Micro-grid economic dispatching method based on dynamic electricity price and battery attenuation

Abstract

The invention relates to the technical field of micro-grid economic dispatching, in particular to a micro-grid economic dispatching method based on dynamic electricity price and battery attenuation, which comprises the following steps of: firstly, constructing a dynamic electricity price mechanism based on supply and demand deviation by adopting a Monte Carlo simulation method so as to adjust the electricity price in real time; and then, designing a load adjustment algorithm based on price elasticity, and optimizing the power consumption behavior of the user side. And then, aiming at the battery energy storage system, a nonlinear SOH attenuation model is constructed, the use efficiency of the energy storage system is optimized, and the service life of the energy storage system is prolonged. And finally, by utilizing a multi-objective optimization model, comprehensively considering the power purchase cost, the carbon emission cost, the wind and light abandoning penalty and the energy storage depreciation maintenance cost, and minimizing the total cost of the system. According to the method, the operation cost, the carbon emission and the battery depreciation cost can be effectively reduced, relatively good robustness is shown in various uncertain scenes, and powerful decision support is provided for planning, operation and management of the micro-grid.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid economic dispatching, and in particular to a microgrid economic dispatching method based on dynamic electricity price and battery attenuation. Background Art

[0002] As two major forms of renewable energy, wind power and photovoltaic power generation have been widely used around the world due to their clean, renewable and abundant resources. However, wind power and photovoltaic power generation have obvious intermittency and uncertainty, which makes their power generation significantly affected by multiple factors such as weather and seasons, bringing new challenges to the stable operation and economy of the power system.

[0003] In order to effectively deal with the volatility of renewable energy generation, the battery energy storage system (BESS) has been widely studied and applied in recent years as an important means of regulation. The battery energy storage system can store energy when there is excess renewable energy generation and release energy when there is insufficient generation, thereby balancing supply and demand and improving the self-sufficiency and stability of the system. In addition, with the gradual opening of the power market and the diversification of electricity price mechanisms, dynamic time-of-use pricing (D-TOU) is an effective means of electricity price regulation. By setting different electricity prices in different time periods, it encourages users to purchase electricity during off-peak hours and sell electricity during peak hours, optimizes the allocation of power resources, and reduces system operating costs.

[0004] In the existing technology, the optimization scheduling problem of renewable energy and battery energy storage mainly focuses on the optimization of system economy, and rarely considers the impact of the health state (SOH) of battery energy storage on system performance. The health state of the battery is directly related to its service life and the long-term economic benefits of the system. Ignoring this factor may lead to excessive use of the battery, shorten its life, and increase system maintenance costs. In addition, although the application of dynamic time-of-use electricity prices in energy management can significantly reduce operating costs, its impact on battery charging and discharging strategies has not been fully studied. Reasonable charging and discharging strategies can not only optimize electricity trading, but also effectively control the number of battery charging and discharging times and extend battery life. At the same time, when dealing with uncertainties in the system, most existing optimization models use deterministic methods, which are difficult to fully reflect the random fluctuations in the actual system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art, and to propose a microgrid economic dispatch method based on dynamic electricity prices and battery attenuation, which can effectively reduce operating costs, carbon emissions and battery depreciation costs, and show good robustness under various uncertainty scenarios, providing strong decision-making support for the planning, operation and management of microgrids.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A microgrid economic dispatch method based on dynamic electricity price and battery attenuation includes the following steps:

[0008] Step 1: Monte Carlo simulation method is used to simulate the uncertainty of load, wind power and photovoltaic power generation in the microgrid. By introducing random disturbances, the uncertainty in the actual grid operation is simulated to improve the robustness of the model.

[0009] Step 2: Based on the supply-demand deviation between renewable energy generation and load demand, the electricity price is adjusted dynamically. When the grid load is high and renewable energy generation is insufficient, the electricity price is increased to reduce the electricity load; when the grid load is low and renewable energy generation is sufficient, the electricity price is reduced to promote electricity consumption;

[0010] Step 3: In order to further improve the flexibility of demand response, a dynamic elasticity coefficient is introduced. The dynamic elasticity coefficient changes with the change of grid load to adapt to the price fluctuations under different load conditions. Based on the dynamic electricity price and elasticity coefficient, the grid load can be adjusted accordingly according to the price fluctuations.

[0011] Step 4: A nonlinear SOH attenuation model of the battery is constructed based on the depth of discharge and energy flux. In addition, in order to reasonably consider the impact of the battery state of health (SOH) in the operating cost of the system and extend the service life, a depreciation cost function related to SOH is introduced into the objective function;

[0012] Step 5. Based on the YALMIP platform, a multi-objective optimization model was established, which comprehensively considered the electricity purchase cost, carbon emission cost, wind and solar power abandonment penalty, and energy storage depreciation and maintenance cost to minimize the total system cost. The mixed integer linear programming (MILP) method was used to solve the optimization problem.

[0013] Preferably, in step 2, the electricity price model is dynamically adjusted as follows:

[0014] The expression of the dynamic adjustment electricity price model is:

[0015]

[0016] In the formula, C d,buy (t) is the dynamic electricity price at time t, Ci,buy (t) is the initial time-of-use electricity price at time t, a is the impact coefficient of grid load on electricity price, b is the impact coefficient of renewable energy power generation on electricity price, L(t) is the grid load at time t, G(t) is the renewable energy power generation at time t, L max is the maximum load of the power grid, G max It is the maximum amount of electricity generated by renewable energy.

[0017] Preferably, in step 3, the load adjustment model based on dynamic electricity price and elasticity coefficient is as follows:

[0018] Step 3.1: The sensitivity of load to electricity price response can be quantified by elasticity coefficient. The dynamic elasticity coefficient ε is introduced to adapt to the price fluctuation under different load conditions. The calculation formula of dynamic elasticity coefficient is:

[0019]

[0020] Where ε(t) is the dynamic elastic coefficient at time t, ε max is the maximum value of the elastic modulus, ε min is the minimum value of the elastic coefficient, L(t) is the grid load at time t, L max is the maximum value of the grid load;

[0021] Step 3.2: Based on the dynamic electricity price mechanism and elasticity coefficient, the grid load can be adjusted accordingly according to the fluctuation of electricity prices, so as to increase electricity consumption during the period of low electricity prices and reduce electricity consumption during the period of high electricity prices. The load change at time t and the adjusted grid load can be expressed as:

[0022]

[0023] In the formula, ΔL(t) is the load change at time t, L(t) is the initial grid load at time t, is the adjusted grid load at time t, ε(t) is the dynamic elastic coefficient, C d (t) is the dynamic electricity price at time t, C i (t) is the initial time-of-use electricity price at time t.

[0024] Step 3.3: To ensure that the adjusted load does not cause a drastic change in the total system load, a load balance constraint is established. The difference between the total original load and the adjusted load is calculated, and the load in each period is compensated by compensating the difference. The load difference compensation formula is as follows:

[0025]

[0026] Where, L final(t) is the grid load after difference compensation at time t, L(t) is the initial grid load at time t, is the adjusted grid load at time t.

[0027] Preferably, in step 4, the nonlinear SOH attenuation model and the depreciation cost function are specifically as follows:

[0028] Step 4.1: As the battery is used for a long time, the available capacity of the battery gradually decreases. The attenuation of SOH is a complex nonlinear process, which is affected by multiple factors, including charge and discharge capacity, charge depth, etc. The SOH calculation formula is as follows:

[0029] SOH(t+1)=SOH(t)-f(t)

[0030] Where SOH(t+1) is the battery health status at time t+1, SOH(t) is the battery health status at time t, and f(t) is the SOH attenuation at time t;

[0031] In order to describe the change of SOH, a nonlinear attenuation model based on energy flux and discharge depth is proposed. At the same time, considering that the attenuation rate of the battery at a higher discharge depth is significantly higher than that at a lower discharge depth, in order to more accurately reflect this nonlinear relationship, an exponential function is used to represent the effect of discharge depth on SOH attenuation. In order to more accurately reflect this nonlinear relationship, the calculation formula for SOH attenuation f(t) at time t is as follows:

[0032]

[0033] In the formula, f(t) is the SOH attenuation at time t, A is the reference coefficient of battery SOH attenuation, c is the nonlinear attenuation amplitude coefficient caused by energy flux, k is the attenuation adjustment coefficient of energy flux, γ is the influence coefficient of discharge depth on SOH attenuation, DoD(t) is the discharge depth at time t, E(t) is the energy flux at time t, which is defined as the charge and discharge energy of the battery in the time period, which can be expressed as:

[0034]

[0035] Where E(t) is the energy flux at time t, η cha is the battery charging efficiency, η dis is the discharge efficiency of the battery, P cha (t) is the charging power of the battery at time t, P dis (t) is the discharge power of the battery at time t, and Δt is the time interval.

[0036] Discharge depth is another key factor affecting battery SOH decay. The greater the discharge depth, the faster the battery decay rate. Discharge depth is calculated by the battery's state of charge:

[0037]

[0038] Where DoD(t) is the battery discharge depth at time t, SOC(t) is the battery state of charge at time t, and SOC max The maximum state of charge, SOC min is the minimum state of charge;

[0039] Step 4.2: In order to reasonably consider the impact of battery health state (SOH) in the operating cost of the system, a depreciation cost function related to SOH is introduced into the objective function. The specific formula is as follows:

[0040]

[0041] In the formula, C SOH (t) is the depreciation cost at time t, C bat is the unit capacity cost of the battery, E rated is the rated capacity of the battery, N cycle is the rated number of cycles, C f is the battery attenuation cost function, f(t) is the SOH attenuation at time t, N eq (t) is the equivalent full cycle number, and the calculation formula is:

[0042]

[0043] Where N eq (t) is the equivalent full cycle number at time t, E(t) is the energy flux of the battery at time t, and E rated is the rated capacity of the battery.

[0044] Preferably, in step 5, the multi-objective optimization model is as follows:

[0045] In order to achieve economical and efficient operation of the microgrid system, an optimization scheduling model that comprehensively considers multiple cost factors is constructed. The objective function of the optimization scheduling model aims to minimize the total operating cost of the system and optimize the power scheduling strategy. The objective function takes into account multiple economic indicators such as power purchase cost, carbon emission cost, battery depreciation and maintenance cost, wind power and photovoltaic power generation operation and maintenance cost, and wind and light abandonment cost to improve the system's operation economy and sustainability. The objective function is as follows:

[0046]

[0047] Where f is the objective function of the scheduling model, representing the total operating cost of the system; Cgb (t) is the electricity purchase cost, C carbon (t) is the carbon emission cost, C battery (t) is the battery depreciation and maintenance cost, C curtail (t) is the penalty cost for wind and solar power abandonment, C DG (t) is the wind and solar operation and maintenance cost. d,buy (t) is the dynamic electricity purchase price at time t, P buy (t) is the power purchased from the power grid at time t; E factor,buy is the carbon emission factor of electricity purchase, C carbon is the carbon price; k b is the maintenance cost coefficient of the battery, E(t) is the energy flux of the battery at time t, C bat is the unit capacity cost of the battery, N eq (t) is the equivalent full cycle number, N cycle is the rated number of cycles, C f is the battery attenuation cost coefficient, f(t) is the SOH attenuation at time t; P wc (t) is the wind abandonment volume at time t, P pvc (t) is the amount of abandoned light at time t, k wc is the wind abandonment penalty coefficient, k pvc k is the light abandonment penalty coefficient; w is the wind power operation and maintenance cost coefficient, k pv is the photovoltaic operation and maintenance cost coefficient; P w (t) is the wind power output power at time t, P pv (t) is the photovoltaic output power at time t.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention adopts the Monte Carlo simulation method, introduces the uncertainty of load fluctuation, wind power and photovoltaic power generation, and improves the robustness of the model to random fluctuations in the actual system.

[0050] 2. The present invention introduces a dynamic electricity price mechanism to adjust the electricity price according to the real-time load and power generation conditions, thereby influencing the user's electricity consumption behavior and achieving more flexible demand response.

[0051] 3. The flexible load adjustment algorithm of the present invention is based on the change of electricity price, which ensures that the total load remains unchanged while reasonably allocating the load differences in each time period, thereby enhancing the flexibility and efficiency of the power system.

[0052] 4. The present invention constructs a nonlinear SOH decay model based on discharge depth and energy flux, which can accurately reflect the decay characteristics of the battery and incorporate it into the optimization process to optimize the battery charging and discharging strategy, extend battery life, and reduce long-term maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the process of the present invention;

[0054] Figure 2 It is a typical daily dynamic electricity price adjustment diagram of the present invention;

[0055] Figure 3 is a typical daily load adjustment diagram of the present invention;

[0056] Figure 4 This is a typical daily SOH attenuation variation diagram of the present invention;

[0057] Figure 5 A total cost comparison diagram of the Monte Carlo simulation of the present invention;

[0058] Figure 6 A comparison chart of carbon emissions from Monte Carlo simulation of the present invention;

[0059] Figure 7 This is the final SOH comparison diagram of the Monte Carlo simulation of the present invention. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the advantages and features of the present invention, thereby making a clearer definition of the protection scope of the present invention. The embodiments described in the present invention are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0061] Reference Figure 1-Figure 7 , a microgrid economic dispatch method based on dynamic electricity price and battery attenuation, comprising the following steps:

[0062] Step 1: Monte Carlo simulation method is used to simulate the uncertainty of load, wind power and photovoltaic power generation in the microgrid. By introducing random disturbances, the uncertainty in the actual grid operation is simulated to improve the robustness of the model. The number of Monte Carlo simulations is 100, the random seed is 123, the wind power and photovoltaic fluctuation coefficients are 10%, and the load fluctuation coefficient is 5%.

[0063] Step 2: Dynamically adjust electricity prices based on the supply and demand deviation between renewable energy generation and load demand.

[0064] Step 3: In order to further improve the flexibility of demand response, a dynamic elasticity coefficient is introduced, which changes with the change of grid load to adapt to the price fluctuations under different load conditions. Based on the dynamic electricity price and elasticity coefficient, the grid load will be adjusted accordingly according to the price fluctuations.

[0065] Step 4: Construct a nonlinear SOH attenuation model for the battery based on the depth of discharge and energy flux. In addition, in order to reasonably consider the impact of the battery state of health (SOH) in the operating cost of the system and extend the service life, a depreciation cost function related to SOH is introduced into the objective function.

[0066] Step 5: Based on the YALMIP platform, a multi-objective optimization model was established to minimize the total system cost by comprehensively considering the power purchase cost, carbon emission cost, wind and solar power abandonment penalty, and energy storage depreciation and maintenance cost. The mixed integer linear programming (MILP) method was used to solve the optimization problem.

[0067] Specifically, in step 2, the electricity price model is dynamically adjusted as follows:

[0068] The expression of the dynamic adjustment electricity price model is:

[0069]

[0070] In the formula, C d,buy (t) is the dynamic electricity price at time t, C i,buy (t) is the initial time-of-use electricity price at time t, a is the impact coefficient of grid load on electricity price, b is the impact coefficient of renewable energy power generation on electricity price, L(t) is the grid load at time t, G(t) is the renewable energy power generation at time t, L max is the maximum load of the power grid, G max It is the maximum amount of electricity generated by renewable energy.

[0071] The influence coefficients of grid load and renewable energy generation on electricity price are 0.4 and 0.5 respectively. The original time-of-use electricity price table is shown in Table 1, and the typical daily dynamic electricity price adjustment chart is shown in Figure 2 As shown in the figure, it can be seen that during the peak load and insufficient renewable energy supply periods (8-11 a.m. and 18-21 p.m.), electricity prices increased significantly, while during the period of sufficient renewable energy (12-16 p.m.), electricity prices decreased, effectively guiding load distribution and resource optimization.

[0072] Table 1 Time-of-use electricity price table

[0073]

[0074] Specifically, in step 3, the load adjustment model based on dynamic electricity price and elasticity coefficient is as follows:

[0075] 3.1. The sensitivity of load to electricity price response can be quantified by the elasticity coefficient. The dynamic elasticity coefficient ε is introduced to adapt to the fluctuation of electricity prices under different load conditions. The calculation formula of the dynamic elasticity coefficient is:

[0076]

[0077] Where ε(t) is the dynamic elastic coefficient at time t, ε max is the maximum value of the elastic coefficient, taking 1, ε min is the minimum value of the elastic coefficient, which is 0.4. L(t) is the grid load at time t. max is the maximum value of the grid load.

[0078] 3.2. Based on the dynamic electricity price mechanism and elasticity coefficient, the grid load will be adjusted accordingly according to the fluctuation of electricity prices, so as to increase electricity consumption during the period of low electricity prices and reduce electricity consumption during the period of high electricity prices. The change in load at time t and the adjusted grid load can be expressed as:

[0079]

[0080] In the formula, ΔL(t) is the load change at time t, L(t) is the initial grid load at time t, is the adjusted grid load at time t, ε(t) is the dynamic elastic coefficient, C d (t) is the dynamic electricity price at time t, C i (t) is the initial time-of-use electricity price at time t.

[0081] 3.3. In order to ensure that the adjusted load does not cause a drastic change in the total system load, a load balance constraint is established. The difference between the total original load and the adjusted load is calculated, and the load in each period is compensated by compensating the difference. The load difference compensation formula is as follows:

[0082]

[0083] Where, L final (t) is the grid load after difference compensation at time t, L(t) is the initial grid load at time t, is the adjusted grid load at time t.

[0084] from Figure 3 It can be seen that the guidance of dynamic electricity prices has significantly reduced the peak load, while increasing the load when renewable energy generation is sufficient, further improving resource utilization efficiency.

[0085] Specifically, in step 4, the nonlinear SOH attenuation model and the depreciation cost function are as follows:

[0086] 4.1. As the battery is used for a long time, the available capacity of the battery gradually decreases. The attenuation of SOH is a complex nonlinear process, which is affected by many factors, including charge and discharge capacity, charge depth, etc. The SOH calculation formula is as follows:

[0087] SOH(t+1)=SOH(t)-f(t)

[0088] Where SOH(t+1) is the battery health state at time t+1, SOH(t) is the battery health state at time t, and f(t) is the SOH attenuation at time t.

[0089] In order to describe the change of SOH, a nonlinear attenuation model based on energy flux and discharge depth is proposed. At the same time, considering that the attenuation rate of the battery at a higher discharge depth is significantly higher than that at a lower discharge depth, in order to more accurately reflect this nonlinear relationship, an exponential function is used to represent the effect of discharge depth on SOH attenuation, so as to more accurately reflect this nonlinear relationship. The calculation formula for SOH attenuation f(t) at time t is as follows:

[0090]

[0091] In the formula, f(t) is the SOH attenuation at time t, A is the reference coefficient of battery SOH attenuation, c is the nonlinear attenuation amplitude coefficient caused by energy flux, k is the attenuation adjustment coefficient of energy flux, γ is the influence coefficient of discharge depth on SOH attenuation, DoD(t) is the discharge depth at time t, E(t) is the energy flux at time t, which is defined as the charge and discharge energy of the battery in the time period, which can be expressed as:

[0092]

[0093] Where E(t) is the energy flux at time t, η cha is the battery charging efficiency, η dis is the discharge efficiency of the battery, P cha (t) is the charging power of the battery at time t, P dis (t) is the discharge power of the battery at time t, and Δt is the time interval.

[0094] Discharge depth is another key factor affecting battery SOH decay. The greater the discharge depth, the faster the battery decay rate. The discharge depth can be calculated by the battery's state of charge:

[0095]

[0096] Where DoD(t) is the battery discharge depth at time t, SOC(t) is the battery state of charge at time t, and SOC max is the maximum value of the state of charge, take 0.8, SOCmin is the minimum value of the state of charge, which is 0.2.

[0097] 4.2. In order to reasonably consider the impact of battery health state (SOH) in the operating cost of the system, a depreciation cost function related to SOH is introduced into the objective function. The specific formula is as follows:

[0098]

[0099] In the formula, C SOH (t) is the depreciation cost at time t, C bat is the unit capacity cost of the battery, E rated is the rated capacity of the battery, N cycle is the rated number of cycles, C f is the battery attenuation cost function, f(t) is the SOH attenuation at time t, N eq (t) is the equivalent full cycle number, and the calculation formula is:

[0100]

[0101] Where N eq (t) is the equivalent full cycle number at time t, E(t) is the charge and discharge energy of the battery at time t, and E rated is the rated capacity of the battery.

[0102] The rated capacity of the battery is 600kWh, the rated power is 300kW, the unit capacity cost is 2000 / kWh, the base coefficient of battery SOH decay is 0.001, the coefficients c and k of energy flux decay are 0.2 and 0.01 respectively, and the coefficient of discharge depth on SOH decay is 5. Figure 4 The battery state of health (SOH) change curve shows that the proposed method effectively controls the battery charge and discharge depth, keeps the SOH at a low attenuation amplitude during the scheduling cycle, and protects the battery life during the load valley period.

[0103] Specifically, in step 5, the specific multi-objective optimization model is as follows:

[0104] In order to achieve economical and efficient operation of the microgrid system, an optimization dispatch model that comprehensively considers multiple cost factors is constructed. The objective function of the model aims to minimize the total operating cost of the system and optimize the power dispatch strategy. The objective function takes into account multiple economic indicators such as power purchase cost, carbon emission cost, battery depreciation and maintenance cost, operation and maintenance cost of wind power and photovoltaic power generation, and wind and solar power abandonment cost to improve the operation economy and sustainability of the system. The objective function is as follows:

[0105]

[0106] Where f is the objective function of the scheduling model, representing the total operating cost of the system; C gb (t) is the electricity purchase cost, C carbon (t) is the carbon emission cost, C battery (t) is the battery depreciation and maintenance cost, C curtail (t) is the penalty cost for wind and solar power abandonment, C DG (t) is the wind and solar operation and maintenance cost. d,buy (t) is the dynamic electricity purchase price at time t, P buy (t) is the power purchased from the power grid at time t; E factor,buy is the carbon emission factor of electricity purchase, C carbon is the carbon price; k b is the maintenance cost coefficient of the battery, E(t) is the energy flux of the battery at time t, C bat is the unit capacity cost of the battery, N eq (t) is the equivalent full cycle number, N cycle is the rated number of cycles, C f is the battery attenuation cost coefficient, f(t) is the SOH attenuation at time t; P wc (t) is the wind abandonment volume at time t, P pvc (t) is the amount of abandoned light at time t, k wc is the wind abandonment penalty coefficient, k pvc k is the light abandonment penalty coefficient; w is the wind power operation and maintenance cost coefficient, k pv is the photovoltaic operation and maintenance cost coefficient; P w (t) is the wind power output power at time t, P pv (t) is the photovoltaic output power at time t.

[0107] Figures 5 to 7 The distribution of key indicators in the Monte Carlo simulation is shown, providing an intuitive analysis for optimizing the performance of the scheduling model. Figure 5 It shows that the total operating cost distribution is concentrated around 1577 yuan, indicating that the optimization model can effectively reduce the system operating cost under various uncertainties. Figure 6 The distribution of carbon emissions is shown, with most results concentrated around 2039 kg CO2, reflecting that the model maintains a high level of environmental friendliness in different scenarios. Figure 7 The distribution of battery state of health (SOH) shows that SOH varies little in different scenarios. It can be seen that the proposed optimized scheduling controls the battery charging and discharging behavior and effectively prolongs the battery life.

[0108] In order to verify the comprehensive advantages of the proposed optimization scheduling method in terms of economy, environmental benefits and system operation efficiency, three scheduling strategies were set up for comparative analysis. To ensure the fairness of the comparison, each scheduling scheme is based on the wind and solar load data of a typical day, and three different scheduling modes are designed for comparative analysis. The specific schemes are as follows:

[0109] Scheduling scheme 1: Unimproved fixed electricity price scheduling mode. This scheme adopts the traditional fixed time-of-use electricity price, does not perform load adjustment and dynamic optimization, and the battery charging and discharging scheduling is completely based on simple peak and valley electricity price rules. The optimization goal is to minimize the single system operating cost.

[0110] Dispatch Scheme 2: Improved dispatch mode with dynamic electricity prices. Based on Scheme 1, a dynamic electricity price mechanism is introduced to consider real-time electricity price fluctuations and promote the consumption of renewable energy by dynamically adjusting electricity prices. The optimization goal is still to minimize operating costs, but the battery health status (SOH) optimization is not included.

[0111] Scheduling Scheme 3: The comprehensive optimization scheduling strategy proposed in this invention. Based on Scheduling Scheme 2, a multi-objective optimization strategy is further adopted to comprehensively consider system operation costs, carbon emission costs, and battery depreciation costs. By introducing a dynamic electricity price mechanism, load adjustment, and battery life optimization strategy, the overall system performance can be improved.

[0112] Table 2 shows the comparison results of the key indicators of the three scheduling schemes. In terms of economy, the total operating cost of scheduling scheme 1 is the highest, which is 1598.49 yuan. Scheduling scheme 2 reduces the cost to 1498.07 yuan by introducing dynamic electricity price optimization, while the multi-objective optimization strategy of scheme 3 takes into account other goals (carbon emissions and battery depreciation costs). The total operating cost is 1561.90 yuan, which is between the two. The battery depreciation cost gradually decreases with the optimization strategy, and scheme 3 is reduced by 25.5% compared with scheme 1, indicating that the proposed SOH decay strategy has significant advantages in extending battery life. In terms of environmental benefits, the carbon emissions and carbon emission costs of scheme 3 are reduced to 2025.06kg CO2 and 101.25 yuan, respectively, which is a decrease of 14.4% compared with scheme 1, which fully reflects the superiority of comprehensive optimization scheduling in emission reduction. In addition, the wind power and photovoltaic operation and maintenance costs, wind curtailment rate and solar curtailment rate of the three schemes are the same, indicating that the optimization strategy has achieved a high level of utilization efficiency of renewable energy, while the final SOH index gradually increases with the improvement of the optimization degree, reaching 0.9883 for scheme 3, further illustrating its protective effect on the health status of the battery.

[0113] Table 2 Comparison of different scheduling strategies

[0114]

[0115]

[0116] In summary, the present invention can effectively reduce operating costs, carbon emissions and battery depreciation costs, show good robustness under various uncertainty scenarios, and provide strong decision-making support for the planning, operation and management of microgrids.

[0117] The description and practice disclosed in the present invention are easy to think and understand for ordinary technicians in the technical field, and several improvements and modifications can be made without departing from the principles of the present invention. Therefore, modifications or improvements made without departing from the spirit of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A microgrid economic dispatch method based on dynamic electricity price and battery attenuation, characterized in that: The steps include: Step 1: Monte Carlo simulation method is used to simulate the uncertainty of load, wind power and photovoltaic power generation in the microgrid. By introducing random disturbances, the uncertainty in the actual grid operation is simulated to improve the robustness of the model. Step 2: Based on the supply-demand deviation between renewable energy generation and load demand, the electricity price is adjusted dynamically. When the grid load is high and renewable energy generation is insufficient, the electricity price is increased to reduce the electricity load; when the grid load is low and renewable energy generation is sufficient, the electricity price is reduced to promote electricity consumption; Step 3: Introduce a dynamic elasticity coefficient, which changes with the change of the grid load to adapt to the price fluctuations under different load conditions; based on the dynamic electricity price and elasticity coefficient, the grid load can be adjusted accordingly according to the electricity price fluctuations; Step 4: A nonlinear SOH attenuation model of the battery is constructed based on the depth of discharge and the energy flux, and a depreciation cost function related to the battery health state SOH is introduced into the objective function; Step 5: Based on the YALMIP platform, a multi-objective optimization model is established to comprehensively consider the electricity purchase cost, carbon emission cost, wind and solar power abandonment penalty, and energy storage depreciation and maintenance cost to minimize the total system cost. The mixed integer linear programming MILP method is used to solve the optimization problem.

2. A microgrid economic dispatch method based on dynamic electricity price and battery attenuation according to claim 1, characterized in that: In step 2, the electricity price model is dynamically adjusted as follows: The expression of the dynamic adjustment electricity price model is: In the formula, C d,buy (t) is the dynamic electricity price at time t, C i,buy (t) is the initial time-of-use electricity price at time t, a is the impact coefficient of grid load on electricity price, b is the impact coefficient of renewable energy power generation on electricity price, L(t) is the grid load at time t, G(t) is the renewable energy power generation at time t, L max is the maximum load of the power grid, G max It is the maximum amount of electricity generated by renewable energy.

3. A microgrid economic dispatch method based on dynamic electricity price and battery attenuation according to claim 1, characterized in that: In step 3, the load adjustment model based on dynamic electricity price and elasticity coefficient is as follows: Step 3.1: The sensitivity of load to electricity price response can be quantified by elasticity coefficient. The dynamic elasticity coefficient ε is introduced to adapt to the price fluctuation under different load conditions. The calculation formula of dynamic elasticity coefficient is: Where ε(t) is the dynamic elastic coefficient at time t, ε max is the maximum value of the elastic modulus, ε min is the minimum value of the elastic coefficient, L(t) is the grid load at time t, L max is the maximum value of the grid load; Step 3.2: Based on the dynamic electricity price mechanism and elastic coefficient, the grid load can be adjusted accordingly according to the fluctuation of electricity prices, so as to increase electricity consumption during the period of low electricity prices and reduce electricity consumption during the period of high electricity prices. The load change at time t and the adjusted grid load can be expressed as: In the formula, ΔL(t) is the load change at time t, L(t) is the initial grid load at time t, is the adjusted grid load at time t, ε(t) is the dynamic elastic coefficient, C d (t) is the dynamic electricity price at time t, C i (t) is the initial time-of-use electricity price at time t; Step 3.3: To ensure that the adjusted load does not cause a drastic change in the total system load, a load balance constraint is established. The difference between the total original load and the adjusted load is calculated, and the load in each period is compensated by compensating the difference. The load difference compensation formula is as follows: Where, L final (t) is the grid load after difference compensation at time t, L(t) is the initial grid load at time t, is the adjusted grid load at time t.

4. A microgrid economic dispatch method based on dynamic electricity price and battery attenuation according to claim 1, characterized in that: In step 4, the nonlinear SOH attenuation model and the depreciation cost function are specifically as follows: Step 4.1, SOH calculation formula is as follows: SOH(t+1)=SOH(t)-f(t) Where SOH(t+1) is the battery health status at time t+1, SOH(t) is the battery health status at time t, and f(t) is the SOH attenuation at time t; In order to describe the change of SOH, a nonlinear attenuation model based on energy flux and discharge depth is proposed. At the same time, considering that the attenuation rate of the battery at a higher discharge depth is significantly higher than that at a low discharge depth, an exponential function is used to represent the effect of discharge depth on SOH attenuation to reflect this nonlinear relationship. The calculation formula of SOH attenuation f(t) at time t is as follows: In the formula, f(t) is the SOH attenuation at time t, A is the reference coefficient of battery SOH attenuation, c is the nonlinear attenuation amplitude coefficient caused by energy flux, k is the attenuation adjustment coefficient of energy flux, γ is the influence coefficient of discharge depth on SOH attenuation, DoD(t) is the discharge depth at time t, E(t) is the energy flux at time t, which is defined as the charge and discharge energy of the battery in the time period, which can be expressed as: Where E(t) is the energy flux at time t, η cha is the battery charging efficiency, η dis is the discharge efficiency of the battery, P cha (t) is the charging power of the battery at time t, P dis (t) is the discharge power of the battery at time t, and Δt is the time interval; The depth of discharge is calculated from the battery's state of charge: Where DoD(t) is the battery discharge depth at time t, SOC(t) is the battery state of charge at time t, and SOC max The maximum state of charge, SOC min is the minimum state of charge; Step 4.2: Introduce the depreciation cost function related to SOH into the objective function. The specific formula is as follows: In the formula, C SOH (t) is the depreciation cost at time t, C bat is the unit capacity cost of the battery, E rated is the rated capacity of the battery, N cycle is the rated number of cycles, C f is the battery attenuation cost function, f(t) is the SOH attenuation at time t, N eq (t) is the equivalent full cycle number, and the calculation formula is: Where N eq (t) is the equivalent full cycle number at time t, E(t) is the energy flux of the battery at time t, and E rated is the rated capacity of the battery.

5. The microgrid economic dispatch method based on dynamic electricity price and battery attenuation according to claim 1 is characterized in that: In step 5, the multi-objective optimization model is as follows: Construct an optimization dispatch model that comprehensively considers multiple cost factors. The objective function of the optimization dispatch model aims to minimize the total operating cost of the system and optimize the power dispatch strategy. The objective function takes into account multiple economic indicators such as power purchase cost, carbon emission cost, battery depreciation and maintenance cost, wind power and photovoltaic power generation operation and maintenance cost, and wind and solar power abandonment cost, so as to improve the operation economy and sustainability of the system. The objective function is as follows: Where f is the objective function of the scheduling model, representing the total operating cost of the system; C gb (t) is the electricity purchase cost, C carbon (t) is the carbon emission cost, C battery (t) is the battery depreciation and maintenance cost, C curtail (t) is the penalty cost for wind and solar power abandonment, C DG (t) is the wind and solar operation and maintenance cost, C d,buy (t) is the dynamic electricity purchase price at time t, P buy (t) is the power purchased from the power grid at time t; E factor,buy is the carbon emission factor of electricity purchase, C carbon is the carbon price; k b is the maintenance cost coefficient of the battery, E(t) is the energy flux of the battery at time t, C bat is the unit capacity cost of the battery, N eq (t) is the equivalent full cycle number, N cycle is the rated number of cycles, C f is the battery attenuation cost coefficient, f(t) is the SOH attenuation at time t; P wc (t) is the wind abandonment volume at time t, P pvc (t) is the amount of abandoned light at time t, k wc is the wind abandonment penalty coefficient, k pvc k is the light abandonment penalty coefficient; w is the wind power operation and maintenance cost coefficient, k pv is the photovoltaic operation and maintenance cost coefficient; P w (t) is the wind power output power at time t, P pv (t) is the photovoltaic output power at time t.

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