Distributed new energy consumption control method and device

Abstract

The invention provides a distributed new energy consumption control method and device. The method comprises the following steps: acquiring a double-layer master-slave game model; the double-layer master-slave game model comprises an upper-layer game model taking a micro-grid group as a main body and a plurality of micro-grids as slave bodies; a lower-layer game model taking the power distribution network as a main body and the micro-grid group and the shared energy storage operator as slave bodies; inputting the load power data and the new energy power generation power data of the plurality of microgrids into an upper layer game model, and outputting an upper layer game result; inputting the upper-layer game result, the capacity of the shared energy storage operator, the lease cost and the load power data of the power distribution network into a lower-layer game model, and outputting a lower-layer game result; the lower-layer game result comprises the optimal time-sharing purchase and sale electricity price of the power distribution network and the optimal interaction power of the power distribution network, the shared energy storage operator and the microgrid group corresponding to each time period. According to the invention, ordered power interaction between the plurality of microgrids and the power distribution network can be realized.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and device for accommodating and controlling distributed new energy. Background Art

[0002] At present, the power grid is accelerating the development of new energy and low-carbon transformation. With the rapid development of new energy and large-scale access to the power grid, the problems faced by the power grid in absorbing new energy are becoming increasingly prominent. Photovoltaic and wind power, as important components of distributed power sources, will have an impact on the stability of the power grid when they are directly connected to the power grid due to the uncertainty of their power generation. Microgrids can integrate distributed power sources in a region and promote the local absorption of new energy.

[0003] However, when multiple microgrids are connected to the distribution network, although they can support the grid, they will also have an impact on the grid. At the same time, due to the uncertainty of their own renewable energy power generation, multiple microgrids will cause wind and solar power abandonment and power waste. Summary of the invention

[0004] The embodiments of the present invention provide a method and device for controlling the consumption of distributed new energy, so as to solve the problem of power waste caused by disordered interaction between multiple microgrids and distribution networks.

[0005] In a first aspect, an embodiment of the present invention provides a method for controlling the consumption of distributed new energy, including:

[0006] Obtain a two-layer master-slave game model; wherein the two-layer master-slave game model includes an upper-layer game model with a microgrid group as the main body and multiple microgrids as slaves; a lower-layer game model with a distribution network as the main body and a microgrid group and a shared energy storage operator as slaves; the upper-layer game model takes the maximum operating income of the microgrid group as the objective function; the lower-layer game model takes the maximum power consumption of new energy as the objective function;

[0007] Input the load power data and renewable energy power data of multiple microgrids into the upper-level game model, and output the upper-level game results; wherein the upper-level game results include the optimal time-of-use electricity purchase and sale price of the microgrid group and the optimal interactive power of each microgrid corresponding to each time period;

[0008] The upper-level game results, the capacity of the shared energy storage operator, the rental cost and the load power data of the distribution network are input into the lower-level game model, and the lower-level game results are output; wherein the lower-level game results include the optimal time-of-use electricity purchase and sale price of the distribution network, and the optimal interactive power of the distribution network, the shared energy storage operator and the microgrid group corresponding to each time period.

[0009] In a second aspect, an embodiment of the present invention provides a distributed new energy consumption control device, including:

[0010] A two-layer master-slave game model acquisition module is used to acquire a two-layer master-slave game model; wherein the two-layer master-slave game model includes an upper-layer game model with a microgrid group as the main body and multiple microgrids as slaves; a lower-layer game model with a distribution network as the main body and a microgrid group and a shared energy storage operator as slaves; the upper-layer game model takes the maximum operating income of the microgrid group as the objective function; the lower-layer game model takes the maximum power consumption of new energy as the objective function;

[0011] An upper-level game result output module is used to input the load power data and new energy power generation data of multiple microgrids into the upper-level game model and output the upper-level game result; wherein the upper-level game result includes the optimal time-of-use purchase and sale price of the microgrid group and the optimal interactive power of each microgrid corresponding to each time period;

[0012] The lower-level game result output module is used to input the upper-level game results, the capacity of the shared energy storage operator, the rental cost and the load power data of the distribution network into the lower-level game model, and output the lower-level game results; wherein the lower-level game results include the optimal time-of-use purchase and sale price of the distribution network, and the optimal interactive power of the distribution network, the shared energy storage operator and the microgrid group corresponding to each time period.

[0013] The embodiment of the present invention provides a method and device for controlling the consumption of distributed new energy. An upper-level game model is constructed by a microgrid group and multiple microgrids to solve the time-of-use electricity purchase and sale price of the optimal microgrid group and the optimal interactive power of each microgrid in each time period. Then, a lower-level game model is constructed by a distribution network, a microgrid group and a shared energy storage operator. According to the lower-level game model, the time-of-use electricity purchase and sale price of the optimal distribution network and the optimal interactive power of each distribution network, microgrid group and shared energy storage operator in each time period are solved. By using the microgrid group as a power transmission intermediary to participate in the power interaction between multiple microgrids and distribution networks, the microgrid group can absorb the renewable energy power that is difficult to absorb by removing its own load and energy storage demand from multiple microgrids based on the power demand of each microgrid in the group by renting the energy storage capacity of the shared energy storage operator, thereby reducing the power waste of the microgrid. Under the premise of ensuring economy, the shared energy storage operator aims to increase the amount of renewable energy absorbed. The microgrid group, shared energy storage operator and distribution network can reasonably and orderly interact with each other in power, so that multiple microgrids can interact with the distribution network in an orderly manner, which has the advantage of improving the new energy absorption capacity of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] 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 described below 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.

[0015] Figure 1 It is a schematic diagram of the structure of a double-layer master-slave game model provided by an embodiment of the present invention;

[0016] Figure 2 It is a flow chart of the implementation of the method for controlling the consumption of distributed new energy provided by an embodiment of the present invention;

[0017] Figure 3 is a schematic diagram of an IEEE-33 node power system provided by an embodiment of the present invention;

[0018] Figure 4 It is a comparison diagram of the net load before and after the microgrid 1 purchases and sells electricity in the simulation provided by the embodiment of the present invention;

[0019] Figure 5 It is a comparison diagram of the net load before and after the microgrid 2 purchases and sells electricity in the simulation provided by the embodiment of the present invention;

[0020] Figure 6 is a comparison diagram of the net load before and after the microgrid 3 purchases and sells electricity in the simulation provided by an embodiment of the present invention;

[0021] Figure 7 It is a time-sharing electricity purchase and sale price diagram of a microgrid group in a simulation provided by an embodiment of the present invention;

[0022] Figure 8 It is a time-sharing electricity purchase and sale price diagram of the distribution network in the simulation provided by an embodiment of the present invention;

[0023] Fig. 9 It is a structural schematic diagram of a distributed new energy consumption control device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] 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.

[0026] Figure 1 It is a schematic diagram of the structure of a double-layer master-slave game model provided by an embodiment of the present invention. Figure 2 The implementation flow chart of the method for controlling the consumption of distributed new energy provided by the embodiment of the present invention is described in detail as follows:

[0027] Step 101: Obtain a two-layer master-slave game model; wherein the two-layer master-slave game model includes an upper-layer game model with a microgrid group as the main body and multiple microgrids as slaves; a lower-layer game model with a distribution network as the main body and microgrid groups and shared energy storage operators as slaves; the upper-layer game model takes maximizing the operating profit of the microgrid group as the objective function; and the lower-layer game model takes maximizing the amount of new energy power consumption as the objective function.

[0028] The upper-level game model is a master-slave game between a microgrid group and each microgrid inside it. The leader of the upper-level game of the present invention is the microgrid group, and the followers are multiple microgrids. First, the microgrid group formulates a reasonable time-of-use electricity purchase and sale price. The microgrid sends electricity demand to the microgrid group based on the time-of-use electricity purchase and sale price. The microgrid group then adjusts the time-of-use electricity purchase and sale price based on the received electricity demand. The optimal time-of-use electricity purchase and sale price of the microgrid group and the corresponding optimal interactive power of each microgrid in each time period are solved by nesting the Cplex solver of the particle swarm algorithm.

[0029] The microgrid adjusts its own controllable load and energy storage, and better absorbs its own renewable energy power generation while ensuring economic efficiency based on the time-of-use power purchase and sales price of the main microgrid group.

[0030] The lower-level game is a master-slave game between microgrid groups, distribution networks and shared energy storage. The leader of the lower-level game of the present invention is the distribution network, and the followers are the microgrid groups and shared energy storage operators. The distribution network first formulates a time-of-use electricity purchase and sale price based on its own net load curve with the goal of maximizing the amount of new energy consumption, and encourages the microgrid groups and shared energy storage operators to participate in the purchase and sale of electricity; the microgrid groups and shared energy storage operators send electricity purchase and sale demands to the distribution network with the goal of maximizing their respective operating profits. The distribution network continuously adjusts the time-of-use electricity purchase and sale price based on the feedback results, and accordingly adjusts the optimal interactive power of the distribution network, shared energy storage operator and microgrid group corresponding to each time period, and outputs the optimal time-of-use electricity purchase and sale price of the distribution network, the distribution network, the shared energy storage operator and the microgrid group corresponding to each time period through the Cplex solver.

[0031] While reducing the curtailment of wind and solar power, it also reduces energy costs and promotes the distribution network to better absorb new energy.

[0032] Step 102: Input the load power data and renewable energy power data of multiple microgrids into the upper-level game model, and output the upper-level game results; wherein the upper-level game results include the optimal time-of-use electricity purchase and sale price of the microgrid group and the optimal interactive power of each microgrid corresponding to each time period.

[0033] The microgrid has two objective functions, one is to minimize the cost of energy storage, and the other is to minimize the net load fluctuation. Because these two objective functions are contradictory, a multi-objective optimization model is constructed with the minimum cost of energy storage and the minimum variance of the microgrid net load.

[0034] The upper-level game model takes the maximum operating profit of the microgrid group as the objective function, generates the time-of-use electricity purchase and sales price of the initial microgrid group, and each microgrid in the group takes the minimum energy cost and net load variance as the goal, selects the interaction power with the microgrid group and other microgrids in each time period, and obtains the optimal microgrid group's time-of-use electricity purchase and sales price and the corresponding optimal interaction power of each microgrid in each time period through the particle swarm algorithm nested Cplex solver.

[0035] Renewable energy generation in microgrids includes photovoltaic power generation and wind power generation, among which:

[0036] The relationship between the output power of photovoltaic power generation equipment, light intensity and ambient temperature is shown in the following formula:

[0037]

[0038] Where: is the power generated by the photovoltaic system during the time period t; f PV , P PV,rate are the derating factor and rated output power of the photovoltaic system respectively; G t , G STC are the total light intensity and the light intensity coefficient on the photovoltaic panel respectively; α PV is the temperature coefficient on the photovoltaic panel; They are the actual temperature on the photovoltaic panel and the temperature under standard test conditions.

[0039] There is the following relationship between the output power of wind power generation and wind speed v:

[0040]

[0041] In the formula is the output power of the wind turbine during the period t; v t P is the wind speed at the geographical location of the wind turbine generator set during the period t; WT,rate is the rated output power of the wind turbine generator set; v rate 、v ci 、v co They are the rated wind speed and cut-in and cut-out wind speeds of the wind turbine respectively.

[0042] In a possible implementation, the upper-level game model includes an objective function of the microgrid and an objective function of the microgrid cluster;

[0043] The load power data of multiple microgrids and the power data of renewable energy generation are input into the upper-level game model, and the upper-level game results are output, including:

[0044] Initialization parameters; the parameters include the load power data and renewable energy power data of each microgrid;

[0045] Generate the time-of-use electricity purchase and sale price of the initial microgrid group according to the objective function of the microgrid group;

[0046] Obtain the objective function value of each microgrid, and calculate the fitness value of each microgrid according to the objective function value;

[0047] The fitness value and the time-of-use electricity purchase and sale price of the initial microgrid group are input into the Cplex solver, and the optimal interactive power of each microgrid in each time period is solved with the goal of maximizing the operating profit of the microgrid group.

[0048] The interactive benefits of the microgrid and the microgrid group are calculated respectively according to the optimal interactive power of each microgrid in each period and the time-of-use electricity purchase and sales price of the microgrid group;

[0049] Update the position and velocity of particles in the particle swarm and determine the new population;

[0050] If the interactive benefit value no longer increases compared with the previous round, then the previous round is a Nash equilibrium solution;

[0051] Output the time-of-use electricity purchase and sales price of the microgrid group in the previous round and the optimal interactive power of each microgrid in each time period.

[0052] Since the objective function of the microgrid is to minimize the energy cost and the net load variance, the inverse of its objective function is used as the fitness function.

[0053] The population is randomly generated and initialized, and each microgrid individual in the population contains its own generator output, its own energy storage, and the power transmitted to the microgrid group.

[0054] Step 103: Input the upper-level game results, the capacity of the shared energy storage operator, the rental cost and the load power data of the distribution network into the lower-level game model, and output the lower-level game results; wherein the lower-level game results include the optimal time-of-use purchase and sale price of the distribution network, the optimal interactive power of the distribution network, the shared energy storage operator and the microgrid group corresponding to each time period.

[0055] The lower-level game model generates time-of-use electricity purchase and sales prices through the distribution network according to its own net load curve, and continuously adjusts the time-of-use electricity purchase and sales prices of the distribution network through iteration, and accordingly adjusts the optimal interaction power of the distribution network, shared energy storage operators and microgrid groups corresponding to each time period, and outputs the optimal time-of-use electricity purchase and sales price of the distribution network, the distribution network, shared energy storage operators and the optimal interaction power of the microgrid group corresponding to each time period.

[0056] Under the premise of ensuring economy, shared energy storage operators aim to increase the amount of renewable energy consumed. Microgrid groups, shared energy storage operators and distribution networks can interact with each other in a reasonable and orderly manner, so that multiple microgrids and distribution networks can interact with each other in an orderly manner, which has the advantage of improving the grid's ability to absorb renewable energy.

[0057] The respective objective functions (i.e., their respective interactive benefits) and the new energy consumption rate of the distribution network are calculated through the optimal purchase and sale of electricity by the distribution network, microgrid group and shared energy storage operators in each period.

[0058] In a possible implementation, the lower-level game model includes an objective function of the distribution network, an objective function of the shared energy storage operator, and an objective function of the microgrid group;

[0059] The upper-level game results, the capacity of the shared energy storage operator, the rental cost, and the load power data of the distribution network are input into the lower-level game model, and the lower-level game results are output, including:

[0060] Obtain upper-level game results, capacity of shared energy storage operators, leasing costs, and distribution network load power data;

[0061] Generate the time-of-use electricity purchase and sale price of the initial distribution network according to the load power data of the distribution network;

[0062] The upper-level game results, the capacity and leasing costs of shared energy storage operators, the load power data of the distribution network, and the time-of-use electricity purchase and sales prices of the distribution network are input into the Cplex solver. With the goal of maximizing the consumption of new energy in the distribution network, the optimal interactive power of the distribution network, microgrid group, and shared energy storage operators in each time period is solved.

[0063] The interactive benefits of the distribution network, microgrid group and shared energy storage operator are calculated based on the optimal interactive power of the distribution network, microgrid group and shared energy storage operator in each period and the time-of-use purchase and sale electricity price of the distribution network;

[0064] Update the time-of-use electricity purchase and sales prices of the distribution network, and solve the optimal interactive power of the new distribution network, microgrid group, and shared energy storage operator in each time period;

[0065] If the interactive benefit value no longer increases compared with the previous round, then the previous round is a Nash equilibrium solution;

[0066] Output the time-of-use electricity purchase and sales price of the distribution network in the previous round, and the optimal interactive power of the corresponding distribution network, microgrid group and shared energy storage operator in each period.

[0067] In this embodiment, the microgrid group and multiple microgrids construct an upper-level game model to solve the optimal microgrid group's time-sharing electricity price and the corresponding optimal interactive power of each microgrid in each period, and then the distribution network, microgrid group and shared energy storage operator construct a lower-level game model. According to the lower-level game model, the optimal distribution network's time-sharing electricity price and the corresponding distribution network, microgrid group and shared energy storage operator's optimal interactive power in each period are solved. By using the microgrid group as a power transmission intermediary to participate in the power interaction of multiple microgrids and distribution networks, the microgrid group, according to the power demand of each microgrid in the group, rents the energy storage capacity of the shared energy storage operator, absorbs the new energy power generation power that is difficult to absorb by multiple microgrids except for their own load and energy storage demand, and reduces the power waste of the microgrid. Under the premise of ensuring economy, the shared energy storage operator aims to increase the amount of new energy consumption, and the microgrid group, shared energy storage operator and distribution network reasonably and orderly interact with each other, so that multiple microgrids and distribution networks can interact with each other in an orderly manner, which has the advantage of improving the new energy consumption capacity of the power grid.

[0068] In a possible implementation, the objective function of the microgrid includes the energy cost of the microgrid and the net load variance of the microgrid. The energy cost formula of the microgrid is:

[0069] J MG =C MGO +C fe +C DG

[0070]

[0071] In the formula, J MG is the energy cost of the microgrid, C MGO is the cost of electricity purchase and sale between microgrids and microgrid groups, C fe is the cost of electricity purchase and sale between microgrids; C DG is the power generation cost of the controllable units in the microgrid itself, It is the electricity purchase and sale price set by the microgrid group for each microgrid. are the interaction power between the microgrid cluster and microgrid e at time t; is the electricity price between other microgrids and microgrid e; They are the input power from other microgrids to microgrid e and the output power from microgrid e to other microgrids at time t;

[0072] The formula for the net load variance of the microgrid is:

[0073]

[0074] Where f is the net load variance of microgrid e after using energy storage; P av,eThey are the load power, new energy power, and equivalent load average of the e-th microgrid at time t;

[0075] The objective function of the microgrid group includes the power purchase and sales revenue between the microgrid group and each microgrid, the power purchase and sales revenue between the microgrid group and the distribution network, the power purchase and sales revenue between the microgrid group and the shared energy storage operator, and the cost of the microgrid group purchasing shared energy storage capacity. The formula is:

[0076] U MGO =I MGΣ +I ADN +I SESO -C SESO

[0077]

[0078] In the formula, I MG∑ is the electricity purchase and sales revenue of the microgrid group and each microgrid; I ADN is the electricity purchase and sale income of the microgrid group and the distribution network; I SESO is the revenue from electricity purchase and sale between the microgrid group and shared energy storage; C SESO is the cost of purchasing shared energy storage capacity for the microgrid group; They are the electricity purchase and sales prices set by the microgrid group for each microgrid, the electricity purchase and sales prices set by the distribution network, and the electricity purchase and sales prices set by the shared energy storage operator and the microgrid group; They are the interaction power between microgrid groups, the interaction power between microgrid groups and distribution networks, and the interaction power between microgrid groups and shared energy storage operators; α m , β m They are the unit capacity leasing cost and unit power leasing cost of shared energy storage operators; They are the capacity demand and power demand required by the microgrid group respectively.

[0079] In a possible implementation, the objective function of the shared energy storage operator includes the cost of the microgrid group purchasing shared energy storage capacity, the power purchase and sales revenue between the microgrid group and the distribution network, the power purchase and sales revenue between the microgrid group and the shared energy storage, and the investment cost of the shared energy storage. The formula is:

[0080]

[0081] In the formula, C SEDO is the cost of the microgrid group purchasing shared energy storage capacity, I SESO is the revenue from the purchase and sale of electricity between the microgrid group and shared energy storage. is the electricity purchase and sale revenue between the microgrid group and the distribution network, C OM is the investment cost of shared energy storage, r is the discount rate, y is the useful life of centralized energy storage, and are the energy capacity and power capacity of shared energy storage, α s and β s They are unit energy capacity cost and unit power capacity cost respectively;

[0082] The objective function of the distribution network is to maximize the amount of electricity consumed by new energy, and its formula is:

[0083]

[0084] In the formula, F N It is the amount of renewable energy consumed by the distribution network in one day; is the interaction power between the microgrid group and the distribution network, is the active power actually consumed by the renewable energy at node i; Ω N is the set of nodes corresponding to wind and solar power, and Δt is the time interval, which is 1h.

[0085] In a possible implementation, the upper-level game model is:

[0086] G1={M;ρ M ;P MG ; U MGO ; {J MG ,f}}

[0087] Where M is the set of participants, including the microgrid group and n microgrids, ρ M It is the time-of-use electricity purchase and sale price of the microgrid group. P MG is the interaction power between the microgrid and the microgrid group at each time period are the interactive power between the microgrid group and the microgrid; U MGO is the revenue of the microgrid group, J MG is the energy cost of the microgrid, f is the net load variance of microgrid e after using energy storage;

[0088] The underlying game model is:

[0089] G2={N;ρ A ; {P M ,P S}; U ADN ; {U MGO ,U SESO}}

[0090] Where N is the set of participants, including the distribution network, microgrid group, and shared energy storage operator; ρ A It is the time-of-use electricity purchase and sale price of the distribution network. P M is the interaction power between the microgrid group and the distribution network and shared energy storage operators at each time period, P M =(P MGO,b ,PMGO,s ,P SESO,b ,P SESO,s ), P S is the interaction power between the shared energy storage operator and the distribution network and microgrid group at each time period, P S =(P ADN,b ,P ADN,s ,P SESO,s ,P SESO,b ), U ADn , U MGO , U SESO They are the revenues of distribution networks, microgrid groups, and shared energy storage operators.

[0091] In the game process, the distribution network aims to maximize the consumption of new energy; the microgrid group and shared energy storage operators aim to maximize their own benefit functions and adjust their own strategy sets until they achieve their respective maximum benefits, that is, reach the game equilibrium point. The condition for the existence of a game equilibrium point in game G is that there is a set of strategy sets The following inequality constraints are satisfied:

[0092]

[0093] That is, the decision variable is is the optimal strategy for this game.

[0094] In a possible implementation, the constraints of the two-layer master-slave game model include distribution network power flow equation constraints, node voltage constraints, line transmission power constraints, upper grid interaction power constraints, shared energy storage power upper and lower bound constraints, and shared energy storage power balance constraints; wherein,

[0095] The power flow equation constraints of the distribution network are:

[0096]

[0097] Where P DGi,t , Q DGi,t are the active and reactive power injected by the generator of node i at time t; P i,t , Q i,t are the injected active and reactive power of node i at time t; P Li,t , Q Li,t are the active and reactive power of the load at node i at time t; θ ij,t is the voltage phase difference between nodes i and j at time t; U i,t , U j,t is the voltage amplitude of nodes i and j at time t; g ij , B ij are the conductance and susceptance of branches i and j respectively;

[0098] The node voltage constraints are:

[0099] V i,min ≤V i,t ≤V i,max

[0100] Where V i,min 、V i,max are the minimum and maximum node voltages;

[0101] Line transmission power constraints:

[0102] S l,t ≤S l,max

[0103] In the formula, S l,t is the transmission power of line l at time t; S l,max is the maximum value of the transmission power allowed by line l;

[0104] Upper-level grid interaction power constraints:

[0105]

[0106] In the formula, are respectively the active and reactive power limits of the interaction between the upper power grid and the distribution network; P A,b , Q A,b They are respectively the active and reactive power of the interaction between the upper power grid and the distribution network;

[0107] The upper and lower bounds of shared energy storage capacity are:

[0108]

[0109] In the formula, is the shared energy storage capacity; SOC min and SOC max It is the minimum and maximum state of charge of energy storage; They are the shared energy storage charging and discharging power, η C , η D They are shared energy storage capacity and charge and discharge efficiency, Δt is the time interval of 1h;

[0110] The shared energy storage power balance constraint is:

[0111]

[0112] In the formula, is a binary [0, 1] variable. When it is 1, it means that the shared energy storage operator purchases and sells electricity with the distribution network and microgrid during period t. hour, hour, is the interactive power between the microgrid group and the shared energy storage operator, It is the power purchased and sold by distribution networks and shared energy storage operators.

[0113] In a possible implementation, the constraints of the microgrid include:

[0114] 1) Power balance constraints

[0115]

[0116] In the formula, is the output power of the gas turbine in microgrid e during period t; are the interaction power between the microgrid cluster and microgrid e at time t; They are respectively the load power and new energy power of the e-th microgrid at time t.

[0117] 2) Distributed photovoltaic output constraints

[0118]

[0119] In the formula, and The upper and lower limits of photovoltaic output.

[0120] 3) Distributed wind power output constraints

[0121]

[0122] In the formula, and The upper and lower limits of wind power output.

[0123] In a possible implementation, the constraints of the microgrid group include:

[0124] 1) Tie-line constraints between microgrid and distribution network

[0125] Power needs to be transmitted through interconnection lines between microgrids and microgrid groups, between microgrid groups and distribution networks, and between shared energy storage operators, so the interconnection line power constraints need to be met.

[0126]

[0127] Where: is the maximum transmission power on the tie line, are the interaction power between the microgrid cluster and the microgrid respectively; are the interaction power between the microgrid cluster and microgrid e at time t; They are the input power from other microgrids to microgrid e and the output power from microgrid e to other microgrids at time t. It is a binary [0,1] variable. When it is 1, it means that the microgrid is purchasing and selling electricity with the microgrid group at time t, and vice versa.

[0128] In other possible ways, the particle swarm algorithm can be embedded in the Cplex solver in other ways. This application mainly uses the particle swarm algorithm embedded in the Cplex solver as an example for explanation.

[0129] The results of solving the net load variance and energy cost of each microgrid for the above model are shown in Table 1. The microgrid reduces the volatility of the net load by interacting with the microgrid group. According to the optimization results, it can be seen that the net load variance of each microgrid is significantly reduced by participating in the power purchase and sales strategy of the model proposed in the present invention. The upper-level game results are transmitted to the lower level, and the distribution network preferentially absorbs the remaining new energy power generation of the microgrid group. After 65 iterations, the microgrid group, the distribution network and the shared energy storage operator are unable to obtain greater benefits by adjusting their own power purchase and sales strategies, that is, to reach Nash equilibrium. At this time, the operating income of the three is shown in Table 2. The solution results show that the coordination and cooperation of the microgrid group, shared energy storage and distribution network can achieve more new energy consumption under the premise of ensuring economy.

[0130] Table 1 Net load variance and energy cost of microgrid

[0131]

[0132]

[0133] Table 2 Game iterative solution data

[0134]

[0135] Figure 3 The simulation is performed on the improved IEEE33 node. Node 0 is connected to the upper grid, and wind turbines, photovoltaics, three microgrids and shared energy storage are connected at different nodes. The optimal power purchase and sales strategy of each microgrid is as follows: Figure 4 , Figure 5 , Figure 6 Taking microgrid 1 as an example, microgrid 1 prioritizes the consumption of its own renewable energy. Figure 4 It can be seen that the net load fluctuates greatly during the time periods of 6:00-9:00 and 17:00-21:00. The volatility of the net load is reduced by purchasing and selling electricity with the microgrid group operator. In the rest of the time periods, the net load fluctuation of the microgrid is small. Considering the energy cost of the microgrid, it is smoothed by the energy storage inside the microgrid. Figure 7 is the time-of-use electricity purchase and sale price optimized by the microgrid group in the upper game model. Figure 8 It is the time-of-use electricity purchase and sale price optimized by the distribution network in the lower-level game model.

[0136] The microgrid group absorbs the new energy power that is difficult to absorb by multiple microgrids except their own load and energy storage demand by renting the energy storage capacity of shared energy storage according to the power demand of each microgrid in the group; then, the microgrid group participates in the power interaction between the distribution network and the shared energy storage operator as a power transfer intermediary to promote the better absorption of new energy by the power grid; finally, a multi-objective optimization solution is adopted by establishing a double-layer master-slave game structure; the optimal interaction power of each subject and the new energy absorption rate are output. The present invention can solve the problems of disordered power interaction and serious wind and solar power abandonment between multiple microgrids, shared energy storage operators and distribution networks in the existing power system.

[0137] 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.

[0138] 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.

[0139] Fig. 9 The following is a schematic diagram of the structure of a distributed renewable energy consumption control device 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:

[0140] like Fig. 9 As shown, the distributed new energy consumption control device 9 includes:

[0141] A two-layer master-slave game model acquisition module 91 is used to acquire a two-layer master-slave game model; wherein the two-layer master-slave game model includes an upper-layer game model with a microgrid group as the main body and multiple microgrids as slaves; a lower-layer game model with a distribution network as the main body and a microgrid group and a shared energy storage operator as slaves; the upper-layer game model takes the maximum operating income of the microgrid group as the objective function; and the lower-layer game model takes the maximum power consumption of new energy as the objective function;

[0142] The upper-level game result output module 92 is used to input the load power data and the new energy generation power data of multiple microgrids into the upper-level game model and output the upper-level game result; wherein the upper-level game result includes the optimal time-of-use purchase and sale price of the microgrid group and the optimal interactive power of each microgrid corresponding to each time period;

[0143] The lower-level game result output module 93 is used to input the upper-level game results, the capacity of the shared energy storage operator, the rental cost and the load power data of the distribution network into the lower-level game model, and output the lower-level game results; wherein the lower-level game results include the optimal time-of-use purchase and sale price of the distribution network, and the optimal interactive power of the distribution network, the shared energy storage operator and the microgrid group corresponding to each time period.

[0144] In a possible implementation, the upper-layer game result output module 92 may be used to:

[0145] The upper-level game model includes the objective function of the microgrid and the objective function of the microgrid cluster;

[0146] The load power data of multiple microgrids and the power data of renewable energy generation are input into the upper-level game model, and the upper-level game results are output, including:

[0147] Initialization parameters; the parameters include the load power data and renewable energy power data of each microgrid;

[0148] Generate the time-of-use electricity purchase and sale price of the initial microgrid group according to the objective function of the microgrid group;

[0149] Obtain the objective function value of each microgrid, and calculate the fitness value of each microgrid according to the objective function value;

[0150] The fitness value and the time-of-use electricity purchase and sale price of the initial microgrid group are input into the Cplex solver, and the optimal interactive power of each microgrid in each time period is solved with the goal of maximizing the operating profit of the microgrid group.

[0151] The interactive benefits of the microgrid and the microgrid group are calculated respectively according to the optimal interactive power of each microgrid in each period and the time-of-use electricity purchase and sales price of the microgrid group;

[0152] Update the position and velocity of particles in the particle swarm and determine the new population;

[0153] If the interactive benefit value no longer increases compared with the previous round, then the previous round is a Nash equilibrium solution;

[0154] Output the time-of-use electricity purchase and sales price of the microgrid group in the previous round and the optimal interactive power of each microgrid in each time period.

[0155] In a possible implementation, the lower-layer game result output module 93 may be used to:

[0156] The lower-level game model includes the objective function of the distribution network, the objective function of the shared energy storage operator, and the objective function of the microgrid group;

[0157] The upper-level game results, the capacity of the shared energy storage operator, the rental cost, and the load power data of the distribution network are input into the lower-level game model, and the lower-level game results are output, including:

[0158] Obtain upper-level game results, capacity of shared energy storage operators, leasing costs, and distribution network load power data;

[0159] Generate the time-of-use electricity purchase and sale price of the initial distribution network according to the load power data of the distribution network;

[0160] The upper-level game results, the capacity and leasing costs of shared energy storage operators, the load power data of the distribution network, and the time-of-use electricity purchase and sales prices of the distribution network are input into the Cplex solver. With the goal of maximizing the consumption of new energy in the distribution network, the optimal interactive power of the distribution network, microgrid group, and shared energy storage operators in each time period is solved.

[0161] The interactive benefits of the distribution network, microgrid group and shared energy storage operator are calculated based on the optimal interactive power of the distribution network, microgrid group and shared energy storage operator in each period and the time-of-use purchase and sale electricity price of the distribution network;

[0162] Update the time-of-use electricity purchase and sales prices of the distribution network, and solve the optimal interactive power of the new distribution network, microgrid group, and shared energy storage operator in each time period;

[0163] If the interactive benefit value no longer increases compared with the previous round, then the previous round is a Nash equilibrium solution;

[0164] Output the time-of-use electricity purchase and sales price of the distribution network in the previous round, and the optimal interactive power of the corresponding distribution network, microgrid group and shared energy storage operator in each period.

[0165] The embodiment of the present invention constructs an upper-layer game model through a microgrid group and multiple microgrids, solves the optimal microgrid group's time-sharing electricity price and the corresponding optimal interactive power of each microgrid in each time period, and then constructs a lower-layer game model through the distribution network, the microgrid group and the shared energy storage operator. According to the lower-layer game model, the optimal distribution network's time-sharing electricity price and the corresponding distribution network, the microgrid group and the shared energy storage operator's optimal interactive power in each time period are solved. By using the microgrid group as a power transmission intermediary to participate in the power interaction of multiple microgrids and the distribution network, the microgrid group, according to the power demand of each microgrid in the group, rents the energy storage capacity of the shared energy storage operator, absorbs the new energy power generation power that is difficult to absorb by multiple microgrids except for their own load and energy storage demand, and reduces the power waste of the microgrid. Under the premise of ensuring economy, the shared energy storage operator aims to increase the amount of new energy consumption, and the microgrid group, the shared energy storage operator and the distribution network reasonably and orderly interact with each other, so that multiple microgrids and the distribution network can interact with each other in an orderly manner, which has the advantage of improving the new energy consumption capacity of the power grid.

[0166] 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.

[0167] Those of ordinary skill in the art will appreciate that the templates, 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.

[0168] If the module 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 processes 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, it can implement the steps of the above-mentioned distributed new energy consumption control method embodiments. 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. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0169] 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 method for accommodating and controlling distributed new energy, characterized in that: include: A two-layer master-slave game model is obtained; wherein the two-layer master-slave game model includes an upper-layer game model with a microgrid group as the main body and multiple microgrids as slaves; a lower-layer game model with a distribution network as the main body and microgrid groups and shared energy storage operators as slaves; the upper-layer game model takes the maximum operating income of the microgrid group as the objective function; the lower-layer game model takes the maximum power consumption of new energy as the objective function; the load power data of the microgrid and the new energy power generation data are input into the upper-layer game model, and the upper-layer game results are output; wherein the upper-layer game results include the optimal time-of-use purchase and sale price of the microgrid group and the optimal interactive power of each microgrid corresponding to each time period; the upper-layer game results, the capacity of the shared energy storage operator, the rental cost and the load power data of the distribution network are input into the lower-layer game model, and the lower-layer game results are output; wherein the lower-layer game results include the optimal time-of-use purchase and sale price of the distribution network, and the optimal interactive power of the distribution network, the shared energy storage operator and the microgrid group corresponding to each time period.

2. The method for controlling the consumption of distributed new energy according to claim 1, characterized in that: The upper-level game model includes the objective function of the microgrid and the objective function of the microgrid group; The step of inputting the load power data of the plurality of microgrids and the new energy generation power data into the upper-layer game model and outputting the upper-layer game result comprises: Initialization parameters; the parameters include load power data and new energy power generation data of each microgrid; Generate a time-of-use electricity purchase and sale price of an initial microgrid group according to the objective function of the microgrid group; Obtaining an objective function value of each microgrid, and calculating a fitness value of each microgrid according to the objective function value; The fitness value and the time-of-use electricity purchase and sale price of the initial microgrid group are input into the Cplex solver, and the optimal interaction power of each microgrid in each time period is solved with the goal of maximizing the operating profit of the microgrid group; The interactive benefits of the microgrid and the microgrid group are calculated respectively according to the optimal interactive power of each microgrid in each period and the time-of-use electricity purchase and sales price of the microgrid group; Update the position and velocity of particles in the particle swarm and determine the new population; If the interactive benefit value no longer increases compared with the previous round, then the previous round is a Nash equilibrium solution; Output the time-of-use electricity purchase and sale price of the microgrid group in the previous round and the optimal interactive power corresponding to each microgrid in each time period.

3. The method for controlling the consumption of distributed new energy according to claim 2, characterized in that: The lower-level game model includes the objective function of the distribution network, the objective function of the shared energy storage operator, and the objective function of the microgrid group; The upper-level game result, the capacity of the shared energy storage operator, the rental cost and the load power data of the distribution network are input into the lower-level game model, and the lower-level game result is output, including: Obtaining the upper-level game result, the capacity and rental cost of the shared energy storage operator, and the load power data of the distribution network; Generate the time-of-use electricity purchase and sale price of the initial distribution network according to the load power data of the distribution network; The upper-level game results, the capacity and leasing cost of the shared energy storage operator, the load power data of the distribution network, and the time-of-use electricity purchase and sale price of the distribution network are input into the Cplex solver, and the optimal interactive power of the distribution network, the microgrid group, and the shared energy storage operator in each time period is solved with the goal of maximizing the new energy consumption of the distribution network; Calculate the interactive benefits of the distribution network, the microgrid group, and the shared energy storage operator respectively according to the optimal interactive power of the distribution network, the microgrid group, and the shared energy storage operator in each time period and the time-of-use purchase and sale electricity price of the distribution network; Update the time-of-use electricity purchase and sale price of the distribution network, and solve the optimal interactive power of the new distribution network, microgrid group, and shared energy storage operator in each time period; If the interactive benefit value no longer increases compared with the previous round, then the previous round is a Nash equilibrium solution; Output the time-of-use electricity purchase and sale price of the distribution network in the previous round, and the optimal interactive power of the corresponding distribution network, the microgrid group and the shared energy storage operator in each time period.

4. The method for controlling the consumption of distributed new energy according to claim 3, characterized in that: The objective function of the microgrid includes the energy cost of the microgrid and the net load variance of the microgrid. The energy cost formula of the microgrid is: J MG =C MGO +C fe +C DG In the formula, J MG is the energy cost of the microgrid, C MGO is the cost of electricity purchase and sale between microgrids and microgrid groups, C fe is the cost of electricity purchase and sale between microgrids; C DG is the power generation cost of the controllable units in the microgrid itself, It is the electricity purchase and sale price set by the microgrid group for each microgrid. are the interaction power between the microgrid cluster and microgrid e at time t; is the electricity price between other microgrids and microgrid e; They are the input power from other microgrids to microgrid e and the output power from microgrid e to other microgrids at time t; The formula for the net load variance of the microgrid is: Where f is the net load variance of microgrid e after using energy storage; P av，e They are respectively the load power, new energy power and equivalent load average value of the e-th microgrid at time t.

5. The method for controlling the consumption of distributed new energy according to claim 3, characterized in that: The objective function of the microgrid group includes the power purchase and sale revenue between the microgrid group and each microgrid, the power purchase and sale revenue between the microgrid group and the distribution network, the power purchase and sale revenue between the microgrid group and the shared energy storage operator, and the cost of the microgrid group purchasing shared energy storage capacity. The formula is: U MGO =I MG∑ +I ADN +I SESO -C SESO In the formula, I MG∑ is the electricity purchase and sales revenue of the microgrid group and each microgrid; I ADN is the electricity purchase and sale income of the microgrid group and the distribution network; I SESO is the revenue from electricity purchase and sale between the microgrid group and shared energy storage; C SESO is the cost of purchasing shared energy storage capacity for the microgrid group; They are the electricity purchase and sales prices set by the microgrid group for each microgrid, the electricity purchase and sales prices set by the distribution network, and the electricity purchase and sales prices set by the shared energy storage operator and the microgrid group; They are the interaction power between microgrid groups, the interaction power between microgrid groups and distribution networks, and the interaction power between microgrid groups and shared energy storage operators; α m , β m They are the unit capacity leasing cost and unit power leasing cost of shared energy storage operators; They are the capacity demand and power demand required by the microgrid group respectively.

6. The method for controlling the consumption of distributed new energy according to claim 4, characterized in that: The objective function of the shared energy storage operator includes the cost of the microgrid group purchasing shared energy storage capacity, the power purchase and sales revenue between the microgrid group and the distribution network, the power purchase and sales revenue between the microgrid group and the shared energy storage, and the investment cost of the shared energy storage. The formula is: In the formula, C SESO is the cost of the microgrid group purchasing shared energy storage capacity, I SESO is the revenue from the purchase and sale of electricity between the microgrid group and shared energy storage. is the electricity purchase and sale revenue between the microgrid group and the distribution network, C OM is the investment cost of shared energy storage, r is the discount rate, y is the useful life of centralized energy storage, and are the energy capacity and power capacity of shared energy storage, α s and β s They are unit energy capacity cost and unit power capacity cost respectively.

7. The method for controlling the consumption of distributed new energy according to claim 4, characterized in that: The objective function of the distribution network is to maximize the amount of electricity consumed by new energy, and its formula is: In the formula, F N It is the amount of renewable energy consumed by the distribution network in one day; is the interaction power between the microgrid group and the distribution network, is the active power actually consumed by the renewable energy at node i; Ω N is the set of nodes corresponding to wind and solar power, and Δt is the time interval, which is 1h.

8. The method for controlling the consumption of distributed new energy according to claim 1, characterized in that: The upper-level game model is: G16{Mρ M 10. P MG N.U MGO {J MG ,f}} Where M is the set of participants, including the microgrid group and n microgrids, ρ M is the time-of-use electricity purchase and sale price of the microgrid group, P MG is the interaction power between the microgrid and the microgrid group at each time period are the interactive power between the microgrid group and the microgrid; U MGO is the revenue of the microgrid group, J MG is the energy cost of the microgrid, f is the net load variance of microgrid e after using energy storage; The lower-level game model is: G2={N; ρA ;{P M ,P S };U ADN ;{U MGO ,U SESO }} Where N is the set of participants, including the distribution network, microgrid group, and shared energy storage operator; ρ A is the time-of-use electricity purchase and sale price of the distribution network, P M is the interaction power between the microgrid group and the distribution network and shared energy storage operators at each time period, P S is the interaction power between the shared energy storage operator and the distribution network and microgrid group at each time period, U ADN , U MGO , U SESO They are the revenues of distribution networks, microgrid groups, and shared energy storage operators.

9. The method for controlling the consumption of distributed new energy according to claim 8, characterized in that: The constraints of the two-layer master-slave game model include distribution network power flow equation constraints, node voltage constraints, line transmission power constraints, upper grid interaction power constraints, shared energy storage power upper and lower bound constraints, and shared energy storage power balance constraints; The power flow equation constraint of the distribution network is: Where P DGi，t , Q DGi，t are the active and reactive power injected by the generator of node i at time t; P i，t , Q i，t are the injected active and reactive power of node i at time t; P Li，t , Q Li，t are the active and reactive power of the load at node i at time t; θ ij，t is the voltage phase difference between nodes i and j at time t; U i，t , U j，t is the voltage amplitude of nodes i and j at time t; G ij , B ij are the conductance and susceptance of branches i and j respectively; The node voltage constraint is: In i，min ≤V i，t ≤V i，max Where V i，min 、V i，max are the minimum and maximum node voltages; The line transmission power constraint is: S l，t ≤S l，max In the formula, S l，t is the transmission power of line l at time t; S l，max is the maximum value of the transmission power allowed by line l; The upper grid interaction power constraint: In the formula, are respectively the active and reactive power limits of the interaction between the upper power grid and the distribution network; P A，b , Q A，b They are respectively the active and reactive power of the interaction between the upper power grid and the distribution network; The upper and lower bounds of the shared energy storage capacity are: In the formula, is the shared energy storage capacity; SOC min and SOC max It is the minimum and maximum state of charge of energy storage; They are the shared energy storage charging and discharging power, η C , η D They are shared energy storage capacity and charge and discharge efficiency, Δt is the time interval of 1h; The shared energy storage power balance constraint is: In the formula, is a binary [0, 1] variable. When it is 1, it means that the shared energy storage operator purchases and sells electricity with the distribution network and microgrid during period t. hour, hour, is the interactive power between the microgrid group and the shared energy storage operator, It is the power purchased and sold by distribution networks and shared energy storage operators.

10. A distributed new energy consumption control device, characterized in that: include: A two-layer master-slave game model acquisition module is used to acquire a two-layer master-slave game model; wherein the two-layer master-slave game model includes an upper-layer game model with a microgrid group as the main body and multiple microgrids as slaves; a lower-layer game model with a distribution network as the main body and a microgrid group and a shared energy storage operator as slaves; the upper-layer game model takes the maximum operating income of the microgrid group as the objective function; the lower-layer game model takes the maximum power consumption of new energy as the objective function; An upper-level game result output module is used to input the load power data and new energy power generation data of multiple microgrids into the upper-level game model and output the upper-level game result; wherein the upper-level game result includes the optimal time-of-use purchase and sale price of the microgrid group and the optimal interactive power of each microgrid corresponding to each time period; The lower-level game result output module is used to input the upper-level game results, the capacity of the shared energy storage operator, the rental cost and the load power data of the distribution network into the lower-level game model, and output the lower-level game results; wherein the lower-level game results include the optimal time-of-use purchase and sale price of the distribution network, and the optimal interactive power of the distribution network, the shared energy storage operator and the microgrid group corresponding to each time period.

Images