Power grid-park interactive coordination method considering photovoltaic access

Abstract

The present application relates to a power distribution network-irrigation park electricity-water interaction coordination method considering photovoltaic access, belongs to the technical field of power distribution network optimization and energy internet, builds a power distribution network-irrigation park electricity-water physical structure; builds a double-layer interactive game framework of power distribution network-irrigation park and constructs a game model; according to the framework, a master-slave game model is constructed, the upper power distribution network takes the comprehensive operation cost, transformer load balancing and irrigation node load peak-valley difference as the optimization target, an optimal decision model of power distribution network operation mode and time-of-use electricity price is established; the lower irrigation park takes the time-of-use electricity price released by the power distribution network as the target, and makes optimal decision of irrigation under photovoltaic access based on its own cost and satisfaction; the interactive game coordination model proposed by the present application is solved by using particle swarm algorithm and CPLEX toolbox, and example verification is carried out; the agricultural irrigation water mode is changed from extensive and inefficient to saving and efficient, and the safe and reliable operation of rural low-voltage power distribution network is effectively supported, achieving a win-win situation.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network optimization and energy internet technology, specifically involving a method for coordinating the interaction between power distribution network and agricultural irrigation park electricity and water, taking into account photovoltaic access. Background Technology

[0002] Traditional agricultural irrigation is generally powered by rural power grids. However, due to my country's complex agricultural terrain and large-scale electric irrigation areas, the concentrated power consumption of irrigation pumps during irrigation cycles puts significant pressure on rural power grids. The unpredictable nature of irrigation water use increases farmers' water and electricity costs and poses safety risks to the rural power grid. Against this backdrop, with the successive introduction of policies related to county-wide rooftop distributed photovoltaic (PV) systems, the integration of PV into agricultural electricity use is rapidly gaining popularity. my country's rural rooftops represent a vast PV power generation resource, with annual power generation approaching 3 trillion kWh in 2020, equivalent to 40% of my country's total power generation that year. In this context, balancing electricity and water consumption, reducing grid pressure while ensuring smooth agricultural irrigation is a common challenge for both agricultural users and power grid companies. Therefore, developing a coordinated approach for power grid-irrigation park electricity-water interaction that considers PV integration is of great significance. Summary of the Invention

[0003] The purpose of this invention is to provide a method for coordinating the interaction between electricity and water in a power distribution network and an agricultural irrigation park, taking into account photovoltaic access. This method aims to solve the technical problems existing in the prior art, balance the contradiction between electricity and water use, reduce the pressure on the power grid, and ensure the smooth operation of agricultural irrigation, which is a common problem faced by agricultural users and power distribution companies.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows:

[0005] A method and system for coordinating electricity and water interaction in a power distribution network and agricultural irrigation park, considering photovoltaic (PV) grid integration. The method includes the following steps:

[0006] Step 1: The physical structure of the power distribution network, agricultural irrigation park electricity and water system was constructed.

[0007] Step 2: A two-layer interactive game framework for the power distribution network and agricultural irrigation park was established, and a game model was constructed.

[0008] Step 3: Based on the framework of Step 2, construct a master-slave game model. The upper-level distribution network takes the comprehensive operating cost, transformer load balancing, and peak-valley load difference of agricultural irrigation nodes as optimization objectives, and establishes an optimal decision-making model for distribution network operation mode and time-of-use electricity price. The lower-level agricultural irrigation park, based on the time-of-use electricity price published by the distribution network, takes its own cost and satisfaction as objectives to make optimal irrigation decisions under photovoltaic access.

[0009] Step 4: Solve the interactive game coordination model proposed in this invention using the particle swarm optimization algorithm and the CPLEX toolbox, and verify it with numerical examples; This invention can realize the transformation of agricultural irrigation water use from extensive and inefficient to economical and efficient, while effectively supporting the safe and reliable operation of rural medium and low voltage power distribution networks, achieving a win-win situation for both parties.

[0010] Furthermore, the optimization objectives of the distribution network include comprehensive operating costs, load balancing indicators, and peak-valley load differences at agricultural irrigation nodes. Using time-of-use pricing and dynamic network reconfiguration schemes as decision variables, the network optimizes its own economic benefits and the load balance of distribution network lines, thereby improving safety and reliability. The optimization objectives of the agricultural irrigation park include electricity costs and farmers' irrigation satisfaction indicators. Using irrigation water plans and electricity consumption as decision variables, the park optimizes its own energy consumption plan.

[0011] The objective function for the distribution network optimization decision is:

[0012] minJ=λ 1,1 F up +λ 1,2 B trans +λ 1,3 D p-v,total

[0013] In the formula: λ 1,1 , λ 1,2 , λ 1,3 F represents the weighting coefficients for each objective; up B is an economic benefit indicator for the 10kV distribution network throughout the entire irrigation cycle. trans D is the load balance index for a 110 / 10kV transformer. p-v,total This refers to the overall peak-to-valley load difference index for agricultural irrigation nodes in the power distribution network.

[0014] Comprehensive operating cost indicators:

[0015] F up =F loss +F switch +F buy -F sell

[0016]

[0017] In the formula, F loss Cost of 10kV network losses Cost per unit of network loss; F swich To reduce the cost of switching operations, E represents the unit cost of switching action. SW This represents the set of various interconnecting switch branches in the reconstructed region. These represent the changes in the operating state of switch branches ij over time period t, and are binary variables. A value of "1" indicates a change from an open state to a closed state. A value of "1" indicates a change from a closed state to an open state; F buy To reduce the cost of purchasing electricity from the upstream grid, To determine the total amount of electricity to be purchased from higher authorities, It has a fixed electricity price; F sell To generate revenue from selling electricity to lower-level networks, This refers to the total electricity sold to lower-level units.

[0018] Transformer load balancing indicators:

[0019]

[0020] In the formula, These represent the active power, load factor, and transformer load balance index of substation node j at time period t, respectively; N sub N represents the total number of substations. trans,j α is the number of transformers connected to substation node j; α(j,f) is the feeder group of transformer f at substation node j;

[0021] The transformer load balance index represents the mean square error of the load on all transformers in a substation. The expression for the mean square error is a nonlinear equation, which can be relaxed to a second-order cone expression with a small relaxation gap in programming simulation:

[0022]

[0023] Agricultural irrigation node load peak-valley difference index

[0024] The fluctuation range of electricity consumption in the lower-level agricultural irrigation park is also one of the optimization objectives of the distribution network. In the objective function, the method of minimizing the standard deviation of the net load curve is used for equivalence.

[0025]

[0026]

[0027] In the formula, D p-v,m Let m be the standard deviation of the load curve for the m-th irrigation node during the total irrigation period. Let be the load value during time period t. This represents the average load over the entire irrigation period.

[0028] The objective function of the irrigation decision model for the agricultural irrigation park is:

[0029] minf = λ 2,1 F down -λ 2,2 S down

[0030] In the formula: λ 2,1 , λ 2,2 F represents the weighting coefficients for each objective; down S represents the total electricity cost for all agricultural irrigation areas throughout the entire irrigation cycle. down This is an indicator of farmer satisfaction.

[0031] Total electricity cost of the agricultural irrigation park:

[0032] F down =F grid +F pv +F bat

[0033]

[0034] In the formula, F grid The total electricity cost for all agricultural irrigation parks; F pv The total operation and maintenance cost of the photovoltaic system. For the actual output of the photovoltaic system installed in the No. m agricultural irrigation park, C pv Unit operating cost of photovoltaic system; F bat The total operation and maintenance cost of the energy storage system These represent the charging and discharging power of the battery during time period t. The unit cost of charging and discharging energy storage systems.

[0035] Farmer satisfaction indicators:

[0036] The farmer satisfaction index is set based on the farmer's working hours and work intensity (irrigation water flow), and represents the farmer's work intention.

[0037]

[0038] In the formula, S m,n For the satisfaction of every worker irrigating every piece of farmland; t This represents the satisfaction coefficient per unit of time. The higher the coefficient, the higher the farmers' satisfaction with irrigation operations during that period.

[0039] Furthermore, the constraints of the distribution network include power flow constraints, security constraints, reconfiguration constraints, and high-voltage main grid constraints; the constraints of the agricultural irrigation park include power balance constraints, irrigation system constraints, groundwater constraints, photovoltaic power output constraints, and energy storage constraints.

[0040] The power flow constraint is:

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] In the formula: α(j) is the set of branch terminal nodes with j as the initial node; β(j) is the set of branch initial nodes with j as the terminal node; r ij x ij and g j b j These represent the branch impedance and the node admittance, respectively. Let j be the voltage at node j. Let be the magnitude of the current at branch ij; These represent the main network and ordinary reactive load of node j in time period t, respectively. These represent the active power loads of node j in the main network, general network, and agricultural irrigation during time period t, respectively. Let represent the active and reactive power of branch ij in the t-th time period, respectively.

[0050] Safety constraints include node voltage constraints and branch current constraints as follows:

[0051]

[0052]

[0053] In the formula, These are the lower and upper limits of the voltage at node j, respectively; These are the lower and upper limits of the current at branch ij, respectively.

[0054] Reconfiguration constraints include network topology constraints and switching action constraints:

[0055] g k ∈G

[0056]

[0057]

[0058] In the formula, g k Let G be the reconstructed network topology, and G be the set of all feasible radial topologies. ijThis indicates the switch status of branch ij. If it is "1", it means that the switch of branch ij is closed.

[0059] High-voltage main grid constraints:

[0060]

[0061]

[0062]

[0063] In the formula: These represent the lower and upper limits of active and reactive power injected from the high-voltage main grid into the distribution network, respectively. The maximum ramp rate limits for the main network.

[0064] Electric power balance constraints:

[0065]

[0066] In the formula, The electricity consumption of the irrigation pumps configured for each piece of farmland during time period t.

[0067] Irrigation system constraints:

[0068]

[0069] In the formula, η0 represents the actual water flow rate for farmland irrigation; H represents the head; η0 and η1 represent the pumping efficiency of the well and the working efficiency of the motor, respectively; ρ and g represent the density of irrigation water and the acceleration due to gravity, respectively.

[0070] During the agricultural irrigation cycle, the irrigation system must meet the following constraints: total irrigation water required for the farmland, pump flow rate constraint, and water flow ramp constraint, respectively:

[0071]

[0072]

[0073]

[0074] In the formula, This represents the total irrigation water required by farmland number n within a time scale T. This is the upper limit of the water flow rate for irrigation pumps; These represent the upward and downward ramp rates of water distribution in the well, respectively.

[0075] Groundwater constraints:

[0076]

[0077] In the formula, These represent the groundwater content at time t and the initial content in farmland area n, respectively.

[0078] The groundwater extraction process involves the relationship between groundwater flow and drawdown, which is crucial to the ecological security of groundwater in the region. This invention uses a linear empirical formula for linear regression. [24-25] This invention takes into account the natural infiltration recharge of groundwater. Satisfy the constraints on the dynamic change of groundwater level over time:

[0079]

[0080]

[0081] In the formula, Let be the groundwater drawdown value for farmland area n, and a and b be the relevant proportionality coefficients. μF is the unit water storage capacity, i.e., the storage capacity provided by the aquifer when the water level drops.

[0082] In summary, this model temporarily disregards the impact of extreme weather events such as precipitation. The groundwater in the area where each farmland is located satisfies the dynamic water balance constraint, which can be approximated as:

[0083]

[0084] In addition, considering the ecological security factors of groundwater, an ecological early warning value is set. If the groundwater balance falls below this value at any point during irrigation, the warning value will be triggered. This indicates that the ecological security of groundwater is affected, and the natural infiltration recharge will be significantly reduced based on equation (35) and cannot be restored in a short period of time.

[0085] Photovoltaic output constraints:

[0086]

[0087]

[0088] In the formula, The predicted full-power output of the photovoltaic system configured for irrigation park No. m; The rated output power of the photovoltaic system under standard conditions; k represents the power temperature coefficient. This refers to the actual operating temperature of the solar panel; T stc,m To configure the rated operating temperature of the solar panels; R stc,m These are the actual solar irradiance and the rated solar irradiance of the agricultural area, respectively.

[0089] Energy storage constraints:

[0090]

[0091]

[0092]

[0093] In the formula, These represent the stored energy (μ) of the battery bank installed in the m-type agricultural irrigation park during time period t and time period t-1, respectively. loss The self-discharge rate of the battery pack. These represent the charging and discharging efficiencies of the battery pack, respectively, with Δt representing the unit time period. These are the upper and lower limits of the battery pack capacity, respectively. These are the upper and lower limits of the charging and discharging power of the energy storage system, respectively. These are 0-1 integer variables representing the charging and discharging states of the energy storage system, where "1" indicates "yes" and "0" indicates "no".

[0094] Furthermore, the aforementioned method for coordinating electricity and water interaction in a distribution network-agricultural irrigation park considering photovoltaic access is characterized in that, in the reconfiguration constraints, adopting a uniform reconfiguration mode throughout the entire time period would increase the computational difficulty for large-scale distribution systems in both time and space dimensions. Therefore, based on the characteristic that the spatiotemporal distribution of the system's net load can represent the flexibility requirements for switch reconfiguration, the spatial distribution of the net load is divided into time periods, and the same reconfiguration mode is adopted for each time period belonging to the same type of center. Reconfiguration under time period division reduces the time calculation dimension while reducing unnecessary spatial calculation costs.

[0095] Furthermore, in the model solution method, the information that the upper-level leader, the distribution network, needs to transmit to the agricultural irrigation park in the game is the time-of-use electricity price. This price signal guides the electricity consumption of lower-level users to maximize their own benefits. The time-of-use electricity price is defined as the main decision variable. Under certain electricity price constraints, the set of time-of-use electricity price decisions is θ1. The lower-level agricultural irrigation park, satisfying its own constraints, calculates the optimal irrigation water plan based on the time-of-use electricity price issued by the upper level, thus deriving the optimal electricity purchase strategy set as θ2. The upper-lower-level master-slave game model can be expressed as:

[0096] G = {(RDN∪AIP), θ1, θ2, J, f}

[0097] In the formula, RDN∪AIP represents the two sides in the game, with the upper-level distribution network as the leader and the lower-level agricultural irrigation park as the follower.

[0098] Furthermore, step 3 combines the particle swarm optimization algorithm and the CPLEX solver to solve the two-layer model of this invention, including the following sub-steps:

[0099] 1) Set the iteration number h = 0, initialize the load demand data, establish the decision model of each subject, set the number of particles, the maximum number of iterations and the maximum iteration convergence error, and randomly initialize the 24-hour time-of-use electricity price parameter particles.

[0100] 2) Lower-level agricultural irrigation areas according to ( (For the time-of-use electricity price set by the upper layer when the number of iterations is h), the CPLEX solver is used to solve for the optimal irrigation water use scheme and electricity use scheme of the lower layer, and the current optimal objective f is calculated and retained. h And feed it back to the upper level for its own power consumption strategy;

[0101] 3) The upper-level distribution network uses the power consumption strategy fed back from the lower level. With the objectives of comprehensive operating cost, transformer load balance, and peak-valley load difference at agricultural irrigation nodes, the upper-level distribution network reconfiguration optimization is performed using the CPLEX solver. The objective function J is calculated and retained. h ;

[0102] 4) Calculate the fitness value of each particle, update the velocity and position of the particles based on the updated learning factor and inertia weight, update the time-sharing electricity price parameter and the fitness function of the particles, and update the local optimum of each particle and the global optimum of the particle swarm.

[0103] 5) Determine if the number of iterations is greater than the maximum number of iterations, and compare the difference between the game optimization result and the previous one with the iteration precision. Let h = h + 1 and repeat step 2) iteration. Otherwise, output the optimal result.

[0104] A method and system for coordinating electricity and water interaction in a power distribution network-agricultural irrigation park considering photovoltaic access, characterized by comprising a status detection module, an information storage module, a reconfiguration scheme decision module, a tie switch decision module, an information interaction module, a time-of-use pricing decision module, a behavior module, and an information acquisition module.

[0105] The status detection module is used to detect real-time data information of load status of distribution network nodes, real-time data information of voltage in distribution network system and real-time data information of power transmission of converter, and store them in the information storage module.

[0106] The information storage module is used to store historical data information of the load;

[0107] The reconfiguration scheme decision module, based on the solution results of the multi-objective dynamic reconfiguration model, suggests appropriate reconfiguration levels and corresponding reconfiguration schemes for distribution network operators.

[0108] The interconnection switch control decision module remotely controls the opening and closing actions of the interconnection switch according to the corresponding reconfiguration scheme;

[0109] The information interaction module is used to transmit real-time electricity price information from the power distribution network to the agricultural irrigation park, and at the same time, the agricultural irrigation park transmits real-time electricity purchase information to the power distribution network through the information interaction module.

[0110] The time-of-use pricing decision module is used to adjust the time-of-use pricing in the distribution network and transmit the pricing information to the information interaction module.

[0111] The aforementioned behavioral module is used for dynamic energy regulation in agricultural irrigation parks.

[0112] The information collection module is used to collect real-time data on well water distribution and farmland groundwater in the agricultural irrigation park, and to guide farmers in the irrigation operation.

[0113] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0114] One of the beneficial effects of this scheme is that the power grid can guide the shift in the timing and intensity of irrigation water use in agricultural irrigation parks through electricity pricing. This can reduce the short-term load impact of electricity consumption in agricultural irrigation parks on the power grid, and also reduce the ecological security impact of concentrated water use on groundwater. This achieves the goal of coordinated optimization of electricity and water, realizes the transformation of agricultural irrigation water use from extensive and inefficient to economical and efficient, and effectively supports the safe and reliable operation of rural medium and low voltage power distribution networks, achieving a win-win situation for both agricultural irrigation parks and power distribution networks.

[0115] One of the beneficial effects of this scheme is that the power distribution network's reconfiguration response to the electricity consumption of agricultural irrigation parks can effectively reduce the imbalance of load on both sides of the high-voltage transformer, avoiding the hidden dangers of one-sided heavy load or one-sided light load. At the same time, it optimizes the power flow of the power grid lines, further reducing network losses and ensuring operational safety and reliability. Furthermore, the electricity consumption response of agricultural irrigation parks can lead to further optimization of time-of-use pricing on the power grid side through bargaining, thereby further enhancing the interests of both parties. Attached Figure Description

[0116] Figure 1 This is a schematic diagram of the power distribution network-agricultural irrigation park electricity-water interaction coordination method of the present invention;

[0117] Figure 2 This is a schematic diagram illustrating the working principle of the power distribution network-agricultural irrigation park electricity-water interaction and coordination of the present invention.

[0118] Figure 3 This is a schematic diagram of the physical structure of the power distribution network-agricultural irrigation park according to the present invention;

[0119] Figure 4 This is a diagram of the two-layer interaction framework of the present invention;

[0120] Figure 5 This is a diagram illustrating the game-theoretic solution steps of the present invention;

[0121] Figure 6 Test diagram of the modified 148-node system;

[0122] Figure 7 This invention presents the optimization results of the distribution network-agricultural irrigation park under different electricity price schemes.

[0123] Figure 8 This invention relates to the changes in water consumption and groundwater content in agricultural irrigation parks under different electricity pricing schemes.

[0124] Figure 9 This invention provides an energy consumption plan for agricultural irrigation parks after increasing the photovoltaic installed capacity. Detailed Implementation

[0125] The following description, in conjunction with the appendix of the present invention, Figure 1 - Appendix Figure 9 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0126] Balancing the conflict between electricity and water use, reducing pressure on the power grid, and ensuring the smooth operation of agricultural irrigation are common challenges faced by agricultural users and power distribution companies.

[0127] Example:

[0128] Therefore, as Figure 1 As shown, a flowchart illustrating a method for coordinating electricity and water interaction between a power distribution network and an agricultural irrigation park, considering photovoltaic (PV) grid integration, is provided, including the following steps:

[0129] Step 1: The physical structure of the power distribution network, agricultural irrigation park electricity and water system was constructed.

[0130] Step 2: A two-layer interactive game framework for the power distribution network and agricultural irrigation park was established, and a game model was constructed.

[0131] Step 3: Based on the framework of Step 2, construct a master-slave game model. The upper-level distribution network takes the comprehensive operating cost, transformer load balancing, and peak-valley load difference of agricultural irrigation nodes as optimization objectives, and establishes an optimal decision-making model for distribution network operation mode and time-of-use electricity price. The lower-level agricultural irrigation park, based on the time-of-use electricity price published by the distribution network, takes its own cost and satisfaction as objectives to make optimal irrigation decisions under photovoltaic access.

[0132] Step 4: Solve the interactive game coordination model proposed in this invention using the particle swarm optimization algorithm and the CPLEX toolbox, and verify it with numerical examples; This invention can realize the transformation of agricultural irrigation water use from extensive and inefficient to economical and efficient, while effectively supporting the safe and reliable operation of rural medium and low voltage power distribution networks, achieving a win-win situation for both parties.

[0133] refer to Figure 2 , Figure 2 This is a schematic diagram of the working principle of the power distribution network-agricultural irrigation park electricity-water interaction coordination system of the present invention. The system mainly includes a status detection module, an information storage module, a reconfiguration scheme decision module, a tie switch decision module, an information interaction module, a time-of-use pricing decision module, a behavior module, and an information collection module. The main functions of the status detection module are to detect real-time load status data of distribution network nodes, real-time voltage data of the distribution network system, and real-time power transmission data of converters, and store them in the information storage module; the main function of the information storage module is to store historical load data; the main function of the reconfiguration scheme decision module is to solve the multi-objective dynamic reconfiguration model and suggest appropriate reconfiguration levels and corresponding reconfiguration schemes for distribution network operators; the main function of the tie switch control decision module is to remotely control the opening and closing actions of tie switches according to the corresponding reconfiguration scheme; the main function of the information interaction module is to transmit real-time electricity price information from the distribution network to the agricultural irrigation park, while the agricultural irrigation park transmits real-time electricity purchase information to the distribution network through the information interaction module; the main function of the time-of-use pricing decision module is to adjust the time-of-use electricity price of the distribution network and transmit the price information to the information interaction module; the main function of the behavior module is to realize dynamic energy regulation in the agricultural irrigation park; and the main function of the information acquisition module is to realize real-time data acquisition of well water distribution and farmland groundwater in the agricultural irrigation park, and further guide farmers in the agricultural irrigation park to carry out irrigation operations.

[0134] refer to Figure 3 , Figure 3This is a schematic diagram of the physical structure of the power distribution network-agricultural irrigation park according to the present invention. The power distribution network has natural hierarchical characteristics. A 110kV high-voltage main grid is introduced, and after passing through 110 / 10kV transformers, a large-scale 10kV rural power distribution network is laid. Furthermore, 10 / 0.4kV transformers cover 380V rural low-voltage power supply areas. An agricultural irrigation park is equivalently represented as a load node on a 10kV power distribution network. The agricultural irrigation park contains multiple farmlands and their corresponding irrigation equipment. Irrigation wells are located between the farmlands, and groundwater is drawn locally for irrigation. Simultaneously, the park itself is equipped with photovoltaic and energy storage equipment, and irrigation power is provided jointly by the upstream power distribution network and its own photovoltaic system. Reconfiguration is one of the important means of distribution network optimization control. When the 110 / 10kV transformers are under single-side load, the load of the agricultural irrigation park downstream of TR1 can be transferred to the feeder by adjusting the switching states of BS1 and FS1, thereby realizing the power flow regulation of the feeder downstream of TR1. By adjusting the switching states of TS1 and BS3, the load of the agricultural irrigation park downstream of TR2 can also be transferred between different transformers, thereby realizing the power flow regulation between TR1 and TR2 and achieving the purpose of balancing the load rate on both sides. In addition, by adjusting the switching states of SS1 and BS4, the load between large substations can be transferred, and the load at the high-voltage incoming line can be balanced.

[0135] refer to Figure 4 , Figure 4 This is a two-layer interactive framework diagram of the present invention. The constructed power distribution network-irrigation park structure forms a master-slave game interaction relationship between two stakeholders. The power distribution network supplies electricity to the irrigation park and collects corresponding fees. Its interests include maximizing its own comprehensive operating costs and power grid operation safety. The irrigation park, by accepting the electricity price from the higher level, formulates its own energy consumption plan. Its interests are to minimize costs and maximize its own satisfaction while meeting its irrigation needs. The power grid has no right to directly control the irrigation water use methods of the irrigation park. It can only guide farmers in the irrigation park to participate in demand response through price signals. However, the power grid is the provider of electricity and the price setter, so it is in a dominant position in the interaction between the two parties and is the leader in the master-slave game relationship. The power grid's adjustment of time-of-use pricing affects the farmers in the irrigation park to change their electricity and water use methods; and the farmers' changed electricity load demand will also affect the power grid to readjust its own pricing strategy. Finally, farmers in the agricultural irrigation park adjust the timing and intensity of their irrigation water use based on the electricity prices published by the power grid, so as to minimize operating costs while ensuring a certain level of satisfaction. The power grid, based on the electricity purchase information transmitted from the lower level, restructures and adjusts the distribution network topology to ensure its economic benefits and operational safety, thus achieving coordinated optimization of electricity and water interaction.

[0136] refer to Figure 5 , Figure 5This is a diagram illustrating the game theory solution steps of this invention. The solution process is as follows:

[0137] 1) Set the iteration number h = 0, initialize the load demand data, establish the decision model of each subject, set the number of particles, the maximum number of iterations and the maximum iteration convergence error, and randomly initialize the 24-hour time-of-use electricity price parameter particles.

[0138] 2) Lower-level agricultural irrigation areas according to ( (For the time-of-use electricity price set by the upper layer when the number of iterations is h), the CPLEX solver is used to solve for the optimal irrigation water use scheme and electricity use scheme of the lower layer, and the current optimal objective f is calculated and retained. h And feed it back to the upper level for its own power consumption strategy;

[0139] 3) The upper-level distribution network uses the power consumption strategy fed back from the lower level. With the objectives of comprehensive operating cost, transformer load balance, and peak-valley load difference at agricultural irrigation nodes, the upper-level distribution network reconfiguration optimization is performed using the CPLEX solver. The objective function J is calculated and retained. h ;

[0140] 4) Calculate the fitness value of each particle, update the velocity and position of the particles based on the updated learning factor and inertia weight, update the time-sharing electricity price parameter and the fitness function of the particles, and update the local optimum of each particle and the global optimum of the particle swarm.

[0141] 5) Determine if the number of iterations is greater than the maximum number of iterations, and compare the difference between the game optimization result and the previous one with the iteration precision. Let h = h + 1 and repeat step 2) iteration. Otherwise, output the optimal result.

[0142] The example analysis is as follows:

[0143] This invention employs an improved 148-node system, such as... Figure 6 As shown, the proposed method was validated using six large agricultural irrigation parks connected to the power grid at nodes 17, 26, 34, 60, 120, and 129. Each park has three farmlands downstream. The equipment configuration within each park is identical, including energy storage, photovoltaic (PV) systems, and irrigation systems. Case studies with different electricity price scenarios and agricultural PV installation ratios were also set up to explore the actual optimization benefits of the electricity-water interaction coordination method. The control group represents the situation without considering PV integration in agriculture; under the traditional fixed electricity price, farmers irrigate haphazardly based entirely on their own satisfaction preferences. Based on this, the first scenario involves farmers irrigating after installing PV systems, considering their own satisfaction levels. The second scenario involves the power grid setting a traditional peak-valley flat electricity price model based on experience, with farmers responding to electricity usage by comprehensively considering their own costs and satisfaction. The third scenario is the two-layer optimization model proposed in this invention, where orderly irrigation is carried out under the incentive of optimized time-of-use electricity prices obtained from actual conditions.

[0144] Coordination of electricity and water interaction between power distribution networks and agricultural irrigation parks under different electricity price scenarios:

[0145] Table 1 Comparison of the benefits of power distribution network-agricultural irrigation park under different scenarios.

[0146]

[0147]

[0148] like Figure 7 As shown in the diagram. In the initial scenario, due to the fixed electricity price throughout the day, farmers irrigate haphazardly according to their own agricultural work schedules, with working hours from 7:00 AM to 12:00 PM and 3:00 PM to 7:00 PM, and the daily irrigation trend is basically the same. Since there is no distributed energy, all irrigation electricity comes from the upstream power purchase, with two peaks occurring around 10:00 AM and 5:00 PM each day. Scenario 1 adds agricultural photovoltaic (PV) systems to the initial scenario, with a cost lower than the fixed electricity price from the upstream power purchase. The results show that irrigation electricity consumption follows a similar trend to the initial scenario, with the highest peak still occurring at 10:00 AM. However, because the peak periods for farmer satisfaction and PV output overlap more in the morning, the morning peak is higher than in the initial scenario, while the afternoon peak is lower, showing a "concentrated peak and low peak" trend. Scenario 2 is a traditional peak-valley electricity pricing model where the agricultural irrigation park responds to time-of-use pricing. To pursue lower costs through off-peak electricity pricing, farmers start irrigation earlier (at 5 AM), reaching peak irrigation intensity at 7 AM. Irrigation then decreases in intensity and continues at a low level until 7 PM, completing the day's irrigation work. Compared to the previous scenario, under the peak-valley electricity pricing system, farmers' irrigation electricity and water consumption achieve peak shaving and off-peak usage, but the time span for irrigation operations increases. Scenario 3 represents the optimization model proposed in this invention. In... Figure 7 As shown, driven by both the 24-hour time-of-use electricity pricing and the farmers' own satisfaction goals, farmers choose to irrigate in the morning from 6:00 to 12:00, reaching a double peak in intensity at 8:00 and 10:00, with a decrease in irrigation intensity at 9:00. After a midday break, they irrigate in the afternoon from 15:00 to 19:00, reaching a double peak in intensity at 16:00 and 17:00. Compared with scenarios 1 and 2, scenario 3 shows a more even distribution of electricity and water throughout the day. Although it forms a multi-peak situation, the peak values ​​are significantly reduced.

[0149] Different water usage patterns can lead to significant variations in groundwater levels, such as... Figure 8As shown in the diagram, in the initial scenario and Scenario 1, farmers concentrated their irrigation in the morning, creating a "concentrated peak and low peak" water usage pattern. This resulted in a significant reduction in groundwater content in the morning. When the level dropped to the warning threshold, it further reduced the natural infiltration recharge of groundwater, which was irrecoverable in the short term. Therefore, it is evident that the large-scale concentrated water use under traditional disordered irrigation can cause a short-term, irrecoverable impact on groundwater content. In Scenario 2, the farmers' water usage pattern changed from a "concentrated peak and low peak" type to a "high impact, low duration" type. Although the average intensity decreased due to the increased water usage time span, a significant peak still existed at 7:00 AM. Furthermore, due to the uninterrupted and long-lasting irrigation, while groundwater content could maintain balance and recover quickly within a day or a short period, continuous irrigation over multiple days might prevent the recovery of groundwater from matching the water usage, indicating a potential decline in groundwater recovery. In scenario 3, farmers' water use pattern further changes to a "decentralized, low-peak" type, with multiple water use peaks within a day, but the intensity is significantly reduced compared to scenarios 1 and 2. The decentralized irrigation feature also allows sufficient time for natural infiltration and replenishment of groundwater, without impacting groundwater content.

[0150] Table 1 shows a comparison of the overall benefits of the distribution network and agricultural irrigation park under different scenarios. It is evident that under the traditional irrigation model, farmer satisfaction is the highest; and under the traditional peak-valley electricity pricing system, farmers' electricity costs are the lowest. However, because farmers excessively pursue low costs, sacrificing their rest time (resulting in a 25% decrease in satisfaction), the overall benefits are not well optimized. In scenario 3, the "decentralized, multi-peak" irrigation model reduces park costs by 27% while only decreasing farmer satisfaction by 6.2%, thus optimizing overall benefits. From the grid side, in the initial scenario, the grid does not respond to the electricity consumption of the agricultural irrigation park. The disorderly irrigation electricity consumption in the park peaks in both the morning and afternoon, causing significant impact on the grid and greatly affecting the unevenness of the grid line load. The peak-valley difference in the load at the agricultural irrigation nodes is also quite significant. However, in Scenario 1, photovoltaic (PV) participation in farmers' electricity consumption alleviates grid pressure, creating a "peak-valley" pattern. Simultaneously, the grid responds to (restructures) the impact load on agricultural irrigation parks, significantly reducing the load imbalance on high-voltage transformers. In Scenario 2, guided by traditional peak-valley pricing, the park's electricity consumption is reduced by peak shaving and staggering, resulting in a slight decrease in the peak-valley difference of node loads while maintaining its economic benefits. In Scenario 3, the "decentralized, multi-peak" irrigation operations in agricultural irrigation parks further significantly reduce the peak-valley load difference at irrigation nodes. Restructuring also ensures grid load balance and reduced grid losses, essentially maintaining its economic benefits, achieving the highest overall optimization ratio.

[0151] Coordination of power and water interaction between power distribution networks and agricultural irrigation parks under different photovoltaic installation ratios:

[0152] Considering the impact of agricultural photovoltaic (PV) policies on farmers' production and daily lives, this section further sets up scenarios 4, 5, and 6. These scenarios increase the installed PV capacity based on scenarios 1, 2, and 3, respectively, to analyze the impact of agricultural PV policy implementation on electricity and water consumption in agricultural irrigation parks and the power grid. Under these three scenarios, the installed PV capacity of farmers increases by 1.0 times, and the corresponding energy storage capacity increases by 0.5 times.

[0153] Table 2 Comparison of the benefits of power distribution network-agricultural irrigation park under different scenarios.

[0154]

[0155]

[0156] After farmers install solar panels, the photovoltaic power output can better meet their electricity needs. Based on their irrigation practices, a typical agricultural irrigation area was analyzed, and the farmers' electricity consumption was as follows: Figure 9 As shown. In Scenario 4, due to the fixed electricity price throughout the day, farmers irrigate according to their own satisfaction. Compared to Scenario 1, due to abundant available sunlight, farmers' electricity purchases are significantly reduced, but the range and intensity of irrigation water use remain roughly the same, still exhibiting a "concentrated peak and off-peak" irrigation water use pattern. In Scenario 5, farmers use water more evenly for irrigation operations, compared to... Figure 9 In Scenario 2, the optimization results show a reduction in irrigation water amplitude, achieving a "peak shaving" effect in the "high-impact, low-sustained" irrigation mode, forming a "multiple low-peak, sustained" trend, and the water use is more inclined towards the optimization results of Scenario 3. In Scenario 6, the new 24-hour time-of-use electricity price is obtained through optimization using the dual-layer model of this invention. The results show that the electricity and water consumption trends of farmers are roughly similar to those in Scenario 3, still belonging to the "decentralized, multiple low-peak" irrigation mode.

[0157] As shown in Table 2, the electricity costs of agricultural irrigation parks can be significantly reduced after the installation of agricultural photovoltaic capacity. Even under a fixed electricity price, the costs are lower than those under the electricity price in Scenario 3, while ensuring that farmers' satisfaction is not significantly affected. The costs in Scenario 5 can even be reduced by as much as 45%. At the same time, for the distribution network, although the installation of photovoltaics by farmers will have some impact on the economic benefits of the grid, the peak-valley difference of the load at the agricultural irrigation nodes is further reduced. In addition, the grid reduces the unbalance of transformer load through reconfiguration response, thus improving the overall efficiency of the grid and potentially ensuring the safety and reliability of grid operation.

[0158] In summary, compared to small-scale photovoltaic (PV) installations, increasing PV capacity optimizes both the power distribution network and agricultural irrigation areas to varying degrees. While fixed electricity prices improve the efficiency of both the power grid and farmers in terms of electricity consumption, they have little impact on the range and intensity of irrigation water use. Traditional peak-valley pricing, though insufficient in the context of low PV penetration, will become more advantageous as agricultural PV policies advance. Furthermore, the proposed method for coordinating the interaction between the power distribution network and agricultural irrigation areas, considering PV integration, ensures a more balanced improvement in the efficiency of both the power grid and agricultural irrigation areas, regardless of whether PV installations are small-scale or expanded.

[0159] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for coordinating electricity and water interaction between a distribution network and an agricultural irrigation park, considering photovoltaic grid integration, characterized in that: Includes the following steps: S1. Establish a two-layer interactive game framework for the power distribution network and agricultural irrigation park and construct a game model; S2. Based on the framework of step S1, construct a master-slave game model. The upper-level distribution network takes the comprehensive operating cost, transformer load balance, and peak-valley difference of agricultural irrigation node load as optimization objectives, and establishes an optimal decision-making model for distribution network operation mode and time-of-use electricity price. The lower-level agricultural irrigation park, based on the time-of-use electricity price published by the distribution network, takes its own cost and satisfaction as objectives, and conducts an optimal decision-making model for irrigation under photovoltaic access. S3. The global optimal result is obtained by solving the game coordination model using the particle swarm optimization algorithm and the CPLEX toolbox. In the two-layer master-slave game model of the power distribution network and agricultural irrigation park, an optimal decision-making model for the operation mode of the power distribution network and time-of-use pricing is established based on the optimization objectives of the upper-level power distribution network. Through the interaction and game between the established time-of-use pricing and the lower-level agricultural irrigation park, the model guides the agricultural irrigation park to accurately determine the irrigation water time and intensity of each farmland well, thereby ensuring the reliability of the power grid operation. The lower-level agricultural irrigation park makes optimal irrigation decisions under photovoltaic access based on the time-of-use pricing published by the power distribution network, realizing the orderly control of irrigation and the interactive coordination and optimization between the power distribution network and the agricultural irrigation park. The optimization objectives of the distribution network include comprehensive operating costs, load balancing indicators, and peak-valley load differences at agricultural irrigation nodes. Time-of-use pricing and dynamic network reconfiguration schemes are used as decision variables to optimize the network's economic benefits and load balance, thereby improving safety and reliability. The optimization goals of agricultural irrigation parks include electricity costs and farmers’ irrigation satisfaction indicators. Using irrigation water use plans and electricity consumption as decision variables, they optimize their own energy use plans. The objective function for distribution network optimization decision-making is: In the formula: , , These are the weighting coefficients for each objective. The economic benefit indicators of the 10kV distribution network throughout the entire irrigation cycle; This is an indicator of the load balance under a 110 / 10kV transformer; The overall peak-to-valley load difference index for agricultural irrigation nodes in the power distribution network; Comprehensive operating cost indicators: In the formula, Cost of 10kV network losses Cost per unit of network loss; To reduce the cost of switching operations, Cost per unit of switch operation; This represents the set of various interconnecting switch branches in the reconstructed region. , Branches of switches ij The change in the running state at time point t is represented by a binary variable. A value of "1" indicates a change from an open state to a closed state. A value of "1" indicates a change from a closed state to an open state; To reduce the cost of purchasing electricity from the upstream grid, To determine the total amount of electricity to be purchased from higher authorities, To set a fixed electricity price for it; To generate revenue from selling electricity to lower-level networks, The total amount of electricity sold to lower-level customers; Transformer load balancing indicators: In the formula, , , Substation nodes j At the point of time t Active power, load factor, and transformer load balance index at the location; This represents the total number of substations. To connect to the substation node j The number of transformers at the location; For substation nodes j Transformer f Downstream feeder set, This indicates the active power of the branch, which here refers to the downstream feeder set. The corresponding branch active power; The transformer load balance index represents the mean square error of the load on all transformers in a substation. The expression for the mean square error is a nonlinear equation, which is relaxed to a second-order cone expression in programming simulation: Agricultural irrigation node load peak-valley difference index: The fluctuation range of electricity consumption in the lower-level agricultural irrigation park is also one of the optimization objectives of the distribution network. In the objective function, the method of minimizing the standard deviation of the net load curve is used for equivalence. In the formula, For the first m The standard deviation of the load curve at each irrigation node during the total irrigation period. For at a certain point in time t The load value, This represents the average load over the entire irrigation period. The objective function of the irrigation decision model for agricultural irrigation parks is: In the formula: , These are the weighting coefficients for each objective. This represents the total electricity cost for all agricultural irrigation parks throughout the entire irrigation cycle. For farmers' satisfaction indicators; Total electricity cost of the agricultural irrigation park: In the formula, The total electricity cost for all agricultural irrigation parks; The total operation and maintenance cost of the photovoltaic system. for m The actual output of the photovoltaic system installed in the No. 1 agricultural irrigation park. Unit operating cost of photovoltaic systems; The total operation and maintenance cost of the energy storage system , The battery at different time points t Internal charging and discharging power, , The unit cost of charging and discharging energy storage systems; Farmer satisfaction indicators: The farmer satisfaction index is set based on the farmer's working hours and workload, and is used to represent the farmer's work intentions. In the formula, To ensure the satisfaction of every worker involved in irrigating farmland; This represents the satisfaction coefficient per unit time. The higher the coefficient, the higher the farmer's satisfaction with the irrigation operation.

2. The method for coordinating electricity and water interaction between the power distribution network and agricultural irrigation park, considering photovoltaic access, as described in claim 1, is characterized in that... Constraints on the distribution network include power flow constraints, security constraints, reconfiguration constraints, and high-voltage main grid constraints; constraints on agricultural irrigation parks include power balance constraints, irrigation system constraints, groundwater constraints, photovoltaic power output constraints, and energy storage constraints. The power flow constraint is: In the formula: For j This is the set of branch terminal nodes of the initial node; For j This is the initial set of branch nodes for the terminal node; , and , These represent the branch impedance and the node admittance, respectively. For nodes j Voltage magnitude, branch road ij The magnitude of the current at the point; , Representing nodes respectively j At the point of time t Main network and ordinary reactive load; , , Representing nodes respectively j At the point of time t Main grid, general grid, and agricultural irrigation active load; , Representing branches ij At the point of time t The active and reactive power; Safety constraints include node voltage constraints and branch current constraints as follows: In the formula, , They are nodes j The lower and upper limits of the voltage; , Branch roads ij The lower and upper limits of the current; Reconfiguration constraints include network topology constraints and switching action constraints: In the formula, For the reconstructed network topology, For the set of all feasible radial topologies; Indicates a branch ij The switch status, if it is "1", indicates that the branch circuit... ij The switch is closed; High-voltage main grid constraints: In the formula: , , , These represent the lower and upper limits of active and reactive power injected from the high-voltage main grid into the distribution network, respectively. , The maximum ramp rate limits for the main network; Electric power balance constraints: In the formula, The irrigation pumps configured for each plot of farmland at specific times t The active power; Irrigation system constraints: In the formula, This refers to the actual water flow rate for farmland irrigation. For Yang Cheng; , These are the water pumping efficiency of the well and the motor operating efficiency, respectively. , These are, respectively, the density of irrigation water and the acceleration due to gravity; During the agricultural irrigation cycle, the irrigation system must meet the following constraints: total irrigation water required for the farmland, pump flow rate constraint, and water flow ramp constraint, respectively: In the formula, Time scale T Inside n The total irrigation water required for farmland No. 1; This refers to the upper limit of the water flow rate for irrigation pumps; , These are the upward and downward ramp rates of the well water distribution, respectively. Groundwater constraints: In the formula, , They are respectively n Groundwater in farmland area No. 1 t Time content and initial content; The groundwater extraction process involves the relationship between groundwater flow and drawdown, which is addressed using a linear regression with a linear empirical formula; natural groundwater recharge is also considered. To satisfy the constraint of groundwater dynamic level change over time: In the formula, for n Groundwater level drawdown in farmland area No. 1 , This refers to the relevant proportionality coefficient; It refers to the amount of water stored per unit volume, i.e., the amount of water stored by the aquifer when the water level drops. Without considering the impact of extreme weather such as precipitation, the groundwater in the area where each farmland is located meets the dynamic water balance constraint, as shown below: In addition, considering the ecological security factors of groundwater, an ecological early warning value is set. If the groundwater balance falls below this value at any point during irrigation, the warning value will be triggered. This indicates that the ecological security of groundwater has been affected; Photovoltaic output constraints: In the formula, for m The predicted full-power output of the photovoltaic system installed in the No. 1 agricultural irrigation park; This refers to the rated output power of the photovoltaic system under standard conditions. k Indicates the power temperature coefficient; This refers to the actual operating temperature of the solar panel. To configure the rated operating temperature of the solar panels; , They are respectively m Actual solar irradiance and rated solar irradiance of the No. 1 agricultural irrigation park; Energy storage constraints: In the formula, , They are respectively m The battery packs installed in the No. 1 agricultural irrigation park at the specified time point t and time point t -1 energy storage capacity The self-discharge rate of the battery pack. , These refer to the charging and discharging efficiencies of the battery pack. For a unit of time period; , These are the upper and lower limits of the battery pack capacity, respectively. , These are the upper and lower limits of the charging and discharging power of the energy storage system, respectively. , These are 0-1 integer variables representing the charging and discharging states of the energy storage system, where "1" indicates "yes" and "0" indicates "no".

3. The method for coordinating electricity and water interaction between the power distribution network and agricultural irrigation park, considering photovoltaic access, as described in claim 2, is characterized in that... Based on the characteristics of the flexibility requirements for switching reconfiguration represented by the spatiotemporal distribution of the system net load, the spatial distribution of the net load is divided into time periods, and the same reconfiguration mode is adopted for each time period belonging to the same type of center.

4. The method for coordinating electricity and water interaction between the power distribution network and agricultural irrigation park, considering photovoltaic access, as described in claim 3, is characterized in that... In the game, the information that the upper-level leaders of the power distribution network need to transmit to the agricultural irrigation park is the time-of-use electricity price. They use the electricity price signal to guide the electricity consumption of lower-level users so that they can obtain greater benefits. Define time-of-use pricing as the primary decision variable. Under certain electricity price constraints, the set of time-of-use pricing decisions is as follows: ; Under the premise of satisfying its own constraints, the lower-level agricultural irrigation park calculates the optimal irrigation water use plan based on the time-of-use electricity price issued by the upper level, thus deriving the optimal set of electricity purchase strategies. ; The master-slave game model between the upper and lower levels is represented as follows: In the formula, This represents the two sides in the game, with the upper-level power distribution network as the leader and the lower-level agricultural irrigation park as the follower.

5. The method for coordinating electricity and water interaction between the power distribution network and agricultural irrigation park, considering photovoltaic access, as described in claim 4, is characterized in that... Solving the two-layer model by combining the particle swarm optimization algorithm and the CPLEX solver includes the following sub-steps: 1) Let the number of iterations be... h =0, initialize load demand data, establish decision models for each subject, set the number of particles, maximum number of iterations and maximum iteration convergence error, and randomly initialize 24-hour time-of-use electricity price parameter particles; 2) Lower-level agricultural irrigation areas according to The CPLEX solver is used to solve for the optimal irrigation water use and electricity consumption schemes at the lower level, and the current optimal objective is calculated and retained. And feed it back to the upper level for its own power consumption strategy; among them, For the number of iterations h Time-of-use electricity pricing set by the higher authorities; 3) The upper-level distribution network uses the power consumption strategy fed back from the lower level. With the objectives of comprehensive operating cost, transformer load balance, and peak-valley load difference at agricultural irrigation nodes, the CPLEX solver is used to optimize the upper-level distribution network reconfiguration, and the objective function is calculated and retained. ; 4) Calculate the fitness value of each particle, update the velocity and position of the particles based on the updated learning factor and inertia weight, update the time-sharing electricity price parameters and the fitness function of the particles, and update the local optimum of each particle and the global optimum of the particle swarm. 5) Determine if the number of iterations is greater than the maximum number of iterations, and compare the difference between the game optimization result and the previous one to see if it is less than the iteration precision. Let Repeat step 2) iteration, otherwise output the optimal result.

6. A photovoltaic-connected power distribution network-agricultural irrigation park electricity-water interaction coordination system, characterized in that, It includes a status detection module, an information storage module, a reconfiguration scheme decision module, a tie switch decision module, an information interaction module, a time-of-use pricing decision module, a behavior module, and an information collection module; The status detection module is used to detect real-time data information of load status of distribution network nodes, real-time data information of voltage in distribution network system and real-time data information of power transmission of converter, and store them in the information storage module. The information storage module is used to store historical data information of the load; The reconfiguration scheme decision module, based on the solution results of the multi-objective dynamic reconfiguration model, suggests appropriate reconfiguration levels and corresponding reconfiguration schemes for distribution network operators. The interconnection switch control decision module remotely controls the opening and closing actions of the interconnection switch according to the corresponding reconfiguration scheme; The information interaction module is used to transmit real-time electricity price information from the power distribution network to the agricultural irrigation park, and at the same time, the agricultural irrigation park transmits real-time electricity purchase information to the power distribution network through the information interaction module. The time-of-use pricing decision module is used to adjust the time-of-use pricing of the distribution network and transmit the pricing information to the information interaction module; The behavior module is used for the dynamic adjustment of energy consumption by farmers in the agricultural irrigation park; The information collection module is used to collect real-time data on well water distribution and farmland groundwater in the agricultural irrigation park, and to guide farmers in the irrigation operation.

7. The power distribution network-agricultural irrigation park electricity-water interaction coordination system considering photovoltaic access as described in claim 6, characterized in that, It also includes a remote communication module, which interacts with the status detection module, information storage module, reconfiguration scheme decision module, tie switch decision module, information interaction module, time-of-use pricing decision module, behavior module, and information collection module.

8. A storage medium, characterized in that, The storage medium stores a computer program, which, when run, executes the power-water interaction coordination method for power distribution networks and agricultural irrigation parks that takes into account photovoltaic access as described in any one of claims 1-6.

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