrids, each microgrid contains a solar photovoltaic power station, a gas turbine, and a compressed air energy storage unit, forming a multi-microgrid network with decentralized power management capabilities;

[0061] Step 2: Application of the unused power generation capacity index: After the system is built, the unused power generation capacity index is introduced to monitor the energy usage of each microgrid in real time, evaluate the power generation and energy storage capacity of each microgrid, help adjust power output and input, and achieve efficient allocation of resources;

[0062] Step 3: Introduction of Chance Constrained Programming Framework: Chance constrained programming methods are used to handle uncertainties in the network, especially the volatility of load demand and renewable energy (such as solar and wind) power generation, to ensure that the entire system operates optimally with high confidence and improve the reliability of power supply;

[0063] Step 4: Integration of compressed air energy storage system: Use compressed air energy storage units to store excess electricity when there is excess photovoltaic power generation, and release compressed air to assist gas turbines in generating electricity when photovoltaic power supply is insufficient or at peak load, thereby ensuring the stability of power supply, especially during periods when solar energy is unsustainable;

[0064] In the present invention, the integrated method of the compressed air energy storage system includes the following steps: when there is excess photovoltaic power generation, the excess electricity is used to drive the compressor to compress the ambient air and store it in the air storage tank as a reserve for subsequent power demand;

[0065] During peak periods of electricity demand or when photovoltaic power generation is insufficient, compressed air is released from the gas storage tank, and after the flow and pressure are adjusted by valves, it enters the regenerator to recover the excess heat during the expansion of the gas turbine and generate electricity for auxiliary power generation;

[0066] In addition, in order to improve the efficiency of the system, two coolers are used during the compression process of compressed air. The coolant is used to recover the waste heat generated during the compression process. The ambient air absorbs heat through the cooler, thereby reducing the temperature of the compressed air and reducing the volume of the air storage tank.

[0067] Furthermore, when the compressed air energy storage unit releases compressed air, the output heat generated still has a high energy density. This heat is used in the heating process of the heater, avoiding the use of fossil fuels for heating and improving the overall energy utilization efficiency of the system. Through this design, the compressed air energy storage system can effectively utilize excess electricity from photovoltaic power generation, reduce dependence on external electricity, and at the same time improve the stability and reliability of the power system under uncertain power supply conditions.

[0068] Step 5: Implementation of demand response: Combine the unused generation capacity index with the dispatch of compressed air energy storage to achieve load management through demand response procedures, smooth the power load curve, reduce peak load pressure, and improve system flexibility;

[0069] In the present invention, the unused power generation capacity index is combined with the scheduling of compressed air energy storage. It should be pointed out that the present invention only incorporates the transferable load into the demand response plan, with the purpose of transferring the load from the peak period to the valley period; the formulas defining the demand response scheme are listed in formulas (12)-(14), however, the transferred load should be compensated in other periods, and this restriction is expressed in formula (15).

[0070] In the formula represents the power increased by the microgrid m in response to demand in time t; γ e It is a coefficient indicating the maximum proportion of load increase, usually a constant less than 1; represents the total load demand of microgrid m at time t; represents the power reduction of microgrid m in response to demand at time t; It represents the total amount of load increase of microgrid m within a period of time, that is, the total amount of power added by the load under the demand response mechanism; It represents the total load reduction of microgrid m in the same time period, that is, the total power reduction of load under the demand response mechanism; represents the actual load demand of microgrid m at time t, which has been adjusted through demand response; represents the total load demand of microgrid m at time t, i.e., the original, unadjusted power demand; It represents the load increased by microgrid m through the demand response mechanism at time t, that is, the increased power demand in the demand response mechanism; It represents the load reduced by microgrid m through the demand response mechanism at time t, that is, the power demand reduced in the demand response mechanism.

[0071] Step 6: Application of artificial intelligence optimization technology: Through artificial neural networks and optimization algorithms, key parameters in the multi-microgrid system, such as the number of photovoltaic panels, the pressure ratio of compressed air energy storage, and the efficiency of gas turbines, are dynamically adjusted to optimize energy production and storage, and improve the exergy efficiency and economic benefits of the entire system.

[0072] In the present invention, artificial intelligence optimization technology is used, specifically including dynamically adjusting key parameters in a multi-microgrid system through a neural network model and an optimization algorithm to achieve optimization of energy production and storage.

[0073] The key parameters include the number of solar panels, the pressure ratio of the compressed air energy storage system, the efficiency of the gas turbine and the efficiency of the compressor.

[0074] Among them, the neural network model is used to establish the nonlinear relationship between the various parts of the multi-microgrid. The input includes variables such as solar power generation, wind power generation, load demand, ambient temperature, etc. The above key parameters are dynamically adjusted through optimization technology, thereby improving the thermodynamic efficiency of the system while reducing the total cost of system operation; especially when the solar power generation and wind power generation fluctuate greatly or there is uncertainty in load demand, the optimization method can respond to environmental changes in real time, adjust the power generation and energy storage strategies, and enable the system to always maintain efficient operation under different working conditions.

[0075] In specific applications, the system's energy storage and release efficiency is optimized by adjusting the pressure ratio of the compressed air energy storage unit; by adjusting the working parameters of the gas turbine, its coordinated work with the compressed air energy storage unit is achieved to improve the overall energy utilization efficiency of the system; this method uses multi-objective optimization to ensure that the microgrid system achieves the best balance in economic benefits and technical performance, maximizes the utilization of clean energy, and significantly reduces dependence on traditional energy.

[0076] The present invention is a multi-microgrid optimization method for compressed air energy storage and demand response. In use, the present invention aims to improve the energy management efficiency and stability of the multi-microgrid system. By constructing a multi-microgrid network including solar power generation, gas turbines and compressed air energy storage systems, the compressed air energy storage unit is used to store excess electricity when photovoltaic power generation is in excess, and compressed air is released to assist power generation when the load is peak or the power supply is insufficient, thereby ensuring the continuity and stability of power supply; at the same time, combined with the demand response mechanism, load balance and peak load reduction are achieved by dynamically adjusting the load demand, thereby improving the flexibility of the system; in addition, artificial intelligence optimization technology is used to dynamically adjust key operating parameters (such as the number of photovoltaic devices, the pressure ratio of the energy storage system and the efficiency of the gas turbine), further optimize energy production and storage, reduce operating costs, and improve energy utilization efficiency; the method of the present invention effectively solves the problem of power instability caused by the volatility of renewable energy power generation, and has the characteristics of high efficiency and energy saving, stable power supply and strong economy; the present invention has the advantages of adopting a dynamic optimization method combining multiple technologies, optimizing energy scheduling, and improving stability and economic benefits.

Claims

1. A multi-microgrid optimization method for compressed air energy storage and demand response, characterized in that: The method comprises the following steps: Step 1: Construction of a multi-microgrid system: By interconnecting multiple microgrids, each microgrid contains a solar photovoltaic power station, a gas turbine, and a compressed air energy storage unit, forming a multi-microgrid network with decentralized power management capabilities; Step 2: Application of the unused generation capacity index: After the system is built, the unused generation capacity index is introduced to monitor the energy usage of each microgrid in real time and evaluate the generation and storage capacity of each microgrid; Step 3: Introduction of Chance Constrained Programming Framework: The uncertainty in the network, especially the volatility of load demand and renewable energy generation, is handled by chance constrained programming method to ensure the optimal operation of the entire system with high confidence. Step 4: Integration of compressed air energy storage system: Use compressed air energy storage units to store excess electricity when there is excess photovoltaic power generation, and release compressed air to assist gas turbines in generating electricity when photovoltaic power supply is insufficient or at peak load, thereby ensuring the stability of power supply, especially during periods when solar energy is unsustainable; Step 5: Implementation of demand response: Combine the unused generation capacity index with the dispatch of compressed air energy storage to achieve load management through demand response procedures, smooth the power load curve, reduce peak load pressure, and improve system flexibility; Step 6: Application of artificial intelligence optimization technology: Through artificial neural networks and optimization algorithms, key parameters in the multi-microgrid system are dynamically adjusted to optimize energy production and storage.

2. A multi-microgrid optimization method for compressed air energy storage and demand response as claimed in claim 1, characterized in that: The unused generation capacity index in step 2 is calculated by dividing the net load by the maximum generation capacity of the microgrid: Where UPC m represents the unused generation capacity index of microgrid m; represents the net load of microgrid m at time t; It represents the maximum total power generation capacity of all power generation equipment in microgrid m within time t; the calculated unused power generation capacity value should meet the set target unused power generation capacity value: In the formula represents the target value of the unused power generation capacity of microgrid m.

3. A multi-microgrid optimization method for compressed air energy storage and demand response as claimed in claim 2, characterized in that: The specific calculation method of the net load in step 2 is based on the existing system components and is defined by the following formula, which includes the scheduling of photovoltaic power generation, wind power generation, gas turbine power generation and battery energy storage units: In the formula represents the net load demand of microgrid m at time t; represents the total load demand of microgrid m at time t; represents the load reduction of microgrid m at time t through the demand response plan; represents the local power generation of microgrid m at time t; represents the net power generated by the photovoltaic power generation equipment in the microgrid m at time t; represents the net power generated by the wind power generation equipment in the microgrid m at time t; represents the power released by the battery energy storage unit of microgrid m at time t; It represents the power consumed by microgrid m to charge the battery energy storage system at time t.

4. A multi-microgrid optimization method for compressed air energy storage and demand response as claimed in claim 3, characterized in that: The net power generated by the photovoltaic power generation device in step 2 for: In the formula represents the total photovoltaic power generation of microgrid m at time t; represents the photovoltaic power generation self-use power of microgrid m at time t; the net power generated by the wind power generation equipment for: In the formula represents the total wind power generation power of microgrid m at time t; It represents the power consumed by the wind power generation system of microgrid m within time t.

5. A multi-microgrid optimization method for compressed air energy storage and demand response according to claim 1, characterized in that: The step 3 deals with the uncertainty in the network by using the chance constrained programming method. The basic steps and framework for dealing with uncertainty can be clearly expressed by formulas (6)-(8): minZ = f(x) (6), Where h k (x) represents the constraint function on the variable x; k∈Ω n The index k representing the constraint belongs to the set Ω n ; Pr{g j (x,ξ)≥0}≥α,j∈Ω j (8), where Pr represents the probability function; g j (x,ξ) represents the constraint function on the variable x and the random variable ξ; α is a confidence level or probability threshold; j∈Ω j The index j representing the constraint belongs to the constraint set Ω j .

6. A multi-microgrid optimization method for compressed air energy storage and demand response as claimed in claim 5, characterized in that: In the energy management problem of multi-microgrids in step 3, the power generation of renewable energy and power demand are uncertain factors affecting the model. According to the formula In the formula For every j that belongs to the set Ω j All constraints g j (x,ξ), these conditions must be met at the same time; for each bus connected to the microgrid, its power balance constraint can be expressed in chance constrained programming as: In the formula It means to conditionally constrain the power generation of each microgrid in the multi-microgrid system at a specified time point to ensure that the power generation of each microgrid can meet its load demand; represents the total power output from microgrid m to other load nodes q; represents the net load demand of microgrid m at time t.

7. A multi-microgrid optimization method for compressed air energy storage and demand response as claimed in claim 6, characterized in that: The power balance constraint formula represents the combined probability that the output power of the power generation side minus the net power flow should meet or exceed the power demand. The combined probability must reach or exceed the set confidence level. Assuming that these uncertain factors follow a Gaussian distribution, the net load can be expressed as follows: In the formula represents the actual net load demand of microgrid m at time t; represents the predicted net load demand of microgrid m at time t; Δe m,t represents the prediction error at time t.

8. A multi-microgrid optimization method for compressed air energy storage and demand response as claimed in claim 1, characterized in that: In step 5, the unused generation capacity index is combined with the dispatch of compressed air energy storage to include only the transferable load in the demand response plan, with the purpose of transferring the load from the peak period to the valley period; Define the demand response scheme as: However, the transferred load should be compensated in other periods, subject to the following constraints: In the formula represents the power increased by the microgrid m in response to demand in time t; γ e It is a coefficient indicating the maximum proportion of load increase, usually a constant less than 1; represents the total load demand of microgrid m at time t; represents the power reduction of microgrid m in response to demand at time t; It represents the total amount of load increase of microgrid m within a period of time; It represents the total load reduction of microgrid m in the same time period; represents the actual load demand of microgrid m at time t; represents the total load demand of microgrid m at time t.

9. A multi-microgrid optimization method for compressed air energy storage and demand response according to claim 1, characterized in that: The artificial intelligence optimization technology in step 6 specifically includes: dynamically adjusting key parameters in the multi-microgrid system through a neural network model and an optimization algorithm to achieve optimization of energy production and storage; Among them, the neural network model is used to establish the nonlinear relationship between the various parts of the multi-microgrid. The input includes variables such as solar power generation, wind power generation, load demand, ambient temperature, etc. The above key parameters are dynamically adjusted through optimization technology, thereby improving the thermodynamic efficiency of the system while reducing the total cost of system operation.

10. A multi-microgrid optimization method for compressed air energy storage and demand response according to claim 9, characterized in that: The key parameters include the number of solar panels, the pressure ratio of the compressed air energy storage system, the efficiency of the gas turbine and the efficiency of the compressor.

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