A user-side based energy control optimization method, medium and device

Abstract

This invention relates to the field of energy dispatching technology, and discloses a user-side energy control optimization method, medium, and equipment. The method includes: establishing a flexible load control model that includes unidirectional and bidirectional interactive flexible loads; establishing an optimization control model involving multiple flexible loads, considering both photovoltaic and energy storage systems, with user-side economic cost, photovoltaic absorption rate, and recreational room comfort as joint objectives; establishing multi-variable joint constraints; and integrating the Latin square model and the PSO model into the LSPSO multi-model ensemble method to obtain the optimal control scheme. This invention constructs mathematical models of unidirectional and bidirectional interactive flexible loads based on energy interaction methods, establishes a user-side coordinated control operation model considering flexible load scheduling, and the proposed LSPSO model shows faster convergence speed in the simulation examples and is better adapted to discrete-continuous mixed data environments, thus achieving a certain advantage in optimization efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy allocation technology, and in particular to a user-side-based energy control optimization method, medium, and device. Background Technology

[0002] Currently, my country's distributed photovoltaic (PV) installed capacity will continue its rapid growth, and with the support of technological innovation, distributed PV will be deeply integrated with new power systems, playing an increasingly significant role in achieving a green and low-carbon energy transition. Therefore, in response to the call and considering the special characteristics of transportation hubs, major cities across the country have built a number of "distributed new energy + energy storage + microgrid" smart energy system projects in highways, waterway service areas, ports, and hub stations. Constructing ecological and green transportation hubs with new energy as the main body has gradually become a current development trend. Traditional highway service area power supply systems generally use municipal power, but with the rapid development of distributed generation units, many flexible loads and intelligent integrated equipment together form a multi-dimensional integrated microgrid. Therefore, how to ensure that new highway service areas meet safety constraints while also being green and intelligent has become a current research hotspot.

[0003] Considering that the multiple flexible loads in new highway service areas are also a nonlinear programming problem, existing technologies can achieve preliminary control of flexible loads. However, the consideration of flexible loads is relatively simplistic, failing to address the coordinated and optimized operation among various flexible loads, and neglecting to consider user-side energy economy. Furthermore, while existing technologies offer different optimization approaches for flexible load control, these methods still suffer from limitations such as high workload and susceptibility to local optima when dealing with mixed discrete-continuous nonlinear optimization problems.

[0004] Therefore, there is an urgent need to provide a more efficient and reasonable user-side-based energy control optimization method to solve the problems existing in the current technology. Summary of the Invention

[0005] The purpose of this invention is to provide a more efficient and reasonable user-side-based energy control optimization method, the specific technical solution of which is as follows:

[0006] A user-side-based energy control optimization method includes the following steps:

[0007] Establish a flexible load control model for the user side, including one-way interactive flexible loads and two-way interactive flexible loads;

[0008] With user-side economic costs, photovoltaic absorption rate, and comfort of recreational rooms as joint objectives, and considering both photovoltaic and energy storage systems, an optimized control model involving multiple flexible loads is established.

[0009] Establish constraints for the joint operation of multiple variables;

[0010] The Latin square model and the PSO model are integrated into the LSPSO multi-model ensemble method to quickly search for the optimal feasible region and obtain the optimal control scheme.

[0011] Furthermore, the flexible load control model is specifically as follows:

[0012] Based on the energy interaction method, flexible loads are divided into two-way interactive flexible loads and one-way interactive flexible loads.

[0013] For all energy users in highway service areas, a one-way interactive flexible load control model is established based on the reducible loads, including central air conditioning, heating equipment, building lighting, some small commercial users, and advertising, as follows:

[0014]

[0015] Where L represents the amount of load reduction by electricity users, This represents the maximum value of the one-way interactive power load reduction. This represents the reduction in unidirectional power load during the t-th time period in the highway service area system.

[0016] In highway service areas, a two-way interactive flexible load control model is established, which includes electric vehicle loads. The charging and discharging states of electric vehicles voluntarily connected to the highway service area microgrid are represented as follows:

[0017]

[0018] In the formula, Q EV (t) represents the electric vehicle's charge at time t, ω represents the charging / discharging efficiency, and P EV The power of charging and discharging an electric vehicle, Δt is the time interval, x EV (t) is the decision variable, where 1 represents the charging and discharging of the electric vehicle during time period t, and 0 represents not participating in the control.

[0019] Furthermore, the specific methods for considering photovoltaic power consumption and energy storage systems are as follows:

[0020] In highway service areas, each rest or recreation room roof is equipped with a distributed solar photovoltaic system, provided that the reflection of photovoltaic modules does not affect the safety of vehicles.

[0021] By adding an energy storage system to the photovoltaic system, the power control situation in highway service areas becomes as follows:

[0022] Photovoltaic power generation is primarily used to meet the daily needs of highway service areas. If the photovoltaic power generation exceeds the daily available load, it will be stored through energy storage devices or electric vehicles.

[0023] Highway service areas select charging times during off-peak hours at night based on the charging and discharging status of buses, taxis, and special-purpose vehicles with transferable load characteristics; if the previous day's passenger volume, weather conditions, or insufficient energy storage system power are obtained in advance, appropriate discharging will be selected for coordinated control.

[0024] Furthermore, the steps for establishing an optimal control model involving multiple flexible loads are as follows:

[0025] With the goal of minimizing user-side energy costs, including the cost of purchasing electricity from the grid, the cost of charging electric vehicles, the cost of discharging electric vehicles, and the cost of battery losses during the charging and discharging of energy storage devices, the user-side economic cost objective function is expressed as follows:

[0026]

[0027] In the formula, C1 is the cost of purchasing electricity from the grid; B(t) is the electricity price at time t; P t C2 is the power consumption; C2 is the charging cost when the electric vehicle is under charging control; Q(t) is the electricity purchase price for the electric vehicle charging at time t; x EV,in,n (t) is set to 1, indicating that the electric vehicle user accepts scheduling during the charging period; otherwise, it is set to 0. max The maximum charging power is α; the compensation rate is α>1; N is the number of electric vehicles; C3 is the cost of electric vehicle discharge when the discharge is controlled; P EV,in,n and P EV,out,n The power output during charging and discharging of electric vehicles, respectively; x EV,out,n (t) is set to 1 to indicate that the electric vehicle user accepts dispatch during the discharge period, otherwise it is set to 0; C4 is the cost of battery loss during the charging and discharging of the energy storage device. The power used to charge and discharge energy storage devices; β ess The cost factor for battery losses during charging and discharging of energy storage devices; Δt is the time interval; T is the control period;

[0028] With the goal of maximizing photovoltaic (PV) grid connection capacity, the objective function for PV grid connection capacity is as follows:

[0029]

[0030] In the formula, P s It is a unidirectional interactive flexible load; P pv P represents photovoltaic power generation. c ess This is due to charging losses; This is discharge loss;

[0031] Room temperature comfort is introduced, which is represented by the weighted deviation between the current room temperature and the standard room temperature. The smaller the objective function, the lower the deviation and the higher the room temperature comfort. The objective function for room temperature comfort is shown below:

[0032]

[0033] In the formula, T p T represents the current room temperature. c This indicates a temperature that is artificially set to provide comfort.

[0034] Furthermore, the constraints for the multivariate joint operation include:

[0035] a. Comfort temperature constraint: As a device for regulating comfort temperature, within the acceptable range for human body temperature, the air conditioner's regulating power must meet the following constraints:

[0036] T min ≤T t ≤T max ;

[0037] 0≤P t ≤P max ac ;

[0038] In the formula T t T represents the indoor temperature after air conditioning adjustment. min and T max These are the lowest and highest acceptable indoor temperatures for the human body, respectively, P. t P is the regulating power of the air conditioner. max ac This represents the maximum allowable power of the air conditioner.

[0039] b. Supply and demand balance constraints for highway service areas: The input power of each highway service area should match its output power. When the load power is less than the power of the power generation unit, the supply and demand balance equation constraint for the traffic power system is established as follows:

[0040] P pv (t)+P ess (t)+P ev (t)=P s (t);

[0041] In the formula, P pv (t) is the output power of the distributed power source at time t; P ess (t) is the output / input power of the energy storage system at time t; P ev (t) is the power of the electric vehicle during charging or discharging at time t; Ps (t) is the power consumed by the system load at time t;

[0042] c. Constraints on the operating state of the energy storage system: In reality, energy storage systems cannot charge and discharge simultaneously, therefore the following constraints must be met:

[0043] 0≤|x ess,in,n (t)|+|x ess,out,n (t)|≤1;

[0044] In the formula, x ess,in,n (t) and x ess,out,n (t) represents the charging and discharging states of the system at time t;

[0045] d. Electric vehicle battery capacity SOC constraint:

[0046] SOC min <<SOC i (t) << SOC max ;

[0047] In the formula, SOC i (t) represents the remaining charge of the battery at time t, SOC. min and SOC max These are the minimum and maximum capacity limits for the battery, respectively.

[0048] e. Rooftop photovoltaic output constraints: Considering the randomness and volatility of rooftop photovoltaic systems, their photovoltaic output must be less than or equal to the maximum value predicted in advance. Therefore, the following constraints are established:

[0049] 0≤P PV ≤P max ;

[0050] Furthermore, the LSPSO multi-model ensemble method is as follows:

[0051] Step 1: Based on the optimization control model and constraints, generate the initial parameters of the objective function using the Latin square sampling model;

[0052] Step 2: Use the initial parameters as the initial population for the particle swarm optimization algorithm;

[0053] Step 3: Set the fitness function as an indicator to evaluate the quality of particles;

[0054] Step 4: Iterative optimization using the particle swarm optimization algorithm;

[0055] Step 5: Update the particle swarm;

[0056] Step 6: Evaluate the optimization results and determine whether the termination conditions are met. If they are met, the optimal control scheme is obtained. If not, adjust the scheme according to the actual situation and return to step 4.

[0057] Furthermore, the termination condition is:

[0058] The maximum number of iterations is reached or the change in the objective function value is less than a set threshold.

[0059] Furthermore, the LSPSO multi-model ensemble method also includes:

[0060] After obtaining the optimal control scheme, check whether it meets the constraints and actual requirements.

[0061] The application of the technical solution of the present invention has the following beneficial effects:

[0062] A user-side-based energy control optimization method includes the following steps: establishing a flexible load control model for the user side, encompassing both unidirectional and bidirectional interactive flexible loads; establishing an optimization control model involving multiple flexible loads, considering both photovoltaic and energy storage systems, with user-side economic cost, photovoltaic absorption rate, and recreational room comfort as joint objectives; establishing multi-variable joint constraints; and integrating the Latin square model and the PSO model into the LSPSO multi-model ensemble method to quickly search the optimal feasible region and obtain the optimal control scheme. This invention constructs mathematical models of unidirectional and bidirectional interactive flexible loads based on energy interaction methods, and establishes a user-side coordinated control operation model considering flexible load scheduling, with user-side energy cost as the objective. This model solves a hybrid nonlinear optimization problem with both discrete and continuous data. It combines the Latin square model with the particle swarm optimization algorithm to form the Latin square particle swarm algorithm, and by combining the Latin square and PSO particle swarm algorithms, it can be integrated into the LSPSO multi-model ensemble method. The LSPSO model proposed in this invention achieves faster convergence in the numerical examples, demonstrating its better adaptability to discrete-continuous mixed data environments, thus offering an advantage in optimization efficiency. Finally, a simulation analysis was conducted based on historical flexible load data of a microgrid in a service area. The simulation results demonstrate that the proposed method possesses strong robustness and fast convergence in the context of discrete-continuous mixed input data, verifying the effectiveness of the proposed scheme.

[0063] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described user-side-based energy control optimization method.

[0064] The present invention also provides an electronic device, comprising:

[0065] processor;

[0066] and memory for storing the executable instructions of the processor;

[0067] The processor is configured to execute the aforementioned user-side-based energy control optimization method by executing the executable instructions.

[0068] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0069] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0070] Figure 1 This is a flowchart illustrating the user-side-based energy control optimization method according to an embodiment of the present invention.

[0071] Figure 2 The above is a graph showing the photovoltaic output versus load curves before optimization in this example.

[0072] Figure 3 The following are before-and-after effect diagrams in the embodiments;

[0073] Figure 4 This is a graph showing the relationship between energy storage charging and discharging power and electricity price fluctuations in the embodiment.

[0074] Figure 5 The optimized load curve is shown in the example. Detailed Implementation

[0075] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0076] Example:

[0077] refer to Figure 1 A user-side-based energy control optimization method includes the following steps:

[0078] Step 1: Taking user-side economic cost, photovoltaic absorption rate and comfort of leisure and entertainment room as joint objectives, and considering photovoltaic system and energy storage system, establish an optimization control model involving multiple flexible loads;

[0079] With the goal of minimizing user-side energy costs, including the cost of purchasing electricity from the grid, the cost of charging electric vehicles, the cost of discharging electric vehicles, and the cost of battery losses during the charging and discharging of energy storage devices, the user-side economic cost objective function is expressed as follows:

[0080]

[0081] In the formula, C1 is the cost of purchasing electricity from the grid; B(t) is the electricity price at time t; P t C2 is the power consumption; C2 is the charging cost when the electric vehicle is under charging control; Q(t) is the electricity purchase price for the electric vehicle charging at time t; x EV,in,n (t) is set to 1, indicating that the electric vehicle user accepts scheduling during the charging period; otherwise, it is set to 0. max The maximum charging power is α; the compensation rate is α>1; N is the number of electric vehicles; C3 is the cost of electric vehicle discharge when the discharge is controlled; P EV,in,n and P EV,out,n The power output during charging and discharging of electric vehicles, respectively; x EV,out,n (t) is set to 1 to indicate that the electric vehicle user accepts dispatch during the discharge period, otherwise it is set to 0; C4 is the cost of battery loss during the charging and discharging of the energy storage device. The power used to charge and discharge energy storage devices; β ess The cost coefficient for charging and discharging the battery of the energy storage device; Δt is the time interval; T is the control cycle; in this embodiment, a 24-hour day is used as a control cycle, and each hour is the control interval.

[0082] With the goal of maximizing photovoltaic (PV) grid connection capacity, the objective function for PV grid connection capacity is as follows:

[0083]

[0084] In the formula, P s It is a unidirectional interactive flexible load; P pv Photovoltaic power generation; This is due to charging losses; This is discharge loss;

[0085] Due to the high population density, the comfort level of highway service areas largely depends on the heat dissipation of passengers, thus affecting the power consumption of their air conditioning. Therefore, a room temperature comfort metric is introduced, represented by the weighted deviation between the current room temperature and the standard room temperature. A smaller objective function indicates a lower deviation and higher room temperature comfort. The room temperature comfort objective function is shown below:

[0086]

[0087] In the formula, T p T represents the current room temperature. c This indicates a temperature that is artificially set to provide comfort.

[0088] The specific methods considering photovoltaic integration and energy storage systems are as follows:

[0089] In highway service areas, to ensure that the reflection from photovoltaic modules does not affect the safety of vehicles, each rest area or recreation room is equipped with a distributed solar photovoltaic system on its roof. Simultaneously, to facilitate the integration of new energy sources and promote clean energy conversion, energy storage systems are added to the photovoltaic systems to facilitate the coordinated control of the highway service area's microgrid. Therefore, the power control situation in highway service areas is as follows:

[0090] 1) The load of highway service areas includes rigid load and flexible load. Flexible load mainly considers central air conditioning and some lighting load.

[0091] 2) Photovoltaic power generation should first meet the daily needs of highway service areas. If the photovoltaic power generation exceeds the daily available load, it will be stored through energy storage devices or electric vehicles.

[0092] Highway service areas primarily consider the charging and discharging of buses, taxis, and dedicated official vehicles. They have the characteristic of being able to transfer loads, allowing charging to be done during off-peak hours at night. When the passenger volume, weather conditions, or insufficient power in the energy storage system are known in advance, appropriate discharging measures can also be selected for coordinated control.

[0093] Step 2: Establish multi-variable joint constraints based on the supply and demand balance of highway service areas, the operating status and capacity of energy storage systems, and photovoltaic output, specifically including:

[0094] a. Comfort temperature constraint: As a device for regulating comfort temperature, within the acceptable range for human body temperature, the air conditioner's regulating power must meet the following constraints:

[0095] T min ≤T t ≤T max ;

[0096] 0≤P t ≤P max ac ;

[0097] In the formula T t T represents the indoor temperature after air conditioning adjustment. min and T max These are the lowest and highest acceptable indoor temperatures for the human body, respectively, P. t P is the regulating power of the air conditioner. max ac This represents the maximum allowable power of the air conditioner; in this embodiment, 26°C is taken as the most comfortable human body temperature.

[0098] b. Supply and demand balance constraints for highway service areas: The input power of each highway service area should match its output power. When the load power is less than the power of the power generation unit, the supply and demand balance equation constraint for the traffic power system is established as follows:

[0099] P pv (t)+P ess (t)+P ev (t)=P s (t);

[0100] In the formula, P pv (t) is the output power of the distributed power source at time t; P ess (t) is the output / input power of the energy storage system at time t; P ev (t) is the power of the electric vehicle during charging or discharging at time t; P s (t) is the power consumed by the system load at time t;

[0101] c. Constraints on the operating state of the energy storage system: In reality, energy storage systems cannot charge and discharge simultaneously, therefore the following constraints must be met:

[0102] 0≤|x ess,in,n (t)|+|x ess,out,n (t)|≤1;

[0103] In the formula, x ess,in,n (t) and x ess,out,n (t) represents the charging and discharging states of the system at time t;

[0104] d. Electric vehicle battery capacity SOC constraint:

[0105] SOC min SOC i (t)《SOC max ;

[0106] In the formula, SOC i (t) represents the remaining charge of the battery at time t, SOC. min and SOC max These are the minimum and maximum capacity limits for the battery, respectively.

[0107] e. Rooftop photovoltaic output constraints: Considering the randomness and volatility of rooftop photovoltaic systems, their photovoltaic output must be less than or equal to the maximum value predicted in advance. Therefore, the following constraints are established:

[0108] 0≤P PV ≤P max ;

[0109] Step 3: Establish a flexible load control model for the user side, including one-way interactive flexible loads and two-way interactive flexible loads;

[0110] The flexible load control model is specifically:

[0111] Based on the energy interaction method, flexible loads are divided into two-way interactive flexible loads and one-way interactive flexible loads.

[0112] For all energy users in highway service areas, a one-way interactive flexible load control model is established based on the reducible loads, including central air conditioning, heating equipment, building lighting, some small commercial users, and advertising, as follows:

[0113]

[0114] Where L represents the amount of load reduction by electricity users, This represents the maximum value of the one-way interactive power load reduction. This represents the reduction in unidirectional power load during the t-th time period in the highway service area system.

[0115] In highway service areas, a two-way interactive flexible load control model is established, which includes electric vehicle loads. The charging and discharging states of electric vehicles voluntarily connected to the highway service area microgrid are represented as follows:

[0116]

[0117] In the formula, Q EV (t) represents the electric vehicle's charge at time t, ω represents the charging / discharging efficiency, and P EV The power of charging and discharging an electric vehicle, Δt is the time interval, x EV (t) is the decision variable, where 1 represents the charging and discharging of electric vehicles during time period t, and 0 represents not participating in control. Whether to participate in transportation hub control is determined by time-of-use electricity pricing and the degree of user willingness.

[0118] Two-way interactive flexible loads achieve optimized control through incentives such as time-of-use pricing and possess mobility, with electric vehicles being a typical example. One-way interactive flexible loads, on the other hand, cannot feed energy back to highway service areas, but can achieve energy conservation and environmental protection by reducing load through control strategies (such as fine-tuning temperature and light intensity) while meeting user needs. Typical examples include central air conditioning, heating equipment, building lighting, some small commercial users, and advertising.

[0119] Step 4: Combine Latin square and PSO and integrate them into the LSPSO multi-model ensemble method to quickly search the optimal feasible region in the context of discrete-continuous mixed data, so as to obtain the optimal control result and improve the optimization efficiency. The LSPSO multi-model ensemble method is as follows:

[0120] Step 4.1: Based on the optimization control model and constraints, generate the initial parameters of the objective function using the Latin square sampling model;

[0121] Step 4.2: Use the initial parameters as the initial population for the particle swarm optimization algorithm;

[0122] Step 4.3: Set the fitness function as an indicator to evaluate the quality of particles;

[0123] Step 4.4: Perform iterative optimization using the particle swarm optimization algorithm;

[0124] Step 4.5: Update the particle swarm;

[0125] Step 4.6: Evaluate the optimization results to determine whether the maximum number of iterations has been reached or the change in the objective function value is less than the set threshold. If the conditions are met, the optimal control scheme is obtained. If not, adjust according to the actual situation and return to step 4.

[0126] Step 4.7: Check whether the constraints and actual requirements are met.

[0127] In summary, based on the various formulas in the model, the combination of multiple parameters and variables on the right side of the formula controls the variables on the left side of the formula to achieve their optimal values.

[0128] The LSPSO model proposed in this invention achieves faster convergence in the numerical examples, demonstrating its better adaptability to discrete-continuous mixed data environments, thus exhibiting a certain advantage in optimization efficiency. Mathematical models of unidirectional and bidirectional interactive flexible loads are constructed based on energy interaction methods. With user-side energy costs as the objective, a coordinated control operation model considering flexible load scheduling on the user side is established. This model addresses the mixed nonlinear optimization problem involving both discrete and continuous data. The Latin square model is combined with the particle swarm optimization algorithm to form a Latin square particle swarm optimization algorithm. Combining the Latin square and PSO particle swarm optimization algorithms, a multi-model ensemble method of LSPSO can be established. Latin square processing is used between single-tangent crossover and mutation operations, and a penalty function is applied to the constraints in the fitness calculation process. Finally, a simulation analysis is conducted based on historical flexible load data from a microgrid in a service area. The simulation results demonstrate that the proposed method has strong robustness and fast convergence in the context of discrete-continuous mixed input data, verifying the effectiveness of the proposed scheme.

[0129] Based on data from a highway service area, the parameters of the microgrid in the transportation hub are as follows:

[0130] This invention references a certain electric vehicle charging price, setting Δt to 1 hour; N to 100 electric vehicles; α compensation rate to 1.2; and maximum charging power P. max For 240kW, β ess The cost coefficient for battery losses during charging and discharging of the energy storage device is 0.25; the electricity purchase price Q(t) from the power grid company is determined based on the electricity price per hour of use (unit: yuan / kWh), as shown in the table below:

[0131]

[0132]

[0133] Taking data from a certain day as an example, the 24-hour photovoltaic power output curve and the daily load curve for that day are as follows: Figure 2 As shown, the unit is kW.

[0134] Simulation Result Analysis:

[0135] Firstly, the system operating efficiency before and after optimization is compared. Through optimization using the proposed model, the changes in various parameters of the simulation system are observed. Figure 3 As shown, combined with Figure 4 and Figure 5 As can be seen, before implementing classification optimization for flexible loads, all loads were grouped into one category. Although this approach had a slight advantage in terms of electric vehicle user subsidies and battery depreciation costs, it could not effectively control the flexible allocation of loads, leading to a significant increase in electricity purchase costs. Conversely, compared to the previous method, implementing classification optimization for flexible loads not only achieved a reasonable allocation of load distribution but also significantly improved the absorption of clean energy and indoor comfort, substantially reduced electricity purchase costs, and ensured the overall operational advantages of the flexible load control optimization mode.

[0136] On the other hand, the effectiveness of the proposed method was verified. For the same computational scenario, the proposed LSPSO optimization model was compared with the standard QGA model, the neural network example algorithm BPNN, and the group theory example algorithm PSO. The comparison mainly focused on optimization performance and efficiency. The results showed that the LSPSO optimization model had the lowest number of iterations compared to the standard QGA model, the neural network example algorithm BPNN, and the group theory example algorithm PSO.

[0137] It is evident that the LSPSO model proposed in this invention has certain advantages in optimization performance. The LSPSO model converges faster and has fewer iterations, demonstrating its ability to better adapt to discrete-continuous mixed data environments, thus achieving a certain advantage in optimization efficiency.

[0138] In one embodiment, a storage medium is provided, which is a computer-readable storage medium storing a user-side flexible load control optimization program. When executed by a processor, the user-side flexible load control optimization program is used to implement the operation of a user-side energy control optimization method as described above.

[0139] In one embodiment, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-described user-side-based energy control optimization method by executing the executable instructions.

[0140] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory.

[0141] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A user-side-based energy control optimization method, characterized in that, Includes the following steps: Establish a flexible load control model for the user side, including one-way interactive flexible loads and two-way interactive flexible loads; With user-side economic costs, photovoltaic absorption rate, and comfort of recreational rooms as joint objectives, and considering both photovoltaic and energy storage systems, an optimized control model involving multiple flexible loads is established. Establish constraints for the joint operation of multiple variables; The Latin square model and the PSO model are integrated into the LSPSO multi-model ensemble method to quickly search the optimal feasible region and obtain the optimal control scheme. The steps for establishing an optimal control model involving multiple flexible loads are as follows: With the goal of minimizing user-side energy costs, including the cost of purchasing electricity from the grid, the cost of charging electric vehicles, the cost of discharging electric vehicles, and the cost of battery losses during the charging and discharging of energy storage devices, the user-side economic cost objective function is expressed as follows: In the formula, C1 is the cost of purchasing electricity from the grid, B(t) is the electricity price at time t, and P... t Let C2 be the power consumption, C2 be the charging cost when the electric vehicle is under charging control, and Q(t) be the electricity purchase price for the electric vehicle charging at time t; x EV,in,n (t) is set to 1, indicating that the electric vehicle user accepts scheduling during the charging period; otherwise, it is set to 0. max The maximum charging power is given by P, where α is the compensation rate (α>1), N is the number of electric vehicles, C3 is the cost of discharging an electric vehicle when it is under control, and P is the maximum charging power. EV,in,n and P EV,out,n The power output during charging and discharging of electric vehicles, respectively; x EV,out,n (t) is set to 1 to indicate that the electric vehicle user accepts dispatch during the discharge period, otherwise it is set to 0; C4 is the cost of battery losses during the charging and discharging of the energy storage device. The power used to charge and discharge energy storage devices, β ess The cost coefficient for charging and discharging battery losses in energy storage devices, where Δt is the time interval and T is the control period; Taking the maximization of photovoltaic (PV) grid connection as the objective, the objective function for PV grid connection is as follows: In the formula, P s P is a unidirectional interactive flexible load. pv For photovoltaic power generation, For charging losses, This is discharge loss; Room temperature comfort is introduced, which is represented by the weighted deviation between the current room temperature and the standard room temperature. The smaller the objective function, the lower the deviation and the higher the room temperature comfort. The objective function for room temperature comfort is shown below: In the formula, T p T represents the current room temperature. c This indicates a temperature that is artificially set to provide comfort.

2. The user-side-based energy control optimization method according to claim 1, characterized in that, The flexible load control model is specifically: Based on the energy interaction method, flexible loads are divided into two-way interactive flexible loads and one-way interactive flexible loads. For all energy users in highway service areas, a one-way interactive flexible load control model is established based on the reducible loads, including central air conditioning, heating equipment, building lighting, some small commercial users, and advertising, as follows: Where L represents the amount of load reduction by electricity users, This represents the maximum value of the one-way interactive power load reduction. This represents the reduction in unidirectional power load during the t-th time period in the highway service area system. In highway service areas, a two-way interactive flexible load control model is established, which includes electric vehicle loads. The charging and discharging states of electric vehicles voluntarily connected to the highway service area microgrid are represented as follows: In the formula, Q EV (t) represents the electric vehicle's charge at time t, ω represents the charging / discharging efficiency, and P EV The power of charging and discharging an electric vehicle, Δt is the time interval, x EV (t) is the decision variable, where 1 represents the charging and discharging of the electric vehicle during time period t, and 0 represents not participating in the control.

3. The user-side-based energy control optimization method according to claim 1, characterized in that, The specific methods for considering photovoltaic integration and energy storage systems are as follows: In highway service areas, each rest or recreation room roof is equipped with a distributed solar photovoltaic system, provided that the reflection of photovoltaic modules does not affect the safety of vehicles. By adding an energy storage system to the photovoltaic system, the power control situation in highway service areas becomes as follows: Photovoltaic power generation is primarily used to meet the daily needs of highway service areas. If the photovoltaic power generation exceeds the daily available load, it will be stored through energy storage devices or electric vehicles. Highway service areas select charging times during off-peak hours at night based on the charging and discharging status of buses, taxis, and special-purpose vehicles with transferable load characteristics; if the previous day's passenger volume, weather conditions, or insufficient energy storage system power are obtained in advance, appropriate discharging will be selected for coordinated control.

4. The user-side-based energy control optimization method according to claim 1, characterized in that, The constraints for the multivariate joint operation include: a. Comfort temperature constraint: As an air conditioner used to regulate comfort temperature, within the acceptable range for human body temperature, its regulating power must meet the following constraints: T min ≤T t ≤T max ; 0≤P t ≤P max ac ; In the formula T t T represents the indoor temperature after air conditioning adjustment. min and T max These are the lowest and highest acceptable indoor temperatures for the human body, respectively, P. t P is the regulating power of the air conditioner. max ac This represents the maximum allowable power of the air conditioner. b. Supply and demand balance constraints for highway service areas: The input power of each highway service area should match its output power. When the load power is less than the power of the power generation unit, the supply and demand balance equation constraint for the traffic power system is established as follows: P pv (t)+P ess (t)+P ev (t)=P s (t); In the formula, P pv (t) is the output power of the distributed power source at time t, P ess (t) is the output / input power of the energy storage system at time t, P ev (t) is the power of the electric vehicle during charging or discharging at time t, P s (t) is the power consumed by the system load at time t; c. Constraints on the operating state of the energy storage system: In reality, energy storage systems cannot charge and discharge simultaneously, therefore the following constraints must be met: 0≤|x ess,in,n (t)|+|x ess,out,n (t)|≤1; In the formula, x ess,in,n (t) and x ess,out,n (t) represents the charging and discharging states of the system at time t; d. Electric vehicle battery capacity SOC constraint: SOC min <<SOC i (t)<<SOC max ; In the formula, SOC i (t) represents the remaining charge of the battery at time t, SOC. min and SOC max These are the minimum and maximum capacity limits for the battery, respectively. e. Rooftop photovoltaic output constraints: Considering the randomness and volatility of rooftop photovoltaic systems, their photovoltaic output must be less than or equal to the maximum value predicted in advance. Therefore, the following constraints are established: 0≤P PV ≤P max 。 5. The user-side-based energy control optimization method according to claim 1, characterized in that, The LSPSO multi-model ensemble method is as follows: Step 1: Based on the optimization control model and constraints, generate the initial parameters of the objective function using the Latin square sampling model; Step 2: Use the initial parameters as the initial population for the particle swarm optimization algorithm; Step 3: Set the fitness function as an indicator to evaluate the quality of particles; Step 4: Iterative optimization using the particle swarm optimization algorithm; Step 5: Update the particle swarm; Step 6: Evaluate the optimization results and determine whether the termination conditions are met; If the conditions are met, the optimal control scheme is obtained; if not, the scheme is adjusted according to the actual situation and the process returns to step ④.

6. The user-side-based energy control optimization method according to claim 5, characterized in that, The termination condition is: The maximum number of iterations is reached or the change in the objective function value is less than a set threshold.

7. The user-side-based energy control optimization method according to claim 6, characterized in that, The LSPSO multi-model ensemble method also includes: After obtaining the optimal control scheme, check whether it meets the constraints and actual requirements.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the user-side-based energy control optimization method according to any one of claims 1-7.

9. An electronic device, characterized in that, include: processor; and memory for storing the executable instructions of the processor; The processor is configured to execute the user-side-based energy control optimization method as described in any one of claims 1-7 by executing the executable instructions.

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