Village-level micro-grid cooperative operation method and device considering optical storage and charging grid load coupling

Abstract

The invention provides a village-level micro-grid cooperative operation method and device considering optical storage and charging grid load coupling, and belongs to the technical field of power system operation. The method comprises the steps that a village-level micro-grid light-storage-charging-grid-load collaborative model is established under multiple scenes, and photovoltaic, energy storage, charging piles, a grid frame and loads of the village-level micro-grid are considered in the collaborative model; based on the collaborative model, taking a village-level micro-grid operator and a load aggregator as participants of a game, and constructing a village-level micro-grid complete information dynamic game model; and solving the game model to realize cooperative operation of the village-level micro-grid under Nash equilibrium. According to the method, the deep coupling relation of light-storage-charging-grid-load is considered, efficient utilization of large-scale distributed resources can be achieved, self-consistent operation of the village-level micro-grid in a source-load fluctuation scene is guaranteed, photovoltaic construction of the whole county of the country is facilitated, and the friendly access capability of the micro-grid is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation, and in particular relates to a village-level microgrid coordinated operation method and device considering photovoltaic storage-charging grid-load coupling. Background Art

[0002] Building village-level microgrids in rural areas with heavy electricity loads and long transmission radiuses is a key measure to promote the development of rural photovoltaics and new energy vehicles, prioritize pilot projects with a high proportion of renewable energy power generation, and is the most effective way to improve power supply reliability and reduce grid investment. Village-level microgrids incorporate a multi-type photovoltaic-energy storage-charging pile-grid-load (abbreviated as photovoltaic-storage-charging-grid-load) system. Through the flexible control of the photovoltaic-storage-charging-grid-load system, the absorption capacity of distributed photovoltaics can be improved, the power fluctuation range of the village microgrid can be reduced, and the accessibility level of the village microgrid can be improved.

[0003] The types of distributed sources and loads in village microgrids are diverse, and photovoltaics, storage, charging, network and loads belong to different entities. The operation and management level cannot organically coordinate village-level photovoltaics, storage, charging, network and loads, resulting in the difficulty in quantifying the adjustable capacity of distributed resources in various scenarios and the difficulty in tapping flexibility, which restricts the level of low-carbon operation. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies by proposing a method and apparatus for the coordinated operation of village-level microgrids that considers the coupling of photovoltaics, storage, charging, grid, and load. By considering the deep coupling relationship between photovoltaics, storage, charging, grid, and load, this invention can achieve efficient utilization of large-scale distributed resources, ensuring the self-consistent operation of village-level microgrids in scenarios with fluctuating sources and loads. This will contribute to the development of national county-wide photovoltaic systems and significantly enhance the friendly accessibility of microgrids.

[0005] The first embodiment of the present invention proposes a village-level microgrid coordinated operation method considering the coupling of photovoltaic storage and charging network and load, including:

[0006] Establish a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model for multiple scenarios. The collaborative model takes into account the photovoltaic, energy storage, charging piles, grid structure, and load of the village-level microgrid.

[0007] Based on the collaborative model, the village microgrid operator and the load aggregator are taken as game participants to construct a complete information dynamic game model of the village microgrid.

[0008] The game model is solved to achieve the coordinated operation of village-level microgrids under Nash equilibrium.

[0009] In a specific embodiment of the present invention, the establishment of a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model under multiple scenarios includes:

[0010] 1) Establish photovoltaic models, load models and their corresponding scenario sets respectively;

[0011] The photovoltaic model expression is as follows:

[0012]

[0013] The load model expression is as follows

[0014]

[0015] in, and They represent the predicted value and prediction error random variables of active power of photovoltaic i at time t respectively; and They represent the active power prediction value and prediction error random variables of load i at time t respectively; represents the actual value of active power of photovoltaic i at time t, Indicates the actual value of active power of load i at time t; represents the actual value of reactive power of photovoltaic i at time t, Indicates the actual value of reactive power of load i at time t; is the power factor of photovoltaic i, is the power factor of load i;

[0016] Based on the k-means algorithm, the historical operation data of photovoltaic and load in the village microgrid are clustered to obtain a multi-scenario dataset with typical photovoltaic and load levels. The expression is as follows:

[0017]

[0018] in, and They are respectively the active data set and reactive data set of photovoltaic; and They are respectively the active data set and reactive data set of the load; and are the active and reactive power of photovoltaic i at time t in scenario s, respectively; and are the active and reactive power of load i at time t in scenario s; S is the total number of scenarios generated by clustering; T is the total number of scheduling periods in any scenario;

[0019] 2) Establish energy storage model;

[0020]

[0021] in, and are the charge and discharge state variables of energy storage i at time t in scenario s, which are 0-1 variables; and are the charging and discharging power of energy storage i at time t in scenario s, respectively; and are the maximum charging and discharging power of energy storage i respectively;

[0022] SOC of energy storage i at time t in scenario s i.s.t The expression is as follows:

[0023]

[0024] SOC min γ ESS ≤SOC i,s,t ≤SOC max γ ESS

[0025] SOC i,s,1 =SOC i,s,T (8)

[0026] Among them, η ch ,η dis They are the charging coefficient and discharging coefficient of energy storage; SOC min and SOC max are the minimum and maximum values of the energy storage charge state respectively; γ ESS is the state of charge ratio;

[0027] 3) Establish a charging load assessment model;

[0028] Record the total number of q charging piles in charging station i in any village microgrid The car-to-pile ratio is Then the number of electric vehicles served by this type of charging pile in charging station i is:

[0029]

[0030] in, is the number of electric vehicles served by the qth type of charging pile in charging station i, [] is rounded up;

[0031] According to the probability distribution of the user's travel and charging behavior, the Monte Carlo sampling method is used to generate the user's charging start time t0 and charging duration T1, so the user's charging end time is: t w =t0+T1, in the period [t0,t w ], if the charging status of the electric vehicle user is charging, the user's charging status is recorded as 1, otherwise it is recorded as 0. The expression is as follows:

[0032]

[0033] in, is the charging status of the g-th user at time t;

[0034] Based on the obtained number of electric vehicle services, number of charging piles and charging status, the charging load model of the charging station is established as follows:

[0035]

[0036] in, is the number of charging piles of the qth type at charging station i at time t in scenario s; The charging status of the g-th electric vehicle charging at this type of charging pile at charging station i at time t in scenario s; is the charging power of the q-type charging pile in charging station i at time t in scenario s; is the rated charging power of the Q-type charging pile; is the charging power of charging station i at time t in scenario s; q0 is the total number of charging pile categories;

[0037] 4) Establish a network model;

[0038]

[0039]

[0040] U i.min ≤U i.s.t ≤U i.max (16)

[0041]

[0042] Among them, P ij.s.t and Q ij.s.t are the active power and reactive power of branch ij at time t in scenario s; g ij and b ij are the conductance and susceptance of branch ij respectively; θ ij.s.t is the voltage phase angle difference between nodes i and j at time t in scenario s; U i.s.t and U j.s.t are the voltage amplitudes of nodes i and j at time t in scenario s, respectively; is the transmission capacity of branch ij; and are the active and reactive powers of controllable distributed generation i at time t in scenario s, respectively; and are the active powers of non-dispatchable load i and dispatchable load i at time t in scenario s, respectively; and are the reactive powers of the non-dispatchable load i and the dispatchable load i at time t in scenario s respectively; U i.min and U i.max are the lower and upper limits of the voltage amplitude at node i respectively; Ω i is the set of branches connected to node i; and is a 0-1 variable, indicating the direction of power exchange between the village microgrid i and the main grid at time t in scenario s, where When the value is 1, it means that the village microgrid i purchases electricity from the main grid. When the value is 1, it means that the village microgrid i sells electricity to the main grid; and are the maximum power purchase and sale between village microgrid i and the main grid; and They represent the purchased power and sold power of village microgrid i at time t in scenario s, respectively; and are respectively the active power and reactive power actually exchanged by village microgrid i at time t; and are the minimum and maximum exchanged reactive powers between the village microgrid i and the main grid, respectively;

[0043] 5) Establish a flexible model at the load end;

[0044]

[0045] in, and are the original active power and reactive power of dispatchable load i at time t in scenario s, respectively; is the load time shift rate of controllable load i at time t in scenario s; is the power regulation capability limit of dispatchable load i.

[0046] In a specific embodiment of the present invention, the construction of a village-level microgrid complete information dynamic game model includes:

[0047] Calculate the revenue function of village microgrid operators;

[0048] B MO =r MO -c MO (twenty two)

[0049]

[0050] Among them, B MO 、r MO and c MOare the net profit, revenue and cost of village-level microgrid operators respectively; NDR is the number of uncontrollable loads; DR, EV and UPP are the number of regulated loads, the number of charging stations, and the number of coupling nodes between microgrids and the main grid in the load aggregator respectively; PV, ESS and DDG are the number of photovoltaic, energy storage and dispatchable distributed generation respectively; is the electricity price of uncontrollable conventional load i at time t in scenario s; is the demand response electricity price of load aggregator i at time t in scenario s; is the electricity selling price of village microgrid i to the main grid at time t in scenario s; p s is the probability of scenario s; and are the electricity price of village microgrid i purchasing electricity from the main grid and the on-grid electricity price of uncontrollable distributed generation i at time t in scenario s, respectively; is the unit charge and discharge depreciation coefficient of energy storage i at time t in scenario s; The output of controllable distributed power generation is The cost of electricity generation at

[0051] Expressed as a piecewise linear function:

[0052]

[0053] Among them, a i is the fixed operating cost of the controllable distributed power source i at the minimum output; u i.s.t is the operating state variable of the controllable distributed power source i at time t in scenario s, which is 1 when it is running and 0 when it is out of operation; is the maximum output of the nth segment of the controllable distributed power source i after piecewise linearization; N i is the number of segments of piecewise linearization; λ i.n is the marginal cost of power generation of the nth segment of controllable distributed generation i after piecewise linearization; The output of the nth segment generator of the controllable distributed power source i after piecewise linearization;

[0054] Calculate the revenue function of each load aggregator:

[0055]

[0056] Among them, B LA.k is the revenue function of load aggregator k; U k (X) is the utility function value when the load aggregator k controls the total load to be X; DR k and EV k are the number of regulated loads and charging stations in load aggregator k, respectively;

[0057] is the active power of dispatchable load i controlled by load aggregator k at time t in scenario s, is the charging power of charging station i controlled by load aggregator k at time t in scenario s; Δt represents the time interval period;

[0058] Establish demand response electricity price constraints in the game model:

[0059]

[0060]

[0061] in, The maximum electricity price for demand response is defined by the policy. As electricity price; is the marginal cost of electricity generation for the village microgrid operator at time t in scenario s;

[0062] Establish a game mechanism based on the sequential bargaining function;

[0063]

[0064] Among them, P x is the probability that the decision-making participant x rejects the strategy proposed by the current bargaining participant. The decision-making participants are the village microgrid operator and the load aggregator, and the bargaining participants are solar-storage-charging-grid-load; u is the Weber coefficient; s0 is the stimulus constant; v x is the reduction in benefits of decision-makers, v M is the just noticeable difference, v x and v M The expressions are:

[0065]

[0066] in, is the original benefit function value of decision participant j; v′ x is the profit function value of the decision-making participants under the new strategy proposed by the bargaining participants.

[0067] In a specific embodiment of the present invention, it also includes:

[0068] Solve the game model and get B MO and B LA.k The optimal solution of .

[0069] The second embodiment of the present invention proposes a village-level microgrid coordinated operation device considering the coupling of photovoltaic storage and charging network and load, including:

[0070] A collaborative model building module is used to establish a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model in multiple scenarios. The collaborative model takes into account the photovoltaic, energy storage, charging piles, grid structure, and load of the village-level microgrid;

[0071] A game model construction module is used to construct a village microgrid complete information dynamic game model based on the collaborative model, taking the village microgrid operator and the load aggregator as game participants;

[0072] The optimization module is used to solve the game model to achieve the coordinated operation of village-level microgrids under Nash equilibrium.

[0073] In a specific embodiment of the present invention, the establishment of a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model under multiple scenarios includes:

[0074] 1) Establish photovoltaic models, load models and their corresponding scenario sets respectively;

[0075] The photovoltaic model expression is as follows:

[0076]

[0077] The load model expression is as follows

[0078]

[0079] in, and They represent the predicted value and prediction error random variables of active power of photovoltaic i at time t respectively; and They represent the active power prediction value and prediction error random variables of load i at time t respectively; represents the actual value of active power of photovoltaic i at time t, Indicates the actual value of active power of load i at time t; represents the actual value of reactive power of photovoltaic i at time t, Indicates the actual value of reactive power of load i at time t; is the power factor of photovoltaic i, is the power factor of load i;

[0080] Based on the k-means algorithm, the historical operation data of photovoltaic and load in the village microgrid are clustered to obtain a multi-scenario dataset with typical photovoltaic and load levels. The expression is as follows:

[0081]

[0082] in, and They are respectively the active data set and reactive data set of photovoltaic; and They are respectively the active data set and reactive data set of the load; and are the active and reactive power of photovoltaic i at time t in scenario s, respectively; and are the active and reactive power of load i at time t in scenario s; S is the total number of scenarios generated by clustering; T is the total number of scheduling periods in any scenario;

[0083] 2) Establish energy storage model;

[0084]

[0085] in, and are the charge and discharge state variables of energy storage i at time t in scenario s, which are 0-1 variables; and are the charging and discharging power of energy storage i at time t in scenario s, respectively; and are the maximum charging and discharging power of energy storage i respectively;

[0086] SOC of energy storage i at time t in scenario s i.s.t The expression is as follows:

[0087]

[0088] SOC min γ ESS ≤SOC i,s,t ≤SOC max γ ESS

[0089] SOC i,s,1 =SOC i,s,T (8)

[0090] Among them, η ch ,η dis They are the charging coefficient and discharging coefficient of energy storage; SOC min and SOC max are the minimum and maximum values of the energy storage charge state respectively; γ ESS is the state of charge ratio;

[0091] 3) Establish a charging load assessment model;

[0092] Record the total number of q charging piles in charging station i in any village microgrid The car-to-pile ratio is Then the number of electric vehicles served by this type of charging pile in charging station i is:

[0093]

[0094] in, is the number of electric vehicles served by the qth type of charging pile in charging station i, [] is rounded up;

[0095] According to the probability distribution of the user's travel and charging behavior, the Monte Carlo sampling method is used to generate the user's charging start time t0 and charging duration T1, so the user's charging end time is: t w =t0+T1, in the period [t0,t w ], if the charging status of the electric vehicle user is charging, the user's charging status is recorded as 1, otherwise it is recorded as 0. The expression is as follows:

[0096]

[0097] Where suzg.t is the charging status of the g-th user at time t;

[0098] Based on the obtained number of electric vehicle services, number of charging piles and charging status, the charging load model of the charging station is established as follows:

[0099]

[0100] in, is the number of charging piles of the qth type at charging station i at time t in scenario s; The charging status of the g-th electric vehicle charging at this type of charging pile at charging station i at time t in scenario s; is the charging power of the q-type charging pile in charging station i at time t in scenario s; is the rated charging power of the Q-type charging pile; is the charging power of charging station i at time t in scenario s; q0 is the total number of charging pile categories;

[0101] 4) Establish a network model;

[0102]

[0103] U i.min ≤U i.s.t ≤U i.max (16)

[0104]

[0105] Among them, P ij.s.t and Q ij.s.t are the active power and reactive power of branch ij at time t in scenario s; g ij and b ij are the conductance and susceptance of branch ij respectively; θ ij.s.tis the voltage phase angle difference between nodes i and j at time t in scenario s; U i.s.t and U j.s.t are the voltage amplitudes of nodes i and j at time t in scenario s, respectively; is the transmission capacity of branch ij; and are the active and reactive powers of controllable distributed generation i at time t in scenario s, respectively; and are the active powers of non-dispatchable load i and dispatchable load i at time t in scenario s, respectively; and are the reactive powers of the non-dispatchable load i and the dispatchable load i at time t in scenario s respectively; U i.min and U i.max are the lower and upper limits of the voltage amplitude at node i respectively; Ω i is the set of branches connected to node i; and is a 0-1 variable, indicating the direction of power exchange between the village microgrid i and the main grid at time t in scenario s, where When the value is 1, it means that the village microgrid i purchases electricity from the main grid. When the value is 1, it means that the village microgrid i sells electricity to the main grid; and are the maximum power purchase and sale between village microgrid i and the main grid; and They represent the purchased power and sold power of village microgrid i at time t in scenario s, respectively; and are respectively the active power and reactive power actually exchanged by village microgrid i at time t; and are the minimum and maximum exchanged reactive powers between the village microgrid i and the main grid, respectively;

[0106] 5) Establish a flexible model at the load end;

[0107]

[0108] in, and are the original active power and reactive power of dispatchable load i at time t in scenario s, respectively; is the load time shift rate of controllable load i at time t in scenario s; is the power regulation capability limit of dispatchable load i.

[0109] In a specific embodiment of the present invention, the construction of a village-level microgrid complete information dynamic game model includes:

[0110] Calculate the revenue function of village microgrid operators;

[0111] B MO =r MO -c MO (twenty two)

[0112]

[0113] Among them, B MO 、r MO and c MO are the net profit, revenue and cost of village-level microgrid operators respectively; NDR is the number of uncontrollable loads; DR, EV and UPP are the number of regulated loads, the number of charging stations, and the number of coupling nodes between microgrids and the main grid in the load aggregator respectively; PV, ESS and DDG are the number of photovoltaic, energy storage and dispatchable distributed generation respectively; is the electricity price of uncontrollable conventional load i at time t in scenario s; is the demand response electricity price of load aggregator i at time t in scenario s; is the electricity selling price of village microgrid i to the main grid at time t in scenario s; p s is the probability of scenario s; and are the electricity price of village microgrid i purchasing electricity from the main grid and the on-grid electricity price of uncontrollable distributed generation i at time t in scenario s, respectively; is the unit charge and discharge depreciation coefficient of energy storage i at time t in scenario s; The output of controllable distributed power generation is The cost of electricity generation at

[0114] Expressed as a piecewise linear function:

[0115]

[0116] Among them, a i is the fixed operating cost of the controllable distributed power source i at the minimum output; u i.s.t is the operating state variable of the controllable distributed power source i at time t in scenario s, which is 1 when it is running and 0 when it is out of operation; is the maximum output of the nth segment of the controllable distributed power source i after piecewise linearization; N i is the number of segments of piecewise linearization; λ i.n is the marginal cost of power generation of the nth segment of controllable distributed generation i after piecewise linearization; The output of the nth segment generator of the controllable distributed power source i after piecewise linearization;

[0117] Calculate the revenue function of each load aggregator:

[0118]

[0119] Among them, B LA.k is the revenue function of load aggregator k; U k (X) is the utility function value when the load aggregator k controls the total load to be X; DR k and EV k are the number of regulated loads and charging stations in load aggregator k, respectively;

[0120] is the active power of dispatchable load i controlled by load aggregator k at time t in scenario s, is the charging power of charging station i controlled by load aggregator k at time t in scenario s; Δt represents the time interval period;

[0121] Establish demand response electricity price constraints in the game model:

[0122]

[0123] in, The maximum electricity price for demand response is defined by the policy. As electricity price; is the marginal cost of electricity generation for the village microgrid operator at time t in scenario s;

[0124] Establish a game mechanism based on the sequential bargaining function;

[0125]

[0126] Among them, P x is the probability that the decision-making participant x rejects the strategy proposed by the current bargaining participant. The decision-making participants are the village microgrid operator and the load aggregator, and the bargaining participants are solar-storage-charging-grid-load; u is the Weber coefficient; s0 is the stimulus constant; v x is the reduction in benefits of decision-makers, v M is the just noticeable difference, v x and v M The expressions are:

[0127]

[0128] in, is the original benefit function value of decision participant j; v x ′ is the profit function value of the decision-making participant under the new strategy proposed by the bargaining participant.

[0129] In a specific embodiment of the present invention, it also includes:

[0130] Solve the game model and get B MO and B LA.k The optimal solution of .

[0131] A third embodiment of the present invention provides an electronic device, including:

[0132] at least one processor; and a memory communicatively coupled to the at least one processor;

[0133] In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-mentioned village-level microgrid collaborative operation method considering the coupling of photovoltaic storage and charging network and load.

[0134] The fourth aspect of the present invention provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned village microgrid collaborative operation method considering the coupling of photovoltaic storage and charging network.

[0135] The characteristics and beneficial effects of the present invention are:

[0136] This paper first considers the different resource characteristics and constructs a photovoltaic-storage-charging-grid-load synergy model for village-level microgrids in multiple scenarios. It then analyzes the multi-stakeholder village microgrid architecture and distinguishes the market players between village-level microgrid operators and load aggregators. Finally, it constructs a fully informed dynamic game model for village-level microgrids with multi-stakeholder participation. Based on the game between bargaining and decision-making players, it achieves a Nash equilibrium.

[0137] The present invention can realize the efficient utilization of large-scale distributed resources, ensure the self-consistent operation of village-level microgrids under source-load fluctuation scenarios, and thus greatly improve the flexibility, safety and economy of multi-scenario and multi-subject collaborative operation of village-level microgrids under scenarios with high distributed photovoltaic penetration and deep coupling of light-storage-charging-grid-load. It is particularly suitable for use in village-level microgrids with deep coupling of light-storage-charging-grid-load, can realize flexible regulation of distributed resources, and is suitable for large-scale promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0138] Figure 1 This is an overall flow chart of a village-level microgrid collaborative operation method considering photovoltaic storage charging network load coupling according to an embodiment of the present invention. DETAILED DESCRIPTION

[0139] The present invention proposes a village-level microgrid collaborative operation method and device considering the coupling of photovoltaic storage and charging network and load, which is further described in detail below with reference to the accompanying drawings and specific implementation.

[0140] The first embodiment of the present invention proposes a village-level microgrid coordinated operation method considering the coupling of photovoltaic storage and charging network and load, including:

[0141] Establish a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model for multiple scenarios. The collaborative model takes into account the photovoltaic, energy storage, charging piles, grid structure, and load of the village-level microgrid.

[0142] Based on the collaborative model, the village microgrid operator and the load aggregator are taken as game participants to construct a complete information dynamic game model of the village microgrid.

[0143] The game model is solved to achieve the coordinated operation of village-level microgrids under Nash equilibrium.

[0144] In a specific embodiment of the present invention, the method for cooperative operation of village-level microgrid considering the coupling of photovoltaic storage and charging network and load is as follows: Figure 1 As shown, the following steps are included:

[0145] 1) Establish a village-level microgrid solar-storage-charging-grid-load synergy model for multiple scenarios. The specific steps are as follows:

[0146] 1-1) Establish photovoltaic model, load model and corresponding scenario sets respectively.

[0147] The photovoltaic model expression is as follows:

[0148]

[0149] The load model expression is as follows

[0150]

[0151] in, and They represent the predicted value and prediction error random variables of active power of photovoltaic i at time t respectively; and They represent the active power prediction value and prediction error random variables of load i at time t respectively; represents the actual value of active power of photovoltaic i at time t, Indicates the actual value of active power of load i at time t; represents the actual value of reactive power of photovoltaic i at time t, Indicates the actual value of reactive power of load i at time t; is the power factor of photovoltaic i, is the power factor of load i.

[0152] The scenario generation method can simulate the uncertainty of photovoltaic and load through random sampling. In this embodiment, the historical photovoltaic and load operation data in the village microgrid are clustered based on the k-means algorithm to obtain a multi-scenario dataset with typical photovoltaic and load levels, which can be expressed as:

[0153]

[0154] in, and They are respectively the active power data set and reactive power data set of photovoltaic. and They are load data set and reactive data set respectively. and are the active and reactive power of photovoltaic i at time t in scenario s, respectively. and are the active and reactive power of load i at time t in scenario s, respectively. S is the total number of scenarios generated by clustering. T is the total number of scheduling periods in any scenario.

[0155] 1-2) Establish an energy storage model.

[0156] In this embodiment, energy storage can be charged or discharged in the village microgrid to smooth out source-load fluctuations. Its output is:

[0157]

[0158]

[0159] in, and are the charging and discharging state variables of energy storage i at time t in scenario s, which are 0-1 variables. and are the charging and discharging power of energy storage i at time t in scenario s, respectively. and are the maximum charging and discharging powers of energy storage i, respectively.

[0160] Based on the charging and discharging power time series of energy storage, the SOC of energy storage i at time t in scenario s can be obtained: i.s.t , which should reflect the upper and lower limits of energy storage capacity, and can be specifically expressed as:

[0161]

[0162] SOC min γ ESS ≤SOC i,s,t ≤SOC max γ ESS

[0163] SOC i,s,1 =SOC i,s,T (8)

[0164] Among them, η ch ,η dis They are the charging coefficient and discharging coefficient of energy storage; SOC minand SOC max are the minimum and maximum values of the energy storage charge state respectively; γ ESS is the state of charge ratio.

[0165] 1-3) Establish a charging load assessment model;

[0166] The car-to-pile ratio can map the relationship between the number of electric vehicles and the number of charging piles. The charging load can be evaluated based on the number of charging piles, the car-to-pile ratio and the charging behavior of electric vehicle users.

[0167] In this embodiment, it is assumed that there are q types of charging piles in charging station i in any village microgrid. The car-to-pile ratio is The number of electric vehicles served by this type of charging pile in charging station i can be estimated as:

[0168]

[0169] in, is the number of electric vehicles served by the qth type of charging pile in charging station i. [] is rounded up.

[0170] According to the probability distribution of the user's travel and charging behavior, the Monte Carlo sampling method is used to generate the user's charging start time t0 and charging duration T1, so the user's charging end time is: t w =t0+T1, in the period [t0,t w ], if the charging status of the electric vehicle user is "charging", the user's charging status is recorded as 1, otherwise it is recorded as 0. The expression is as follows:

[0171]

[0172] in, is the charging status of the g-th user at time t.

[0173] Based on the obtained number of electric vehicle services, number of charging piles and charging status, the charging load of the charging station can be modeled as:

[0174]

[0175] Wherein, formula (11) represents the number of charging piles at the charging station, formula (12) represents the charging power of the charging piles, and formula (13) represents the charging power of the charging station. is the number of charging piles of the qth type at charging station i at time t in scenario s. It is the charging status of the g-th electric vehicle charging at this type of charging pile at charging station i at time t in scenario s. is the charging power of type q charging pile in charging station i at time t in scenario s. is the rated charging power of the Q-type charging pile. is the charging power of charging station i at time t in scenario s. q0 is the total number of charging pile categories.

[0176] 1-4) Establish a network model.

[0177] In this embodiment, the network model expression is as follows:

[0178]

[0179] U i.min ≤U i.s.t ≤U i.max (16)

[0180]

[0181] Wherein, Equation (14) represents the branch power flow constraint. Equation (15) represents the node power balance. Equation (16) represents the node voltage constraint. Equation (17) represents the line capacity constraint. Equations (18)-(19) represent the power exchange constraint with the main network. P ij.s.t and Q ij.s.t are the active power and reactive power of branch ij at time t in scenario s, respectively. ij and b ij are the conductance and susceptance of branch ij respectively. ij.s.t is the voltage phase angle difference between nodes i and j at time t in scenario s. i.s.t and U j.s.t are the voltage amplitudes of nodes i and j at time t in scenario s, respectively. is the transmission capacity of branch ij. and are the active and reactive powers of controllable distributed generation i at time t in scenario s, respectively. and are the active powers of the non-dispatchable load i and the dispatchable load i at time t in scenario s, respectively. and are the reactive powers of the non-dispatchable load i and the dispatchable load i at time t in scenario s, respectively. i.min and U i.max are the lower and upper limits of the voltage amplitude at node i, respectively. Ω i is the set of branches connected to node i. and is a 0-1 variable, indicating the direction of power exchange between the village microgrid i and the main grid at time t in scenario s, where When the value is 1, it means that the village microgrid i purchases electricity from the main grid. When the value is 1, it means that the village microgrid i sells electricity to the main grid. and are the maximum power purchase and sale between village microgrid i and the main grid respectively. and They represent the village microgrid at time t in scenario s.

[0182] i's purchased and sold electricity power. and are the active and reactive powers actually exchanged by village microgrid i at time t, respectively. and are the minimum and maximum exchanged reactive powers between the village microgrid i and the main grid, respectively.

[0183] 1-5) Establish a flexible model at the load end.

[0184] In this embodiment, some loads of the village microgrid, such as greenhouses, are highly controllable, enabling cross-temporal and spatial transfer of electricity consumption and achieving a good peak-shaving and valley-filling effect, as follows:

[0185]

[0186] Among them, formula (20) represents the power regulation capability constraint of controllable loads such as greenhouses when participating in the coordinated scheduling of multiple flexible resources. Formula (21) represents that the total power consumption of the dispatchable loads within a certain period remains unchanged. and are the original active and reactive powers of dispatchable load i at time t in scenario s, respectively. is the load time shift rate of controllable load i at time t in scenario s. is the power regulation capability limit of dispatchable load i.

[0187] 2) Based on the results of step 1), a complete information dynamic game model of the village microgrid is established; the specific steps are as follows:

[0188] 2-1) Determine the participants of the village-level microgrid game model.

[0189] In this embodiment, the participants in the village microgrid game model are the village microgrid operator and the load aggregator. They are assumed to be individually rational and aim to maximize their own profit function. During the game, each participant has access to all the strategies and profit information of the other participants.

[0190] 2-2) Calculate the revenue function of village-level microgrid operators;

[0191] B MO =r MO -c MO (twenty two)

[0192]

[0193] Among them, B MO 、r MO and c MO are the net revenue, income, and costs of village-level microgrid operators, respectively. NDR is the number of uncontrollable loads. DR, EV, and UPP are the number of regulated loads, the number of charging stations, and the number of microgrid-to-maingrid coupling nodes in the load aggregator, respectively. PV, ESS, and DDG are the number of photovoltaic, energy storage, and dispatchable distributed generation, respectively. is the electricity price of uncontrollable conventional load i at time t in scenario s. is the demand response electricity price of load aggregator i at time t in scenario s. is the electricity selling price of village microgrid i to the main grid at time t in scenario s. s is the probability of scene s. and They are the electricity price of village microgrid i purchasing electricity from the main grid and the on-grid electricity price of uncontrollable distributed generation i at time t in scenario s, respectively. is the unit charge and discharge depreciation coefficient of energy storage i at time t in scenario s. The output of controllable distributed power generation is The cost of electricity generation at that time.

[0194] It can be further expressed as a piecewise linear function:

[0195]

[0196] Among them, a i is the fixed operating cost of the controllable distributed power source i at the minimum output. i.s.t is the operating state variable of the controllable distributed power source i at time t in scenario s, which is 1 when it is operating and 0 when it is out of operation. is the maximum output of the nth segment of the controllable distributed power source i after piecewise linearization. N i is the number of segments of piecewise linearization. i.n is the marginal cost of power generation of the nth segment of controllable distributed generation i after piecewise linearization. It is the generator output of the nth section of the controllable distributed power source i after piecewise linearization.

[0197] Calculate the revenue function of each load aggregator:

[0198]

[0199] Among them, B LA.k is the revenue function of load aggregator k. k (X) is the utility function value when the load aggregator k controls the total load to be X; DR k and EV k are the regulated loads and the number of charging stations in load aggregator k, respectively.

[0200] is the active power of dispatchable load i controlled by load aggregator k at time t in scenario s, is the charging power of charging station i at time t in scenario s, controlled by load aggregator k. Δt represents the time interval.

[0201] 2-3) Establishing demand response electricity price constraints in the game model:

[0202]

[0203] in, The maximum electricity price for demand response is set by the policy. In order to increase users’ enthusiasm for participating in demand response, For electricity prices; is the marginal cost of electricity generation for the village microgrid operator at time t in scenario s.

[0204] 2-4) Establish a game mechanism based on the sequential bargaining function.

[0205] In this embodiment, in order to improve the fairness of the dynamic game while ensuring the smooth progress of the game, the participants in the game need to make a certain degree of concessions to the strategy proposed by the first mover without seriously damaging their own interests. The sequential bargaining function is used to describe the process of decision-making participants (in this embodiment, village-level microgrid operators and load aggregators) judging whether to make concessions and accept the strategy proposed by the bargaining participants (in this embodiment, solar-storage-charging-grid-load).

[0206]

[0207] Among them, P x is the probability that decision participant x rejects the strategy proposed by the current bargaining participant. u is the Weber coefficient (which can be obtained from historical experience). s0 is the stimulus constant (which can be obtained from historical experience). v x is the reduction in benefits of decision-makers, v M is the just noticeable difference, v x and v M The expressions are:

[0208]

[0209] in, is the original benefit function value of decision participant j. v′ x is the profit function value of the decision-making participants under the new strategy proposed by the bargaining participants.

[0210] 3) Use the mixed integer linear programming method to solve the model established in step 2) and obtain the benefits of the village microgrid operator and load aggregator, that is, B MOand B LA.k The optimal solution of .

[0211] With this benefit, village-level microgrid operators and load aggregators can effectively improve the power flow distribution of village-level microgrids, promote the consumption of new energy, and ensure the safe operation of village-level microgrids.

[0212] To implement the above embodiment, the second embodiment of the present invention proposes a village-level microgrid collaborative operation device considering the coupling of photovoltaic storage and charging network and load, including:

[0213] A collaborative model building module is used to establish a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model in multiple scenarios. The collaborative model takes into account the photovoltaic, energy storage, charging piles, grid structure, and load of the village-level microgrid;

[0214] A game model construction module is used to construct a village microgrid complete information dynamic game model based on the collaborative model, taking the village microgrid operator and the load aggregator as game participants;

[0215] The optimization module is used to solve the game model to achieve the coordinated operation of village-level microgrids under Nash equilibrium.

[0216] In a specific embodiment of the present invention, the establishment of a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model under multiple scenarios includes:

[0217] 1) Establish photovoltaic models, load models and their corresponding scenario sets respectively;

[0218] The photovoltaic model expression is as follows:

[0219]

[0220] The load model expression is as follows

[0221]

[0222] in, and They represent the predicted value and prediction error random variables of active power of photovoltaic i at time t respectively; and They represent the active power prediction value and prediction error random variables of load i at time t respectively; represents the actual value of active power of photovoltaic i at time t, Indicates the actual value of active power of load i at time t; represents the actual value of reactive power of photovoltaic i at time t, Indicates the actual value of reactive power of load i at time t; is the power factor of photovoltaic i, is the power factor of load i;

[0223] Based on the k-means algorithm, the historical operation data of photovoltaic and load in the village microgrid are clustered to obtain a multi-scenario dataset with typical photovoltaic and load levels. The expression is as follows:

[0224]

[0225] in, and They are respectively the active data set and reactive data set of photovoltaic; and They are respectively the active data set and reactive data set of the load; and are the active and reactive power of photovoltaic i at time t in scenario s, respectively; and are the active and reactive power of load i at time t in scenario s; S is the total number of scenarios generated by clustering; T is the total number of scheduling periods in any scenario;

[0226] 2) Establish energy storage model;

[0227]

[0228] in, and are the charge and discharge state variables of energy storage i at time t in scenario s, which are 0-1 variables; and are the charging and discharging power of energy storage i at time t in scenario s, respectively; and are the maximum charging and discharging power of energy storage i respectively;

[0229] SOC of energy storage i at time t in scenario s i.s.t The expression is as follows:

[0230]

[0231] SOC min γ ESS ≤SOC i,s,t ≤SOC max γ ESS

[0232] SOC i,s,1 =SOC i,s,T (8)

[0233] Among them, η ch ,η dis They are the charging coefficient and discharging coefficient of energy storage; SOC min and SOC maxare the minimum and maximum values of the energy storage charge state respectively; γ ESS is the state of charge ratio.

[0234] 3) Establish a charging load assessment model;

[0235] Record the total number of q charging piles in charging station i in any village microgrid The car-to-pile ratio is Then the number of electric vehicles served by this type of charging pile in charging station i is:

[0236]

[0237] in, is the number of electric vehicles served by the qth type of charging pile in charging station i, [] is rounded up;

[0238] According to the probability distribution of the user's travel and charging behavior, the Monte Carlo sampling method is used to generate the user's charging start time t0 and charging duration T1, so the user's charging end time is: t w =t0+T1, in the period [t0,t w ], if the charging status of the electric vehicle user is charging, the user's charging status is recorded as 1, otherwise it is recorded as 0. The expression is as follows:

[0239]

[0240] Where suzg.t is the charging status of the g-th user at time t;

[0241] Based on the obtained number of electric vehicle services, number of charging piles and charging status, the charging load model of the charging station is established as follows:

[0242]

[0243] in, is the number of charging piles of the qth type at charging station i at time t in scenario s; The charging status of the g-th electric vehicle charging at this type of charging pile at charging station i at time t in scenario s; is the charging power of the q-type charging pile in charging station i at time t in scenario s; is the rated charging power of the Q-type charging pile; is the charging power of charging station i at time t in scenario s; q0 is the total number of charging pile categories;

[0244] 4) Establish a network model;

[0245]

[0246]

[0247] U i.min ≤U i.s.t ≤U i.max (16)

[0248]

[0249] Among them, P ij.s.t and Q ij.s.t are the active power and reactive power of branch ij at time t in scenario s; g ij and b ij are the conductance and susceptance of branch ij respectively; θ ij.s.t is the voltage phase angle difference between nodes i and j at time t in scenario s; U i.s.t and U j.s.t are the voltage amplitudes of nodes i and j at time t in scenario s, respectively; is the transmission capacity of branch ij; and are the active and reactive powers of controllable distributed generation i at time t in scenario s, respectively; and are the active powers of non-dispatchable load i and dispatchable load i at time t in scenario s, respectively; and are the reactive powers of the non-dispatchable load i and the dispatchable load i at time t in scenario s respectively; U i.min and U i.max are the lower and upper limits of the voltage amplitude at node i respectively; Ω i is the set of branches connected to node i; and is a 0-1 variable, indicating the direction of power exchange between the village microgrid i and the main grid at time t in scenario s, where When the value is 1, it means that the village microgrid i purchases electricity from the main grid. When the value is 1, it means that the village microgrid i sells electricity to the main grid; and are the maximum power purchase and sale between village microgrid i and the main grid; and They represent the purchased power and sold power of village microgrid i at time t in scenario s, respectively; and are respectively the active power and reactive power actually exchanged by village microgrid i at time t; and are the minimum and maximum exchanged reactive powers between the village microgrid i and the main grid, respectively;

[0250] 5) Establish a flexible model at the load end;

[0251]

[0252]

[0253] in, and are the original active power and reactive power of dispatchable load i at time t in scenario s, respectively; is the load time shift rate of controllable load i at time t in scenario s; is the power regulation capability limit of dispatchable load i.

[0254] In a specific embodiment of the present invention, the construction of a village-level microgrid complete information dynamic game model includes:

[0255] Calculate the revenue function of village microgrid operators;

[0256] B MO =r MO -c MO (twenty two)

[0257]

[0258] Among them, B MO 、r MO and c MO are the net profit, revenue and cost of village-level microgrid operators respectively; NDR is the number of uncontrollable loads; DR, EV and UPP are the number of regulated loads, the number of charging stations, and the number of coupling nodes between microgrids and the main grid in the load aggregator respectively; PV, ESS and DDG are the number of photovoltaic, energy storage and dispatchable distributed generation respectively; is the electricity price of uncontrollable conventional load i at time t in scenario s; is the demand response electricity price of load aggregator i at time t in scenario s; is the electricity selling price of village microgrid i to the main grid at time t in scenario s; p s is the probability of scenario s; and are the electricity price of village microgrid i purchasing electricity from the main grid and the on-grid electricity price of uncontrollable distributed generation i at time t in scenario s, respectively; is the unit charge and discharge depreciation coefficient of energy storage i at time t in scenario s; The output of controllable distributed power generation is The cost of electricity generation at

[0259] Expressed as a piecewise linear function:

[0260]

[0261] Among them, a iis the fixed operating cost of the controllable distributed power source i at the minimum output; u i.s.t is the operating state variable of the controllable distributed power source i at time t in scenario s, which is 1 when it is running and 0 when it is out of operation; is the maximum output of the nth segment of the controllable distributed power source i after piecewise linearization; N i is the number of segments of piecewise linearization; λ i.n is the marginal cost of power generation of the nth segment of controllable distributed generation i after piecewise linearization; The output of the nth segment generator of the controllable distributed power source i after piecewise linearization;

[0262] Calculate the revenue function of each load aggregator:

[0263]

[0264] Among them, B LA.k is the revenue function of load aggregator k; U k (X) is the utility function value when the load aggregator k controls the total load to be X; DR k and EV k are the number of regulated loads and charging stations in load aggregator k, respectively;

[0265] is the active power of dispatchable load i controlled by load aggregator k at time t in scenario s, is the charging power of charging station i controlled by load aggregator k at time t in scenario s; Δt represents the time interval period;

[0266] Establish demand response electricity price constraints in the game model:

[0267]

[0268] in, The maximum electricity price for demand response is defined by the policy. As electricity price; is the marginal cost of electricity generation for the village microgrid operator at time t in scenario s;

[0269] Establish a game mechanism based on the sequential bargaining function;

[0270]

[0271] Among them, P x is the probability that the decision-making participant x rejects the strategy proposed by the current bargaining participant. The decision-making participants are the village microgrid operator and the load aggregator, and the bargaining participants are solar-storage-charging-grid-load; u is the Weber coefficient; s0 is the stimulus constant; v x is the reduction in benefits of decision-makers, v M is the just noticeable difference, vx and v M The expressions are:

[0272]

[0273] in, is the original benefit function value of decision participant j; v x ′ is the profit function value of the decision-making participant under the new strategy proposed by the bargaining participant.

[0274] In a specific embodiment of the present invention, it also includes:

[0275] Solve the game model and get B MO and B LA.k The optimal solution of .

[0276] This can take into account the deep coupling relationship between light, storage, charging, grid and load, realize the efficient use of large-scale distributed resources, ensure the self-consistent operation of village-level microgrids under source-load fluctuation scenarios, contribute to the construction of national county-wide photovoltaics, and greatly improve the friendly access capabilities of microgrids.

[0277] To implement the above embodiment, a third aspect of the present invention provides an electronic device, including:

[0278] at least one processor; and a memory communicatively coupled to the at least one processor;

[0279] In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-mentioned village-level microgrid collaborative operation method considering the coupling of photovoltaic storage and charging network and load.

[0280] To implement the above embodiments, the fourth aspect of the present invention proposes a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned village-level microgrid collaborative operation method considering the coupling of photovoltaic storage and charging network.

[0281] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0282] The computer-readable medium may be included in the electronic device or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When executed by the electronic device, the one or more programs cause the electronic device to execute the aforementioned embodiment of the method for coordinated operation of a village-level microgrid that considers photovoltaic, storage, charging, and grid-load coupling.

[0283] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0284] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0285] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0286] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0287] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0288] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0289] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0290] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0291] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A village-level microgrid coordinated operation method considering the coupling of photovoltaic storage and charging network, characterized by: include: Establish a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model for multiple scenarios. The collaborative model takes into account the photovoltaic, energy storage, charging piles, grid structure, and load of the village-level microgrid. Based on the collaborative model, the village microgrid operator and the load aggregator are taken as game participants to construct a complete information dynamic game model of the village microgrid. The game model is solved to achieve the coordinated operation of village-level microgrids under Nash equilibrium.

2. The method according to claim 1, characterized in that The establishment of a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model in multiple scenarios includes: 1) Establish photovoltaic models, load models and their corresponding scenario sets respectively; The photovoltaic model expression is as follows: The load model expression is as follows in, and They represent the predicted value and prediction error random variables of active power of photovoltaic i at time t respectively; and They represent the active power prediction value and prediction error random variables of load i at time t respectively; represents the actual value of active power of photovoltaic i at time t, Indicates the actual value of active power of load i at time t; represents the actual value of reactive power of photovoltaic i at time t, Indicates the actual value of reactive power of load i at time t; is the power factor of photovoltaic i, is the power factor of load i; Based on the k-means algorithm, the historical operation data of photovoltaic and load in the village microgrid are clustered to obtain a multi-scenario dataset with typical photovoltaic and load levels. The expression is as follows: in, and They are respectively the active data set and reactive data set of photovoltaic; and They are respectively the active data set and reactive data set of the load; and are the active and reactive power of photovoltaic i at time t in scenario s, respectively; and are the active and reactive power of load i at time t in scenario s; S is the total number of scenarios generated by clustering; T is the total number of scheduling periods in any scenario; 2) Establish energy storage model; in, and are the charge and discharge state variables of energy storage i at time t in scenario s, which are 0-1 variables; and are the charging and discharging power of energy storage i at time t in scenario s, respectively; and are the maximum charging and discharging power of energy storage i respectively; SOC of energy storage i at time t in scenario s i.s.t The expression is as follows: SOC min c ESS ≤SOC i,s,t ≤SOC max c ESS SOCIETY i,s,1 =SOC i,s,T (8) Among them, η ch ,η dis They are the charging coefficient and discharging coefficient of energy storage; SOC min and SOC max are the minimum and maximum values of the energy storage charge state respectively; γ ESS is the state of charge ratio; 3) Establish a charging load assessment model; Record the total number of q charging piles in charging station i in any village microgrid The car-to-pile ratio is Then the number of electric vehicles served by this type of charging pile in charging station i is: in, is the number of electric vehicles served by the qth type of charging pile in charging station i, [] is rounded up; According to the probability distribution of the user's travel and charging behavior, the Monte Carlo sampling method is used to generate the user's charging start time t0 and charging duration T1, so the user's charging end time is: t w =t0+T1, in the period [t0,t w ], if the charging status of the electric vehicle user is charging, the user's charging status is recorded as 1, otherwise it is recorded as 0. The expression is as follows: in, is the charging status of the g-th user at time t; Based on the obtained number of electric vehicle services, number of charging piles and charging status, the charging load model of the charging station is established as follows: in, is the number of charging piles of the qth type at charging station i at time t in scenario s; The charging status of the g-th electric vehicle charging at this type of charging pile at charging station i at time t in scenario s; is the charging power of the q-type charging pile in charging station i at time t in scenario s; is the rated charging power of the Q-type charging pile; is the charging power of charging station i at time t in scenario s; q0 is the total number of charging pile categories; 4) Establish a network model; IN i.min ≤U i.s.t ≤U i.max (16) Among them, P ij.s.t and Q ij.s.t are the active power and reactive power of branch ij at time t in scenario s; g ij and b ij are the conductance and susceptance of branch ij respectively; θ ij.s.t is the voltage phase angle difference between nodes i and j at time t in scenario s; U i.s.t and U j.s.t are the voltage amplitudes of nodes i and j at time t in scenario s, respectively; is the transmission capacity of branch ij; and are the active and reactive powers of controllable distributed generation i at time t in scenario s, respectively; and are the active powers of non-dispatchable load i and dispatchable load i at time t in scenario s, respectively; and are the reactive powers of the non-dispatchable load i and the dispatchable load i at time t in scenario s respectively; U i.min and U i.max are the lower and upper limits of the voltage amplitude at node i respectively; Ω i is the set of branches connected to node i; and is a 0-1 variable, indicating the direction of power exchange between the village microgrid i and the main grid at time t in scenario s, where When the value is 1, it means that the village microgrid i purchases electricity from the main grid. When the value is 1, it means that the village microgrid i sells electricity to the main grid; and are the maximum power purchase and sale between village microgrid i and the main grid; and They represent the purchased power and sold power of village microgrid i at time t in scenario s, respectively; and are respectively the active power and reactive power actually exchanged by village microgrid i at time t; and are the minimum and maximum exchanged reactive powers between the village microgrid i and the main grid, respectively; 5) Establish a flexible model at the load end; in, and are the original active power and reactive power of dispatchable load i at time t in scenario s, respectively; is the load time shift rate of controllable load i at time t in scenario s; is the power regulation capability limit of dispatchable load i.

3. The method according to claim 2, characterized in that The construction of the village-level microgrid complete information dynamic game model includes: Calculate the revenue function of village microgrid operators; B MO =r MO -c MO (22) Among them, B MO 、r MO and c MO are the net profit, revenue and cost of village-level microgrid operators respectively; NDR is the number of uncontrollable loads; DR, EV and UPP are the number of regulated loads, the number of charging stations, and the number of coupling nodes between microgrids and the main grid in the load aggregator respectively; PV, ESS and DDG are the number of photovoltaic, energy storage and dispatchable distributed generation respectively; is the electricity price of uncontrollable conventional load i at time t in scenario s; is the demand response electricity price of load aggregator i at time t in scenario s; is the electricity selling price of village microgrid i to the main grid at time t in scenario s; p s is the probability of scenario s; and are the electricity price of village microgrid i purchasing electricity from the main grid and the on-grid electricity price of uncontrollable distributed generation i at time t in scenario s, respectively; is the unit charge and discharge depreciation coefficient of energy storage i at time t in scenario s; The output of controllable distributed power generation is The cost of electricity generation at Expressed as a piecewise linear function: Among them, a i is the fixed operating cost of the controllable distributed power source i at the minimum output; u i.s.t is the operating state variable of the controllable distributed power source i at time t in scenario s, which is 1 when it is running and 0 when it is out of operation; is the maximum output of the nth segment of the controllable distributed power source i after piecewise linearization; N i is the number of segments of piecewise linearization; λ i.n is the marginal cost of power generation of the nth segment of controllable distributed generation i after piecewise linearization; The output of the nth segment generator of the controllable distributed power source i after piecewise linearization; Calculate the revenue function of each load aggregator: Among them, B LA.k is the revenue function of load aggregator k; U k (X) is the utility function value when the load aggregator k controls the total load to be X; DR k and EV k are the number of regulated loads and charging stations in load aggregator k, respectively; is the active power of dispatchable load i controlled by load aggregator k at time t in scenario s, is the charging power of charging station i controlled by load aggregator k at time t in scenario s; Δt represents the time interval period; Establish demand response electricity price constraints in the game model: in, The maximum electricity price for demand response is defined by the policy. As electricity price; is the marginal cost of electricity generation for the village microgrid operator at time t in scenario s; Establish a game mechanism based on the sequential bargaining function; Among them, P x is the probability that the decision-making participant x rejects the strategy proposed by the current bargaining participant. The decision-making participants are the village microgrid operator and the load aggregator, and the bargaining participants are solar-storage-charging-grid-load; u is the Weber coefficient; s0 is the stimulus constant; v x is the reduction in benefits of decision-makers, v M is the just noticeable difference, v x and v M The expressions are: in, is the original benefit function value of decision participant j; v′ x is the profit function value of the decision-making participants under the new strategy proposed by the bargaining participants.

4. The method according to claim 3, characterized in that Also includes: Solve the game model and get B MO and B LA.k The optimal solution of .

5. A village-level microgrid collaborative operation device considering the coupling of photovoltaic storage and charging network, characterized by: include: A collaborative model building module is used to establish a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model in multiple scenarios. The collaborative model takes into account the photovoltaic, energy storage, charging piles, grid structure, and load of the village-level microgrid; A game model construction module is used to construct a village microgrid complete information dynamic game model based on the collaborative model, taking the village microgrid operator and the load aggregator as game participants; The optimization module is used to solve the game model to achieve the coordinated operation of village-level microgrids under Nash equilibrium.

6. The device according to claim 5, characterized in that The establishment of a village-level microgrid photovoltaic-storage-charging-grid-load collaborative model in multiple scenarios includes: 1) Establish photovoltaic models, load models and their corresponding scenario sets respectively; The photovoltaic model expression is as follows: The load model expression is as follows in, and They represent the predicted value and prediction error random variables of active power of photovoltaic i at time t respectively; and They represent the active power prediction value and prediction error random variables of load i at time t respectively; represents the actual value of active power of photovoltaic i at time t, Indicates the actual value of active power of load i at time t; represents the actual value of reactive power of photovoltaic i at time t, Indicates the actual value of reactive power of load i at time t; is the power factor of photovoltaic i, is the power factor of load i; Based on the k-means algorithm, the historical operation data of photovoltaic and load in the village microgrid are clustered to obtain a multi-scenario dataset with typical photovoltaic and load levels. The expression is as follows: in, and They are respectively the active data set and reactive data set of photovoltaic; and They are respectively the active data set and reactive data set of the load; and are the active and reactive power of photovoltaic i at time t in scenario s, respectively; and are the active and reactive power of load i at time t in scenario s; S is the total number of scenarios generated by clustering; T is the total number of scheduling periods in any scenario; 2) Establish energy storage model; in, and are the charge and discharge state variables of energy storage i at time t in scenario s, which are 0-1 variables; and are the charging and discharging power of energy storage i at time t in scenario s, respectively; and are the maximum charging and discharging power of energy storage i respectively; SOC of energy storage i at time t in scenario s i.s.t The expression is as follows: SOC min c ESS ≤SOC i,s,t ≤SOC max c ESS SOCIETY i,s,1 =SOC i,s,T (8) Among them, η ch ,η dis They are the charging coefficient and discharging coefficient of energy storage; SOC min and SOC max are the minimum and maximum values of the energy storage charge state respectively; γ ESS is the state of charge ratio; 3) Establish a charging load assessment model; Record the total number of q charging piles in charging station i in any village microgrid The car-to-pile ratio is Then the number of electric vehicles served by this type of charging pile in charging station i is: in, is the number of electric vehicles served by the qth type of charging pile in charging station i, [] is rounded up; According to the probability distribution of the user's travel and charging behavior, the Monte Carlo sampling method is used to generate the user's charging start time t0 and charging duration T1, so the user's charging end time is: t w =t0+T1, in the period [t0,t w ], if the charging status of the electric vehicle user is charging, the user's charging status is recorded as 1, otherwise it is recorded as 0. The expression is as follows: in, is the charging status of the g-th user at time t; Based on the obtained number of electric vehicle services, number of charging piles and charging status, the charging load model of the charging station is established as follows: in, is the number of charging piles of the qth type at charging station i at time t in scenario s; The charging status of the g-th electric vehicle charging at this type of charging pile at charging station i at time t in scenario s; is the charging power of the q-type charging pile in charging station i at time t in scenario s; is the rated charging power of the Q-type charging pile; is the charging power of charging station i at time t in scenario s; q0 is the total number of charging pile categories; 4) Establish a network model; IN i.min ≤U i.s.t ≤U i.max (16) Among them, P ij.s.t and Q ij.s.t are the active power and reactive power of branch ij at time t in scenario s; g ij and b ij are the conductance and susceptance of branch ij respectively; θ ij.s.t is the voltage phase angle difference between nodes i and j at time t in scenario s; U i.s.t and U j.s.t are the voltage amplitudes of nodes i and j at time t in scenario s, respectively; is the transmission capacity of branch ij; and are the active and reactive powers of controllable distributed generation i at time t in scenario s, respectively; and are the active powers of non-dispatchable load i and dispatchable load i at time t in scenario s, respectively; and are the reactive powers of the non-dispatchable load i and the dispatchable load i at time t in scenario s respectively; U i.min and U i.max are the lower and upper limits of the voltage amplitude at node i respectively; Ω i is the set of branches connected to node i; and is a 0-1 variable, indicating the direction of power exchange between the village microgrid i and the main grid at time t in scenario s, where When the value is 1, it means that the village microgrid i purchases electricity from the main grid. When the value is 1, it means that the village microgrid i sells electricity to the main grid; and are the maximum power purchase and sale between village microgrid i and the main grid; and They represent the purchased power and sold power of village microgrid i at time t in scenario s, respectively; and are respectively the active power and reactive power actually exchanged by village microgrid i at time t; and are the minimum and maximum exchanged reactive powers between the village microgrid i and the main grid, respectively; 5) Establish a flexible model at the load end; in, and are the original active power and reactive power of dispatchable load i at time t in scenario s, respectively; is the load time shift rate of controllable load i at time t in scenario s; is the power regulation capability limit of dispatchable load i.

7. The device according to claim 6, characterized in that The construction of the village-level microgrid complete information dynamic game model includes: Calculate the revenue function of village microgrid operators; B MO =r MO -c MO (22) Among them, B MO 、r MO and c MO are the net profit, revenue and cost of village-level microgrid operators respectively; NDR is the number of uncontrollable loads; DR, EV and UPP are the number of regulated loads, the number of charging stations, and the number of coupling nodes between microgrids and the main grid in the load aggregator respectively; PV, ESS and DDG are the number of photovoltaic, energy storage and dispatchable distributed generation respectively; is the electricity price of uncontrollable conventional load i at time t in scenario s; is the demand response electricity price of load aggregator i at time t in scenario s; is the electricity selling price of village microgrid i to the main grid at time t in scenario s; p s is the probability of scenario s; and are the electricity price of village microgrid i purchasing electricity from the main grid and the on-grid electricity price of uncontrollable distributed generation i at time t in scenario s, respectively; is the unit charge and discharge depreciation coefficient of energy storage i at time t in scenario s; The output of controllable distributed power generation is The cost of electricity generation at Expressed as a piecewise linear function: Among them, a i is the fixed operating cost of the controllable distributed power source i at the minimum output; u i.s.t is the operating state variable of the controllable distributed power source i at time t in scenario s, which is 1 when it is running and 0 when it is out of operation; is the maximum output of the nth segment of the controllable distributed power source i after piecewise linearization; N i is the number of segments of piecewise linearization; λ i.n is the marginal cost of power generation of the nth segment of controllable distributed generation i after piecewise linearization; The output of the nth segment generator of the controllable distributed power source i after piecewise linearization; Calculate the revenue function of each load aggregator: Among them, B LA.k is the revenue function of load aggregator k; U k (X) is the utility function value when the load aggregator k controls the total load to be X; DR k and EV k are the number of regulated loads and charging stations in load aggregator k, respectively; is the active power of dispatchable load i controlled by load aggregator k at time t in scenario s, is the charging power of charging station i controlled by load aggregator k at time t in scenario s; Δt represents the time interval period; Establish demand response electricity price constraints in the game model: in, The maximum electricity price for demand response is defined by the policy. As electricity price; is the marginal cost of electricity generation for the village microgrid operator at time t in scenario s; Establish a game mechanism based on the sequential bargaining function; Among them, P x is the probability that the decision-making participant x rejects the strategy proposed by the current bargaining participant. The decision-making participants are the village microgrid operator and the load aggregator, and the bargaining participants are solar-storage-charging-grid-load; u is the Weber coefficient; s0 is the stimulus constant; v x is the reduction in benefits of decision-makers, v M is the just noticeable difference, v x and v M The expressions are: in, is the original benefit function value of decision participant j; v′ x is the profit function value of the decision-making participants under the new strategy proposed by the bargaining participants.

8. The device according to claim 7, characterized in that Also includes: Solve the game model and get B MO and B LA.k The optimal solution of .

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method according to any one of claims 1 to 4.

Images