A coordinated control method for multi-energy fusion system for smart communities

Abstract

The present invention discloses a coordinated control method for a multi-energy fusion system for a smart community. First, an interactive framework for the multi-energy fusion system of a smart community is constructed. Second, an output model of the photovoltaic power generation system and the wind power generation system, a model of the energy storage equipment, and a model of the power load are constructed within the smart community. Next, a model for the virtual energy storage control capability of electric private cars within the smart community is established. Finally, based on the interactive framework and model of the multi-energy fusion system of a smart community, a method for controlling and collaboratively optimizing the operation of the multi-energy fusion system of a smart community is proposed. The present invention fully utilizes the virtual energy storage control capability of electric private cars to participate in the research of the multi-energy fusion control strategy of the smart community, maximizes the enthusiasm of users to participate in system control, and can effectively realize the multi-energy fusion control and collaborative optimization operation of the smart community.

Description

Technical Field

[0001] The present invention belongs to the field of multi-energy fusion coordinated control in smart communities, and involves the construction of an interactive framework and model of a multi-energy fusion system in a smart community, the construction of a virtual energy storage control capability model for electric private cars in a smart community, and the formulation of a control strategy for a multi-energy fusion system in a smart community. Specifically, it relates to a multi-energy fusion system coordinated control method for a smart community. Background Art

[0002] To reduce the proportion of thermal power, minimize grid expansion costs, and effectively improve grid reliability, multiple distributed power sources, energy storage devices, and conventional loads are being integrated with the main grid in the form of smart communities. This not only allows for the utilization of distributed power sources, meeting the power demands of loads, but also ensures the stability of power generation and supply. Consequently, smart communities have gradually developed as emerging microgrid systems. However, due to the complex structure of smart communities, multi-energy integration control methods still have shortcomings, such as difficulty in achieving coordinated cooperation between distributed power sources and energy storage devices, and difficulty in achieving interaction between smart communities and the main grid, and thus have not yet achieved the desired results.

[0003] Therefore, a new technical solution is needed to solve these problems. Summary of the Invention

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, a multi-energy fusion system coordination control method for smart communities is provided. The present invention fully considers the coordinated operation among the photovoltaic power generation system, wind power generation system, energy storage equipment, power load, electric private cars and the large power grid within the smart community, and fully utilizes the virtual energy storage control capabilities of electric private cars to participate in the research on the multi-energy fusion control strategy of smart communities, thereby mobilizing users' enthusiasm for participating in system control to the greatest extent, and can effectively realize the multi-energy fusion control and coordinated optimization operation of smart communities.

[0005] Technical solution: To achieve the above objectives, the present invention provides a coordinated control method for a multi-energy fusion system for a smart community, comprising the following steps:

[0006] S1: Constructed an interactive framework for the multi-energy integration system of smart communities;

[0007] S2: Constructed the output model of the photovoltaic power generation system and wind power generation system within the community, the model of the energy storage equipment, and the model of the power load;

[0008] S3: A model for the virtual energy storage control capability of electric private cars in smart communities was established;

[0009] S4: Through the interactive framework of the smart community multi-energy fusion system, the model constructed in step S2, and the virtual energy storage control capability model of electric private cars in the smart community, the smart community multi-energy fusion system control and collaborative optimization are realized according to the smart community multi-energy fusion system control and operation strategy.

[0010] Furthermore, the interactive framework of the smart community multi-energy fusion system in step S1 mainly includes seven parts, namely: photovoltaic power generation system, wind power generation system, energy storage equipment, power load, electric private car, bidirectional intelligent charger and smart community control center.

[0011] Furthermore, the photovoltaic and wind power output models in step S2 include a photovoltaic power generation system output power model and a wind power generation system output power model.

[0012] (1) The output power model of the photovoltaic power generation system is:

[0013]

[0014] Where: P PV (t) is the actual output power during period t; P STC is the rated output power; L C (t) is the actual light intensity during the t period; L STC The standard light intensity is 1kW / m 2 ; μ is the power temperature coefficient, which is -0.0047 / ℃; T C is the actual temperature of the battery surface; T STC It is the surface temperature of the photovoltaic unit under standard conditions, which is 25℃.

[0015] (2) The output power model of the wind power generation system is:

[0016]

[0017] Where: P WT (t) is the actual output power of the wind turbine during period t; P e is the rated power of the wind turbine; v(t) is the actual wind speed during the t period; v r is the cut-in wind speed; v c is the cut-off wind speed; v e is the rated wind speed.

[0018] (3) The mathematical model of the energy storage device is:

[0019] Charging:

[0020] S ES (t+1)=(1-σ)S ES (t)+P ES(t)Δtη c (3)

[0021] Constraints:

[0022]

[0023] Discharging:

[0024]

[0025] Constraints:

[0026] 0≤P ES (t)≤min{[(1-σ)S ES (t)-S ES.min ]Δtη disc ,P ES.max} (6)

[0027] Where: S ES.min is the minimum capacity of the energy storage device; S ES.max is the maximum capacity of the energy storage device; S ES (t+1) is the remaining energy storage capacity during the t+1 period; S ES (t) is the remaining energy storage capacity in period t; P ES (t) is the charge and discharge power during the t period; P ES.max is the maximum value of charge and discharge power; σ is the self-discharge rate; η c is the charging efficiency; η disc is the discharge efficiency; Δt is the time interval.

[0028] (4) The model of power load is:

[0029]

[0030] Where: P L (t) is the total load of residents in period t; P it is the power consumption of the i-th load in period t; n is the load type.

[0031] Furthermore, in step S3, the virtual energy storage control capability model of electric private cars in the smart community is:

[0032]

[0033]

[0034]

[0035]

[0036] Where: C cha(t) is the charging capacity of the electric private car cluster during period t, C j,cha (t) is the charging capacity of a single electric private car during period t, C dis (t) is the discharge capacity of the electric private car cluster during period t, C j,dis (t) is the discharge capacity of a single electric private car during period t, P cha (t) is the charging power of the electric private car cluster during period t, P j,cha (t) is the charging power of a single electric private car during period t, P dis (t) is the discharge power of the electric private car cluster during period t, P j,dis (t) is the discharge power of a single electric private car during period t, N EV is the number of electric private cars in the smart community.

[0037] Furthermore, the control and operation strategy of the smart community multi-energy fusion system in step S4 is:

[0038] Optimization goal:

[0039]

[0040] in:

[0041]

[0042] Where: f S represents the volatility of the equivalent load composed of the smart community resident load, photovoltaic power generation, wind power generation and electric private car control power; P H (t) is the original load of the smart community during period t; P EV (t) is the control power of the electric private car cluster during period t; P PV (t) is the photovoltaic power generation output during period t; P WT (t) is the wind power output during period t; P AVE is the average value of the comprehensive load; T is the total number of time periods.

[0043] Furthermore, the constraints of the smart cell multi-energy fusion collaborative optimization operation strategy in step S4 include the following constraints (1) to (4):

[0044] Constraints (1) Constraints on the virtual energy storage control capability of electric private car clusters

[0045] P dis (t)≤P EV (t)≤P cha (t) t=1,2,3,…,96 (14)

[0046] Where: P dis(t) is the lower limit of discharge control power during period t, P cha (t) is the upper limit of charging control power during period t.

[0047] Constraint (2) Constraints on the interaction power between electric private car clusters and the power grid

[0048] P g,min (t)≤P EV (t)≤P g,max (t) t=1,2,3,…,96 (15)

[0049] Where: P g,min (t) is the minimum value of the interaction power between the electric private car cluster and the power grid during period t, P g,max (t) is the maximum value of the interaction power between the electric private car cluster and the power grid during period t.

[0050] Constraint (3) Charge and discharge power constraints for a single electric private car

[0051] P dis,max ≤P j (t)≤P cha,max t=1,2,3,…,96 (16)

[0052] Where: P dis,max is the maximum discharge power that a single electric private car can withstand, P cha,max The maximum charging power that a single electric private car can withstand.

[0053] Constraint (4) SOC constraint for a single electric private car

[0054] S j,min ≤S j (t)≤1 (17)

[0055] Where: S j,min It is the minimum SOC value of a single electric vehicle, and its purpose is to prevent discharge from causing too low a value and affecting the battery life of the electric vehicle.

[0056] The operation method of the smart community multi-energy fusion system control and collaborative optimization in step S4 is:

[0057] Step 1: The smart community control center reads the data of distributed power supply, energy storage equipment and residents' load in the system, and calculates the photovoltaic output P through the established photovoltaic system output model. PV (t), wind system output model is used to calculate the wind turbine output P WT (t) and the electricity load model are used to calculate the residential load P L (t), obtain the maximum active power P of the transformer M ;

[0058] Step 2: Determine whether ΔP1 = P PV (t)+P WT (t)-P L (t) Whether it is 0, that is, whether the remaining power after the photovoltaic output power and wind power output power supply the residential load power is greater than 0. If it is greater than 0, go to step 3; if it is less than 0, go to step 8;

[0059] Step 3: Obtain the curtailed solar and wind power and the required charging power ∑P for all uncharged electric private vehicles EV.n (t) Based on the effect of tracking the curtailment curves of solar and wind power by the smart community control center, guide electric private car loads to participate in absorbing the curtailment of solar and wind power;

[0060] Step 4: Determine ΣP EV.n (t)+P L Is (t) less than P? M , that is, whether the charging power of the electric private car plus the residential load power is less than the transformer gate value, if so, go to step 6, if it is greater than P M , then go to step 5;

[0061] Step 5: Prioritize charging electric private cars whose power levels do not meet user expectations based on vehicle usage demand, and calculate the updated charging power ∑P′ of electric private cars. EV.n (t), return to step 3, now ∑P′ EV.n (t) is equivalent to ∑P EV.n (t) into the calculation;

[0062] Step 6: Calculate ΔP2 = ΔP1 - ∑P EV.n Whether the value of (t) is greater than 0, that is, whether the remaining power after calculating the photovoltaic output power and wind power output power supplying the residential load and the electric private car load is greater than 0, if it is greater than 0, go to step 7, if it is less than 0, go to step 8;

[0063] Step 7: Charge the vehicles based on the community's vehicle usage, and use the energy storage device as a coordination auxiliary device for the electric private vehicle load to continue absorbing excess abandoned solar and wind power, and then proceed to step 10;

[0064] Step 8: Determine whether the energy storage SOC is greater than 20%. If so, the energy storage device discharges the load and goes to step 9; otherwise, go to step 11.

[0065] Step 9: Calculate whether the energy storage device can still meet the user load demand after discharge. If so, go to step 12; otherwise, go to step 11.

[0066] Step 10: Determine whether the energy storage SOC is less than 80%. If so, use the excess distributed energy to charge the energy storage device. If not, shut down the excess distributed energy output and proceed to step 12.

[0067] Step 11: Based on the virtual energy storage control capability of electric private cars in the smart community, obtain the dischargeable capacity of the electric vehicle's virtual energy storage, and determine whether the discharge of the electric private car's virtual energy storage can meet the community load demand. If so, go to step 12; if not, the smart community purchases electricity from the power grid, thus realizing the multi-energy integration and optimized operation of the smart community.

[0068] Beneficial effects: Compared with the existing technology, the present invention fully considers the coordinated operation between the photovoltaic power generation system, wind power generation system, energy storage equipment, power load, electric private cars, and the large power grid within the smart community, and fully utilizes the virtual energy storage control capability of electric private cars to participate in the research of the multi-energy fusion control strategy of the smart community, mobilizing the enthusiasm of users to participate in system control to the greatest extent, and can effectively realize the multi-energy fusion control and coordinated optimization operation of the smart community. The multi-energy fusion system coordination control method for smart communities proposed in the present invention can achieve coordinated cooperation between photovoltaic power generation, wind power generation, energy storage systems, residential loads, electric private cars and the large power grid, while reducing the peak-to-valley difference of residential loads, improving the power consumption structure, and improving the reliability of power supply. At the same time, it also reduces the cost of purchasing electricity from the large power grid, improves the economy of system operation, and realizes the coordinated operation of the multi-energy fusion system of the smart community. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is the interactive framework diagram of the smart community system;

[0070] Figure 2 Flowchart of the control strategy for the multi-energy fusion system of the smart community;

[0071] Figure 3 A diagram of adjustable charging power levels for virtual energy storage of electric private car clusters;

[0072] Figure 4 A diagram of adjustable charging capacity levels for virtual energy storage of electric private car clusters;

[0073] Figure 5 A diagram of the adjustable discharge power levels for virtual energy storage in a cluster of electric private cars;

[0074] Figure 6 A diagram of the adjustable discharge capacity level of virtual energy storage for electric private car clusters;

[0075] Figure 7 Result diagram of multi-energy integration and collaborative optimization for smart communities;

[0076] Figure 8 A comparison chart of electric private cars before and after participating in smart community optimization and regulation using virtual energy storage;

[0077] Figure 9 It is a graph showing the output of renewable energy power generation on weekdays and the corresponding abandoned solar and wind power;

[0078] Figure 10 This is the curve tracing effect diagram;

[0079] Figure 11 A comparison chart of abandoned solar and wind power before and after optimization;

[0080] Figure 12 This is the power control curve of the electric private car cluster. DETAILED DESCRIPTION

[0081] The present invention is further illustrated below with reference to the accompanying drawings, tables and specific examples. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0082] The present invention provides a multi-energy fusion system coordinated control method for a smart community, comprising the following steps:

[0083] S1: Constructing an interactive framework for a multi-energy fusion system in a smart community:

[0084] Smart community system interaction framework Figure 1 As shown, the smart community control center primarily includes: a photovoltaic power generation system, a wind power generation system, energy storage equipment, residential loads, electric vehicles, a bidirectional smart charger, and a smart community control center. Six photovoltaic units are located on the rooftops of residential buildings, while the control center and six wind turbines are located on the ground floor of the community. The electric vehicles, bidirectional smart charger, and energy storage equipment are located underground. The smart community power grid also interacts with the broader power grid. The smart community control center's functions primarily encompass two aspects: first, it collects and processes information from each component from the bottom up; second, it makes control decisions based on the processed and integrated information from each component, issuing control commands to each component from the top down. Examples include operating control of new energy generators, managing the charging and discharging of energy storage equipment, guiding the charging and discharging of electric vehicles, and optimizing the coordinated energy scheduling between components.

[0085] Figure 1The specific process of internal integration and interaction within a smart community is as follows: First, the smart community control center collects information from various components within the system, including wind / solar output, residential load, and electric vehicle charging needs. Then, it develops optimization strategies, including the optimal combination of new energy generators and energy storage equipment, and the control center's flexible regulation of electric vehicle charging and discharging, thereby achieving the optimal energy scheduling strategy. Specifically, the virtual energy storage of electric vehicles participates in smart community grid regulation. This allows electric vehicles to charge from the grid when electricity load is low, reducing the phenomenon of "wind and solar curtailment" and avoiding resource waste while meeting their own charging needs. During peak electricity load, the vehicles discharge energy to the grid through V2G technology, meeting the smart community's electricity load while generating certain economic benefits from the price difference between charging and discharging.

[0086] S2: Construct the output model of photovoltaic and wind power, the model of energy storage equipment, and the model of power load within the smart community:

[0087] (1) The output power model of the photovoltaic power generation system is:

[0088]

[0089] Where: P PV (t) is the actual output power; P STC is the rated output power; L C (t) is the actual light intensity; L STC The standard light intensity is 1kW / m 2 ; μ is the power temperature coefficient, which is -0.0047 / ℃ in this embodiment; T C is the actual temperature of the battery surface; T STC is the surface temperature of the photovoltaic unit under standard conditions, which is 25° C. in this embodiment.

[0090] (2) The mathematical model of the wind power generation system output power is:

[0091]

[0092] Where: P WT (t) is the actual output power of the wind turbine during period t; P e is the rated power of the wind turbine; v(t) is the actual wind speed during the t period; v r is the cut-in wind speed; v c is the cut-off wind speed; v e is the rated wind speed.

[0093] (3) The mathematical model of the energy storage device is:

[0094] Charging:

[0095] SES (t+1)=(1-σ)S ES (t)+P ES (t)Δtη c (20)

[0096] Constraints:

[0097]

[0098] Discharging:

[0099]

[0100] Constraints:

[0101] 0≤P ES (t)≤min{[(1-σ)S ES (t)-S ES.min ]Δtη disc ,P ES.max} (twenty three)

[0102] Where: S ES.min is the minimum capacity of the energy storage device; S ES.max is the maximum capacity of the energy storage device; S ES (t+1) is the remaining energy storage capacity during the t+1 period; S ES (t) is the remaining energy storage capacity in period t; P ES (t) is the charge and discharge power during the t period; P ES.max is the maximum value of charge and discharge power; σ is the self-discharge rate; η c is the charging efficiency; η disc is the discharge efficiency; Δt is the time interval.

[0103] (4) The model of power load is:

[0104]

[0105] Where: P L (t) is the total load of residents in period t; P it is the power consumption of the i-th load in period t; n is the load type.

[0106] S3: A model for the virtual energy storage control capability of electric private vehicles in smart communities was established:

[0107]

[0108]

[0109]

[0110]

[0111] Where: C cha (t) is the charging capacity of the electric private car cluster during period t, C j,cha (t) is the charging capacity of a single electric private car during period t, C dis (t) is the discharge capacity of the electric private car cluster during period t, C j,dis (t) is the discharge capacity of a single electric private car during period t, P cha (t) is the charging power of the electric private car cluster during period t, P j,cha (t) is the charging power of a single electric private car during period t, P dis (t) is the discharge power of the electric private car cluster during period t, P j,dis (t) is the discharge power of a single electric private car during period t, N EV is the number of electric private cars in the smart community.

[0112] S4: Based on the interactive framework and model of the multi-energy fusion system in smart communities, as well as the virtual energy storage control capability model of electric private cars in smart communities, a method for the control and coordinated optimization of the multi-energy fusion system in smart communities is proposed:

[0113] Optimization goal:

[0114]

[0115] in:

[0116]

[0117] Where: f S represents the volatility of the equivalent load composed of the smart community resident load, photovoltaic power generation, wind power generation and electric private car control power; P H (t) is the original load of the smart community during period t; P EV (t) is the control power of the electric private car cluster during period t; P PV (t) is the photovoltaic power generation output during period t; P WT (t) is the wind power output during period t; P AVE is the average value of the comprehensive load; T is the total number of time periods.

[0118] The constraints include the following constraints (1) to (4):

[0119] Constraints (1) Constraints on the virtual energy storage control capability of electric private car clusters

[0120] P dis (t)≤P EV (t)≤P cha (t) t=1,2,3,…,96 (31)

[0121] Where: P dis (t) is the lower limit of discharge control power during period t, P cha (t) is the upper limit of charging control power during period t.

[0122] Constraint (2) Constraints on the interaction power between electric private car clusters and the power grid

[0123] P g,min (t)≤P EV (t)≤P g,max (t) t=1,2,3,…,96 (32)

[0124] Where: P g,min (t) is the minimum value of the interaction power between the electric private car cluster and the power grid during period t, P g,max (t) is the maximum value of the interaction power between the electric private car cluster and the power grid during period t.

[0125] Constraint (3) Charge and discharge power constraints for a single electric private car

[0126] P dis,max ≤P j (t)≤P cha,max t=1,2,3,…,96 (33)

[0127] Where: P dis,max is the maximum discharge power that a single electric private car can withstand, P cha,max The maximum charging power that a single electric private car can withstand.

[0128] Constraint (4) SOC constraint for a single electric private car

[0129] S j,min ≤S j (t)≤1 (34)

[0130] Where: S j,min It is the minimum SOC value of a single electric private car, and its purpose is to prevent discharge from being too low and affecting the battery life of the electric private car.

[0131] The flow chart of the smart community multi-energy fusion system control strategy in this embodiment is as follows: Figure 2 The steps are as follows:

[0132] Step 1: The smart community control center reads the data of distributed power supply, energy storage equipment and residents' load in the system, including the light intensity, ambient temperature, wind speed and other data in the community, and calculates the photovoltaic output P through the established photovoltaic system output model. PV (t), wind system output model is used to calculate the wind turbine output PWT (t) and the electricity load model are used to calculate the residential load P L (t); obtain the maximum active power P of the transformer M ;

[0133] Step 2: Determine whether ΔP1 = P PV (t)+P WT (t)-P L (t) Whether it is 0, that is, whether the remaining power after the photovoltaic output power and wind power output power supply the residential load power is greater than 0. If it is greater than 0, go to step 3; if it is less than 0, go to step 8;

[0134] Step 3: Obtain the curtailed solar and wind power and the required charging power ∑P for all uncharged electric private vehicles EV.n (t) Based on the effect of tracking the curtailment curves of solar and wind power by the smart community control center, guide electric private car loads to participate in absorbing the curtailment of solar and wind power;

[0135] Step 4: Determine ΣP EV.n (t)+P L Is (t) less than P? M , that is, whether the charging power of the electric private car plus the residential load power is less than the transformer gate value, if so, go to step 6, if it is greater than P M , then go to step 5;

[0136] Step 5: Prioritize charging electric private cars whose power levels do not meet user expectations based on vehicle usage demand, and calculate the updated charging power ΣP′ of electric private cars. EV.n (t), return to step 3, now ΣP′ EV.n (t) is equivalent to ∑P EV.n (t) into the calculation;

[0137] Step 6: Calculate ΔP2 = ΔP1 - ∑P EV.n Whether the value of (t) is greater than 0, that is, whether the remaining power after calculating the photovoltaic output power and wind power output power supplying the residential load and the electric private car load is greater than 0, if it is greater than 0, go to step 7, if it is less than 0, go to step 8;

[0138] Step 7: Charge the vehicles based on the community's vehicle usage, and use the energy storage device as a coordination auxiliary device for the electric private vehicle load to continue absorbing excess abandoned solar and wind power, and then proceed to step 10;

[0139] Step 8: Determine whether the energy storage SOC is greater than 20%. If so, the energy storage device discharges the load and goes to step 9; otherwise, go to step 11.

[0140] Step 9: Calculate whether the energy storage device can still meet the user load demand after discharge. If so, go to step 12; otherwise, go to step 11.

[0141] Step 10: Determine whether the energy storage SOC is less than 80%. If so, use the excess distributed energy to charge the energy storage device. If not, shut down the excess distributed energy output and proceed to step 12.

[0142] Step 11: Based on the virtual energy storage control capability of electric vehicles in the smart community, the dischargeable capacity of the electric vehicle virtual energy storage is obtained, and it is determined whether the discharge of the electric vehicle virtual energy storage can meet the community load demand. If so, the process proceeds to step 12; if not, the smart community purchases electricity from the grid.

[0143] Step 12: The electricity demand of residents in the smart community and the charging and discharging needs of electric private car users are met, realizing the multi-energy integration and optimized operation of the smart community;

[0144] Step 13: End.

[0145] Based on the multi-energy fusion system coordinated control method of the smart community, a simulation analysis is performed in this embodiment, as follows:

[0146] 1. Smart Community System Interaction Framework Figure 1 As shown, the smart community system primarily includes: a photovoltaic power generation system, a wind power generation system, energy storage equipment, residential loads, electric vehicles, a bidirectional smart charger, and a smart community control center. Six photovoltaic units are located on the rooftops of residential buildings, the control center and six wind turbines are located on the ground floor of the community, and the electric vehicles, bidirectional smart charger, and energy storage equipment are located underground. The smart community grid also interacts with the larger power grid.

[0147] 2. The output models of the photovoltaic power generation system and the wind power generation system, the model of the energy storage device and the model of the power load have been established in step S2 of the specific implementation method.

[0148] 3. Evaluate the virtual energy storage of electric private cars. Assume that the number of electric private cars is 100, the rated capacity of the battery is 30kWh, the maximum charge and discharge power of the bidirectional smart charger is 6kW, the power consumption of the electric private car is 15kWh per 100 kilometers, and the time of a day is discretized into 96 time periods with an interval of 15 minutes. The time period corresponding to 00:00-00:15 is numbered 1, the time period corresponding to 00:15-00:30 is numbered 2, and so on. Evaluate the control capability of electric private cars in each time period, assuming that the charge and discharge power remains unchanged within the time interval. According to reference

[88] , when the electric private car is not connected, the exchange power inside the smart community is shown in Table 1. Indicates the switching power within the smart cell on weekdays.

[0149] Table 1 - Switching power within a smart community

[0150]

[0151]

[0152] The real-time electricity prices are shown in Table 2, and the charging and discharging prices of electric private cars are shown in Table 3.

[0153] Table 2 - Real-time electricity prices

[0154]

[0155] Table 3 - Charging and discharging prices for electric private cars

[0156]

[0157] By analyzing the travel behavior of electric private car users in smart communities, it is found that on weekdays, users usually leave around 7:30 and return around 18:00, as shown in Table 4.

[0158] Table 4 - Travel behavior of electric private car users

[0159]

[0160] It should be noted that, to better reflect the data collection methods used by electric private vehicles in real-world scenarios, the above probability distribution is used to simulate the travel behavior of electric private vehicle users on weekdays. Electric private vehicle users expect the SOC to be above 80% when leaving the smart community, and the SOC to be no less than 0.2 and no more than 0.9 during charging and discharging. To ensure uniformity in calculations, the electric private vehicle battery capacity is 30kWh, and the maximum charge and discharge power provided by the bidirectional smart charger is 6kW, with seven settings: -6, -4, -2, 0, 2, 4, and 6kW. Positive values ​​indicate charging the electric private vehicle, and negative values ​​indicate discharging the electric private vehicle.

[0161] Through simulation analysis, Figure 3 Adjustable charging power level for virtual energy storage of electric private car clusters, Figure 4 Adjustable charging capacity level for virtual energy storage of electric private car clusters, Figure 5 The adjustable discharge power level of the virtual energy storage for electric private car clusters, Figure 6 Adjustable discharge capacity level for virtual energy storage of electric private car clusters.

[0162] 4. Based on the output models of the photovoltaic power generation system and wind power generation system, the model of the energy storage equipment and the model of the power load, as well as the evaluation results of the virtual energy storage control capability of electric private cars in the smart community, multi-energy integration control is carried out on the smart community. The smart community has 100 households, 6 groups of 20kW photovoltaic units, 6 groups of 10kW wind turbines and 1 group of energy storage equipment with a rated power of 30kW and a capacity of 100kWh. The load data of the smart community is shown in Table 1, the light and temperature data are shown in Table 5, and the wind speed data are shown in Table 6.

[0163] Table 5 - Light and temperature data

[0164]

[0165]

[0166] Table 6 - Wind speed data

[0167]

[0168] Figure 7 As the control result, the figure includes the load data curve of smart community residents, the output curve of photovoltaic power generation equipment, the output curve of wind power generation equipment, the power sales curve of the power grid, and the charging and discharging curve of energy storage equipment. Figure 7 As can be seen, photovoltaic power generation only occurs during daytime sunlight, reaching its maximum at noon. Wind power generation output remains largely stable throughout the day, with minimal fluctuations. From 12:00 AM to 9:00 AM, wind / solar power output is less than the residential load, and the smart community purchases power from the grid. From 9:00 AM to 4:30 PM, wind / solar power output exceeds the residential load, at which point the smart community stops purchasing power from the grid. To avoid curtailment, the energy storage device begins charging. To reduce the cost of purchasing power from the grid, the energy storage system is not charged at night. From 4:30 PM to midnight, wind / solar power output again falls short of the residential load, causing the energy storage device to release stored power while the smart community purchases power from the grid. The figure shows that peak-to-valley load variations are effectively reduced, avoiding the problem of inadequate absorption of excess wind and solar power.

[0169] Before the optimization, electric private cars were only used as regular charging loads in the smart community, rather than virtual energy storage loads with flexible charging and discharging control capabilities. Figure 8 This is a comparison chart before and after the virtual energy storage of electric private cars participates in the optimization and regulation of smart communities on weekdays. Figure 8 It can be seen from the data that on weekdays, after the virtual energy storage of electric private cars participated in the optimization and regulation of smart communities, the smart communities significantly reduced the power purchased from the power grid.

[0170] according to Figure 8As can be seen from Table 7, after optimization, the power purchased from the grid between 9:00 and 16:00 is 0, which effectively reduces the cost of purchasing electricity from the grid for users in the smart community and improves economic efficiency.

[0171] Table 7-Grid electricity sales power between 9:00 and 16:00

[0172]

[0173] according to Figure 8 As can be seen from Tables 8 and 9, after optimization, the residential electricity load during the peak hours of 6:00-9:00 and 18:00-24:00 is significantly reduced.

[0174] Table 8-Residential electricity load between 6:00 and 9:00

[0175]

[0176] Table 9-Residential electricity load between 18:00 and 24:00

[0177]

[0178]

[0179] In addition, according to Figure 8 As can be seen from Table 10, after optimization, the residential electricity load during the low electricity consumption period of 13:00-16:00 is significantly improved, and the peak-to-valley difference is reduced, thereby improving the electricity consumption structure and enhancing the reliability of power supply.

[0180] Table 10-Residential electricity load between 13:00 and 16:00

[0181]

[0182] Figure 9 Figure A shows the output of renewable energy power generation on weekdays and the corresponding abandoned solar and wind power. Figure A shows the output of renewable energy power generation on weekdays, and Figure B shows the abandoned solar and wind power on weekdays.

[0183] Figure 10 The following is a curve tracking effect diagram, where Figure A shows the curtailed wind and solar power on weekdays, and Figure B shows the continuous curve tracking on weekdays. At every moment of the weekday, the smart community control center releases the curtailed solar and wind power curves, guiding the virtual energy storage of electric private cars and the energy storage equipment as a coordinated supplementary resource to achieve the tracking and absorption of the curtailed solar and wind power curves. The effect is as follows Figure 11 shown.

[0184] Figure 11Figure A in the middle is a comparison of the abandoned solar power before and after optimization. Since the sunlight intensity is 0 in the early morning and at night, the photovoltaic output is 0 and the abandoned solar power is also 0. During the period of 06:00-14:00, the abandoned solar power shows an upward trend from 0. The reason is that light begins to appear during the day, and the light intensity is increasing, the photovoltaic output power increases, and the travel needs of residents lead to a decrease in the residents' load and a decrease in the load of electric private cars in the community; during the period of 14:00-18:00, the abandoned solar power shows a downward trend until it reaches 0. This is because as the light intensity weakens, the photovoltaic output power decreases, and the residents' load and the load of electric private cars in the community increase due to the return of residents to the smart community. Figure 11 Figure B in the middle is a comparison of wind power curtailment before and after optimization. Due to the high load of residents and electric private cars in the early morning and at night, the curtailed solar power is relatively low. The curtailed wind power begins to show an upward trend from 06:00 to 14:00, which is due to the reduction of residents' load due to the travel needs of residents and the reduction of electric private car load in the community; the curtailed wind power shows a downward trend from 14:00 to 18:00, which is due to the increase of residents' load due to the return of residents to the smart community and the increase of electric private car load in the community. Figure 11 It can be clearly seen that the abandoned solar power and abandoned wind power after optimization on weekdays are significantly lower than the abandoned solar power and abandoned wind power before optimization, further proving that the optimization strategy proposed in the present invention can utilize photovoltaic and wind power generation to a greater extent, reduce the amount of abandoned solar power and abandoned wind power, and reduce the waste of photovoltaic and wind resources.

[0185] Figure 12 This is the power control curve for the electric vehicle cluster. The "upper power limit" and "lower power limit" in the figure define the power range within which the electric vehicle cluster can participate in bidirectional grid interaction. As can be seen from the figure, the charging and discharging power of the electric vehicle cluster does not exceed these upper and lower limits. The "Charge and Discharge Strategy" curve in the figure illustrates the charge and discharge power control of the electric vehicle virtual energy storage on weekdays. Between 12:00 AM and 5:00 AM, electric vehicles charge, reducing charging costs due to lower charging prices. Between 5:00 AM and 9:00 AM, during the morning peak electricity load, electric vehicles discharge, reducing peak load. Between 9:00 AM and 4:30 PM, when wind and solar power output exceeds residential load, the smart community stops purchasing electricity from the grid. To avoid wind and solar curtailment, electric vehicles charge, improving the absorption of wind and solar power. Between 4:30 PM and midnight, during peak electricity load, electric vehicles discharge, effectively reducing peak load.

[0186] To sum up, it can be seen from the control results that, under the premise of ensuring the stable operation of the smart community and the travel needs of electric private car users, based on the interactive framework of the smart community system and the evaluation results of the virtual energy storage control capabilities of electric private cars in the smart community, the multi-energy fusion system coordination control method for smart communities proposed in the present invention can enable photovoltaic power generation, wind power generation, energy storage systems, residential loads, electric private cars and large power grids to cooperate with each other, while reducing the peak-to-valley difference of residential loads, improving the power consumption structure, and improving the reliability of power supply; at the same time, it can utilize photovoltaic and wind power generation to a greater extent, reduce the amount of abandoned light and wind, and reduce the waste of photovoltaic and wind resources; at the same time, it also reduces the cost of purchasing electricity from the large power grid, improves the economy of system operation, and realizes the coordinated operation of the multi-energy fusion system of the smart community.

Claims

1. A coordinated control method for a multi-energy fusion system for a smart community, characterized by: The steps include: S1: Constructing an interactive framework for the multi-energy integration system of smart communities; S2: Construct the output model of photovoltaic and wind power within the community, the model of energy storage equipment, and the model of power load; S3: Establish a virtual energy storage control capability model for electric private cars in smart communities; S4: Through the interactive framework of the smart community multi-energy fusion system, the model constructed in step S2, and the virtual energy storage control capability model of electric private cars in the smart community, the smart community multi-energy fusion system control and collaborative optimization are realized according to the smart community multi-energy fusion system control and operation strategy; The operation method of the smart community multi-energy fusion system control and collaborative optimization in step S4 is: Step 1: The smart community control center reads the data of distributed power supply, energy storage equipment and residents' load in the system, and calculates the photovoltaic output P through the established photovoltaic system output model. PV (t), wind system output model is used to calculate the wind turbine output P WT (t) and the electricity load model are used to calculate the residential load P L (t), obtain the maximum active power P of the transformer M ; Step 2: Determine whether ΔP1 = P PV (t)+P WT (t)-P L (t) Whether it is 0, that is, whether the remaining power after the photovoltaic output power and wind power output power supply the residential load power is greater than 0. If it is greater than 0, go to step 3; if it is less than 0, go to step 8; Step 3: Obtain the curtailed solar and wind power and the required charging power ∑P for all uncharged electric private vehicles EV.n (t) Based on the effect of tracking the curtailment curves of solar and wind power by the smart community control center, guide electric private car loads to participate in absorbing the curtailment of solar and wind power; Step 4: Determine ∑P EV.n (t)+P L Is (t) less than P? M , that is, whether the charging power of the electric private car plus the residential load power is less than the transformer gate value, if so, go to step 6, if it is greater than P M , then go to step 5; Step 5: Prioritize charging electric private cars whose power levels do not meet user expectations based on vehicle usage demand, and calculate the updated charging power ∑P′ of electric private cars. EV.n (t), return to step 3, now ∑P′ EV.n (t) is equivalent to ∑P EV.n (t) into the calculation; Step 6: Calculate ΔP2 = ΔP1 - ∑P EV.n Whether the value of (t) is greater than 0, that is, whether the remaining power after calculating the photovoltaic output power and wind power output power supplying the residential load and the electric private car load is greater than 0, if it is greater than 0, go to step 7, if it is less than 0, go to step 8; Step 7: Charge the vehicles based on the community's vehicle usage, and use the energy storage device as a coordination auxiliary device for the electric private vehicle load to continue absorbing excess abandoned solar and wind power, and then proceed to step 10; Step 8: Determine whether the energy storage SOC is greater than 20%. If so, the energy storage device discharges the load and goes to step 9; otherwise, go to step 11. Step 9: Calculate whether the energy storage device can still meet the user load demand after discharge. If so, go to step 12; otherwise, go to step 11. Step 10: Determine whether the energy storage SOC is less than 80%. If so, use the excess distributed energy to charge the energy storage device. If not, shut down the excess distributed energy output and proceed to step 12. Step 11: Based on the virtual energy storage control capability of electric private cars in the smart community, obtain the dischargeable capacity of the electric vehicle's virtual energy storage, and determine whether the discharge of the electric private car's virtual energy storage can meet the community load demand. If so, go to step 12; if not, the smart community purchases electricity from the power grid, thus realizing the multi-energy integration and optimized operation of the smart community.

2. The multi-energy fusion system coordination control method for smart communities according to claim 1 is characterized in that: The interactive framework of the smart community multi-energy fusion system in step S1 includes seven parts: photovoltaic power generation system, wind power generation system, energy storage equipment, power load, electric private car, bidirectional intelligent charger and smart community control center.

3. The multi-energy fusion system coordination control method for smart communities according to claim 1 is characterized in that: The photovoltaic and wind power output models in step S2 include a photovoltaic power generation system output power model and a wind power generation system output power model. The output power model of the photovoltaic power generation system is: Where: P PV (t) is the actual output power during period t; P STC is the rated output power; L C (t) is the actual light intensity during the t period; L STC is the standard light intensity; μ is the power temperature coefficient; T C is the actual temperature of the battery surface; T STC is the surface temperature of the photovoltaic unit under standard conditions; The output power model of the wind power generation system is: Where: P WT (t) is the actual output power of the wind turbine during period t; P e is the rated power of the wind turbine; v(t) is the actual wind speed during the t period; v r is the cut-in wind speed; v c is the cut-off wind speed; v e is the rated wind speed; The mathematical model of the energy storage device is: Charging: S ES (t+1)=(1-σ)S ES (t)+P ES (t)Δtη c (3) Constraints: Discharging: Constraints: 0≤P ES (t)≤min{[(1-σ)S ES (t)-S ES.min ]Dtη disc ,P ES.max } (6) Where: S ES.min is the minimum capacity of the energy storage device; S ES.max is the maximum capacity of the energy storage device; S ES (t+1) is the remaining energy storage capacity during the t+1 period; S ES (t) is the remaining energy storage capacity in period t; P ES (t) is the charge and discharge power during the t period; P ES.max is the maximum value of charge and discharge power; σ is the self-discharge rate; η c is the charging efficiency; η disc is the discharge efficiency; Δt is the time interval; The model of the electricity load is: Where: P L (t) is the total load of residents in period t; P it is the power consumption of the i-th load in period t; n is the load type.

4. The multi-energy fusion system coordination control method for smart communities according to claim 1 is characterized in that: The virtual energy storage control capability model of electric private cars in the smart community in step S3 is: Where: C cha (t) is the charging capacity of the electric private car cluster during period t, C j,cha (t) is the charging capacity of a single electric private car during period t, C dis (t) is the discharge capacity of the electric private car cluster during period t, C j,dis (t) is the discharge capacity of a single electric private car during period t, P cha (t) is the charging power of the electric private car cluster during period t, P j,cha (t) is the charging power of a single electric private car during period t, P dis (t) is the discharge power of the electric private car cluster during period t, P j,dis (t) is the discharge power of a single electric private car during period t, N EV is the number of electric private cars in the smart community.

5. The multi-energy fusion system coordination control method for smart communities according to claim 1 is characterized in that: The control and operation strategy of the smart community multi-energy fusion system in step S4 is: Optimization goal: in: Where: f S represents the volatility of the equivalent load composed of the smart community resident load, photovoltaic power generation, wind power generation and electric private car control power; P H (t) is the original load of the smart community during period t; P EV (t) is the control power of the electric private car cluster during period t; P PV (t) is the photovoltaic power generation output during period t; P WT (t) is the wind power output during period t; P AVE is the average value of the comprehensive load, and T is the total number of time periods.

6. The method for coordinated control of a multi-energy fusion system for a smart community according to claim 5, characterized in that: The constraints in the optimization objective include the following constraints (1) to (4): Constraints (1) Constraints on the virtual energy storage control capability of electric private car clusters P dis (t)≤P EV (t)≤P cha (t)t=1,2,3,…,96(14) Where: P dis (t) is the lower limit of discharge control power during period t, P cha (t) is the upper limit of charging control power during period t; Constraint (2) Constraints on the interaction power between electric private car clusters and the power grid P g,min (t)≤P EV (t)≤P g,max (t)t=1,2,3,…,96(15) Where: P g,min (t) is the minimum value of the interaction power between the electric private car cluster and the power grid during period t, P g,max (t) is the maximum value of the interaction power between the electric private car cluster and the power grid during period t; Constraint (3) Charge and discharge power constraints for a single electric private car P dis,max ≤P j (t)≤P cha,max t=1,2,3,…,96 (16) Where: P dis,max is the maximum discharge power that a single electric private car can withstand, P cha,max The maximum charging power that a single electric private car can withstand; Constraint (4) SOC constraint for a single electric private car S j,min ≤S j (t)≤1 (17) Where: S j,min It is the minimum SOC value of a single electric private car, and its purpose is to prevent discharge from being too low and affecting the battery life of the electric private car.

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