Electricity utilization optimization management method for photoelectric equipment based on weight optimization model predictive control

Abstract

The invention discloses a photoelectric equipment power utilization optimization management method based on weight optimization model predictive control, and relates to the technical field of smart grid power utilization adaptive demand response. Comprising the following steps: S1, establishing a photoelectric equipment load model, and deducing a nonlinear state-space equation according to the model; s2, establishing a value function of multiple targets with dynamically changed weights for power utilization of the photoelectric equipment; s3, dynamically adjusting an inertia weight parameter of the particle swarm algorithm through the improved particle swarm algorithm, controlling by using the weight configuration of the current particle, and calculating a target function value; according to the fitness of the particles, the historical optimal position and the global optimal position, the positions of the particles are updated, the weight of multiple targets is determined, and then the multi-target value function of the load is optimized in a rolling optimization mode; and S4, adjusting a power utilization optimization instruction according to an optimization result, distributing the load, and realizing power utilization fine management of the photoelectric building load equipment. According to the invention, the target optimization of the power utilization of the photoelectric equipment is combined with real-time control, and the problem that the difference of the power utilization demands of the photoelectric equipment cannot be predicted in time is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grid power adaptive demand response, and in particular to a method for optimizing power consumption management of photovoltaic equipment based on weight optimization model predictive control. Background Art

[0002] As global awareness of climate change and environmental protection increases, reducing greenhouse gas emissions has become a major goal for all countries. Photovoltaic power generation, as a clean energy source, can significantly reduce carbon dioxide emissions and help alleviate global warming and air pollution. Many countries are seeking to transform their energy structure to reduce their dependence on traditional fossil fuels. Photovoltaic power generation is one of the key technologies to achieve energy diversification and enhance energy security. By developing photovoltaic technology, the demand for non-renewable resources such as coal, oil and natural gas can be reduced. The advancement of photovoltaic technology and the reduction of production costs have made photovoltaic power generation more economical and feasible. Efficient photovoltaic modules and power generation systems continue to emerge, while improvements in production processes and large-scale production have reduced the cost of photovoltaic power generation.

[0003] Photovoltaic buildings are an innovative concept that combines photovoltaic technology with architectural design, providing buildings with efficient and intelligent solutions to achieve energy conservation and self-sufficient energy systems. The goal of this architectural design is to optimize energy use, improve comfort and reduce environmental impact by integrating photovoltaic equipment. Photovoltaic buildings can generate electricity through photovoltaic systems, reduce dependence on external power supply, thereby reducing energy costs and significantly reducing carbon emissions.

[0004] Due to the intermittent and random characteristics of photovoltaic power generation, the grid-connected operation of photovoltaic power generation and the power grid may have a certain impact and influence on the security, stability of the power grid and the power quality on the output side of the converter.

[0005] In view of this, a method for optimizing power consumption of photovoltaic equipment based on predictive control of weight optimization model is needed. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a method for optimizing the power consumption of photovoltaic equipment based on predictive control of a weight optimization model, which can effectively alleviate the instability and randomness of power output of renewable energy power generation and ensure the stable operation of the power grid. The specific technical solution is as follows:

[0007] A method for optimizing power consumption of photovoltaic equipment based on predictive control of a weighted optimization model, comprising:

[0008] The photovoltaic equipment load model is established, and the nonlinear state space equation is derived based on the model;

[0009] For optoelectronic equipment, a multi-objective value function with dynamically changing weights is established;

[0010] Through the improved particle swarm algorithm, the inertia weight parameters of the particle swarm algorithm are dynamically adjusted, the current particle weight configuration is used for MPC control, and the objective function value is calculated, and the multi-objective value function of the load is optimized through rolling optimization;

[0011] By adjusting the power optimization instructions through the optimization results, the load is distributed to achieve refined power management of photovoltaic building load equipment.

[0012] Preferably, the optoelectronic equipment includes photovoltaic power generation components, energy storage batteries and intelligent power consumption equipment.

[0013] Preferably, the nonlinear state space equation of the photovoltaic power generation assembly is:

[0014] P PV =P MT ×S×[1+k(T c -T R )] / S MT

[0015] Among them, P MT is the maximum test power under standard test conditions, S MT is the solar radiation intensity under standard test conditions, T c is the component operating temperature, T R is the reference temperature, S is the actual solar radiation intensity, and k is the power temperature coefficient.

[0016] Preferably, the nonlinear state space equation of the energy storage battery is:

[0017]

[0018] Among them, SOC max , SOC min are the upper and lower limits of the battery state of charge respectively; C bat is the battery capacity; C bat (t) is the battery terminal voltage; Δt is the unit time interval; are the maximum chargeable power and maximum dischargeable power of the battery in the tth period respectively; are the maximum charging current and maximum discharging current allowed by the battery, respectively. The number of battery packs.

[0019] Preferably, the nonlinear state space equation of the intelligent electrical device is:

[0020] W g (t+1)=max[Wg (t)-Z g (t),0]+a g (t)

[0021] Among them, Z g (t) is the energy allocated to the smart appliance, W g (t) is the energy demand backlog of smart appliances, a g (t) is the energy requirement for storage.

[0022] Preferably, the expression of the multi-objective value function is as follows:

[0023]

[0024] x(t) is the system state space equation, They are the reference value of photovoltaic power generation, the reference value of energy storage battery power and the reference value of smart power load power; ρ, τ correspond to the weighting factors of each function term.

[0025] Preferably, the improved particle swarm algorithm is used to dynamically adjust the inertia weight parameters of the particle swarm algorithm, use the current particle weight configuration to perform MPC control, calculate the objective function value, and optimize the multi-objective value function of the load by rolling optimization, which specifically includes the following steps:

[0026] S31, initialize the load parameters of the photovoltaic equipment and initialize the population, including the population size n pop , the maximum number of iterations G max , upper and lower limits of function dimensions and particle update speed;

[0027] S32. In the initialized population, the fitness value of each particle is calculated, and its historical optimal position is recorded as the individual extreme value; the optimal solution is found in the individual extreme value set of all particles, and it is compared with the current global optimal solution to determine the global extreme value;

[0028] S33, updating the position and speed of the particle according to the speed and position update formula of each particle in the algorithm;

[0029] ν j+1 =w*v j +s1*a1*(tbest j -x j )+s2*a2*(abest-x j )

[0030] x j+1 =x j +ν j+1

[0031] Where w represents the inertia weight; j is the current iteration number, s1, s2 represent the learning factors, a1, a2 are random numbers in the range of [0, 1], tbest represents the local optimal solution, and abest represents the global optimal solution;

[0032] S34, calculate the fitness value of the particle, find the individual optimal tbest that each particle can find by continuously updating the position, and compare the optimal value with each new tbest. The optimal one is updated as the global optimal abest. With the update iteration of each particle, the global optimal solution abest is updated step by step.

[0033] S35, the optimized dynamic inertia weight is as follows, where n Pop represents the number of iterations, j represents the number of iterations, and b is an additional parameter;

[0034]

[0035] Determine whether the maximum number of iterations has been reached. If not, add 1 to the number of iterations and go to S33. If so, the algorithm terminates the iteration, outputs the optimal weight solution set, brings the weight into the value function, performs rolling optimization, and optimizes the multi-objective value function of the load through rolling optimization.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention establishes a photoelectric equipment load model, proposes an algorithm for dynamically adjusting the inertia weight through an improved particle swarm algorithm, dynamically adjusts the inertia weight parameter of the particle swarm algorithm, uses the current particle weight configuration for MPC control, and calculates the weight value of the objective function. The power optimization instructions are adjusted through the optimization results, the load is distributed, and the photovoltaic output, energy storage battery and intelligent load are coordinated to uniformly participate in active power control, ensuring the stability and economic benefits of photovoltaic power generation, equipment power consumption and energy storage.

[0038] The present invention combines the target optimization of the power consumption of the photovoltaic equipment with the real-time control, and solves the problem that the differentiated power consumption demand of the photovoltaic equipment cannot be predicted in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0040] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0043] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0044] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0045] Example

[0046] like Figure 1 As shown, a method for optimizing power consumption of photovoltaic equipment based on predictive control of a weighted optimization model comprises:

[0047] A photovoltaic equipment load model is established. The photovoltaic equipment includes photovoltaic power generation, energy storage batteries and intelligent power consumption equipment. Based on this, a multi-objective load model of photovoltaic equipment is established. The details are as follows:

[0048] The nonlinear state space equation of photovoltaic power generation components can be expressed as:

[0049] P PV =P MT ×S×[1+k(T c -T R )] / S MT

[0050] Among them, P MT is the maximum test power under standard test conditions, S MT is the solar radiation intensity under standard test conditions, T c is the component operating temperature, T Ris the reference temperature, S is the actual solar radiation intensity, and k is the power temperature coefficient.

[0051] When the light intensity is weak or the load is heavy, the energy storage battery discharges through the energy storage device to supply the load, thereby improving the utilization efficiency of renewable energy. The charging and discharging process of the energy storage battery during system operation is limited by the state of charge (SOC min ≤SOC≤SOC max ), its maximum charge and discharge power is:

[0052]

[0053] Among them, SOC max , SOC min are the upper and lower limits of the battery state of charge respectively; C bat is the battery capacity; C bat (t) is the battery terminal voltage; Δt is the unit time interval; are the maximum chargeable power and maximum dischargeable power of the battery in the tth period respectively; are the maximum charging current and maximum discharging current allowed by the battery, respectively. The number of battery packs.

[0054] Smart power equipment: refers to intelligent and adjustable power loads that can adjust the load according to demand response. Energy demand is divided into two categories: elastic and inelastic. Elastic energy demand means that users allow a certain delay in the energy they demand, thereby shifting the working time period of this type of load to a time period with lower power demand; inelastic energy demand means that the user's energy demand must be met immediately (without delay). The energy demand queue update formula for smart appliances is:

[0055] W g (t+1)=max[W g (t)-Z g (t),0]+a g (t)

[0056] Among them, Z g (t) is the energy allocated to the smart appliance, W g (t) is the energy demand backlog of smart appliances, a g (t) is the energy requirement for storage

[0057] For the electricity consumption of photovoltaic equipment, a multi-objective value function with dynamically changing weights is established:

[0058] The goal of the MPC controller is to enable the management system to timely control the changing power components and improve the source-load matching degree within the system through the photovoltaic equipment control system in the photovoltaic building when the electrical parameters change. The value function is expressed as:

[0059]

[0060] x(t) is the system state space equation, They are the reference value of photovoltaic power generation, the reference value of energy storage battery power and the reference value of smart power load power; ρ and τ correspond to the weighting factors of each function term. The weighting factors are constantly changing with the environmental parameters (light intensity).

[0061] The controller solves the optimization problem in the prediction range in each time step t when the change in the control input is minimized. The first goal of the optimization problem is to ensure that the photovoltaic power can track the reference value of the power obtained by the conductance increment method; the second goal is to limit the power of the energy storage battery within the set range; the third goal is to maintain the intelligent power load within the set threshold range.

[0062] The improved particle swarm algorithm is used to dynamically adjust the inertia weight parameters of the particle swarm algorithm, use the current particle weight configuration to perform MPC control, calculate the objective function value, and optimize the multi-objective value function of the load through a rolling optimization method, which specifically includes the following steps:

[0063] S31, initialize the load parameters of the photovoltaic equipment and initialize the population, including the population size n pop , the maximum number of iterations G max , the upper and lower limits of function dimensions and particle update speed;

[0064] S32. In the initialized population, the fitness value of each particle is calculated, and its historical optimal position is recorded as the individual extreme value; the optimal solution is found in the individual extreme value set of all particles, and it is compared with the current global optimal solution to determine the global extreme value;

[0065] S33, updating the position and speed of the particle according to the speed and position update formula of each particle in the algorithm;

[0066] ν j+1 =w*v j +s1*a1*(tbest j -x j )+s2*a2*(abest-x j )

[0067] x j+1 =x j +ν j+1

[0068] Where w represents the inertia weight; j is the current iteration number, s1, s2 represent the learning factors, a1, a2 are random numbers in the range of [0, 1], tbest represents the local optimal solution, and abest represents the global optimal solution;

[0069] S34, calculate the fitness value of the particle, find the individual optimal tbest that each particle can find by continuously updating the position, and compare the optimal value with each new tbest. The optimal one is updated as the global optimal abest. With the update iteration of each particle, the global optimal solution abest is updated step by step.

[0070] S35, the optimized dynamic inertia weight is as follows, where n Pop represents the number of iterations, j represents the number of iterations, and b is an additional parameter;

[0071]

[0072] Determine whether the maximum number of iterations has been reached. If not, add 1 to the number of iterations and go to step S33. If so, the algorithm terminates the iteration and outputs the optimal weight solution set. The weights are brought into the value function for rolling optimization. The multi-objective value function of the load is optimized through rolling optimization. The power optimization instructions are adjusted through the optimization results to distribute the load and realize the refined management of power consumption of photovoltaic building load equipment.

[0073] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0074] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0075] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0076] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for optimizing power consumption of photovoltaic equipment based on predictive control of weight optimization model, characterized in that: include: The photovoltaic equipment load model is established, and the nonlinear state space equation is derived based on the model; For optoelectronic equipment, a multi-objective value function with dynamically changing weights is established; Through the improved particle swarm algorithm, the inertia weight parameters of the particle swarm algorithm are dynamically adjusted, the current particle weight configuration is used for MPC control, and the objective function value is calculated, and the multi-objective value function of the load is optimized through rolling optimization; By adjusting the power optimization instructions through the optimization results, the load is distributed to achieve refined power management of photovoltaic building load equipment.

2. According to claim 1, a method for optimizing power consumption of photovoltaic equipment based on predictive control of weight optimization model, characterized in that: The optoelectronic equipment includes photovoltaic power generation components, energy storage batteries and intelligent electrical equipment.

3. The method for optimizing power consumption of photovoltaic equipment based on predictive control of weight optimization model according to claim 2 is characterized in that: The nonlinear state space equation of the photovoltaic power generation component is: P PV =P MT ×S×[1+k(T c -T R )] / S MT Among them, P MT is the maximum test power under standard test conditions, S MT is the solar radiation intensity under standard test conditions, T c is the component operating temperature, T R is the reference temperature, S is the actual solar radiation intensity, and k is the power temperature coefficient.

4. The method for optimizing power consumption of photovoltaic equipment based on predictive control of weight optimization model according to claim 2 is characterized in that: The nonlinear state space equation of the energy storage battery is: Among them, SOC max , SOC min are the upper and lower limits of the battery state of charge respectively; C bat is the battery capacity; C bat (t) is the battery terminal voltage; Δt is the unit time interval; are the maximum chargeable power and maximum dischargeable power of the battery in the tth period respectively; are the maximum charging current and maximum discharging current allowed by the battery, respectively. The number of battery packs.

5. The method for optimizing power consumption of photovoltaic equipment based on predictive control of weight optimization model according to claim 2 is characterized in that: The nonlinear state space equation of the intelligent electrical device is: W g (t+1)=max[W g (t)-Z g (t),0]+a g (t) Among them, Z g (t) is the energy allocated to the smart appliance, W g (t) is the energy demand backlog of smart appliances, a g (t) is the energy requirement for storage.

6. The method for optimizing power consumption of photovoltaic equipment based on predictive control of weight optimization model according to claim 1 is characterized in that: The expression of the multi-objective value function is as follows: x(t) is the system state space equation, They are the reference value of photovoltaic power generation, the reference value of energy storage battery power and the reference value of smart power load power; ρ, τ correspond to the weighting factors of each function term.

7. The method for optimizing power consumption of photovoltaic equipment based on predictive control of weight optimization model according to claim 1 is characterized in that: The improved particle swarm algorithm is used to dynamically adjust the inertia weight parameters of the particle swarm algorithm, use the current particle weight configuration to perform MPC control, calculate the objective function value, and optimize the multi-objective value function of the load through a rolling optimization method, which specifically includes the following steps: S31, initialize the load parameters of the photovoltaic equipment and initialize the population, including the population size n pop , the maximum number of iterations G max , the upper and lower limits of function dimensions and particle update speed; S32. In the initialized population, the fitness value of each particle is calculated, and its historical optimal position is recorded as the individual extreme value; the optimal solution is found in the individual extreme value set of all particles, and it is compared with the current global optimal solution to determine the global extreme value; S33, updating the position and speed of the particle according to the speed and position update formula of each particle in the algorithm; n j+1 =w*v j +s1*a1*(tbest j -x j )+s2*a2*(abest-x j ) x j+1 =x j +n j+1 Where w represents the inertia weight; j is the current iteration number, s1, s2 represent the learning factors, a1, a2 are random numbers in the range of [0, 1], tbest represents the local optimal solution, and abest represents the global optimal solution; S34, calculate the fitness value of the particle, find the individual optimal tbest that each particle can find by continuously updating the position, and compare the optimal value with each new tbest. The optimal one is updated as the global optimal abest. With the update iteration of each particle, the global optimal solution abest is updated step by step. S35, the optimized dynamic inertia weight is as follows, where n Pop represents the number of iterations, j represents the number of iterations, and b is an additional parameter; Determine whether the maximum number of iterations has been reached. If not, add 1 to the number of iterations and go to step S33. If so, the algorithm terminates the iteration, outputs the optimal weight solution set, brings the weight into the value function, performs rolling optimization, and optimizes the multi-objective value function of the load through rolling optimization.

Images