Wind-solar-storage rural microgrid system scheduling method considering operation aging cost of energy storage system

Abstract

The application discloses a wind-solar-storage rural micro-grid system dispatching method considering the operation aging cost of a storage system, and relates to a wind-solar-storage rural micro-grid system dispatching method. The application is aimed at solving the problem that the existing wind-solar-storage micro-grid system dispatching method ignores the aging cost of the storage system caused by the charging and discharging cycle, leads to the increase of the comprehensive operation cost of the micro-grid day-ahead dispatching, and long-term neglect of the performance aging of the storage system causes the decline of the system safety and the limitation of the dispatching flexibility. The application comprehensively considers the wind-solar power generation loss cost and the operation aging cost of the storage system, improves the comprehensive benefit of the micro-grid day-ahead operation, and introduces the constant-current-constant-voltage charging characteristic curve to ensure the safety of the storage system. The application belongs to the technical field of micro-grid dispatching.

Description

Technical Field

[0001] This invention relates to a scheduling method for a rural microgrid system with wind, solar and energy storage, belonging to the field of microgrid scheduling technology. Background Technology

[0002] With the rapid development of the world economy, energy shortages and environmental protection issues have become increasingly prominent, making renewable energy power generation methods such as wind and solar power 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 are prone to extreme weather events. These rural areas are far from the main power grid and have low dependence on it. Relying on the main power 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 new energy power generation technologies, how to economically utilize local new energy resources has become an urgent problem to be solved. Furthermore, rural areas generally have open terrain and abundant wind resources, making them very suitable for the development of wind-solar hybrid microgrids. However, wind and solar power generation technologies have significant randomness and volatility, posing many challenges to the operation and scheduling of microgrids.

[0004] Although configuring energy storage devices for microgrids to form a wind-solar-storage complementary power supply system can alleviate the power shortage problem in rural microgrids to some extent, ensuring the normal operation of microgrids and improving the service life of energy storage systems remains an urgent problem to be solved in rural areas such as Northeast China where extreme weather occurs frequently.

[0005] The team led by Xu Jianwei at Nanjing Normal University proposed a day-ahead-intraday optimization scheduling method for integrated energy systems to address the impact of wind and solar uncertainties on the operation optimization of integrated energy systems. The method takes the minimum operating cost as the objective function, introduces time-of-use electricity and time-of-use heat prices, and then narrows the time scale to construct a multi-objective optimization model with the minimum operating cost and the minimum scheduling adjustment cost.

[0006] The team led by Shi Zhaodi at the China Electric Power Research Institute proposed a two-tiered planning method for the capacity of energy storage and thermal storage systems. They introduced the conditional value-at-risk (VAT) method to consider the impact of uncertainties in new energy power generation output on the planning results of energy storage capacity.

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

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

[0009] I. Currently, the upper limit of charging power of energy storage systems in wind-solar-storage microgrid dispatch is usually set to the rated power value of the energy storage system. When the state of charge (SOC) level of the energy storage system is high, continuous charging at the rated power value may cause the internal voltage of the energy storage system to be too high, resulting in safety hazards.

[0010] Second, current day-ahead dispatch strategies for wind-solar-storage microgrids often overlook the aging costs of energy storage systems due to charge-discharge cycles, leading to an increase in the overall operating costs of day-ahead dispatch for microgrids. Long-term neglect of the performance aging of energy storage systems can cause problems such as decreased system safety and limited dispatch flexibility. Summary of the Invention

[0011] This invention addresses the problem that existing wind-solar-storage microgrid system dispatching methods neglect the aging costs of energy storage systems due to charge-discharge cycles, leading to increased overall day-ahead dispatching operating costs for microgrids. Furthermore, it addresses the issue that neglecting the performance aging of energy storage systems can cause decreased system safety and limited dispatching flexibility. Therefore, this invention proposes a wind-solar-storage rural microgrid system dispatching method that takes into account the aging costs of energy storage systems.

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

[0013] Step 1: For the type of energy storage technology to be used in the microgrid system, conduct charge-discharge cycle tests on the energy storage system, record the performance parameters after each cycle, and establish a lifespan degradation model of the energy storage system through statistical analysis.

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

[0015] Step 3: Establish mathematical models for wind power output and solar power output;

[0016] Step 4: Perform day-ahead forecasts on local climate data; obtain day-ahead wind power forecasts and solar power forecasts based on wind turbine output models and solar power output models;

[0017] Step 5: Perform day-ahead forecasting of local electricity load to generate day-ahead electricity load time series;

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

[0019] Step 7: In microgrid dispatching, the SOC level of the energy storage system is maintained continuously and stably in various time periods to ensure a stable 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 for the maximum and minimum values ​​of the economic objective function.

[0021] Furthermore, the energy storage system lifetime degradation 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 Let C be the capacity loss of the energy storage system, T be the ambient temperature of the energy storage system, Ah be the current throughput of the energy storage system within the corresponding time period, B be a constant coefficient under different discharge rates C, a1, a2, a3 and a4 be fitting coefficients, and SOH be the health status of the energy storage system.

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

[0027]

[0028] In formulas (4), (5) and (6), SOC t Let SOC be the SOC level of the energy storage system at any time t. C This represents the SOC level of the energy storage system when switching from the CC phase to the CV phase. The rated charging power for the energy storage system, The charging power of the energy storage system at 100% SOC is the termination power, and α is the linear descent coefficient of the CV curve.

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

[0030]

[0031] In formulas (7) and (8), P WT The predicted output power of the wind turbine is given by v, where v is the wind speed. ci To cut off the wind speed, v r To cut off the wind speed, v co For safe wind speed, E p For the predicted power generation of photovoltaics, H A E represents the total solar irradiance on a horizontal surface.s P represents the irradiance under standard conditions. AZ K represents the installed capacity of the photovoltaic modules, and K is the overall efficiency coefficient, which includes the tilt angle of the photovoltaic array, the azimuth correction coefficient, and the conversion efficiency correction coefficient of the photovoltaic modules.

[0032] Furthermore, in step 6, when wind and solar power output can meet load demand, priority is given to supplying the load, and the microgrid operates in an islanded state, reducing the operating cost of the microgrid; when wind and solar power output exceeds load demand, the energy storage system absorbs excess electricity through charging, improving the utilization rate of new energy power generation; when wind and solar power output does not meet load demand, the energy storage system supplies power to the load side through discharging, ensuring the normal operation of the grid; when wind and solar power output and the capacity of the energy storage system do not meet load demand, the microgrid switches to grid-connected operation, and the grid side supplies power to the load side, ensuring the normal operation of the grid.

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

[0034]

[0035] In formulas (9) and (10), For the total wind and solar power output on the power generation side in hour t, The total demand on the load side in hour t. This represents the charge / discharge state of the energy storage system in hour t. This represents the operating mode of the microgrid in hour t. Let t represent the total wind and solar power output and the available capacity of the energy storage system of the microgrid in hour t.

[0036] Furthermore, in step 7, maintaining the continuity and stability of the SOC level of the energy storage system across different time periods to ensure a stable and efficient power supply is represented as follows:

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

[0038] SOC min ≤SOC t ≤SOC max (12),

[0039] In formulas (11) and (12), SOC 0 The SOC level of the energy storage system at the initial moment before the day. 24 The SOC level at the day-ahead termination of the energy storage system. t Let SOC be the SOC level of the energy storage system at time t before the day-ahead. min SOC maxThese represent the minimum and maximum daily SOC levels of the energy storage system, respectively.

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

[0041]

[0042] In formula (13), Let t be the wind turbine output power at time t during the day-ahead operation of the microgrid. Let Q be the photovoltaic output power of the microgrid at time t during its day-ahead operation. loss This refers to the capacity loss of the energy storage system during day-ahead operation. p1 represents the power that the microgrid obtains from the grid at time t during the day-ahead operation, p2 represents the price of wind turbines and photovoltaic power generation in the microgrid system, p3 represents the unit price of electricity obtained by the microgrid from the grid, and p4 represents the equivalent cost of capacity loss of the microgrid energy storage system.

[0043] The beneficial effects of this invention are:

[0044] 1. This invention avoids the internal voltage safety problem of electrochemical energy storage systems caused by setting the upper limit of the charging power of electrochemical energy storage systems to the rated power value in traditional microgrid dispatching methods;

[0045] 2. This invention introduces a lifespan model of the energy storage system into the day-ahead dispatch model of the microgrid. Under the premise of ensuring the stable and safe operation of the microgrid, it further optimizes the power allocation strategy, which not only reduces the loss of new energy power generation during the dispatch process, but also improves the lifespan of the energy storage system.

[0046] 3. The energy storage system lifetime model proposed in this invention is a semi-empirical model. By adjusting the fitting parameters, it can meet the aging simulation requirements of different types of electrochemical energy storage technologies in different microgrid application scenarios. Attached Figure Description

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

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

[0049] Figure 3 A schematic diagram of the interaction power and electricity price curves for that day;

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

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

[0052] Figure 6 This is a detailed flowchart of the present invention. Detailed Implementation

[0053] Specific implementation method one: as follows Figures 1 to 6 As shown, the dispatching method for a rural microgrid system that considers the aging costs of the energy storage system includes the following specific steps:

[0054] Step 1: For the type of energy storage technology to be used in the microgrid system, conduct charge-discharge cycle tests on the energy storage system, record the performance parameters after each cycle, and establish a lifespan degradation model of the energy storage system through statistical analysis.

[0055] The lifespan degradation model of an energy storage system is expressed as follows:

[0056]

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

[0058]

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

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

[0061] The CC phase defined within the SOC domain is represented by formula (4), and the CV phase is represented by formulas (5) and (6).

[0062]

[0063] In formulas (4), (5) and (6), SOC t Let SOC be the SOC level of the energy storage system at any time t. C This represents the SOC level of the energy storage system when switching from the CC phase to the CV phase. The rated charging power for the energy storage system, The charging power of the energy storage system at 100% SOC is the termination power, and α is the linear descent coefficient of the CV curve.

[0064] Step 3: Establish mathematical models for wind power output and solar power output;

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

[0066]

[0067] In formulas (7) and (8), P WT The predicted output power of the wind turbine is given by v, where v is the wind speed. ci To cut off the wind speed, v r To cut off the wind speed, v co For safe wind speed, E p For the predicted power generation of photovoltaics, H A E represents the total solar irradiance on a horizontal surface. s P represents the irradiance under standard conditions. AZ K represents the installed capacity of the photovoltaic modules, and K is the overall efficiency coefficient, which includes the tilt angle of the photovoltaic array, the azimuth correction coefficient, and the conversion efficiency correction coefficient of the photovoltaic modules.

[0068] Step 4: Perform day-ahead forecasts on local climate data; obtain day-ahead wind power forecasts and solar power forecasts based on wind turbine output models and solar power output models;

[0069] Step 5: Perform day-ahead forecasting of local electricity load to generate day-ahead electricity load time series;

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

[0071] When wind and solar power output can meet load demand, priority is given to supplying the load, and the microgrid operates in an islanded state, reducing the operating costs of the microgrid. When wind and solar power output exceeds load demand, the energy storage system absorbs excess electricity through charging, improving the utilization rate of new energy power generation. When wind and solar power output does not meet load demand, the energy storage system supplies power to the load side through discharging, ensuring the normal operation of the grid. When wind and solar power output and the capacity of the energy storage system do not meet load demand, the microgrid switches to grid-connected operation, and the grid side supplies power to the load side, ensuring the normal operation of the grid.

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

[0073]

[0074]

[0075] In formulas (9) and (10), For the total wind and solar power output on the power generation side in hour t, The total demand on the load side in hour t. This represents the charge / discharge state of the energy storage system in hour t. This represents the operating mode of the microgrid in hour t. Let t represent the total wind and solar power output and the available capacity of the energy storage system of the microgrid in hour t.

[0076] Step 7: In microgrid dispatching, the SOC level of the energy storage system is maintained continuously and stably in various time periods to ensure a stable and efficient power supply.

[0077] The continuity and stability of the SOC level of the energy storage system across different time periods, ensuring a stable and efficient power supply, 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 The SOC level of the energy storage system at the initial moment before the day. 24 The SOC level at the day-ahead termination of the energy storage system. t Let SOC be the SOC level of the energy storage system at time t before the day-ahead. min SOC max These represent the minimum and maximum day-ahead SOC levels of the energy storage system, respectively.

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

[0082] The economic objective function is expressed as:

[0083]

[0084] In formula (13), Let t be the wind turbine output power at time t during the day-ahead operation of the microgrid. Let Q be the photovoltaic output power of the microgrid at time t during its day-ahead operation. loss This refers to the capacity loss of the energy storage system during day-ahead operation. p1 represents the power that the microgrid obtains from the grid at time t during the day-ahead operation, p2 represents the price of wind turbines and photovoltaic power generation in the microgrid system, p3 represents the unit price of electricity obtained by the microgrid from the grid, and p4 represents 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 is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A dispatching method for a wind-solar-storage rural microgrid system that takes into account the aging costs of the energy storage system, characterized in that, The specific steps include: Step 1: For the type of energy storage technology to be used in the microgrid system, conduct charge-discharge cycle tests on the energy storage system, record the performance parameters after each cycle, and establish a lifespan degradation model of the energy storage system through statistical analysis. Step 2: For the CV stage in charging mode, the maximum allowable charging power that decreases exponentially with time is equivalently transformed into the maximum allowable charging power that decreases linearly with SOC, thus completing the conversion from the time domain to the SOC domain. Step 3: Establish mathematical models for wind power output and solar power output; Step 4: Perform day-ahead forecasts on local climate data; obtain day-ahead wind power forecasts and solar power forecasts based on wind turbine output models and solar power output models; Step 5: Perform day-ahead forecasting of local electricity load to generate day-ahead electricity load time series; Step 6: Embed charging and discharging strategies into the rural microgrid energy storage system; Step 7: In microgrid dispatching, the SOC level of the energy storage system is maintained continuously and stably in various time periods to ensure a stable and efficient power supply. Step 8: In actual scheduling, considering the operating characteristics of the microgrid, the optimal scheduling strategy is obtained by solving for the maximum and minimum values ​​of the economic objective function.

2. The dispatching method for wind-solar-storage rural microgrid systems considering the aging costs of energy storage systems according to claim 1, characterized in that, The energy storage system lifetime degradation model in step 1 is expressed as follows: B(C)=a1C 3 +a2C 2 +a3C+a4(2), In formulas (1), (2) and (3), Q loss Let C be the capacity loss of the energy storage system, T be the ambient temperature of the energy storage system, Ah be the current throughput of the energy storage system within the corresponding time period, B be a constant coefficient under different discharge rates C, a1, a2, a3 and a4 be fitting coefficients, and SOH be the health status of the energy storage system.

3. The dispatching method for wind-solar-storage rural microgrid systems considering the aging costs of energy storage systems according to claim 1, characterized in that, In step 2, the CC stage defined within the SOC domain is represented by formula (4), and the CV stage is represented by formulas (5) and (6). In formulas (4), (5) and (6), SOC t Let SOC be the SOC level of the energy storage system at any time t. C This represents the SOC level of the energy storage system when switching from the CC phase to the CV phase. The rated charging power for the energy storage system, The charging power of the energy storage system at 100% SOC is the termination power, and α is the linear descent coefficient of the CV curve.

4. The dispatching method for wind-solar-storage rural microgrid systems considering the aging costs of energy storage systems 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 power output is expressed as formula (8); In formulas (7) and (8), P WT The predicted output power of the wind turbine is given by v, where v is the wind speed. ci To cut off the wind speed, v r To cut off the wind speed, v co For safe wind speed, E p For the predicted power generation of photovoltaics, H A E represents the total solar irradiance on a horizontal surface. s P represents the irradiance under standard conditions. AZ K represents the installed capacity of the photovoltaic modules, and K is the overall efficiency coefficient, which includes the tilt angle of the photovoltaic array, the azimuth correction coefficient, and the conversion efficiency correction coefficient of the photovoltaic modules.

5. The dispatching method for a wind-solar-storage rural microgrid system considering the aging costs of the energy storage system according to claim 1, characterized in that, In step 6, when wind and solar power output can meet load demand, priority is given to supplying the load, and the microgrid operates in an islanded state, reducing the operating cost of the microgrid; when wind and solar power output exceeds load demand, the energy storage system absorbs excess electricity through charging, improving the utilization rate of new energy power generation; when wind and solar power output does not meet load demand, the energy storage system supplies power to the load side through discharging, ensuring the normal operation of the grid; when wind and solar power output and the capacity of the energy storage system do not meet load demand, the microgrid switches to grid-connected operation, and the grid side supplies power to the load side, ensuring the normal operation of the grid. Among them, the energy charging and discharging strategy of the energy storage system is expressed by formula (9), and the microgrid working mode switching strategy is expressed by formula (10); In formulas (9) and (10), For the total wind and solar power output on the power generation side in hour t, The total demand on the load side in hour t. This represents the charge / discharge state of the energy storage system in hour t. This represents the operating mode of the microgrid in hour t. Let t represent the total wind and solar power output and the available capacity of the energy storage system of the microgrid in hour t.

6. The dispatching method for a wind-solar-storage rural microgrid system considering the aging costs of the energy storage system according to claim 1, characterized in that, In step 7, maintaining the continuity and stability of the SOC level of the energy storage system across different time periods to ensure a stable and efficient power supply is represented as follows: SOCIETY 0 =SOC 24 (11), SOC min ≤SOC t ≤SOC max (12), In formulas (11) and (12), SOC 0 The SOC level of the energy storage system at the initial moment before the day. 24 The SOC level at the day-ahead termination of the energy storage system. t Let SOC be the SOC level of the energy storage system at time t before the day-ahead. min SOC max These represent the minimum and maximum daily SOC levels of the energy storage system, respectively.

7. The dispatching method for a wind-solar-storage rural microgrid system considering the aging costs 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), Let t be the wind turbine output power at time t during the day-ahead operation of the microgrid. Let Q be the photovoltaic output power of the microgrid at time t during its day-ahead operation. loss This refers to the capacity loss of the energy storage system during day-ahead operation. p1 represents the power that the microgrid obtains from the grid at time t during the day-ahead operation, p2 represents the price of wind turbines and photovoltaic power generation in the microgrid system, p3 represents the unit price of electricity obtained by the microgrid from the grid, and p4 represents the equivalent cost of capacity loss of the microgrid energy storage system.

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