Energy storage coordination and mutual aid method for rural power grid power distribution network area

Abstract

An energy storage coordination mutual aid method for a rural power grid distribution network district belongs to the technical field of power systems, and comprises the steps of formulating a rural micro-grid multistage scheduling control flow, establishing an energy storage and photovoltaic power generation mutual aid mechanism, and establishing a distribution network district operation optimization model including a light storage complementary regulation and control mechanism optimization model. And a rural power grid power distribution network zone area operation optimization model is established. According to the method, the power grid pressure is effectively relieved under the high-load condition, the purpose of peak load shifting is achieved by improving the utilization rate of the energy storage system, and the overall economical efficiency of power grid operation is further optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and particularly relates to a method for coordinated mutual assistance of energy storage for rural distribution network substations. Background Art

[0002] With the growth of distributed new energy such as rural rooftop photovoltaics, the traditional low-voltage distribution network with a "single power source" has been transformed into a "multi-power source" network, and the distribution characteristics of the power flow have undergone fundamental changes. Due to the volatility, intermittency, and randomness of photovoltaic power generation, after household photovoltaics are connected to the grid, the low-voltage distribution network will face many risks, such as voltage over-limit and fluctuations [1-2] , three-phase imbalance problems [3-4] become more prominent and the harmonic hazards are aggravated [5] and other power quality problems.

[0003] To solve the above problems, the technical solutions focus on the shared energy storage and power mutual assistance mechanism. By deploying an inverter and a DC-side parallel energy storage device at the end of the substation, a multi-substation interconnected topology structure is constructed, and the output of distributed power sources and the energy storage charge and discharge strategies are dynamically regulated to improve the local consumption rate of photovoltaics and reduce the reverse power [6-7] .

[0004] In a low-voltage distribution network with a high photovoltaic penetration rate, a significantly high resistance, and a long transmission line, it is often difficult to achieve the expected effect by only using a single device to regulate the network. Therefore, sometimes it is necessary to consider multiple devices during regulation. Aiming at the problems of the characteristics of the regulation devices themselves and the differences in regulation objectives, a coordinated regulation method of OLTC and energy storage is adopted. The regulation objectives and devices are determined based on the existence of reverse power flow, and a coordinated optimization control strategy for OLTC and photovoltaic inverters is proposed to make up for the problems that the capacity of photovoltaic inverters is limited and it is difficult for OLTC to achieve full-line voltage regulation [8] . Aiming at the problems of the grid operation environment such as the three-phase imbalance degree of LVDN caused by single-phase grid connection of photovoltaics, considering the three-phase four-wire structure of LVDN and the problem of active and reactive coordinated control, a collaborative optimization method for LVDN photovoltaic inverters and energy storage based on the three-phase four-wire system is proposed, and a three-phase balance coordinated optimization control scheme considering photovoltaic phase selection switching, reactive power regulation of photovoltaic inverters, and active power regulation of energy storage is established [9] .

[0005] Currently, there are generally large prediction errors in distributed power sources and loads, and the short-term power fluctuations have a greater impact on the safe and economic operation of the system. Therefore, "multi-level coordination and gradual refinement" has become a research direction for AC-DC distribution networks. A two-stage flexible soft switch (FID) and tie switch coordinated optimization control architecture of "day-ahead - intra-day" is established to absorb distributed power sources and improve the operation economy of the distribution network

[10] Combining the security issues brought by the uncertainty of distributed power sources and load power with the economic issues of distribution network operation, a multi-time scale optimization strategy considering AC-DC hybrid voltage risk perception is proposed.

[11] Considering the randomness of distributed photovoltaic output, a multi-stage stochastic optimization model for AC-DC hybrid distribution network based on scenario tree is proposed.

[12] 。

[0006] It is of great significance to study the optimal operation of a new type of rural power grid distribution network facing source-network-load-storage charging.

[0007] In rural microgrids, distributed photovoltaic power generation can interact with energy storage systems to achieve efficient coordinated control. Although there are many outstanding advantages in the construction of rural microgrids with photovoltaic power generation, there are also some problems to be considered. Here mainly refer to two aspects:

[0008] One is poor stability, resulting in difficult operation in some periods. Photovoltaic power generation is greatly affected by time and climate. At noon, when the sunlight is strong, the system output will increase; at night, without the sun, the system output is zero. In case of cloudy or rainy days, insufficient sunlight will also affect the system output. In addition, environmental factors will affect the conversion efficiency of the battery panels. This randomness of output is not conducive to the operation of electrical equipment for self-sufficient household users, and may even cause equipment shutdown or burnout in severe cases; it is also not conducive to system grid connection, which will cause node voltage fluctuations and affect the power quality of the rural power grid.

[0009] The other is poor economy, resulting in difficulties in construction and operation promotion. To obtain sufficient electric energy, a large number of crystalline silicon battery panels must be equipped. On the other hand, the current price of photovoltaic power generation equipment in the market is high, and it has little attraction for private capital.

[0010] Therefore, the existing technology urgently needs a new technical solution to solve the above problems. Summary of the Invention

[0011] The technical problem to be solved by the present invention is: to provide a method for energy storage coordination and mutual assistance for rural power grid distribution network areas, to propose an optimized mutual assistance and collaborative scheduling strategy for energy storage in multiple distribution network areas, to develop a prototype of the energy storage scheduling module for distribution network areas and conduct demonstration applications at the local dispatching level, and to improve operation economy and power supply reliability.

[0012] A method for energy storage coordination and mutual assistance for rural power grid distribution network areas includes the following steps, and the following steps are carried out sequentially.

[0013] Step 1: Develop a multi-level scheduling and control process for rural microgrids, monitor the operating status of all microgrids in real time, dispatch the operating status of energy storage in each microgrid group according to the optimization goal of the optimal operation of the system, and send it to the local control level of each microgrid; through the interface connected to the local dispatching system, send the collected parameters to the local dispatching system and execute the instructions of the local dispatching system.

[0014] Step 2: Establish a mutual assistance mechanism between energy storage and photovoltaic power generation. Utilize the complementarity of distributed power sources in time to make the overall output curve of distributed power sources tend to be flat; or configure the energy storage capacity for the photovoltaic power generation system. According to the load status and light resource distribution, bundle the energy storage device with the photovoltaic, and interact by adjusting the battery status.

[0015] Step 3: Based on the mutual assistance mechanism between energy storage and photovoltaic power generation in Step 2, establish an optimal operation model for the operation of the distribution network substation area, including an optimized model for the complementary regulation mechanism of light and storage, and an optimized model for the operation of the rural distribution network substation area.

[0016] The microgrid operating status described in Step 1 includes photovoltaic parameters, energy storage parameters, and load parameters.

[0017] The optimization goal described in Step 1 is to fully absorb photovoltaic power, ensure the supply voltage, and minimize the line loss.

[0018] The objective function of the optimized model for the complementary regulation mechanism of light and storage described in Step 3 is:

[0019] min(ΔP) 2 =min(P new -P now )

[0020] In the formula, P represents the new cycle and the current cycle during scheduling; P new represents the new cycle during scheduling, and P now represents the current cycle during scheduling.

[0021] The optimized model for the operation of the rural distribution network substation area described in Step 3 is:

[0022]

[0023] In the formula: F is the daily comprehensive operation benefit of the rural power grid; and are the daily power sales revenue of the rural power grid, the daily comprehensive operation cost of the energy storage device, and the daily payment cost of demand response respectively; T and Δt are the total number of scheduling times and the time interval between adjacent scheduling times respectively; P s,t e 、P s,t ESS,d 、P s,t ESS,c and ρ s,tare the daily sold electric power of the rural power grid, the discharging power, charging power of the battery energy storage device, and the time-of-use electricity price respectively; and are the comprehensive operation cost of the battery energy storage device and the demand response compensation cost respectively; P s,tSS ESd d is the load shedding power of the demand response at time t; and P s,t L are the photovoltaic contribution and the load value in time period t respectively; is the charging power of the electric vehicle charging pile at time t; is the rated power of the transformer in the rural power grid substation area; and P ESS min are the boundary values of the charging / discharging active power of the battery energy storage device respectively; S ESS max and S ESS min are the SOC boundary values of the battery energy storage device respectively; is the SOC of the battery energy storage device at time t; S ESS 0 and S ESS T are the SOC values of the battery energy storage device at the start point and end point of the scheduling period respectively; N ESS is the actual charge and discharge times of the energy storage device within a scheduling period; N ESS max is the upper limit of the charge and discharge times of the energy storage device within a scheduling period; P DR max is the upper limit of the load shedding power of the demand response; N DR and N DR max are the actual number of times of the demand response and the maximum number of times of the demand response within a scheduling period respectively; is the upper limit of the charging power of the electric vehicle charging pile.

[0024] Through the above design scheme, the present invention can bring the following beneficial effects: A method for coordinated mutual assistance of energy storage for rural power grid distribution network substations not only effectively relieves the power grid pressure under high load conditions, but also realizes the purpose of peak shaving and valley filling by improving the utilization rate of the energy storage system, and further optimizes the overall economy of the power grid operation.

[0025] The proposed method of mutual power supply among the terminal power sources in the substation area has achieved remarkable results in improving the load power supply reliability after the high-voltage line outage. This method effectively improves the power supply guarantee ability of the power grid, reduces the economic losses and user dissatisfaction caused by power outages, and provides new technical support for the stable operation of the rural power grid.

[0026] In the demonstration application, the research and deployment of the substation area energy storage scheduling module have been successfully verified. Through the actual operation in multiple pilot substation areas, the system demonstrates good scheduling capabilities and adaptability, successfully improving the operation efficiency of the power grid and the consumption capacity of new energy, and significantly reducing the phenomenon of wind and light abandonment. User feedback is positive, and it is generally believed that the module has high practicality and reliability and can effectively assist the daily scheduling work of the power grid. Description of the Drawings

[0027] The present invention will be further described below in conjunction with the drawings and specific embodiments:

[0028] Figure 1 Typical daily load curve of a certain substation area in April for the specific implementation method of an energy storage coordination and mutual assistance method for rural distribution network substations of the present invention

[0029] Figure 2 Photovoltaic curve of a certain substation area in April for the specific implementation method of an energy storage coordination and mutual assistance method for rural distribution network substations of the present invention.

[0030] Figure 3 Typical daily operation optimization strategy diagram for tea frying in a certain substation area in April for the specific implementation method of an energy storage coordination and mutual assistance method for rural distribution network substations of the present invention Specific Embodiments

[0031] An energy storage coordination and mutual assistance method for rural distribution network substations mainly includes three aspects.

[0032] 1) Multi-level scheduling control process for rural microgrids;

[0033] 2) Mutual assistance mechanism between energy storage and photovoltaic power generation;

[0034] 3) Operation optimization model for rural distribution network substations.

[0035] Among them, the multi-level scheduling control process is specifically as follows:

[0036] Microgrid group scheduling master station level: In a graphical mode, it real-time monitors the operation status of all underlying microgrids, including all parameters of photovoltaic, energy storage, and load; according to the optimization objectives of the optimal operation of the system (fully consuming photovoltaic power, ensuring power supply voltage, minimizing line losses, etc.), it schedules the operation status of the energy storage in each microgrid group and sends it to the local control level of each microgrid. At the same time, it has an interface for networking with the local dispatching system for subsequent upgrade applications, and can send the collected parameters to the local dispatching system and execute the instructions of the local dispatching system.

[0037] The mutual assistance mechanism between the energy storage and photovoltaic power generation is specifically as follows:

[0038] The complementarity of different distributed power sources at different times can be utilized to make the overall output curve of distributed power sources tend to be flat, or it can also be achieved by configuring energy storage capacity for the photovoltaic power generation system. For the situation of Luwei Cooperative, after equipping a certain capacity of energy storage device, the energy storage device can be bundled with the photovoltaic according to the specific load status and light resource distribution, and the interaction can be realized by adjusting the battery state during specific periods to improve the economy and power quality of the system.

[0039] The specific operation optimization model of the rural power distribution network substation area is as follows:

[0040] The dispatching mechanism of the rural microgrid with a distributed photovoltaic energy storage system will operate according to this process in each dispatching cycle until it is manually stopped or interrupted due to fault handling. After obtaining the optimized results, the power of each node needs to be adjusted, and a power distribution strategy is formulated for DG and DES respectively. The average power distribution strategy is similar to the method of traffic balance in the network, and its main purpose is to balance the load or power supply of all nodes. For power supply equipment, fair power supply according to the power supply capacity helps to extend the service life of the equipment; for user loads, fair adjustment of the load according to the adjustment ability can obtain an overall better user satisfaction. The research on the power adjustment strategy can be used as a separate research point, and the research focus is on the overall dispatching mechanism. In practice, different power distribution strategies can be adopted for different parts according to the regional characteristics to achieve better results, such as adopting the principle of the least number of devices for the power distribution of DG. There is no requirement for the power adjustment order of DG and DES, and the power adjustment of each part does not affect each other, because the power adjustment of each part is carried out asynchronously according to the optimization results, and only requires that the power adjustment values of each node be calculated before execution, and the calculated power is obtained according to the calculated value for control.

[0041] Furthermore, the optimization model of the photovoltaic energy storage complementary regulation mechanism

[0042] 1) Objective function

[0043] To achieve the purpose of improving power quality and economy, the considered method is to minimize the change in the total load power of each dispatching cycle in the microgrid of Luwei Cooperative to change the operation state of the photovoltaic energy storage system, that is:

[0044] min(ΔP) 2 =min(P new -P now ) (1)

[0045] In the formula, P is the value of P G +P GC , P new represents the new cycle during dispatching, and P now represents the current cycle of dispatching.

[0046] 2) Constraint conditions

[0047] Power balance and power flow constraints of rural microgrids. The condition for safe and stable operation in the entire microgrid is that the generated power is equal to the consumed power, i.e., energy conservation, which is:

[0048] P DES +P SC +P CL +P load +P loss =P DG +P G (2)

[0049] In the formula, P G is the access power of the rural microgrid; P CL is the total power accessed by all nodes in the controllable load part; P load is the total value of the conventional load P loss ; is the sum of the power losses in the entire distribution network line; P DES is the total power of all energy storage nodes, with a positive value indicating the charging power and a negative value indicating the discharging power; P sc is the total power of the charging pile set, with a positive value indicating the charging power and a negative value indicating the discharging power; P DG is the total power of the distributed power generation part.

[0050] DG output range. The predicted output value in the next scheduling period is a constant term and satisfies:

[0051]

[0052] In the formula, is the minimum power of the distributed power generation part; is the maximum power of the distributed power generation part.

[0053] DES status and output range. In the DES part, obtain the charge and discharge status of each DES, and the output range of the effective power value in the discharging state is:

[0054]

[0055] In the formula: P dismax is the maximum power limit in the discharging state of the DES.

[0056] The effective power consumption range in the charging state is:

[0057]

[0058] In the formula: P charmax is the maximum power limit in the discharging state of the DES.

[0059] For rechargeable and dischargeable nodes, the power regulation range is as follows:

[0060]

[0061] The current capacity and state of the SC. For each SC, the power output or consumption range in the charging and discharging states is as follows:

[0062]

[0063] In the formula: is the minimum limit value of the charging and discharging power of the charging pile set; is the maximum limit value of the charging and discharging power of the charging pile set.

[0064] Taking the Luwei Cooperative as an example, the power range for the energy storage device to charge is calculated from the charging plan time and given by the upper-layer application; the minimum value of the discharging power is usually 0, that is, there is only the maximum discharging power limit.

[0065] Controllable load range limit. The CL part obtains the power load conditions of all controllable loads and satisfies:

[0066]

[0067] In the formula: is the size of the controllable load of the i-th node; is the minimum value of the controllable load of the i-th node.

[0068] Finally, it should be pointed out that in the next scheduling period, calculated according to the theoretical calculation method by taking the maximum load at the next scheduling period, what is obtained is P loss , and when the power in the active distribution network fluctuates within a small range, the active distribution network will autonomously adjust the power supply and consumption power relationship so that the power consumption is always equal to the power supply.

[0069] Embodiment 2

[0070] Rural power distribution network substation operation optimization model

[0071] The objective function of the rural power grid optimization operation model proposed in this study is to maximize the daily comprehensive operation benefit of the rural power grid, and the expression is as follows:

[0072]

[0073] In the formula, F is the daily comprehensive operation benefit of the rural power grid; and are the daily electricity sales revenue of the rural power grid, the daily comprehensive operation cost of the energy storage device, and the daily payment cost of demand response respectively; T and Δt are the total number of scheduling moments and the time interval between adjacent scheduling moments; P s,t e 、Ps,t ESS,d 、P s,t ESS,c and ρ s,t are respectively the daily electricity sales power of the rural power grid, the discharge power, the charging power of the battery energy storage device, and the time-of-use electricity price during a period; and are respectively the comprehensive operation cost of the battery energy storage device (converting costs such as losses and depreciation) and the demand response compensation cost; P s,tSS ESd d is the load shedding power of the demand response at time t.

[0074] Distribution network side constraint conditions:

[0075] (1) Power balance constraint:

[0076]

[0077] In the formula: and P s,t L are respectively the photovoltaic contribution and the load value during period t; is the charging power of the electric vehicle charging pile at time t.

[0078] (2) Power supply power constraint of the rural power grid substation transformer:

[0079]

[0080] In the formula: is the rated power of the rural power grid substation transformer.

[0081] Load side constraint conditions:

[0082] Charge / discharge power constraint of the energy storage device:

[0083]

[0084] In the formula: and P ESS min are respectively the boundary values of the charge / discharge active power of the battery energy storage device.

[0085] SOC constraint of the energy storage device:

[0086]

[0087] In the formula: S ESS max and S ESS min are respectively the SOC boundary values of the battery energy storage device; is the SOC of the battery energy storage device at time t; S ESS0 and S ESS T are the SOC values of the battery energy storage device at the start and end times of the scheduling period, respectively.

[0088] Charge and discharge times constraint of the energy storage device:

[0089] 0 ≤ N ESS ≤ N ESS max (15)

[0090] In the formula: N ESS is the actual charge and discharge times of the energy storage device within a scheduling period; N ESS max is the upper limit of the charge and discharge times of the energy storage device within a scheduling period.

[0091] Demand response constraint:

[0092]

[0093] In the formula: P DR max is the upper limit of the load shedding power of the demand response; N DR and N DR max are the actual number of times of the demand response and the maximum number of times of the demand response within a scheduling period, respectively.

[0094] Electric vehicle charging pile constraint:

[0095]

[0096] In the formula: is the upper limit of the charging power of the electric vehicle charging pile.

[0097] To ensure that the distribution transformer in the substation area is not overloaded, this model stipulates that when the power supplied by the transformer exceeds 70% of the rated power, the charging pile stops charging.

[0098] The proposed coordinated and mutual aid method of energy storage in the substation area takes a rice milling point in a certain substation area as the research object of the calculation example.

[0099] The typical daily load curves of a certain substation area during the rice milling seasons in March and April have obvious characteristics such as Figure 1 and Figure 2As shown: The night load is significantly higher than the day load because most of the rice milling work in a certain substation area is carried out at night, making the substation load at the rice milling point in this substation area different from that of most substation areas. Taking the typical rice milling day in April as an example, the highest load at the rice milling point in this substation area during the rice milling season in April can reach 330 kW, while the transformer capacity at the rice milling point in this substation area is only 315 kW. Without using measures such as energy storage devices and demand response, the transformer in this substation area will operate overloaded and is easily damaged. The parameter settings of the rice milling point in this substation area are shown in Table 1, and the time-of-use electricity price is shown in Table 2.

[0100] Table 1 Parameter settings of the example in this substation area

[0101]

[0102] Table 2 Parameter settings of the example in this substation area

[0103]

[0104]

[0105] Taking the typical load situation as an example, an operation optimization model for this substation area is established. Below, taking the typical day in the rice milling season in April in this substation area as the research object, the GAMS software platform is used to solve the established model, and the optimized dispatching strategy for the typical day in the rice milling season in April in this substation area is obtained. According to the dispatching strategy, the loads in the time periods of 18:30 - 20:15 and 21:45 - 23:00 both exceed 252 kW (i.e., 80% of the rated capacity of 315 kW), and there is no sunlight at night, so the photovoltaic power generation is zero. The optimal dispatching strategy given by the proposed optimized operation model can avoid the overload of the substation transformer. Below, the dispatching strategies of energy storage devices and demand response are analyzed separately. During the peak load periods when the load exceeds 252 kW, the battery energy storage device is in the discharging state, restricting the power supplied by the substation transformer to 252 kW, reducing the power supply pressure of the substation transformer and successfully solving the problem of transformer heavy load. In addition, because the SOC of the battery energy storage device needs to be kept unchanged within a dispatching cycle, the battery energy storage device is adjusted to the charging state at low electricity price time periods such as 06:45, 22:00, and 22:30. The above dispatching scheme for the battery energy storage device can also achieve low charging and high discharging, enabling this substation area to profit through the dispatching of the battery energy storage device. The demand response strategy in this area is from Figure 3It can be seen that due to the fact that battery energy storage devices are restricted by factors such as SOC and discharge power limit values, relying solely on battery energy storage devices cannot ensure that the power supplied by the distribution transformer in the substation area is below 80% of the rated power. Therefore, the optimal dispatching scheme solved by the optimized operation model proposed in this embodiment applies demand response, interrupting the loads at 19:00 and 19:45 by 43.66 kW and 21.01 kW respectively, greatly reducing the power supply pressure of the distribution transformer in the substation area, and at the same time preventing problems such as excessive reduction of SOC caused by over-discharge of the battery energy storage device and shortening of the operating life of the battery energy storage device.

[0106] References

[0107] [1] Nour A, Hatata A, Helal A, et al. Voltage Violation in Four-wire Distribution Networks Integrated with Rooftop PV Systems[J]. IET Renewable Power Generation, 2020.

[0108] [2] Zhang Shizhong, Pei Wei, Ma Tengfei, et al. Nash Bargaining Method for Reactive Power Incentive Rewards of Residential PV Systems for Distribution Networks[J]. Power System Technology, 2021, 45(8): 3079-3086.

[0109] [3] Su Xiangjing, Peng He, Mi Yang, et al. Real-time Optimization of Voltage and Reactive Power in Medium- and Low-Voltage Unbalanced Distribution Networks Based on Linear Programming[J / OL]. Power System Technology. Power System Technology, 2021, 45(8): 3079-3086.

[0110] [4] Erlich I, El-Naggar A. Control approach of three-phase grid-connected PV inverters for voltage unbalance mitigation in low-voltage distribution grids[J]. IET Power Generation, 2017.

[0111] [5] Fekete K, Klaic Z, Majdandzic L. Expansion of the residential photovoltaic systems and its harmonic impact on the distribution grid[J]. Renewable Energy, 2012(43), 140 - 148.

[0112] [6] Yong Jing, Xu Xin, Zeng Liqiang, et al. Review of low - voltage DC power supply systems[J]. Proceedings of the CSEE, 2013, 33(7): 42 - 52.

[0113] [7] Zeng Rong, Zhao Yuming, Zhao Biao, et al. Key technologies research and application prospects of DC power distribution and utilization[J]. Proceedings of the CSEE, 2018, 38(23): 6790 - 6801.

[0114] [8] Tang Wei, Li Tianrui, Zhang Lu, et al. Photovoltaic - energy storage coordinated control in low - voltage distribution network based on three - phase four - wire optimal power flow[J]. Automation of Electric Power Systems, 2020, 44(12): 31 - 42.

[0115] [9] Chen Jiachao, Li Qinhao, Tang Yuan, et al. Optimization of three - phase balance in low - voltage distribution network considering photovoltaic phase - selection switching[J]. Electric Power Automation Equipment, 2022, 42(4): 71 - 78.

[0116]

[10] Cong Pengwei, Tang Wei, Lou Chengwei, et al. Coordinated optimization control of two - stage flexible soft - switch and tie - switch in active distribution network with high - penetration renewable energy[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1263 - 1272.

[11] Huang Kun, Liu Zhihan, Fu Ming, et al. Optimal dispatch of AC - DC distribution network considering voltage risk perception[J]. Automation of Electric Power Systems, 2021, 45(15): 45 - 54, 3.

[0117]

[12] Pei Lei, Wei Zhinong, Chen Sheng, et al. Multi - stage stochastic optimal dispatch model for AC - DC hybrid distribution network[J]. Power System Protection and Control, 2022, 50(14): 23 - 32.

Claims

1. A method for energy storage coordination and mutual assistance in rural power distribution network areas, characterized by: The method comprises the following steps, and the following steps are performed sequentially, Step 1: Develop a multi-level dispatching and control process for rural microgrids, monitor the operating status of all microgrids in real time, dispatch the operating status of energy storage in each microgrid group according to the optimization goal of the optimal operation of the system, and send it to the local control level of each microgrid; send the collected parameters to the local dispatching system through the interface connected to the local dispatching system, and execute the instructions of the local dispatching system; Step 2: Establish a mutual assistance mechanism between energy storage and photovoltaic power generation, and use the complementarity of distributed power sources at the time to make the overall output curve of distributed power sources tend to be gentle; or configure energy storage capacity for the photovoltaic power generation system, and bundle the energy storage device with photovoltaic power generation according to the load status and light resource distribution, and interact by adjusting the battery status; Step 3: Based on the mutual assistance mechanism between energy storage and photovoltaic power generation in step 2, establish a distribution network substation operation optimization model, including an optimization model for the photovoltaic-storage complementary regulation mechanism and an optimization model for the rural power distribution network substation operation.

2. According to claim 1, a method for energy storage coordination and mutual assistance for rural power distribution network stations is characterized by: The microgrid operating status described in step 1 includes photovoltaic parameters, energy storage parameters and load parameters.

3. The energy storage coordination and mutual assistance method for rural power distribution network areas according to claim 1 is characterized by: The optimization goal of step one is to fully absorb photovoltaic power and ensure the supply voltage and minimize line losses.

4. The energy storage coordination and mutual assistance method for rural power distribution network areas according to claim 1 is characterized by: The objective function of the optimization model of the photovoltaic-storage complementary regulation mechanism described in step 3 is: min(ΔP) 2 =min(P new -P now ) Where P represents the new cycle and the current cycle during scheduling; P new represents the scheduling period, P now Indicates the scheduling of the current cycle.

5. The energy storage coordination and mutual assistance method for rural power distribution network areas according to claim 1 is characterized by: The operation optimization model of the rural power distribution network area described in step 3 is: Where: F is the daily comprehensive benefit of the rural power grid; and are the daily electricity sales revenue of the rural power grid, the daily comprehensive operating cost of the energy storage device and the daily payment cost of the demand response, respectively; T and Δt are the total number of dispatching times and the time interval between adjacent dispatching times, respectively; P s,t e , P s,t ESS,d , P s,t ESS,c and ρ s,t They are the daily power sales of the rural power grid, the discharge power of the battery energy storage equipment, the charging power and the time-of-use electricity price; and are the comprehensive operating costs of battery energy storage equipment and demand response compensation costs; P s,tSS ESD d is the load shedding power of demand response at time t; and P s,t L are the photovoltaic contribution and load value in period t respectively; is the charging power of the electric vehicle charging pile at time t; P e max The rated power of the transformer in the rural power grid area; and P ESS min are the limit values ​​of the charging / discharging active power of the battery energy storage device; S ESS max and S ESS min They are the SOC limit values ​​of the battery energy storage equipment; is the SOC of the battery energy storage device at time t; S ESS 0 and S ESS T are the SOC values ​​of the battery energy storage equipment at the start and end of the scheduling cycle respectively; N ESS N is the actual number of charge and discharge times of the energy storage device in a scheduling cycle; ESS max P is the upper limit of the number of times the energy storage device is charged and discharged within a scheduling cycle; DR max The upper limit of load shedding power for demand response; N DR and N DR max They are the actual number of demand responses and the maximum number of demand responses in a scheduling cycle respectively; The upper limit of charging power for electric vehicle charging piles.

Images