Two-stage optimization scheduling method and device for photovoltaic power generation and energy supply system of agricultural greenhouse

Abstract

The invention provides a two-stage optimization scheduling method and device for an agricultural greenhouse photovoltaic power generation and energy supply system. The method comprises the following steps: establishing a photovoltaic and energy storage combined scheduling model of the agricultural greenhouse in a day-ahead optimization scheduling stage based on operation data of each device of the agricultural greenhouse; solving the photovoltaic and energy storage combined scheduling model to obtain a scheduling strategy of the agricultural greenhouse; based on the scheduling strategy and the operation data of the power distribution network accessed by the agricultural greenhouse, establishing an optimal scheduling model of the power distribution network in an intra-day optimal scheduling stage; solving the optimal scheduling model, and judging whether each node of the power distribution network has voltage out-of-limit; if yes, reactive power device optimization is carried out firstly, and then whether voltage out-of-limit still exists in each node of the power distribution network is judged; and if the voltage out-of-limit still exists, adjusting the active resources of the energy storage device and / or the photovoltaic device until the voltage out-of-limit does not exist in each node of the power distribution network. The photovoltaic power generation acceptance capability of the power distribution network can be improved, and the operation cost of the power distribution network is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of clean energy coordinated scheduling, and in particular to a two-stage optimization scheduling method and device for an agricultural greenhouse photovoltaic power generation energy supply system. Background Art

[0002] Modern agricultural greenhouses require electricity in all seasons, especially in winter when a lot of heat is needed for heating. The traditional greenhouse heating method is coal-fired heating, but coal-fired boilers emit a lot of polluting gases when they are running, causing huge environmental pollution. In recent years, natural gas boilers have become popular. Although natural gas boilers are pollution-free for heating, the price of natural gas, pipeline laying or gas storage costs are high.

[0003] Distributed photovoltaic power generation refers specifically to a distributed power generation system that uses photovoltaic modules to directly convert solar energy into electrical energy. It is a new type of power generation and comprehensive energy utilization method with broad development prospects. It advocates the principle of generating electricity nearby, connecting to the grid nearby, converting nearby, and using nearby. It can not only effectively increase the power generation of photovoltaic power stations of the same scale, but also effectively solve the problem of power loss in voltage boosting and long-distance transportation.

[0004] A new agricultural model combining photovoltaic power generation systems with agricultural production is gradually emerging. By installing photovoltaic power generation systems on farmland or greenhouse roofs, electricity can be provided for agricultural production, while also increasing farmers' income. Therefore, vigorously developing distributed photovoltaic power generation and realizing "farmland planting electricity", "agricultural photovoltaic complementarity" and "one land for two uses" are the general trends in the development of my country's facility agriculture.

[0005] However, with the increasing number of distributed power sources and the continuous growth of new energy power generation, the problem of efficient utilization and consumption of clean energy in rural areas has become increasingly prominent, posing major challenges to the safe and stable operation of distribution networks.

[0006] It is imperative to optimize and control the output of different distributed power sources, improve the rural distribution network's ability to accept photovoltaic power generation, reduce the total cost of distribution network operation, reduce the phenomenon of abandoned light and wind, and achieve scientific and reasonable coordinated scheduling of distributed energy. Summary of the invention

[0007] The present invention provides a two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system to solve the problem of unreasonable coordinated scheduling of rural distributed energy, resulting in high operating costs and unstable operation of the distribution network.

[0008] In a first aspect, the present invention provides a two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system, comprising: based on the operation data of each device in the agricultural greenhouse, establishing a photovoltaic and energy storage joint scheduling model for the agricultural greenhouse in the day-ahead optimization scheduling stage;

[0009] Solve the photovoltaic and energy storage joint scheduling model to obtain the scheduling strategy for agricultural greenhouses;

[0010] Based on the dispatching strategy and the operating data of the distribution network connected to the agricultural greenhouse, an optimal dispatching model of the distribution network in the intraday optimal dispatching stage is established;

[0011] The optimization dispatch model is solved to determine whether there is voltage over-limit at each node of the distribution network; if so, the reactive device is optimized first, and then it is determined whether there is still voltage over-limit at each node of the distribution network; if there is still voltage over-limit, the active resources of the energy storage device and / or photovoltaic device are adjusted until there is no voltage over-limit at each node of the distribution network.

[0012] In one possible implementation, the devices in the agricultural greenhouse include: LED lighting device, physical insecticide device, space electric field, plasma nitrogen fixation device, photovoltaic power generation device, air source heat pump device, phase change heat storage device, water energy storage device and electrical energy storage device.

[0013] In one possible implementation, the photovoltaic and energy storage joint scheduling model for agricultural greenhouses includes a comprehensive electric and thermal operation cost objective function, which is expressed as:

[0014]

[0015] In the formula, minC is the minimum cost of comprehensive electric heating operation of agricultural greenhouse, T is a scheduling cycle, e buy (t) is the price of electricity purchased from the distribution network during period t, e sell (t) is the price of electricity sold to the distribution network during period t; γ is the switch for buying and selling electricity. When γ=1, electricity is sold to the distribution network, and when γ=0, electricity is purchased from the distribution network; P buy.grid (t) is the active power purchased from the distribution network during period t, P sell.grid (t) is the active power sold to the distribution network during period t; C i (t) is the unit power maintenance cost of the i-th power device, P i (t) is the power of the ith power device, n is the number of power devices; C r (t) is the unit thermal power maintenance cost of the rth thermal power device, Q r (t) is the thermal power of the rth thermal power device, and m is the number of thermal power devices;

[0016] Among them, the electric power device includes: photovoltaic power generation device, water energy storage device and electric energy storage device; the thermal power device includes: air source heat pump device and phase change heat storage device.

[0017] In one possible implementation, the photovoltaic and energy storage joint scheduling model for agricultural greenhouses includes an electric power balance constraint. When purchasing active power from the distribution network, the electric power balance constraint is expressed as:

[0018] P buy.grid (t)+P PV (t)+P bat (t) = P e (t)+P EHP (t)+P PUMP (t)

[0019] Where P buy.grid (t) is the active power purchased from the distribution network during period t, P PV (t) is the electrical power of the photovoltaic power generation device, P bat (t) is the active power of the electric energy storage device, P e (t) is the sum of the power of the LED lighting device, the power of the physical insecticide device, the power of the space electric field and the power of the plasma nitrogen fixation device, P EHP (t) is the electrical power of the air source heat pump device, P PUMP (t) is the electrical power of the water storage pump.

[0020] When selling active power to the distribution network, the power balance constraint is expressed as:

[0021] P sell.grid (t)+P PV (t)+P bat (t) = P e (t)+P EHP (t)+P PUMP (t)

[0022] Where P sell.grid (t) is the active power sold to the distribution network during period t, P PV (t) is the electrical power of the photovoltaic power generation device, P bat (t) is the active power of the electric energy storage device, P e (t) is the sum of the power of the LED lighting device, the power of the physical insecticide device, the power of the space electric field and the power of the plasma nitrogen fixation device, P EHP (t) is the electrical power of the air source heat pump device, P PUMP (t) is the electrical power of the water storage pump.

[0023] In a possible implementation, the optimal dispatching model of the distribution network includes an active power loss objective function, which is expressed as:

[0024]

[0025] Where n is the number of load nodes in the distribution network, N is the number of branches in the distribution network, and I n is the branch current value of the nth branch, R n is the resistance value of the nth branch, and T is a scheduling period.

[0026] In a possible implementation, solving the optimization dispatch model to determine whether voltage exceeds the limit at each node of the distribution network includes:

[0027] Solve the optimization dispatch model, calculate the voltage of each node of the distribution network using power flow constraints, and determine whether there is voltage over-limit at each node of the distribution network;

[0028] The power flow constraint is expressed as:

[0029]

[0030] Where P is the active power in the distribution network, Q is the reactive power in the distribution network, and U i is the voltage amplitude of node i, U j is the voltage amplitude at node j, G ij is the conductance between nodes i and j in the distribution network, B ij is the susceptance between nodes i and j in the distribution network, θ ij is the phase difference between nodes i and j in the distribution network.

[0031] In a possible implementation, the voltage constraint of each node is:

[0032] U i,min ≤U i ≤U i,max

[0033] Where U i is the voltage value of the distribution network node i, U i,min is the minimum value of the voltage allowed at the node i of the distribution network, U i,max is the maximum value of the voltage allowed at node i in the distribution network.

[0034] In a possible implementation, the reactive power compensation device includes: a photovoltaic inverter reactive power compensation device, an electric energy storage inverter reactive power compensation device and a distribution network reactive power compensation device;

[0035] The constraints of the PV inverter reactive power compensation device are:

[0036]

[0037] In the formula, is the reactive power of the PV inverter installed at node i, is the minimum reactive power of the PV inverter installed at node i, is the maximum reactive power of the PV inverter installed at node i.

[0038] The constraints of the reactive power compensation device of the energy storage inverter are:

[0039]

[0040] Where P ch,i,t is the charging power of the phase change thermal energy storage device at node i at time t, P dis,i,t is the heat release power of the phase change thermal energy storage device at node i at time t, Q bat,i,t is the reactive power at the energy storage inverter node i at time t, is the maximum apparent power at the energy storage inverter node i at time t.

[0041] The constraints of the reactive power compensation device in the distribution network are:

[0042]

[0043] In the formula, is the capacity of the reactive power compensation device installed at node i, is the minimum capacity of the reactive power compensation device installed at node i, is the maximum capacity of the reactive power compensation device installed at node i.

[0044] In a possible implementation, if the voltage is still over-limited, the active resources of the energy storage device and / or the photovoltaic device are adjusted until the voltage at each node of the distribution network is no longer over-limited, including:

[0045] If the voltage still exceeds the limit, the active resources of the energy storage device are adjusted first. When the active resources of the energy storage device reach the maximum capacity, the active resources of the photovoltaic device are adjusted again until there is no voltage exceeding the limit at each node of the distribution network.

[0046] In a second aspect, the present invention provides a two-stage optimization scheduling device for an agricultural greenhouse photovoltaic power generation energy supply system, comprising:

[0047] The model building module is used to establish a photovoltaic and energy storage joint scheduling model for agricultural greenhouses in the day-ahead optimization scheduling stage based on the operating data of various devices in the agricultural greenhouses;

[0048] The model building module is also used to establish an optimal dispatching model of the distribution network in the intraday optimal dispatching stage based on the dispatching strategy and the operating data of the distribution network connected to the agricultural greenhouse;

[0049] The solution module is used to solve the photovoltaic and energy storage joint scheduling model to obtain the scheduling strategy of the agricultural greenhouse;

[0050] The solution module is also used to solve the optimal dispatch model of the distribution network in the intraday optimal dispatch stage;

[0051] A judgment module is used to judge whether the voltage of each node of the distribution network exceeds the limit;

[0052] Optimization module, used for reactive power device optimization;

[0053] The optimization module is also used to adjust the active resources of the energy storage device and / or the photovoltaic device until there is no voltage exceeding the limit at each node of the distribution network.

[0054] The present invention provides a two-stage optimization scheduling method and device for an agricultural greenhouse photovoltaic power generation energy supply system. The method includes establishing a photovoltaic and energy storage joint scheduling model for the agricultural greenhouse in the day-ahead optimization scheduling stage based on the operation data of each device in the agricultural greenhouse; solving the photovoltaic and energy storage joint scheduling model to obtain the scheduling strategy of the agricultural greenhouse; establishing an optimization scheduling model for the distribution network in the intraday optimization scheduling stage based on the scheduling strategy and the operation data of the distribution network connected to the agricultural greenhouse; solving the optimization scheduling model to determine whether there is a voltage over-limit at each node of the distribution network; if so, first optimize the reactive device, and then determine whether there is still a voltage over-limit at each node of the distribution network; if there is still a voltage over-limit, adjust the active resources of the energy storage device and / or the photovoltaic device until there is no voltage over-limit at each node of the distribution network. The present invention ensures the full and reasonable use of photovoltaic power generation by establishing scheduling models for the day-ahead stage and the intraday stage, and at the same time fully mobilizes the reactive device resources and the energy storage device and / or the photovoltaic device to ensure that the node voltage does not exceed the limit, so that the distribution network operates safely. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0056] Figure 1 It is a schematic diagram of a scenario of an agricultural greenhouse and its access to a power distribution network provided by an embodiment of the present invention;

[0057] Figure 2 It is a flow chart for implementing a two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system provided by an embodiment of the present invention;

[0058] Figure 3 It is a flow chart of a two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system provided by an embodiment of the present invention;

[0059] Figure 4It is a structural schematic diagram of a two-stage optimization scheduling device for an agricultural greenhouse photovoltaic power generation energy supply system provided by an embodiment of the present invention;

[0060] Figure 5 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0063] Figure 1 Schematic diagram of an agricultural greenhouse and a scene of agricultural greenhouse connected to a distribution network provided by an embodiment of the present invention. Figure 1 As shown, it includes agricultural greenhouse area 1, agricultural greenhouse area 2...agricultural greenhouse area n. Taking agricultural greenhouse area 1 as an example, its internal devices include: LED supplementary lighting device, physical insecticide device, space electric field, plasma nitrogen fixation device, photovoltaic power generation device, air source heat pump device, phase change heat storage device, water storage energy storage device, electric energy storage device, photovoltaic inverter device and electric energy storage inverter device.

[0064] Each agricultural greenhouse area is connected to the distribution network through the PCC interconnection line. Taking agricultural greenhouse area 1 as an example, the node where agricultural greenhouse area 1 is connected to the distribution network is i, and the longitudinal component of the voltage drop at i is:

[0065]

[0066] Where U N Indicates the line voltage, P i and Q i Respectively represent the active power and reactive power at node i; P i and Q i The size and direction of P changes with the nature of its load or power supply. i and Q i Both can affect the voltage of node i. Since the resistance and reactance of the distribution network are large, the node voltage is greatly affected by the active power. Limiting the active power injected into the distribution network by photovoltaic power generation can fundamentally prevent the occurrence of overvoltage.

[0067] Therefore, the present invention divides the optimization and scheduling method of the agricultural greenhouse photovoltaic power generation energy supply system into a day-ahead optimization and scheduling stage and an intra-day optimization and scheduling stage.

[0068] The current optimization scheduling stage is to establish a joint scheduling model of agricultural greenhouse photovoltaic and energy storage based on the mathematical models of various loads and energy storage devices in the agricultural greenhouse. The objective function is to minimize the comprehensive electric and thermal operation cost of each device in the agricultural greenhouse. Considering various relevant constraints, the particle swarm algorithm is used to solve the problem, and the scheduling strategy for each agricultural greenhouse is formulated according to the solution results.

[0069] The day-ahead optimization dispatching stage did not consider the specific distribution network structure. The objective function was to minimize the comprehensive operating cost based on the photovoltaic output forecast and the load forecast of each device in the agricultural greenhouse. The uncertainty of photovoltaic active output and load forecast can easily cause the voltage at the grid connection point to exceed the limit. Intraday optimization dispatching is to minimize the active network loss of the distribution network by adjusting the reactive adjustable resources in the distribution network and the reactive adjustable resources in the agricultural greenhouse. During the optimization process, if some nodes in the distribution network do not meet the voltage constraints, it is necessary to adjust the energy storage device and photovoltaic active output in the agricultural greenhouse to eliminate the voltage limit at the distribution network nodes. For specific methods, see Figure 2 .

[0070] Figure 2 is a flowchart of the implementation of the two-stage optimization scheduling method for the agricultural greenhouse photovoltaic power generation energy supply system provided by the embodiment of the present invention, referring to Figure 2 , as detailed below:

[0071] In step 201, based on the operating data of each device in the agricultural greenhouse, a photovoltaic and energy storage joint scheduling model for the agricultural greenhouse in the day-ahead optimization scheduling stage is established.

[0072] The photovoltaic and energy storage joint scheduling model for agricultural greenhouses covers the scheduling strategies of power grids, new energy photovoltaics and energy storage devices. The model takes into account factors such as time-of-use electricity prices, equipment capacity, and charging and discharging restrictions, aiming to minimize operating costs.

[0073] In one possible implementation, the photovoltaic and energy storage joint scheduling model for agricultural greenhouses includes a comprehensive electric and thermal operation cost objective function, which is expressed as:

[0074]

[0075] In the formula, minC is the minimum cost of comprehensive electric heating operation of agricultural greenhouse, T is a scheduling cycle, e buy (t) is the price of electricity purchased from the distribution network during period t, e sell (t) is the price of electricity sold to the distribution network during period t; γ is the switch for buying and selling electricity. When γ=1, electricity is sold to the distribution network, and when γ=0, electricity is purchased from the distribution network; Pbuy.grid (t) is the active power purchased from the distribution network during period t, P sell.grid (t) is the active power sold to the distribution network during period t; C i (t) is the unit power maintenance cost of the i-th power device, P i (t) is the power of the ith power device, n is the number of power devices; C r (t) is the unit thermal power maintenance cost of the rth thermal power device, Q r (t) is the thermal power of the rth thermal power device, and m is the number of thermal power devices;

[0076] Among them, the electric power device includes: photovoltaic power generation device, water energy storage device and electric energy storage device; the thermal power device includes: air source heat pump device and phase change heat storage device.

[0077] Exemplarily, a scheduling cycle T may be 24 hours, and the time period t may be 1 hour.

[0078] In a possible implementation, the photovoltaic and energy storage joint scheduling model of the agricultural greenhouse includes an electric power balance constraint. When purchasing active power of electric energy from the distribution network, the electric power balance constraint is expressed as:

[0079] P buy.grid (t)+P PV (t)+P bat (t) = P e (t)+P EHP (t)+P PUMP (t)

[0080] Where P buy.grid (t) is the active power purchased from the distribution network during period t, P PV (t) is the electrical power of the photovoltaic power generation device, P bat (t) is the active power of the electric energy storage device, P e (t) is the sum of the power of the LED lighting device, the power of the physical insecticide device, the power of the space electric field and the power of the plasma nitrogen fixation device, P EHP (t) is the electrical power of the air source heat pump device, P PUMP (t) is the electrical power of the water storage pump.

[0081] When selling active power to the distribution network, the power balance constraint is expressed as:

[0082] P sell.grid (t)+P PV (t)+P bat (t) = P e (t)+P EHP (t)+PPUMP (t)

[0083] Where P sell.grid (t) is the active power sold to the distribution network during period t, P PV (t) is the electrical power of the photovoltaic power generation device, P bat (t) is the active power of the electric energy storage device, P e (t) is the sum of the power of the LED lighting device, the power of the physical insecticide device, the power of the space electric field and the power of the plasma nitrogen fixation device, P EHP (t) is the electrical power of the air source heat pump device, P PUMP (t) is the electrical power of the water storage pump.

[0084] In a possible implementation, the photovoltaic and energy storage joint scheduling model for agricultural greenhouses also includes a thermal power balance constraint, which is expressed as:

[0085] Q HP (t)+Q hstor (t) = Q h (t)

[0086] In the formula, Q HP (t) is the output thermal power of the air source heat pump at time t, Q hstor (t) is the charging or releasing power of the phase change thermal energy storage device, Q h (t) is the heat load in the agricultural greenhouse.

[0087] In a possible implementation, the photovoltaic and energy storage joint scheduling model of the agricultural greenhouse also includes a phase change thermal energy storage device constraint, and the phase change thermal energy storage device constraint is expressed as:

[0088] E min ≤E(t)≤E max

[0089] 0≤P dis (t)≤P dmax

[0090] 0≤P ch (t)≤P cmax

[0091] Where E(t) is the capacity of the phase change thermal energy storage device at time t, E min is the minimum capacity of the phase change thermal energy storage device, E max is the maximum capacity of the phase change thermal energy storage device; P dis (t) is the heat release power of the phase change thermal energy storage device at time t, P dmax is the maximum heat release power of the phase change thermal energy storage device; P ch(t) is the charging power of the phase change thermal energy storage device at time t, P cmax It is the maximum charging power of the phase change thermal energy storage device.

[0092] In a possible implementation, the photovoltaic and energy storage joint scheduling model for agricultural greenhouses also includes the constraints of the electric energy storage device, which is expressed as:

[0093] E batmin ≤E bat (t)≤E batmax

[0094] 0≤P batdis (t)≤P batdmax

[0095] 0≤P batch (t)≤P batcmax

[0096] In the formula, E bat (t) is the capacity of the energy storage device at time t, E batmin is the minimum capacity of the electric energy storage device, E batmax is the maximum capacity of the electric energy storage device; P batdis (t) is the heat release power of the electric energy storage device at time t, P batdmax is the maximum heat release power of the electric energy storage device; P batch (t) is the charging power of the electric energy storage device at time t, P batcmax It is the maximum charging power of the electric energy storage device.

[0097] In a possible implementation, the photovoltaic and energy storage joint scheduling model of the agricultural greenhouse also includes water storage device constraints, which are expressed as:

[0098] 0≤E pump (t)≤E pumpmax

[0099] 0≤P pumpch (t)≤P pumpmax

[0100] In the formula, E pump (t) is the capacity of the water storage device at time t, E pumpmax is the maximum capacity of the electric energy storage device; P pumpch (t) is the output power of the water storage pump at time t, P pumpmax is the maximum output power of the water storage pump at time t.

[0101] In a possible implementation, the photovoltaic and energy storage joint scheduling model for agricultural greenhouses also includes power supply device constraints, which are expressed as:

[0102]

[0103] Where P i (t) is the power of the ith power supply device in time period t, is the minimum power of the ith power supply device, is the maximum power of the i-th power supply device.

[0104] In the present invention, the power supply device includes a photovoltaic power generation device, an electric energy storage device and active power purchased from the power distribution network.

[0105] In a possible implementation, the photovoltaic and energy storage joint scheduling model for agricultural greenhouses also includes heating device constraints, which are expressed as:

[0106]

[0107] In the formula, Q r (t) is the power of the rth heating device in period t, is the minimum power of the rth heating device, is the maximum power of the rth heating device.

[0108] In step 202, the photovoltaic and energy storage joint scheduling model is solved to obtain the scheduling strategy of the agricultural greenhouse.

[0109] Exemplarily, a particle swarm algorithm or optimization tool software may be used to solve the photovoltaic and energy storage joint scheduling model.

[0110] Steps 201 to 202 complete the optimization of the joint scheduling of photovoltaics and energy storage in the agricultural greenhouse. On this basis, the problem of node voltage exceeding the limit after the agricultural greenhouse is connected to the distribution network should be considered.

[0111] In step 203, based on the dispatching strategy and the operation data of the distribution network to which the agricultural greenhouse is connected, an optimization dispatching model of the distribution network in the intraday optimization dispatching stage is established.

[0112] In a possible implementation, the optimal dispatching model of the distribution network includes an active power loss objective function, which is expressed as:

[0113]

[0114] Where n is the number of load nodes in the distribution network, N is the number of branches in the distribution network, and I n is the branch current value of the nth branch, R n is the resistance value of the nth branch, and T is a scheduling period.

[0115] Exemplarily, a scheduling cycle T may be 24 hours, and the time period t may be 1 hour.

[0116] In step 204, the optimization dispatch model is solved to determine whether voltage exceeds the limit at each node of the distribution network.

[0117] In a possible implementation, solving the optimization dispatch model to determine whether voltage exceeds the limit at each node of the distribution network includes:

[0118] Solve the optimization dispatch model, calculate the voltage of each node of the distribution network using power flow constraints, and determine whether there is voltage over-limit at each node of the distribution network;

[0119] The power flow constraint is expressed as:

[0120]

[0121] Where P is the active power in the distribution network, Q is the reactive power in the distribution network, and U i is the voltage amplitude of node i, U j is the voltage amplitude at node j, G ij is the conductance between nodes i and j in the distribution network, B ij is the susceptance between nodes i and j in the distribution network, θ ij is the phase difference between nodes i and j in the distribution network.

[0122] In a possible implementation, the voltage constraint of each node is:

[0123] U i,min ≤U i ≤U i,max

[0124] Where U i is the voltage value of the distribution network node i, U i,min is the minimum value of the voltage allowed at the node i of the distribution network, U i,max is the maximum value of the voltage allowed at node i in the distribution network.

[0125] In step 205, if yes, then the reactive device optimization is performed first, and then it is determined whether the voltage at each node of the distribution network is still out of limit.

[0126] Reactive power refers to the situation in which the electric field or magnetic field in an AC circuit with reactance absorbs energy from the power supply during part of a cycle and releases energy during the other part. The average power in the entire cycle is zero, but energy is constantly exchanged between the power supply and the reactance element. The maximum value of the exchange rate is the reactive power.

[0127] When the reactive power demand in the system increases, if reactive compensation devices are not installed artificially in the system, the power plant must increase the reactive power output by adjusting the phase. Since the capacity of the generator is limited, it is necessary to reduce the output of active power, that is, to reduce the output capacity of the generator. In order to meet the electricity requirements, the capacity of the generator, power supply line and transformer must be increased, which will not only increase the power supply investment and reduce the equipment utilization rate, but also increase the line loss.

[0128] In a possible implementation, the reactive power compensation device includes: a photovoltaic inverter reactive power compensation device, an electric energy storage inverter reactive power compensation device and a distribution network reactive power compensation device;

[0129] The constraints of the PV inverter reactive power compensation device are:

[0130]

[0131] In the formula, is the reactive power of the PV inverter installed at node i, is the minimum reactive power of the PV inverter installed at node i, is the maximum reactive power of the PV inverter installed at node i.

[0132] The constraints of the reactive power compensation device of the energy storage inverter are:

[0133]

[0134] Where P ch,i,t is the charging power of the phase change thermal energy storage device at node i at time t, P dis,i,t is the heat release power of the phase change thermal energy storage device at node i at time t, Q bat,i,t is the reactive power at the energy storage inverter node i at time t, is the maximum apparent power at the energy storage inverter node i at time t.

[0135] The constraints of the reactive power compensation device in the distribution network are:

[0136]

[0137] In the formula, is the capacity of the reactive power compensation device installed at node i, is the minimum capacity of the reactive power compensation device installed at node i, is the maximum capacity of the reactive power compensation device installed at node i.

[0138] In a possible implementation, the constraints of the optimization scheduling model also include: power constraints of agricultural greenhouse photovoltaic power generation and distribution network interconnection lines. The power constraints of agricultural greenhouse photovoltaic power generation and distribution network interconnection lines are expressed as:

[0139] P pcc_min ≤P pcc,t ≤P pcc_max

[0140] Q pcc_min ≤Q pcc,t ≤Q pcc_max

[0141] Where P pcc,t is the interactive active power between agricultural greenhouse photovoltaic power generation and distribution network, Q pcc,t is the interactive reactive power between agricultural greenhouse photovoltaic power generation and distribution network; P pcc_min is the lower limit of the interactive active power between agricultural greenhouse photovoltaic power generation and distribution network, P pcc_max is the upper limit of the interactive active power between agricultural greenhouse photovoltaic power generation and distribution network; Q pcc_min is the lower limit of the interactive reactive power between the photovoltaic power generation in the agricultural greenhouse and the distribution network during period t, Q pcc_max It is the upper limit of the interactive reactive power between the agricultural greenhouse photovoltaic power generation and the distribution network during period t.

[0142] After step 205 is performed, if there is still a node voltage exceeding the limit, step 206 is performed.

[0143] In step 206 , if the voltage still exceeds the limit, the active resources of the energy storage device and / or the photovoltaic device are adjusted until the voltage at each node of the distribution network does not exceed the limit.

[0144] In a possible implementation, if the voltage is still over-limited, the active resources of the energy storage device and / or the photovoltaic device are adjusted until the voltage at each node of the distribution network is no longer over-limited, including:

[0145] If the voltage still exceeds the limit, the active resources of the energy storage device are adjusted first. When the active resources of the energy storage device reach the maximum capacity, the active resources of the photovoltaic device are adjusted again until there is no voltage exceeding the limit at each node of the distribution network.

[0146] For example, after the reactive device is fully adjusted, if there is still a node voltage exceeding the limit, the electric energy storage device, the water energy storage device and the phase change thermal energy storage device are adjusted first to store energy to the maximum capacity. If there is still a node voltage exceeding the limit, the photovoltaic device can be shaded to reduce its power generation.

[0147] The embodiment of the present invention ensures the full absorption and utilization of green new energy of photovoltaic power generation by establishing a joint scheduling model of photovoltaic and energy storage for agricultural greenhouses in the day-ahead optimization scheduling stage. By establishing an optimization scheduling model for the distribution network in the intraday optimization scheduling stage, the reactive compensation device and resources as well as the energy storage device and / or photovoltaic device are fully adjusted to ensure that the node voltage does not exceed the limit, so that the distribution network operates safely.

[0148] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0149] To further illustrate the implementation of this application, please refer to Figure 3 , Figure 3 It is a flow chart of a two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system provided by an embodiment of the present invention.

[0150] First, the operating data and parameters of each device in the agricultural greenhouse are input, and based on this, the day-ahead optimal dispatching strategy in the agricultural greenhouse is established. Based on the day-ahead optimal dispatching strategy in the agricultural greenhouse, the intraday optimal dispatching considering the distribution network is established.

[0151] Calculate the voltage of each node and determine whether there is a voltage over-limit at each node of the distribution network. If there is a voltage over-limit, first perform reactive compensation adjustment on the photovoltaic inverter reactive compensation device, the electric energy storage inverter reactive compensation device and the distribution network reactive compensation device. After fully mobilizing reactive resources, if there is still a node voltage over-limit, then perform the electric energy storage device, the water storage energy storage device and the phase change thermal energy storage device to store energy to the maximum capacity. If there is still a node voltage over-limit, the photovoltaic device can be shaded to reduce its power generation.

[0152] In this process, the application also sets the maximum number of iterations T for network loss optimization. max , when the number of network loss optimization iterations T is greater than T max The optimization process ends when

[0153] In summary, this application first performs day-ahead optimization scheduling of agricultural greenhouse microgrids, and secondly performs day-ahead optimization scheduling of distribution networks, thereby ensuring full absorption and utilization of green new energy from photovoltaic power generation.

[0154] In solving the problem of voltage over-limit, we first make full use of the reactive output control of agricultural greenhouse photovoltaic inverters and energy storage inverters. After fully mobilizing the reactive compensation devices of the distribution network, the reactive power of agricultural greenhouse photovoltaic inverters and the reactive power of energy storage inverters, if the node voltage still exceeds the limit, we are forced to adjust the photovoltaic active output. This not only reasonably distributes energy, but also ensures the safe operation of the distribution network.

[0155] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0156] Figure 4 This is a schematic diagram of the structure of a two-stage optimization scheduling device for an agricultural greenhouse photovoltaic power generation system provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows:

[0157] Reference Figure 4 The two-stage optimization scheduling device 4 of the agricultural greenhouse photovoltaic power generation energy supply system includes: a model building module 41, a solution module 42, a judgment module 43 and an optimization module 44.

[0158] The model building module 41 is used to establish a photovoltaic and energy storage joint scheduling model for the agricultural greenhouse in the day-ahead optimization scheduling stage based on the operating data of each device in the agricultural greenhouse.

[0159] The model building module 41 is also used to build an optimal dispatching model of the distribution network in the intraday optimal dispatching stage based on the dispatching strategy and the operation data of the distribution network to which the agricultural greenhouse is connected.

[0160] The solution module 42 is used to solve the photovoltaic and energy storage joint scheduling model to obtain the scheduling strategy of the agricultural greenhouse.

[0161] The solving module 42 is also used to solve the optimal dispatching model of the distribution network in the intra-day optimal dispatching stage.

[0162] The judgment module 43 is used to judge whether the voltage of each node of the distribution network exceeds the limit.

[0163] The optimization module 44 is used to optimize the reactive device.

[0164] The optimization module 44 is further used to adjust the active resources of the energy storage device and / or the photovoltaic device until there is no voltage exceeding the limit at each node of the distribution network.

[0165] Figure 5 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, the steps in the above-mentioned two-stage optimization scheduling method for the agricultural greenhouse photovoltaic power generation system are implemented, such as Figure 2 Alternatively, when the processor 50 executes the computer program 52, the functions of the modules in the above-mentioned device embodiments are realized, for example, Figure 4 The functions of the modules 41 to 44 are shown.

[0166] Exemplarily, the computer program 52 may be divided into one or more modules, which are stored in the memory 51 and executed by the processor 50 to implement the present invention. The one or more modules may be a series of computer program instruction segments that can implement specific functions, and the instruction segments are used to describe the execution process of the computer program 52 in the electronic device 5. For example, the computer program 52 may be divided into Figure 4 Modules 41 to 44 are shown.

[0167] The electronic device 5 may be a desktop computer or a notebook. The electronic device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will appreciate that Figure 5 It is only an example of the electronic device 5 and does not constitute a limitation of the electronic device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0168] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0169] The memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 51 may also be an external storage device of the electronic device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. Further, the memory 51 may also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 51 is used to store the computer program and other programs and data required by the electronic device. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0170] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0171] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0172] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0173] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0174] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0176] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned two-stage optimization scheduling method embodiment of each agricultural greenhouse photovoltaic power generation system can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0177] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system, characterized in that: The agricultural greenhouse is deployed with photovoltaic devices and energy storage devices, and the method comprises: based on the operation data of each device in the agricultural greenhouse, establishing a photovoltaic and energy storage joint scheduling model for the agricultural greenhouse in the day-ahead optimization scheduling stage; Solving the photovoltaic and energy storage joint scheduling model to obtain the scheduling strategy of the agricultural greenhouse; Based on the dispatching strategy and the operation data of the distribution network to which the agricultural greenhouse is connected, an optimization dispatching model of the distribution network in the intraday optimization dispatching stage is established; The optimization scheduling model is solved to determine whether there is a voltage over-limit at each node of the distribution network; if so, the reactive device is optimized first, and then it is determined whether there is still a voltage over-limit at each node of the distribution network; if there is still a voltage over-limit, the active resources of the energy storage device and / or the photovoltaic device are adjusted until there is no voltage over-limit at each node of the distribution network.

2. According to claim 1, a two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system is characterized in that: The devices of the agricultural greenhouse include: LED lighting device, physical insecticide device, space electric field, plasma nitrogen fixation device, photovoltaic power generation device, air source heat pump device, phase change heat storage device, water energy storage device and electric energy storage device.

3. A two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system according to claim 1, characterized in that: The photovoltaic and energy storage joint scheduling model of the agricultural greenhouse includes a comprehensive electric heating operation cost objective function, which is expressed as: In the formula, minC is the minimum cost of comprehensive electric heating operation of agricultural greenhouse, T is a scheduling cycle, e buy (t) is the price of electricity purchased from the distribution network during period t, e sell (t) is the price of electricity sold to the distribution network during period t; γ is the switch for buying and selling electricity. When γ=1, electricity is sold to the distribution network, and when γ=0, electricity is purchased from the distribution network; P buy.grid (t) is the active power purchased from the distribution network during period t, P sell.grid (t) is the active power sold to the distribution network during period t; C i (t) is the unit power maintenance cost of the i-th power device, P i (t) is the power of the ith power device, n is the number of power devices; C r (t) is the unit thermal power maintenance cost of the rth thermal power device, Q r (t) is the thermal power of the rth thermal power device, and m is the number of thermal power devices; Among them, the electric power device includes: photovoltaic power generation device, water energy storage device and electric energy storage device; the thermal power device includes: air source heat pump device and phase change heat storage device.

4. A two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system according to claim 1, characterized in that: The photovoltaic and energy storage joint scheduling model of the agricultural greenhouse includes an electric power balance constraint. When the active power of electric energy is purchased from the distribution network, the electric power balance constraint is expressed as: P buy.grid (t)+P PV (t)+P bat (t)=P e (t)+P EHP (t)+P PUMP (t) Where P buy.grid (t) is the active power purchased from the distribution network during period t, P PV (t) is the electrical power of the photovoltaic power generation device, P bat (t) is the active power of the electric energy storage device, P e (t) is the sum of the power of the LED lighting device, the power of the physical insecticide device, the power of the space electric field and the power of the plasma nitrogen fixation device, P EHP (t) is the electrical power of the air source heat pump device, P PUMP (t) is the electrical power of the water storage pump. When selling active power of electric energy to the distribution network, the electric power balance constraint is expressed as: P sell.grid (t)+P PV (t)+P bat (t)=P e (t)+P EHP (t)+P PUMP (t) Where P sell.grid (t) is the active power sold to the distribution network during period t, P PV (t) is the electrical power of the photovoltaic power generation device, P bat (t) is the active power of the electric energy storage device, P e (t) is the sum of the power of the LED lighting device, the power of the physical insecticide device, the power of the space electric field and the power of the plasma nitrogen fixation device, P EHP (t) is the electrical power of the air source heat pump device, P PUMP (t) is the electrical power of the water storage pump.

5. A two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system according to claim 1, characterized in that: The distribution network optimization dispatching model includes an active power loss objective function, which is expressed as: Where n is the number of load nodes in the distribution network, N is the number of branches in the distribution network, and I n is the branch current value of the nth branch, R n is the resistance value of the nth branch, and T is a scheduling period.

6. A two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system according to claim 1, characterized in that: Solving the optimization dispatch model to determine whether voltage exceeds a limit at each node of the distribution network includes: Solving the optimization scheduling model, calculating the voltage of each node of the distribution network using power flow constraints, and determining whether there is a voltage over-limit at each node of the distribution network; The power flow constraint is expressed as: In the formula, P is the active power in the distribution network, Q is the reactive power in the distribution network, and U is i is the voltage amplitude of node i, U j is the voltage amplitude at node j, G ij is the conductance between nodes i and j in the distribution network, B ij is the susceptance between nodes i and j in the distribution network, θ ij is the phase difference between nodes i and j in the distribution network.

7. A two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system according to claim 6, characterized in that: The constraint conditions of the optimization scheduling model include node voltage constraints, which are: IN i,min ≤U i ≤U i,max Where U i is the voltage value of the distribution network node i, U i,min is the minimum value of the voltage allowed at the node i of the distribution network, U i,max is the maximum value of the voltage allowed at node i in the distribution network.

8. A two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system according to claim 1, characterized in that: The reactive power compensation device includes: a photovoltaic inverter reactive power compensation device, an electric energy storage inverter reactive power compensation device and a distribution network reactive power compensation device; The constraints of the optimization scheduling model include the reactive power compensation constraint of the photovoltaic inverter, the reactive power compensation capacity constraint of the electric energy storage inverter and the reactive power compensation capacity constraint of the distribution network. The reactive power compensation constraint of the photovoltaic inverter is: In the formula, is the reactive power of the PV inverter installed at node i, is the minimum reactive power of the PV inverter installed at node i, is the maximum reactive power of the PV inverter installed at node i; The reactive power compensation constraint of the electric energy storage inverter is: Where P ch,i,t is the charging power of the phase change thermal energy storage device at node i at time t, P dis,i,t is the heat release power of the phase change thermal energy storage device at node i at time t, Q bat,i,t is the reactive power at the energy storage inverter node i at time t, is the maximum apparent power at the energy storage inverter node i at time t; The reactive power compensation capacity constraint of the distribution network is: In the formula, is the capacity of the reactive power compensation device installed at node i, is the minimum capacity of the reactive power compensation device installed at node i, is the maximum capacity of the reactive power compensation device installed at node i.

9. A two-stage optimization scheduling method for an agricultural greenhouse photovoltaic power generation energy supply system according to claim 1, characterized in that: If the voltage still exceeds the limit, the active resources of the energy storage device and / or the photovoltaic device are adjusted until the voltage at each node of the distribution network does not exceed the limit, including: If the voltage still exceeds the limit, the active resources of the energy storage device are adjusted first. When the active resources of the energy storage device reach the maximum capacity, the active resources of the photovoltaic device are adjusted again until there is no voltage exceeding the limit at each node of the distribution network.

10. A two-stage optimization scheduling device for an agricultural greenhouse photovoltaic power generation system, characterized in that: include: The model building module is used to establish a photovoltaic and energy storage joint scheduling model for agricultural greenhouses in the day-ahead optimization scheduling stage based on the operating data of various devices in the agricultural greenhouses; The model building module is also used to establish an optimal dispatching model of the distribution network in the intraday optimal dispatching stage based on the dispatching strategy and the operation data of the distribution network connected to the agricultural greenhouse; The solution module is used to solve the photovoltaic and energy storage joint scheduling model to obtain the scheduling strategy of the agricultural greenhouse; The solving module is further used to solve the optimization dispatching model of the distribution network in the intra-day optimization dispatching stage; A judgment module is used to judge whether the voltage of each node of the distribution network exceeds the limit; Optimization module, used for reactive power device optimization; The optimization module is also used to adjust the active resources of the energy storage device and / or the photovoltaic device until there is no voltage exceeding the limit at each node of the distribution network.

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