Wind and light storage rural micro-grid system scheduling method considering operation aging cost of energy storage system

Abstract

The invention relates to a wind and light storage rural micro-grid system dispatching method, in particular to a wind and light storage rural micro-grid system dispatching method considering the operation aging cost of an energy storage system. The invention aims to solve the problems that the aging cost of an energy storage system caused by charging and discharging circulation is neglected in the existing wind and light storage micro-grid system scheduling method, so that the day-ahead scheduling comprehensive operation cost of the micro-grid is increased, and the system safety is reduced and the scheduling flexibility is limited due to long-term neglecting of the performance aging of the energy storage system. The method comprehensively considers the loss cost of wind and light power generation and the operation aging cost of the energy storage system, improves the comprehensive benefits of the day-ahead operation of the microgrid, introduces the constant current-constant voltage charging characteristic curve, and guarantees the safety of the energy storage system. The invention belongs to the technical field of micro-grid dispatching.

Description

Technical Field

[0001] The present invention relates to a wind, solar and energy storage rural microgrid system dispatching method, belonging to the technical field of microgrid dispatching. Background Art

[0002] With the rapid development of the current world economy, energy shortages and environmental protection issues are becoming increasingly prominent. Therefore, renewable energy power generation methods such as wind and solar energy have become a research hotspot. In recent years, research on the efficient utilization of large-scale distributed renewable energy has continued to deepen.

[0003] Some rural areas in Northeast my country are geographically remote, with complex and diverse topography and frequent extreme weather events. These rural areas are far from the main power grid and have a low reliance on it. Relying on the main grid for power supply would require laying additional long-distance transmission lines, which is not only costly but also economically inefficient. With the rapid development of renewable energy generation technologies, how to fully utilize local renewable energy resources in an economical manner has become a pressing issue. Furthermore, rural areas generally have open terrain and abundant wind resources, making them ideal for the development of wind-solar hybrid microgrids. However, renewable energy generation technologies such as wind and solar energy exhibit significant randomness and volatility, posing numerous challenges to the operation and scheduling of microgrids.

[0004] Although configuring microgrids with energy storage equipment to form a wind-solar-storage complementary power supply system can alleviate the problem of power shortage in rural microgrids to a certain extent, in rural areas such as the Northeast where extreme weather is frequent, how to ensure the normal operation of microgrids and improve the service life of energy storage systems remains a difficult problem that needs to be solved urgently.

[0005] In response to the impact of wind and solar uncertainty on the operation optimization of integrated energy systems, Xu Jianwei's team from Nanjing Normal University proposed a day-ahead and intraday optimization scheduling method for integrated energy systems. With the lowest operating cost as the objective function, they introduced time-of-use electricity prices and time-of-use heat prices, and then narrowed the time scale to construct a multi-objective optimization model with the lowest operating cost and the lowest scheduling adjustment cost.

[0006] Shi Zhaodi's team from the China Electric Power Research Institute proposed a two-level planning method for the capacity of energy storage and heat storage systems, introduced the conditional value at risk method, and considered the impact of the uncertainty of renewable energy power generation output on the energy storage capacity planning results.

[0007] Li Kuining's team at Chongqing University proposed a balanced charging strategy to address the aging problem of automotive energy storage batteries caused by charging strategies, thereby increasing the lifespan of automotive energy storage batteries.

[0008] However, the existing scheduling methods still have the following defects:

[0009] First, the charging power limit of the energy storage system in the current wind-solar-storage microgrid is usually set at the rated power value of the energy storage system. When the energy storage system's state of charge (SOC) level is high, continuous charging at the rated power value may cause the internal voltage of the energy storage system to be too high, posing a safety hazard.

[0010] Second, the current day-ahead dispatch strategies for wind-solar-storage microgrids often ignore the aging costs of the energy storage system due to the charge-discharge cycle, resulting in an increase in the overall operating costs of the microgrid's day-ahead dispatch. Long-term neglect of the aging of the energy storage system's performance will lead to problems such as reduced system security and limited dispatch flexibility. Summary of the Invention

[0011] The present invention aims to solve the problem that the existing wind-solar-storage microgrid system scheduling method ignores the aging cost of the energy storage system caused by the charge and discharge cycle, resulting in an increase in the comprehensive operating cost of the microgrid scheduling. Long-term neglect of the performance aging of the energy storage system will cause a decrease in system safety and limited scheduling flexibility. Therefore, a wind-solar-storage rural microgrid system scheduling method is proposed that takes into account the aging cost of the energy storage system.

[0012] The technical solution adopted by the present invention to solve the above problems is: the steps of the present invention include:

[0013] Step 1: Conduct charge and discharge cycle tests on the energy storage system for the type of energy storage technology to be adopted by the microgrid system, record the performance parameters after each cycle, and establish a life degradation model for the energy storage system through statistical analysis;

[0014] Step 2: For the CV phase in the charging mode, the maximum allowable charging power that decreases exponentially with time is equivalently converted to a maximum allowable charging power that decreases linearly with SOC, completing the conversion from the time domain to the SOC domain.

[0015] Step 3: Establishing a mathematical model of wind power output and a mathematical model of photovoltaic output;

[0016] Step 4: Perform a day-ahead forecast of local climate data; obtain day-ahead wind power forecast data and photovoltaic forecast data based on the wind turbine output model and photovoltaic output model;

[0017] Step 5: Predict the local electricity load on the day before and generate a time series of the electricity load on the day before;

[0018] Step 6: Embed charging and discharging strategies into the rural microgrid energy storage system;

[0019] Step 7: During microgrid scheduling, the energy storage system SOC level maintains continuity and stability within each time period to ensure smooth and efficient power supply;

[0020] Step 8: In actual scheduling, considering the operating characteristics of the microgrid, the optimal scheduling strategy is obtained by solving the maximum value of the economic objective function.

[0021] Furthermore, the energy storage system life attenuation model in step 1 is expressed as:

[0022]

[0023] B(C)=a1C 3 +a2C 2 +a3C+a4(2),

[0024]

[0025] In formulas (1), (2) and (3), Q loss is the capacity loss of the energy storage system, C is the discharge rate, T is the ambient temperature of the energy storage system, Ah is the current throughput of the energy storage system in the corresponding time, B is the constant coefficient under different discharge rates C, a1, a2, a3 and a4 are fitting coefficients, and SOH is the health status of the energy storage system.

[0026] Furthermore, the CC phase defined in the SOC domain in step 2 is expressed by formula (4), and the CV phase is expressed by formulas (5) and (6);

[0027]

[0028] In formulas (4), (5) and (6), SOC t is the SOC level of the energy storage system corresponding to any time t, SOC C is the SOC level of the energy storage system when the CC phase switches to the CV phase, is the rated charging power of the energy storage system, is the termination charging power of the energy storage system corresponding to 100% SOC, and α is the linear decrease coefficient of the CV curve.

[0029] Furthermore, the mathematical model of wind power output in step 3 is expressed as formula (7), and the mathematical model of photovoltaic output is expressed as formula (8);

[0030]

[0031] In formulas (7) and (8), P WT is the predicted output power of the wind turbine, v is the wind speed, v ci is the cut-in wind speed, v r is the cut-out wind speed, v co is the safe wind speed, E p is the predicted photovoltaic power generation, H A is the total solar radiation on the horizontal surface, Es is the irradiance under standard conditions, P AZ is the installed capacity of photovoltaic modules, and K is the comprehensive efficiency coefficient, which includes the inclination angle of the photovoltaic array, the azimuth correction coefficient, and the photovoltaic module conversion efficiency correction coefficient.

[0032] Furthermore, in step 6, when the wind and solar output can meet the load demand, the load is supplied first, and the microgrid is in an island operation state, reducing the operating cost of the microgrid; when the wind and solar output is greater than the load demand, the energy storage system absorbs the excess electricity by charging, thereby improving the utilization rate of renewable energy power generation; when the wind and solar output does not meet the load demand, the energy storage system supplies power to the load side by discharging, ensuring the normal operation of the grid; when the wind and solar output and the capacity of the energy storage system do not meet the load demand, the microgrid switches to the grid-connected operation state, and the grid side supplies power to the load side, ensuring the normal operation of the grid;

[0033] Among them, the energy storage system energy charging and discharging strategy is expressed as formula (9), and the microgrid working mode switching strategy is expressed as formula (10);

[0034]

[0035] In formulas (9) and (10), is the total wind and solar power output on the power generation side in the tth hour, is the total demand on the load side in hour t, is the charge and discharge status of the energy storage system in the tth hour, is the working mode of the microgrid in the tth hour, is the total wind and solar power output of the microgrid and the available capacity of the energy storage system in the tth hour.

[0036] Furthermore, in step 7, the SOC level of the energy storage system maintains continuity and stability in each time period, ensuring a smooth and efficient power supply, which can be expressed as:

[0037] SOC 0 =SOC 24 (11),

[0038] SOC min ≤SOC t ≤SOC max (12),

[0039] In formulas (11) and (12), SOC 0 is the SOC level of the energy storage system at the initial moment of the day before, SOC 24 is the SOC level of the energy storage system at the time of termination. t is the SOC level of the energy storage system at the tth moment the day before, SOC min , SOC maxare the minimum and maximum SOC levels of the energy storage system on the day before.

[0040] Furthermore, the economic objective function in step 8 is expressed as:

[0041]

[0042] In formula (13), is the wind turbine output power at time t of the microgrid operation a day ago, is the photovoltaic output power of the microgrid at time t the day before, Q loss is the capacity loss of the energy storage system during the day-ahead operation, is the power obtained from the grid at time t the day before the microgrid is running, p1 is the price of wind turbine and photovoltaic power generation in the microgrid system, p2 is the unit price of electricity obtained by the microgrid from the grid, and p3 is the equivalent cost of capacity loss of the microgrid energy storage system.

[0043] The beneficial effects of the present invention are:

[0044] 1. The present invention avoids the internal voltage safety problem of the electrochemical energy storage system caused by the traditional microgrid scheduling method setting the upper limit of the electrochemical energy storage system charging power to the rated power value;

[0045] 2. This invention introduces a lifespan model for the energy storage system into the microgrid's day-ahead dispatch model. While ensuring stable and safe operation of the microgrid, it further optimizes the power allocation strategy, reducing renewable energy power generation losses during the dispatch process and extending the lifespan of the energy storage system.

[0046] 3. The energy storage system life model proposed in this invention is a semi-empirical model. By adjusting the fitting parameters, it can meet the operation aging simulation requirements of different types of electrochemical energy storage technologies in different microgrid application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of the present invention;

[0048] Figure 2 It is a schematic diagram of the wind turbine output, photovoltaic output and residential electricity load curve;

[0049] Figure 3 Schematic diagram of the interactive power and electricity price curve for the day;

[0050] Figure 4 This is a schematic diagram of the operating output of wind, solar and storage units;

[0051] Figure 5 It is a schematic diagram of the state of charge of the energy storage system;

[0052] Figure 6 It is a specific flow chart of the present invention. DETAILED DESCRIPTION

[0053] Specific implementation method 1: Figures 1 to 6 As shown in Figure 1, a dispatching method for a rural microgrid system with wind, solar and energy storage, taking into account the aging cost of the energy storage system, includes the following specific steps:

[0054] Step 1: Conduct charge and discharge cycle tests on the energy storage system for the type of energy storage technology to be adopted by the microgrid system, record the performance parameters after each cycle, and establish a life degradation model for the energy storage system through statistical analysis;

[0055] The energy storage system life attenuation model is expressed as:

[0056]

[0057] B(C)=a1C 3 +a2C 2 +a3C+a4(2),

[0058]

[0059] In formulas (1), (2) and (3), Q loss is the capacity loss of the energy storage system, C is the discharge rate, T is the ambient temperature of the energy storage system, Ah is the current throughput of the energy storage system in the corresponding time, B is the constant coefficient under different discharge rates C, a1, a2, a3 and a4 are fitting coefficients, and SOH is the health status of the energy storage system;

[0060] Step 2: For the CV phase in the charging mode, the maximum allowable charging power that decreases exponentially with time is equivalently converted to a maximum allowable charging power that decreases linearly with SOC, completing the conversion from the time domain to the SOC domain.

[0061] The CC phase defined in the SOC domain is expressed by formula (4), and the CV phase is expressed by formulas (5) and (6);

[0062]

[0063] In formulas (4), (5) and (6), SOC t is the SOC level of the energy storage system corresponding to any time t, SOC C is the SOC level of the energy storage system when the CC phase switches to the CV phase, is the rated charging power of the energy storage system, is the energy storage system termination charging power corresponding to 100% SOC, α is the linear decrease coefficient of the CV curve;

[0064] Step 3: Establishing a mathematical model of wind power output and a mathematical model of photovoltaic output;

[0065] The mathematical model of wind power output is expressed as formula (7), and the mathematical model of photovoltaic output is expressed as formula (8);

[0066]

[0067] In formulas (7) and (8), P WT is the predicted output power of the wind turbine, v is the wind speed, v ci is the cut-in wind speed, v r is the cut-out wind speed, v co is the safe wind speed, E p is the predicted photovoltaic power generation, H A is the total solar radiation on the horizontal surface, E s is the irradiance under standard conditions, P AZ is the installed capacity of photovoltaic modules, K is the comprehensive efficiency coefficient, which includes the inclination angle of the photovoltaic array, the azimuth correction coefficient, and the photovoltaic module conversion efficiency correction coefficient;

[0068] Step 4: Perform a day-ahead forecast of local climate data; obtain day-ahead wind power forecast data and photovoltaic forecast data based on the wind turbine output model and photovoltaic output model;

[0069] Step 5: Predict the local electricity load on the day before and generate a time series of the electricity load on the day before;

[0070] Step 6: Embed charging and discharging strategies into the rural microgrid energy storage system;

[0071] When the wind and solar output can meet the load demand, the load is supplied first, and the microgrid is in an island operation state, reducing the operating costs of the microgrid; when the wind and solar output exceeds the load demand, the energy storage system absorbs the excess electricity through charging, improving the utilization rate of renewable energy power generation; when the wind and solar output does not meet the load demand, the energy storage system discharges power to the load side to ensure the normal operation of the grid; when the wind and solar output and the capacity of the energy storage system do not meet the load demand, the microgrid switches to the grid-connected operation state, and the grid side supplies power to the load side to ensure the normal operation of the grid;

[0072] Among them, the energy storage system energy charging and discharging strategy is expressed as formula (9), and the microgrid working mode switching strategy is expressed as formula (10);

[0073]

[0074]

[0075] In formulas (9) and (10), is the total wind and solar power output on the power generation side in the tth hour, is the total demand on the load side in hour t, is the charge and discharge status of the energy storage system in the tth hour, is the working mode of the microgrid in the tth hour, is the total wind and solar power output and available capacity of the energy storage system of the microgrid in hour t;

[0076] Step 7: During microgrid scheduling, the energy storage system SOC level maintains continuity and stability within each time period to ensure smooth and efficient power supply;

[0077] The SOC level of the energy storage system maintains continuity and stability in all time periods, ensuring a smooth and efficient power supply. It is expressed as follows:

[0078] SOC 0 =SOC 24 (11),

[0079] SOC min ≤SOC t ≤SOC max (12),

[0080] In formulas (11) and (12), SOC 0 is the SOC level of the energy storage system at the initial moment of the day before, SOC 24 is the SOC level of the energy storage system at the time of termination. t is the SOC level of the energy storage system at the tth moment the day before, SOC min , SOC max are the minimum and maximum SOC levels of the energy storage system on the day before;

[0081] Step 8: In actual scheduling, considering the operating characteristics of the microgrid, the optimal scheduling strategy is obtained by solving the maximum value of the economic objective function;

[0082] The economic objective function is expressed as:

[0083]

[0084] In formula (13), is the wind turbine output power at time t of the microgrid operation a day ago, is the photovoltaic output power of the microgrid at time t the day before, Q loss is the capacity loss of the energy storage system during the day-ahead operation, is the power obtained from the grid at time t the day before the microgrid is running, p1 is the price of wind turbine and photovoltaic power generation in the microgrid system, p2 is the unit price of electricity obtained by the microgrid from the grid, and p3 is the equivalent cost of capacity loss of the microgrid energy storage system.

[0085] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the present profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical content disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement of the above embodiments made according to the technical essence of the present invention, within the spirit and principles of the present invention, without departing from the content of the technical solution of the present invention, shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A dispatching method for a wind-solar-storage rural microgrid system taking into account the aging cost of the energy storage system, characterized by: The specific steps include: Step 1: Conduct charge and discharge cycle tests on the energy storage system for the type of energy storage technology to be adopted by the microgrid system, record the performance parameters after each cycle, and establish a life degradation model for the energy storage system through statistical analysis; Step 2: For the CV phase in the charging mode, the maximum allowable charging power that decreases exponentially with time is equivalently converted to a maximum allowable charging power that decreases linearly with SOC, completing the conversion from the time domain to the SOC domain. Step 3: Establishing a mathematical model of wind power output and a mathematical model of photovoltaic output; Step 4: Perform a day-ahead forecast of local climate data; obtain day-ahead wind power forecast data and photovoltaic forecast data based on the wind turbine output model and photovoltaic output model; Step 5: Predict the local electricity load on the day before and generate a time series of the electricity load on the day before; Step 6: Embed charging and discharging strategies into the rural microgrid energy storage system; Step 7: During microgrid scheduling, the energy storage system SOC level maintains continuity and stability within each time period to ensure smooth and efficient power supply; Step 8: In actual scheduling, considering the operating characteristics of the microgrid, the optimal scheduling strategy is obtained by solving the maximum value of the economic objective function.

2. The method for dispatching a wind, solar and energy storage rural microgrid system taking into account the aging cost of the energy storage system according to claim 1 is characterized in that: The energy storage system life attenuation model in step 1 is expressed as: B(C)=a1C 3 +a2C 2 +a3C+a4(2), In formulas (1), (2) and (3), Q loss is the capacity loss of the energy storage system, C is the discharge rate, T is the ambient temperature of the energy storage system, Ah is the current throughput of the energy storage system in the corresponding time, B is the constant coefficient under different discharge rates C, a1, a2, a3 and a4 are fitting coefficients, and SOH is the health status of the energy storage system.

3. The method for dispatching a wind, solar and energy storage rural microgrid system taking into account the aging cost of the energy storage system according to claim 1, characterized in that: The CC phase defined in the SOC domain in step 2 is expressed by formula (4), and the CV phase is expressed by formulas (5) and (6); In formulas (4), (5) and (6), SOC t is the SOC level of the energy storage system corresponding to any time t, SOC C is the SOC level of the energy storage system when the CC phase switches to the CV phase, is the rated charging power of the energy storage system, is the termination charging power of the energy storage system corresponding to 100% SOC, and α is the linear decrease coefficient of the CV curve.

4. The method for dispatching a wind, solar and energy storage rural microgrid system taking into account the aging cost of the energy storage system according to claim 1, characterized in that: In step 3, the mathematical model of wind power output is expressed as formula (7), and the mathematical model of photovoltaic output is expressed as formula (8); In formulas (7) and (8), P WT is the predicted output power of the wind turbine, v is the wind speed, v ci is the cut-in wind speed, v r is the cut-out wind speed, v co is the safe wind speed, E p is the predicted photovoltaic power generation, H A is the total solar radiation on the horizontal surface, E s is the irradiance under standard conditions, P AZ is the installed capacity of photovoltaic modules, and K is the comprehensive efficiency coefficient, which includes the inclination angle of the photovoltaic array, the azimuth correction coefficient, and the photovoltaic module conversion efficiency correction coefficient.

5. The method for dispatching a wind, solar and energy storage rural microgrid system taking into account the aging cost of the energy storage system according to claim 1, characterized in that: In step 6, when the wind and solar output can meet the load demand, the load is supplied first, and the microgrid is in an island operation state, reducing the operating cost of the microgrid; when the wind and solar output is greater than the load demand, the energy storage system absorbs the excess electricity by charging, improving the utilization rate of renewable energy power generation; when the wind and solar output does not meet the load demand, the energy storage system discharges power to the load side to ensure the normal operation of the grid; when the wind and solar output and the capacity of the energy storage system do not meet the load demand, the microgrid switches to the grid-connected operation state, and the grid side supplies power to the load side to ensure the normal operation of the grid; Among them, the energy storage system energy charging and discharging strategy is expressed as formula (9), and the microgrid working mode switching strategy is expressed as formula (10); In formulas (9) and (10), is the total wind and solar power output on the power generation side in the tth hour, is the total demand on the load side in hour t, is the charge and discharge status of the energy storage system in the tth hour, is the working mode of the microgrid in the tth hour, is the total wind and solar power output of the microgrid and the available capacity of the energy storage system in the tth hour.

6. The method for dispatching a wind, solar and energy storage rural microgrid system taking into account the aging cost of the energy storage system according to claim 1, characterized in that: In step 7, the SOC level of the energy storage system maintains continuity and stability in each time period, ensuring a smooth and efficient power supply. This is expressed as: SOCIETY 0 =SOC 24 (11), SOC min ≤SOC t ≤SOC max (12), In formulas (11) and (12), SOC 0 is the SOC level of the energy storage system at the initial moment of the day before, SOC 24 is the SOC level of the energy storage system at the time of termination. t is the SOC level of the energy storage system at the tth moment the day before, SOC min , SOC max are the minimum and maximum SOC levels of the energy storage system on the day before.

7. The method for dispatching a wind, solar and energy storage rural microgrid system taking into account the aging cost of the energy storage system according to claim 1, characterized in that: The economic objective function in step 8 is expressed as: In formula (13), is the wind turbine output power at time t when the microgrid was running a few days ago, is the photovoltaic output power of the microgrid at time t the day before, Q loss is the capacity loss of the energy storage system during the day-ahead operation, is the power obtained from the grid at time t the day before the microgrid is running, p1 is the price of wind turbine and photovoltaic power generation in the microgrid system, p2 is the unit price of electricity obtained by the microgrid from the grid, and p3 is the equivalent cost of capacity loss of the microgrid energy storage system.

Images