An electric energy demand optimization operation method based on a pelican optimization algorithm

Abstract

The application provides an electric energy demand optimization operation method based on a pelican optimization algorithm, which has the following advantages: 1. Higher reliability: the mixed dynamic demand response strategy can significantly improve the reliability of the micro-grid by reducing the peak load demand, and reduce the pressure on the power system caused by the peak load. 2. Significant optimization effect: the pelican optimization algorithm used in the patent can effectively solve the energy management problem of the micro-grid, and combined with the mixed dynamic demand response strategy, it can better balance the power supply and demand, reduce the operation cost and increase the operation benefit. The micro-grid electric energy demand optimization based on the mixed dynamic demand response strategy proposed in the patent can effectively reduce the peak load demand, improve the operation efficiency and reliability of the micro-grid by combining the traditional fixed price incentive demand response and the variable price incentive demand response based on the peak.

Description

TECHNICAL FIELD

[0001] The application relates to the field of power systems, in particular to an electric energy demand optimization operation method based on a pelican optimization algorithm. BACKGROUND

[0002] With the rapid growth of global power demand, the power system is facing the problems of resource waste and low operation efficiency. Relying entirely on traditional power systems to meet the power demand of modern society is considered to be an inefficient and unsustainable solution. Therefore, the electric energy demand optimization operation strategy, as an advanced management method, has gradually become a key means to improve the efficiency and reliability of the power system. Through intelligent scheduling and optimization control, the electric energy demand optimization operation strategy can improve the utilization efficiency of power resources, reduce waste and peak load, and thus improve the overall performance of the power system. SUMMARY

[0003] The application aims to solve the problems in the prior art and provides an electric energy demand optimization operation method based on a pelican optimization algorithm.

[0004] To achieve the above-mentioned purpose, the application adopts the following technical scheme: an electric energy demand optimization operation method based on a pelican optimization algorithm, which specifically comprises:

[0005] S1: Microgrid modeling:

[0006] Modeling the electric energy demand in the power system, establishing a power demand prediction model to predict future changes in power demand;

[0007] The microgrid is composed of renewable energy sources, and the load data is read from the energy power station, including photovoltaic power sources and wind turbines as well as conventional diesel engine power sources;

[0008] Through the connection between the main grid and the microgrid, power trading occurs between them, and all power can be purchased or sold to the main grid to obtain the electricity demand of customers in turn;

[0009] Establish a load model to calculate the sum of individual customer loads;

[0010] Classify the willingness of customers to participate in the demand response plan, and take C(θ;x) as the cost generated by the customer; wherein θ is the customer type, indicating the willingness of the customer to participate in the demand response plan, and the customer type value changes between 0 and 1, the customer most willing to participate in the demand response plan sets the value to 1, and the customer with lower willingness sets the value to 0; x represents power outage or reduced power consumption of the user;

[0011] S2: Hybrid dynamic demand response strategy;

[0012] According to the specific load value, a traditional incentive demand response or a predetermined incentive demand response strategy is selected and applied;

[0013] When the demand is greater than the average value, an incentive demand response scheme is applied;

[0014] In the case of peak load far higher than the average value, a predetermined incentive demand response is applied; By combining CIDR and PIDR and HDDR technology, flexible and stable demand response strategies can be provided under different load conditions, improving the overall reliability and efficiency of the microgrid system;

[0015] S3: Application of pelican optimization algorithm:

[0016] By simulating the pelican predation behavior, the power generation of the microgrid, the energy trading volume and the demand response strategy are optimized to achieve the goal of minimizing the total operating cost or maximizing the economic benefit;

[0017] Through repeated iteration, updating the solution set and optimizing the parameters, until the optimization goal is met or the stopping condition is reached.

[0018] Preferably, the method for establishing the microgrid model in step S1 is as follows:

[0019] S11: The power generation characteristics of the microgrid are described by a model combining photovoltaic power generation, wind power generation and diesel engine power generation;

[0020] S12: The expected output power of wind power generation is calculated considering the factors of wind speed and tower height;

[0021] S13: The power generation of photovoltaic power generation at different time points is calculated, considering the changes of environmental temperature and incident solar radiation;

[0022] S14: The fuel cost of diesel engine power generation is represented by a binary model to determine the relationship between the fuel cost of the diesel generator and the power;

[0023] S15: The total power generation of the microgrid should be equal to the load demand minus the demand response reduction.

[0024] Preferably, in step S2, the mixed dynamic includes a traditional incentive demand response strategy and a peak-based variable price demand response strategy; The traditional incentive demand response strategy is applied during periods of low load to provide a fixed incentive price; The peak-based variable price demand response strategy is applied during periods of high load to dynamically adjust the incentive price.

[0025] Preferably, in step S3, the pelican optimization algorithm has the following specific method:

[0026] S31: Initialize parameters and pelican position;

[0027] An initial solution set is generated, each solution representing a possible energy management configuration, including diesel power generation, photovoltaic power generation, wind power generation, and demand response strategies;

[0028] S32: Read load data and calculate initial fitness value;

[0029] The fitness value of each solution, i.e. the total operating cost, is calculated, which usually includes fuel cost, power trading cost and demand response incentive cost;

[0030] S33: Generate prey by calculating objective function randomly;

[0031] Simulate the hunting behavior of pelicans: by simulating the search and capture behavior of pelicans in the process of hunting, a new prey is generated, i.e. a new solution is generated;

[0032] S34-1: First calculation of objective function, iteration after data judgment;

[0033] S34-2: Second calculation of objective function, iteration after data judgment;

[0034] Update population: select better solutions to update the population according to the fitness value, keep better solutions and eliminate poor solutions

[0035] S35: Save the data of the second calculation of the objective function after iteration, and output after judgment;

[0036] Repeat the simulation of hunting behavior and population update until the stopping condition is reached.

[0037] Preferably, the willingness of the demand response plan in step S1 needs to be constrained.

[0038] Preferably, in step S2, the incentive obtained by the hybrid dynamic demand response technology during the peak period depends not only on the amount of power cut, but also on the ratio of current load to peak load.

[0039] Compared with the prior art, the present application has the following advantages: first, it has higher reliability. By reducing peak load demand, the hybrid dynamic demand response strategy can significantly improve the reliability of the microgrid and reduce the pressure on the power system caused by peak load. Second, the optimization effect is significant. The pelican optimization algorithm used in this patent can effectively solve the energy management problem of the microgrid. Combined with the hybrid dynamic demand response strategy, it can better balance power supply and demand, reduce operating costs and increase operating income. The microgrid energy optimization based on the hybrid dynamic demand response strategy proposed in this patent can effectively reduce peak load demand, improve the operating efficiency and reliability of the microgrid by combining traditional fixed price incentive demand response and variable price incentive demand response based on peak load and using the pelican optimization algorithm. Attached Figure Description

[0040] Fig. 1 This is the overall flowchart;

[0041] Fig. 2 A flowchart for hybrid dynamic demand response;

[0042] Fig. 3 Flowchart of the optimized algorithm for Pelicans. Detailed Implementation

[0043] To provide a further understanding of the purpose, structure, features, and functions of the present invention, detailed descriptions are provided below with reference to specific embodiments.

[0044] Please refer to the reference. Figs. 1 to 3 This invention provides a method for optimizing power demand operation based on the Pelican optimization algorithm.

[0045] S1: Microgrid Modeling

[0046] Microgrids consist of renewable energy sources, such as photovoltaic power and wind turbines, as well as conventional diesel generators.

[0047] There is a connection between the main grid and the MG, and it is assumed that there is an electricity transaction between them. All electricity can be bought or sold to the main grid.

[0048] Wind power generation is inherently probabilistic; the amount of electricity generated by wind depends on the height of the tower and the wind speed.

[0049] Its expected output power is calculated as follows: ;

[0050] in and v represent output power and wind speed, respectively; where V is the rated power of the fan; nom For the rated wind speed, V ci To cut in wind speed;

[0051] The hourly power generation of photovoltaic power generation in a given area is: in For photovoltaic power generation area; The efficiency of a photovoltaic array is the rate of change of its efficiency with respect to ambient temperature and incident solar irradiance at time t. And change.

[0052] Diesel generators are an important power source in microgrids, and operators can adjust them flexibly. When renewable energy sources in the microgrid cannot meet the load demand, diesel generators will serve as a supplementary power source to satisfy the load.

[0053] The fuel cost of the ith conventional diesel generator at time t is represented by a quadratic model as follows: ; where and are the fuel cost coefficients of the conventional diesel generator i, is the power output of the ith diesel generator at time t.

[0054] The total power generation of the microgrid at each time t should equal the load demand minus the demand response curtailment: ;

[0055] where represents the power trading with the main grid (positive value means buying power, negative value means selling power), is the total load demand at time t; is the output power of the wind turbine at time t; is the output power of the photovoltaic generator at time t;

[0056] The main optimization objective of this system is to minimize the operation cost of the system and maximize the economic benefit. Specifically, the optimization methods include: through the demand response plan, especially the hybrid dynamic demand response, to encourage users to reduce electricity consumption during peak hours, thereby reducing peak load and improving the economic benefit of the microgrid (MG) operator. Through the optimization of energy management, the fuel cost and power trading cost of the conventional generator are reduced. The objective function is as follows:

[0057] MAX ]

[0058] where is the power interruption cost of the jth customer; is the power curtailment of the jth customer at time t, is the incentive obtained by the jth customer at time t for curtailment of electricity consumption; is the fuel cost of the ith diesel generator at time t; is the power trading cost.

[0059] The MG optimization problem is solved through an accurate load calculation and microgrid benefit model. Usually, load calculation is the sum of individual customer loads. However, in this work, customers are classified according to their willingness to participate in the demand response plan.

[0060] The present application takes C(theta;x) as the cost generated by the customer; wherein theta is the customer type, indicating the willingness of the customer to participate in the demand response program, the customer type value varies between 0 and 1, the customer most willing to participate in the demand response program sets the value to 1, and the customer with lower willingness sets the value to 0; x represents the user outage (or reduced power consumption), the unit is KW or MW. The demand response complete model is constrained by the following equation: .

[0061] Wherein k1 and k2 are cost coefficients, therefore the customer's benefit is mathematically expressed as:

[0062] ;

[0063] From the above formula, when the customer's benefit function is F1>=0 in demand response, the customer will reduce consumption, wherein B represents the total incentive that the customer will obtain for reduced consumption. The micro-grid benefit can also be expressed as:

[0064] ;

[0065] Wherein lambda is the cost of power reduction from a specific customer, according to the customer contract formula, if the customer obtains an incentive, then according to the customer cost function, the benefit of customer J is calculated as:

[0066] ;

[0067] In addition, the overall point energy demand optimization operation strategy benefit of the micro-grid is calculated as:

[0068] .

[0069] S2: Hybrid dynamic demand response strategy;

[0070] According to the specific load value, select to apply the traditional incentive demand response or scheduled incentive demand response strategy.

[0071] The traditional demand response scheme uses a fixed incentive rate, that is, the reward obtained by the user when responding to demand adjustment is fixed; such a scheme lacks flexibility when facing dynamic changes in energy supply and demand, and is difficult to adapt to actual situations; in order to improve the reliability of the micro-grid, we propose a hybrid dynamic demand response technology; this strategy provides flexibility for the system to maximize the use of available renewable energy, such as wind and solar energy, which are only effective during specific time periods of the day.

[0072] During peak hours, by mixing dynamic demand response, users are encouraged to reduce electricity consumption, which can reduce the dependence on traditional generators (such as diesel generators), thereby reducing fuel consumption and costs. Through the mixed dynamic demand response plan, the load demand can be dynamically adjusted to maximize the use of renewable energy (such as photovoltaic and wind energy), reduce dependence on fossil fuels, reduce environmental pollution, and improve the overall economic efficiency of the system through reasonable scheduling of POA.

[0073] The mixed dynamic demand response technology selects the application of traditional incentive demand response (CIDR) or peak-based variable price demand response (PIDR) strategy according to the specific load value in each time period.

[0074] When the demand is greater than the average, the incentive demand response scheme is applied;

[0075] In the case of peak load much higher than the average, the predetermined incentive demand response is applied; by combining CIDR and PIDR and HDDR technology, flexible and stable demand response strategies can be provided under different load conditions, improving the overall reliability and efficiency of the microgrid system.

[0076] When the demand is greater than the average, the CIDR strategy is applied; in the case of peak load (much higher than the average), the PIDR strategy is applied. By combining CIDR and PIDR, the mixed dynamic demand response technology can provide flexible and stable demand response strategies under different load conditions, thereby improving the overall reliability and efficiency of the microgrid system.

[0077] The calculation formula of customer incentive is as follows:

[0078] ;

[0079] Where

[0080] ;

[0081] The minimum incentive that the customer can get to ensure their interests is γ, which is a proportional factor, and the patent uses 0.2.

[0082] As can be seen from the above formula, the incentive obtained by the customer through the mixed dynamic demand response technology during peak hours not only depends on the amount of electricity reduced, but also depends on the ratio of the current load to the peak load.

[0083] Through precise demand response and optimization strategies, the peak load and energy procurement costs of the microgrid are reduced. The optimization algorithm can manage energy resources more meticulously, reduce equipment maintenance costs, and thus significantly reduce the overall operating costs of the microgrid.

[0084] S3: Pelican optimization algorithm application:

[0085] By simulating the behavior of the pelican's predation, the power generation, energy trading volume and demand response strategy of the micro-grid are optimized to achieve the goal of minimizing the total operating cost or maximizing the economic benefit.

[0086] Through repeated iteration, updating the solution set and optimizing the parameters until the optimization goal is met or the stopping condition is reached.

[0087] Initialization: POA is a population-based algorithm, where each pelican member is considered as a candidate solution. The optimization process is started by randomly initializing each member in the population using the following formula:

[0088] ;

[0089] Where N is the number of total members, x i is the candidate solution of the value, Rand is a random vector in the interval [0, 1]; using these initial candidate solutions to evaluate the objective function of the given problem; calculate the objective function vector; this process is simulated in the following two stages:

[0090] Exploration stage: This stage simulates the strategy of the pelican scanning in the search space to identify food sources. After identifying the prey, the pelican begins to move towards the prey area. An important feature of POA is to randomly generate prey positions, thereby enhancing the exploration ability. The new state of the i-th pelican candidate solution in this stage can be mathematically simulated by the following formula:

[0091] ;

[0092] Where I is a randomly generated vector with values of 1 or 2, x p is a randomly generated prey position, F(x p ) is the value of its objective function; based on the new position, the solution is updated as follows:

[0093] .

[0094] Exploitation stage: In this stage, the pelican begins to spread its wings on the water surface, allowing the prey to move upwards. Specifically, the algorithm adjusts the position of the candidate solution to search more meticulously within the neighborhood of the current solution, thereby improving the ability of local search. The new state of the i-th pelican candidate solution in stage 2 can be mathematically simulated by the following formula:

[0095] ;

[0096] Where t is the current iteration, T is the maximum number of iterations, R is a constant equal to 0.2.

[0097] Then, according to the new position, the update of the solution is performed:

[0098]

[0099] In the formula is The target function value of the candidate solution.

[0100] The hybrid dynamic demand response strategy and the pelican optimization algorithm (POA) finely describe the response mechanism of the micro-grid under different load conditions. By optimizing energy distribution, the micro-grid can more efficiently utilize renewable energy resources, reduce energy waste, and improve overall energy utilization efficiency.

[0101] The hybrid dynamic demand response strategy and the POA algorithm can help the micro-grid maintain stability when facing load fluctuations and uncertainties, and enhance the reliability of the system. By dynamically adjusting the demand response strategy, the micro-grid can ensure continuous power supply and improve the stability and reliability of the system.

[0102] The present application has been described by the above-mentioned related embodiments, however, the above-mentioned embodiments are only examples for implementing the present application. It must be pointed out that the disclosed embodiments do not limit the scope of the present application. On the contrary, modifications and improvements made without departing from the spirit and scope of the present application are within the scope of the patent protection of the present application.

Claims

1. A method for optimizing electricity demand operation based on the Pelican optimization algorithm, characterized in that: The method specifically includes: S1: Microgrid Modeling Model the electricity demand in the power system, establish an electricity demand forecasting model, and predict future changes in electricity demand. Microgrids consist of renewable energy sources that read load data from power plants, including photovoltaic power, wind turbines, and conventional diesel generators. Wind power generation is inherently probabilistic; the amount of electricity generated by a wind turbine depends on the tower height and wind speed; its expected output power is calculated as follows: ;in and v represent output power and wind speed, respectively; where V is the rated power of the fan; nom For the rated wind speed, V ci The cutoff wind speed; the hourly power generation of photovoltaic power generation in a given area is: ;in For photovoltaic power generation area; The efficiency of a photovoltaic power generation array is the rate of change of its efficiency with respect to ambient temperature and incident solar irradiance at time t. And change; The total power generation of the microgrid at each time t should equal the load demand. Subtract demand response reductions: ;in: Let be the power output of the i-th diesel generator at time t; For power trading with the main power grid; This represents the output power of the wind turbine at time t. Let be the output power of photovoltaic power generation at time t; By optimizing energy management, the fuel costs and electricity trading costs of traditional generators can be reduced. The target formula is as follows: ; in This is the power outage cost for the j-th customer; It is the amount of electricity reduction by the j-th customer at time t. It is the incentive that the j-th customer receives at time t for reducing electricity consumption; The fuel cost of the i-th diesel generator at time t; It is the cost of electricity transactions; The connection between the main grid and the microgrid enables electricity trading between them. All electricity can be bought or sold to the main grid, thereby obtaining customers' electricity demand. Establish a load model and calculate the sum of the loads of individual customers; The willingness of customers to participate in the demand response program is categorized, and C(θ;x) is used as the cost incurred by each customer. Here, θ represents the customer type, indicating their willingness to participate in the demand response program. The customer type value varies between 0 and 1, with the most willing customers assigned a value of 1 and the least willing customers assigned a value of 0. x represents the user's power outage or reduced electricity consumption. The specific expression for the cost function C(θ;x) is as follows: Where k1 and k2 are cost coefficients, corresponding to the cost weights of high-willingness users and low-willingness users, respectively; therefore, the customer's benefit is mathematically expressed as: ; As can be seen from the formula above, when the customer's benefit function is F1≥0, the customer will reduce consumption, where B represents the total incentive the customer will receive to reduce consumption. The benefits of microgrids can also be expressed as: ; Where λ represents the electricity reduction cost from a specific customer, and according to the customer contract formula, if it is an incentive received by the customer, then according to the customer cost function, the revenue of customer J is calculated as follows: ； Furthermore, the overall point energy demand optimization operation strategy benefit calculation for microgrids is as follows: ； S2: Hybrid dynamic demand response strategy; Depending on the specific load value, choose to apply either the traditional incentive demand response strategy or the predetermined incentive demand response strategy. When demand exceeds the average, a traditional incentive-based demand response scheme should be applied. When peak loads are much higher than average, pre-incentivized demand response is applied; by combining CIDR, PIDR, and HDDR technologies, flexible and stable demand response strategies can be provided under different load conditions, improving the overall reliability and efficiency of the microgrid system. S3: Application of Pelican Optimization Algorithm: By simulating pelican predation behavior, the algorithm optimizes the power generation, energy trading volume, and demand response strategy of microgrids to achieve the goal of minimizing total operating costs or maximizing economic benefits. By iterating repeatedly, updating the solution set, and optimizing the parameters, the optimization objective is met or the stopping condition is reached.

2. The energy demand optimization operation method based on the pelican optimization algorithm as described in claim 1, characterized in that: The specific method for establishing the microgrid model in step S1 is as follows: S11: The power generation characteristics of the microgrid are described by a model combining photovoltaic power generation, wind power generation and diesel engine power generation. S12: Considering the wind speed and tower height of wind power generation, calculate the expected output power of wind power generation; S13: Calculate the power generation of photovoltaic power generation at different points in time, taking into account the changes in ambient temperature and incident solar irradiance; S14: Using a binary model of diesel generator power generation to represent fuel costs, determine the relationship between the fuel cost of a diesel generator and its power output; the specific binary model of diesel generator power generation is as follows: ； S15: Determine that the total generation of the microgrid should be equal to the load demand minus the demand response reduction.

3. The energy demand optimization operation method based on the pelican optimization algorithm as described in claim 1, characterized in that: In step S2, the hybrid dynamic includes a traditional incentive demand response strategy and a peak-based variable price demand response strategy; the traditional incentive demand response strategy is applied during periods of low load to provide a fixed incentive price; and the peak-based variable price demand response strategy is applied during periods of high load to dynamically adjust the incentive price.

4. The energy demand optimization operation method based on the pelican optimization algorithm as described in claim 1, characterized in that: In step S3, the specific method of the Pelican optimization algorithm is as follows: S31: Initialize parameters and Pelicans' positions; Generate an initial solution set, where each solution represents a possible energy management configuration, including diesel power generation, photovoltaic power generation, wind power generation, and demand response strategies; S32: Read the load data and calculate the initial fitness value; Calculate the fitness value for each solution, which is the total operating cost; the fitness value typically includes fuel costs, electricity trading costs, and demand response incentive costs. S33: Randomly generate prey by calculating the objective function; Simulate pelican hunting behavior: By simulating the pelican's search and capture behavior for prey during the hunting process, new prey is generated, that is, new solutions are generated; S34-1: Perform the first calculation of the objective function, evaluate the obtained data, and then iterate; S34-2: Perform a second calculation of the objective function, evaluate the obtained data, and iterate; update the population: select the better solution based on the fitness value to update the population, and retain the better solutions. And eliminate the worse solutions. S35: Save the data after the second iteration of the objective function calculation, make a judgment and output it; repeat the predation behavior simulation and population update until the stopping condition is met.

5. The energy demand optimization operation method based on the pelican optimization algorithm as described in claim 1, characterized in that: The intention of the demand response plan in step S1 needs to be constrained.

6. The energy demand optimization operation method based on the pelican optimization algorithm as described in claim 1, characterized in that: In step S2, the incentive obtained during peak hours through hybrid dynamic demand response technology depends not only on the amount of electricity reduced, but also on the ratio of current load to peak load.

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