Operating optimization method of optical storage integrated energy station based on photovoltaic absorption and peak and frequency modulation

Abstract

The invention discloses an operation optimization method of an optical storage integrated energy station based on photovoltaic absorption and peak and frequency modulation, and belongs to the technical field of application of an electric energy storage system in an alternating current distribution network to reduce power oscillation. The invention provides a calendar aging and cycle aging integrated battery energy storage aging cost model. The process of embedding the aging model in the scheduling optimization formula is simplified, so that the influence of battery aging can be quantified and brought into the optimization scheduling problem. According to the method, the photovoltaic operation model is established by considering factors such as photovoltaic light abandoning quantity, and the photovoltaic operation model and the energy storage battery aging model are combined with interaction of the photovoltaic storage comprehensive energy station and the power distribution network to establish the operation model for the photovoltaic storage comprehensive energy station to provide peak regulation and frequency modulation services for the power distribution network. The method is used for optimizing power scheduling of the optical storage, charging and switching integrated station so as to provide peak regulation and frequency modulation services and minimize light abandoning at the same time, and the economic feasibility of the optical storage, charging and switching integrated station is improved.

Description

TECHNICAL FIELD

[0001] The application relates to an operation optimization method of a photovoltaic storage comprehensive energy station based on photovoltaic consumption and peak regulation and frequency regulation, and belongs to the technical field of reducing power oscillation (H02J3 / 24) by applying an electric energy storage system to an alternating current distribution network. BACKGROUND

[0002] The light-storage-charging integrated station has wide application scenarios and market demand. Through the battery energy storage system stored in the battery compartment, the light-storage-charging integrated station can absorb electric energy during the off-peak period of night load and release electric energy to the distribution network during the on-peak period of day load, and supplement the electric energy through a photovoltaic power generation system, so that the load peak-valley difference of the fast-charging station can be effectively balanced. In addition, the light-storage-charging integrated station can also provide various power grid services such as frequency regulation, peak clipping and valley filling, standby capacity and the like, and support the stable operation of the power grid. However, the multi-service supply can significantly improve the utilization efficiency and economy of the energy storage, but also brings more complex scheduling problems. In the case of gradual marketization of electric power, the peak-valley price difference and the peak regulation and frequency regulation service price serve as indexes of power system generation and consumption imbalance and frequency regulation resource margin, form an incentive signal to promote the light-storage-charging integrated station to participate in arbitrage and auxiliary services through charging and discharging strategies, but frequent charging and discharging will accelerate the battery degradation and increase the whole life cycle cost.

[0003] The existing technology cannot fully reflect that the light-storage-charging integrated station is a power system link coupled by market benefits, battery degradation, photovoltaic consumption and power grid peak regulation and frequency regulation. The existing technology is insufficient in evaluating the sustainability of the battery energy storage deployment in the multi-service supply under the influence of the battery energy storage aging and capacity attenuation. Meanwhile, the complexity of the multi-service scene and the challenge of integrating the accurate battery energy storage aging model into an efficient control strategy framework are also not considered. Therefore, there is an urgent need for a general battery energy storage aging model that can comprehensively reflect the whole life cycle of the battery energy storage and its economic influence, and effectively integrate the general battery energy storage aging model into an optimization framework, so as to realize the efficient operation and economic scheduling of the light-storage-charging integrated station in the distribution network. SUMMARY

[0004] The technical problem to be solved by the application is how to obtain a power interaction curve that enables the light-storage comprehensive energy station to provide peak regulation and frequency regulation services to the distribution network while maximizing the consumption of photovoltaic power generation.

[0005] To solve the above technical problems, the technical solution provided by the application is an operation optimization method of a light-storage comprehensive energy station based on photovoltaic consumption and peak regulation and frequency regulation, comprising the following steps:

[0006] Step 1: dividing the time T of interaction between the photovoltaic comprehensive energy station and the distribution cabinet into a plurality of time intervals according to a fixed time length The photovoltaic operation model of the photovoltaic and energy storage integrated energy station is established, as shown in the following formula (1),

[0007] (1)

[0008] In formula (1), is the predicted output of the photovoltaic in the kth time period in the N time periods in the integrated energy station; is the power of the photovoltaic in the kth time period in the N time periods in the integrated energy station charging the energy storage battery; is the power of the photovoltaic in the kth time period in the N time periods in the integrated energy station injecting power into the power grid; is the photovoltaic inverter efficiency of the photovoltaic; is the unabsorbed power of the photovoltaic in the kth time period in the N time periods in the integrated energy station;

[0009] Step 2: Establishing a battery aging model of the photovoltaic and energy storage integrated energy station;

[0010] Step 3: According to the photovoltaic operation model and the battery aging model, an operation model of the photovoltaic and energy storage integrated energy station providing peak shaving and frequency modulation services to the power grid is established, as shown in the following formula (3),

[0011] (3)

[0012] In formula (2), is the selling price of the surplus power accepted by the power grid; is the peak shaving weight of the photovoltaic and energy storage integrated energy station participating in the power grid; is the frequency modulation weight of the photovoltaic and energy storage integrated energy station participating in the power grid; is the power of the photovoltaic and energy storage integrated energy station participating in the peak shaving of the power grid in the nth time period in the N time periods; is the peak shaving compensation unit price given by the power grid; is an event function, which is 1 when the event is true, and 0 otherwise; is the valley filling compensation unit price given by the power grid; is the frequency modulation weight of the photovoltaic and energy storage integrated energy station participating in the power grid; is the frequency modulation price given by the power grid; is the power of the photovoltaic and energy storage integrated energy station participating in the frequency modulation of the power grid in the nth time period in the N time periods; is the charging efficiency coefficient of the energy conversion system; is the discharging efficiency coefficient of the energy conversion system;

[0013] Step 4: setting the photovoltaic power generation constraint condition, the main transformer constraint condition, the energy storage power constraint condition and the energy storage battery state of charge constraint condition of the operation model;

[0014] Step 5: substituting the operation model and all constraint conditions into the MATLAB platform, according to the predicted photovoltaic output of the photovoltaic storage comprehensive energy station and the predicted electricity price of the power distribution network, taking the maximum benefit of the photovoltaic storage comprehensive energy station providing peak shaving and frequency modulation services to the power distribution network as the target, the optimal power interaction curve of the photovoltaic storage comprehensive energy station providing peak shaving and frequency modulation services to the power distribution network while maximizing the consumption of photovoltaic power generation is calculated.

[0015] Further, the photovoltaic power generation constraint condition of the operation model is shown in the following formula (4),

[0016] (4)

[0017] In formula (4), is the power conversion efficiency ratio related to the output of the photovoltaic generated electric energy to the energy storage battery and the power distribution network; is the total power of the photovoltaic power generation of the photovoltaic storage comprehensive energy station in the kth time period in N time periods;

[0018] The main transformer constraint condition of the operation model is shown in the following formula (5),

[0019] (5)

[0020] In formula (5), is the rated power of the main transformer on the power distribution network;

[0021] The energy storage power constraint condition of the operation model is shown in the following formula (6),

[0022] (6)

[0023] In formula (6), and are charging binary variables and discharging binary variables, respectively, taking values of 0 or 1; if 1, it means that the energy storage battery is charging / discharging, if 0, it means that the energy storage battery is not charging / discharging; and are the lower limit of the charging power and the upper limit of the charging power of the energy storage battery, respectively; and are the lower limit of the discharging power and the upper limit of the discharging power of the energy storage battery, respectively;

[0024] The energy storage battery state of charge constraint condition of the operation model is shown in the following formula (7),

[0025] (7)

[0026] In formula (7), is the state of charge of the energy storage battery in the process of peak shaving and frequency modulation of the photovoltaic energy storage integrated energy station to the power grid in the kth time period of N time periods; is the state of charge in the process of outputting power to the power grid in the process of peak shaving and frequency modulation of the photovoltaic energy storage integrated energy station to the power grid in the kth time period of N time periods; and are the minimum and maximum states of charge of the energy storage battery, respectively; is the energy consumption of the energy storage battery itself during operation.

[0027] Further, the battery aging model of the photovoltaic energy storage integrated energy station is shown in the following formula (2),

[0028] (2)

[0029] In formula (2), and are the cycle aging coefficient and the calendar aging coefficient of the energy storage battery, respectively; is the total construction cost of the energy storage battery; is the cycle life of the energy storage battery; is the effective capacity of the energy storage battery; is the length of time from production to scrap due to natural aging of the energy storage battery; is the installed capacity of the energy storage battery; is the maximum discharge depth of the energy storage battery.

[0030] The beneficial effects of the present application are: 1. The present application proposes a battery energy storage aging cost model integrating calendar aging and cycle aging; simplifies the process of embedding the aging model in the scheduling optimization formula, so that the influence of battery aging can be quantified and included in the optimization scheduling problem. 2. The present application establishes a photovoltaic operation model by considering factors such as photovoltaic light rejection, combines the photovoltaic operation model and the energy storage battery aging model together with the interaction of the photovoltaic energy storage integrated energy station and the power grid to establish an operation model of the photovoltaic energy storage integrated energy station providing peak shaving and frequency modulation services to the power grid, which is used to optimize the power scheduling of the photovoltaic energy storage integrated energy station to minimize light rejection while providing peak shaving and frequency modulation services, and improve the economic feasibility of the photovoltaic energy storage integrated energy station. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 is a flowchart of the operation optimization method of the photovoltaic energy storage integrated energy station based on photovoltaic consumption and peak shaving and frequency modulation according to the present application. DETAILED DESCRIPTION

[0032] The operation optimization method of the photovoltaic storage comprehensive energy station based on photovoltaic consumption and peak regulation and frequency regulation will be further described below in combination with the drawings and specific embodiments

[0033] Embodiments

[0034] The operation optimization method in this embodiment, as shown in Figure 1 includes the following steps:

[0035] Step 1: dividing the time T of interaction between the photovoltaic comprehensive energy station and the power distribution cabinet into N time periods according to a fixed time length ,

[0036] Through the power that the photovoltaic storage comprehensive energy station fails to consume due to the limitation of the receiving capacity in a certain time period, the power directly injected into the power distribution network by the photovoltaic, and the power injected into the energy storage battery by the photovoltaic, a photovoltaic operation model of the photovoltaic storage comprehensive energy station is established, as shown in the following formula (1),

[0037] (1)

[0038] In formula (1), is the predicted output of the photovoltaic in the kth time period of the N time periods in the comprehensive energy station; is the power of the photovoltaic in the kth time period of the N time periods in the comprehensive energy station for charging the energy storage battery; is the power of the photovoltaic in the kth time period of the N time periods in the comprehensive energy station for injecting power into the power distribution network; is the photovoltaic inverter efficiency of the photovoltaic; is the non-consumed power of the photovoltaic in the kth time period of the N time periods in the comprehensive energy station;

[0039] Step 2: introducing a cycle aging cost index and a calendar aging cost index, establishing a battery aging model of the photovoltaic storage comprehensive energy station, as shown in the following formula (2),

[0040] (2)

[0041] In formula (2), and are the cycle aging coefficient and the calendar aging coefficient of the energy storage battery, respectively; is the total construction cost of the energy storage battery; is the cycle life of the energy storage battery; is the effective capacity of the energy storage battery; is the length of the scrap time of the energy storage battery from production to being unsuitable for continuous use due to natural aging; is the installed capacity of the energy storage battery; is the maximum discharge depth of the energy storage battery.

[0042] Step 3: According to the photovoltaic operation model and the battery aging model, an operation model of the integrated energy station providing peak shaving and frequency modulation services to the power distribution network is established, as shown in the following formula (3),

[0043] (3)

[0044] In formula (2), is the selling price of the surplus electricity accepted by the power distribution network; is the peak shaving weight of the integrated energy station participating in the power distribution network; is the frequency modulation weight of the integrated energy station participating in the power distribution network; is the power of the integrated energy station participating in the peak shaving of the power distribution network in the th time period of N time periods; is the peak cutting compensation unit price given by the power distribution network; is an event function, which is 1 when the event is true, and 0 otherwise; is the valley filling compensation unit price given by the power distribution network; is the frequency modulation weight of the integrated energy station participating in the power distribution network; is the frequency modulation price given by the power distribution network; is the power of the integrated energy station participating in the frequency modulation of the power distribution network in the th time period of N time periods; is the charging efficiency coefficient of the energy conversion system; is the discharging efficiency coefficient of the energy conversion system;

[0045] Step 4: Set the photovoltaic power generation constraint condition, main transformer constraint condition, energy storage power constraint condition and energy storage battery state of charge constraint condition of the operation model;

[0046] Considering that the photovoltaic power generated by the photovoltaic panel is always greater than or equal to the sum of the photovoltaic grid-connected power injected after inversion and the photovoltaic storage power injected into the energy storage battery, the photovoltaic power generation constraint condition of the operation model is set, as shown in the following formula (4),

[0047] (4)

[0048] In formula (4), is the power conversion efficiency ratio related to the energy output of the photovoltaic to the energy storage battery and the power distribution network; is the total photovoltaic power generation of the photovoltaic in the kth time period of N time periods in the integrated energy station;

[0049] Considering the whole operation, the total alternating current power injected into the grid should not exceed the rated power of the main transformer, and the main transformer constraint condition of the operation model is set, as shown in the following formula (5),

[0050] (5)

[0051] In formula (5), is the rated power of the main transformer on the power distribution network.

[0052] Considering that the energy acquisition and output of the energy storage battery from the photovoltaic and power distribution network cannot exceed the upper and lower limits of the discharging power of the energy storage battery, the power exchange constraint condition of the energy storage battery of the operation model is set, as shown in the following formula (6),

[0053] (6)

[0054] In formula (6), and are charging binary variables and discharging binary variables, respectively, and take values of 0 or 1; if 1, it means that the energy storage battery is charging / discharging, and if 0, it means that the energy storage battery is not charging / discharging; and are the lower limit of the charging power and the upper limit of the charging power of the energy storage battery, respectively; and are the lower limit of the discharging power and the upper limit of the discharging power of the energy storage battery, respectively;

[0055] Considering the real-time state of charge of the energy storage battery and the budget of the interactive state of charge of the energy storage battery participating in the peak shaving process of the power distribution network, the state of charge constraint condition of the energy storage battery of the operation model is set, as shown in the following formula (7),

[0056] (7)

[0057] In formula (7), is the state of charge of the energy storage battery of the photovoltaic and energy storage integrated energy station in the kth time period in the N time periods in the peak shaving and frequency modulation process to the power distribution network; is the state of charge in the process of outputting energy to the power distribution network in the peak shaving and frequency modulation process to the power distribution network in the kth time period in the N time periods of the photovoltaic and energy storage integrated energy station; and are the minimum and maximum states of charge of the energy storage battery, respectively; is the energy consumption of the energy storage battery itself in the operation process.

[0058] Step 5: Substitute the operation model and all the constraint conditions into the MATLAB platform, and according to the predicted photovoltaic output of the photovoltaic and energy storage integrated energy station and the predicted price of the power distribution network, the maximum income of the photovoltaic and energy storage integrated energy station providing peak shaving and frequency modulation services to the power distribution network is taken as the target, and the optimal power interaction curve of the photovoltaic and energy storage integrated energy station providing peak shaving and frequency modulation services to the power distribution network while maximizing the consumption of photovoltaic power generation is calculated.

[0059] Below for a certain specific light storage comprehensive energy station to carry out operation optimization

[0060] This patent optimizes the operation of a certain light storage comprehensive energy station in East China (24-hour cycle). The system configuration is as follows: the installed capacity of photovoltaic power is 5 MW, typical of industrial park rooftop photovoltaic; the power of the energy storage system is 1 MW, and the capacity is 2 MWh (lithium ion battery, DoD=80%); the capacity of the main transformer is 10 MVA; the local load is equipped with 3 MW of base load plus 2 MW of adjustable peak load.

[0061] The market parameters refer to the local typical daily electricity prices at different times: peak electricity price (10:00-15:00, 18:00-21:00): 1.2 yuan / kWh; flat section electricity price (7:00-10:00, 15:00-18:00): 0.7 yuan / kWh;

[0062] Low valley electricity price (21:00-7:00): 0.3 yuan / kWh. Auxiliary service compensation: peak load shifting compensation: 0.2 yuan / kWh (charging during low valley period); peak load cutting compensation: 0.5 yuan / kWh (discharging during peak period); frequency modulation capacity compensation: 0.15 yuan / kW·h (settled according to provided power).

[0063] The battery aging parameters are as follows: cycle life: 5000 times (80% DoD); calendar life: 10 years; total construction cost: 1.6 million yuan (800 yuan / kWh); aging coefficient: cycle aging coefficient a = 1.6 million yuan / (5000 x 1600) kWh = 0.2 yuan / kWh; calendar aging coefficient b = 1.6 million yuan / (10 x 365 x 24 x 1600) kWh ≈ 0.00114 yuan / kWh / h.

[0064] Substituting the above parameters into the optimization process of this patent, the optimization results for a typical day (24h) can be obtained, as shown in the following table:

[0065]

[0066] From the above table, we can observe the key benefits. The photovoltaic consumption rate reaches 98.2% (only 0.1 MWh is not consumed). From the perspective of aging cost savings, the original cycle aging cost accounts for 22% of the income, while after the optimization of this patent, the cycle aging cost is reduced to 12% (a reduction of 45%). And the frequency modulation reserve contributes to the income of 18% (about 1120 yuan), and the valley filling / peak cutting compensation accounts for 31% (about 1930 yuan).

[0067]

[0068] By integrating battery aging model and multi-service coordinated scheduling, the total revenue is increased by 29.5% and the battery life loss is reduced by 36.8% while ensuring high PV consumption rate, which verifies the effectiveness of the patent method.

Claims

1. An operation optimization method for a photovoltaic integrated energy station based on photovoltaic absorption and peak and frequency regulation, characterized by: The following steps are involved: Step 1: The time T for the photovoltaic integrated energy station to interact with the distribution cabinet is fixed Divide into N time periods and establish the photovoltaic operation model of the photovoltaic storage integrated energy station, as shown in the following formula (1): （1） In formula (1), is the predicted output of the photovoltaic power plant in the integrated energy station in the kth time period within N time periods; is the power of the photovoltaic power plant in the integrated energy station charging the energy storage battery in the kth time period within N time periods; is the power of the photovoltaic power plant in the integrated energy station injected into the distribution network in the kth time period within N time periods; is the photovoltaic inverter efficiency of the photovoltaic; is the unabsorbed power of photovoltaic power in the integrated energy station in the kth time period within N time periods; Step 2: Establishing a battery aging model for the photovoltaic and energy storage integrated energy station; Step 3: Based on the photovoltaic operation model and the battery aging model, establish an operation model for the photovoltaic integrated energy station to provide peak load and frequency regulation services to the distribution network, as shown in the following formula (3): （3） In formula (2), is the selling price of surplus electricity received by the distribution network; is the peak load regulation weight of the photovoltaic integrated energy station participating in the distribution network; is the frequency regulation weight of the photovoltaic integrated energy station participating in the distribution network; is the power of the photovoltaic integrated energy station participating in the peak regulation of the distribution network in the th time period within N time periods; is the peak shaving compensation unit price given by the distribution network; Is the event function, in the event If it is established, it takes 1, otherwise it takes 0; is the valley filling compensation unit price given by the distribution network; is the frequency regulation weight of the photovoltaic integrated energy station participating in the distribution network; is the frequency regulation electricity price given by the distribution network; is the power of the photovoltaic integrated energy station participating in the frequency regulation of the distribution network in the th time period within N time periods; is the charging efficiency coefficient of the energy conversion system; is the discharge efficiency coefficient of the energy conversion system; Step 4: Setting photovoltaic power generation constraints, main transformer constraints, energy storage power constraints, and energy storage battery state of charge constraints for the operation model; Step 5: Substitute the operating model and all constraints into the MATLAB platform. Based on the photovoltaic output predicted by the photovoltaic and storage integrated energy station and the predicted electricity price of the distribution network, with the goal of maximizing the benefits of the photovoltaic and storage integrated energy station providing peak-shaving and frequency regulation services to the distribution network, calculate the optimal power interaction curve for the photovoltaic and storage integrated energy station to provide peak-shaving and frequency regulation services to the distribution network while maximizing the absorption of photovoltaic power generation.

2. The operation optimization method according to claim 1, characterized in that: The photovoltaic power generation constraint condition of the operation model is shown in the following formula (4): （4） In formula (4), is the power conversion efficiency ratio when the photovoltaic power is output to the energy storage battery and the distribution network; is the total photovoltaic power generation power of the photovoltaic integrated energy station in the kth time period within N time periods; The main transformer constraint condition of the operation model is shown in the following equation (5): （5） In formula (5), is the rated power of the main transformer on the distribution network; The energy storage power constraint of the operation model is shown in the following formula (6): （6） In formula (6), and They are charging binary variables and discharging binary variables, with values ​​of 0 or 1; if it is 1, it means that the energy storage battery is charging / discharging, and if it is 0, it means that the energy storage battery is not charging / discharging; and are respectively the lower limit and upper limit of charging power of the energy storage battery; and are the lower and upper discharge power limits of the energy storage battery respectively; The state of charge constraint of the energy storage battery in the operation model is shown in the following formula (7): （7） In formula (7), is the state of charge of the energy storage battery during the process of the photovoltaic integrated energy station performing peak load regulation and frequency regulation on the distribution network in the kth time period among N time periods; is the charge state of the photovoltaic integrated energy station during the process of outputting electric energy to the distribution network during the process of peak regulation and frequency regulation to the distribution network in the kth time period among N time periods; and are respectively the minimum and maximum states of charge of the energy storage battery; It is the energy consumption of the energy storage battery itself during operation.

3. The operation optimization method according to claim 1, characterized in that: The battery aging model of the photovoltaic and storage integrated energy station is shown in the following formula (2): （2） In formula (2), and They are the cycle aging coefficient and calendar aging coefficient of the energy storage battery; is the total construction cost of the energy storage battery; is the cycle life of the energy storage battery; is the effective capacity of the energy storage battery; It is the length of time from the time the energy storage battery is produced to the time it is scrapped due to natural aging and is no longer suitable for continued use; is the installed capacity of the energy storage battery; is the maximum discharge depth of the energy storage battery.

Images