Oil field micro-grid multi-objective optimization scheduling method based on IPSO-EPM

Abstract

The invention provides an oil field micro-grid multi-objective optimization scheduling method based on an IPSO-EPM. The oil field micro-grid multi-objective optimization scheduling method based on the IPSO-EPM comprises the steps that 1, an oil field micro-grid mathematical model is built; step 2, constructing an oil field micro-grid multi-objective optimization scheduling model; step 3, performing multi-objective planning solution; 4, solving a planning problem by adopting an IPSO-EPM algorithm; and 5, performing simulation calculation to obtain an optimal scheduling scheme of each micro source of the oil field micro-grid. The IPSO-EPM-based oil field micro-grid multi-objective optimization scheduling method is clear in physical significance, comprehensively considers the economical efficiency, the load stability and the renewable energy consumption rate of oil field micro-grid operation, and has the advantages of being scientific and reasonable, high in applicability and effective in scheduling strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield microgrids, and in particular to an IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method. Background Art

[0002] In my country, oil extraction consumes a lot of electricity. At the same time, a series of environmental pollution problems are also affecting people's daily lives. Therefore, how to reduce production costs and improve environmental friendliness has become a key issue for oilfield companies. Photovoltaic microgrids can effectively alleviate the transmission pressure of high-voltage power grids, reduce power grid losses, and improve the voltage quality of terminal power grids. They are of great practical significance to the safe and economic operation of oilfield power grids and the overall energy conservation and consumption reduction of oilfields.

[0003] For the optimized operation of oilfield microgrids, traditional economic dispatch models often cannot comprehensively improve the comprehensive indicators of microgrid operation. If the economic and environmental performance of the system is to be comprehensively improved, a multi-objective optimization dispatch model needs to be established. At the same time, most of the existing methods for processing multi-objective functions are relatively simple and provide little reference value for decision makers to formulate dispatch strategies. Therefore, based on the above problems, it is difficult for existing optimization methods to achieve reasonable and effective dispatch.

[0004] In the Chinese patent application with application number: CN202110389022.1, a power coordination and optimization method for an offshore oilfield group based on shore-based power supply is involved. The power data of the platform equipment in the offshore oilfield platform is obtained, and the power data is sent to the power coordination platform. The power coordination platform receives the power data sent by the offshore oilfield platform, performs line loss analysis and power allocation on the power data, obtains the line loss analysis results and the power allocation results, and sends the results to the remote control platform. The remote control platform is used to obtain the results sent by the power coordination platform and display them for power monitoring personnel to view, and perform remote optimization and adjustment to improve the efficiency of power coordination and optimization of offshore oilfield groups.

[0005] In the Chinese patent application with application number: CN201810088180.1, a method and system for short-term power load forecasting of offshore oilfield microgrids are involved, which has strong global search capabilities, higher prediction accuracy and computational efficiency. The method includes: setting the upper and lower limits of the penalty parameter C and the kernel parameter σ of the support vector machine SVM; setting the dimension of the position vector of the improved dragonfly algorithm IDA, the maximum number of iterations, and the number of dragonfly individuals; initializing the behavior parameters of the dragonfly individuals; calculating the current fitness value of the dragonfly individuals in IDA; sorting the two generations of dragonfly individuals by mapping, calculating and saving the corresponding maximum fitness value; updating the location of food and natural enemies in IDA; updating the behavior of dragonfly individuals in IDA; updating the location of dragonfly individuals in IDA; when the maximum number of iterations is reached, according to the location of the dragonfly individuals corresponding to the saved maximum fitness value, setting the penalty parameter C and the kernel parameter σ of the SVM, and building a prediction model based on the SVM to predict the short-term power load of the offshore oilfield microgrid.

[0006] In the Chinese patent application with application number: CN201410040921.0, an online simulation calculation method for the expected operation of an offshore oilfield group power grid is involved, which includes the following steps: generating pseudo-measurement data of an offshore oilfield group power grid; performing topological analysis on the oilfield group power grid based on the pseudo-measurement data to realize the conversion of the pseudo-measurement data into an IEEE power flow data file; modifying the quasi-steady-state model of the offshore oilfield group power grid according to the offshore oilfield group power grid operator and the power grid control characteristics; performing power flow calculation on the modified quasi-steady-state model; according to the power flow calculation results, combined with the reactive power / voltage control of the offshore oilfield group power grid, if it is necessary to change the node type and node injection power of the node where the generator is located, then after changing the corresponding node type and node injection power and re-finding the balance node, perform power flow calculation again; convert the power flow calculation results into corresponding equipment information. This calculation method can help operators grasp the impact of expected operations on the power grid in advance, which is conducive to the stable operation of the offshore oilfield group power grid and the safety and continuity of oilfield production.

[0007] In the Chinese patent application with application number: CN201310006694.5, a power optimization control method for an offshore oilfield power grid is involved. It establishes a large-scale, multi-constraint, nonlinear power optimization mathematical model, takes power generation fuel costs, active network losses, reactive power balance, and voltage residual as optimization targets, and realizes a multi-target comprehensive control mode through user control; with flow constraints, voltage constraints, branch transmission power constraints, etc. as constraint conditions, the safety of the system is guaranteed. The interior point method is used for programming on the IntelliJIDEA platform. The invention sets an optimization target suitable for offshore oilfield power grids, realizes coordinated control of active and reactive power of power grids, and realizes optimized automatic control of power grid power, which can reduce the fuel consumption of offshore oilfield power grids, improve their power generation efficiency, improve the safety and stability of power grids, and reduce fuel consumption and corresponding pollutant emissions.

[0008] The above existing technologies are all significantly different from the present invention and fail to solve the technical problem we want to solve. Therefore, we have invented a new multi-objective optimization scheduling method for oilfield microgrids based on IPSO-EPM. Summary of the invention

[0009] The purpose of the present invention is to provide an IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method that can well meet the operation requirements of the oilfield microgrid and improve various indicators.

[0010] The object of the present invention can be achieved by the following technical measures: a multi-objective optimization scheduling method for oilfield microgrid based on IPSO-EPM, the multi-objective optimization scheduling method for oilfield microgrid based on IPSO-EPM includes:

[0011] Step 1, build a mathematical model of oilfield microgrid;

[0012] Step 2: construct a multi-objective optimization scheduling model for oilfield microgrid;

[0013] Step 3, solving multi-objective planning;

[0014] Step 4, using IPSO-EPM algorithm to solve the planning problem;

[0015] Step 5: Perform simulation calculations to obtain the optimal scheduling plan for each micro-source in the oilfield microgrid.

[0016] The purpose of the present invention can also be achieved by the following technical measures:

[0017] In step 1, the following parameter information needs to be collected:

[0018] a) Power forecast curves of renewable energy and oil pumping unit loads for the next day;

[0019] b) The capacity, maximum and minimum power, ramp rate and unit dispatch cost of distributed generation;

[0020] c) Capacity, charging and discharging power and efficiency of the energy storage unit.

[0021] In step 1, based on the collected parameter information, the oilfield microgrid central controller takes into account the economy of the scheduling strategy, the stability of the load and the absorption rate of renewable energy, and formulates an effective scheduling strategy on the basis of ensuring the safe and stable operation of the microgrid.

[0022] In step 2, a multi-objective optimization scheduling model is established with the goals of minimizing the operating cost of the photovoltaic microgrid, minimizing the abandonment rate, and minimizing the variance of the total output power curve of the well group.

[0023] In step 2, the objective function of the multi-objective optimization scheduling model is established as follows:

[0024] minF1=C1+C2+C3+C4 1)

[0026]

[0027]

[0028] Where F1 represents the microgrid operation cost, F2 represents the variance of the total output power curve of the well group, F3 represents the photovoltaic absorption rate, C1 represents the photovoltaic operation and maintenance cost, C2 represents the gas turbine fuel and operation and maintenance cost, C3 represents the energy storage operation and maintenance cost, C4 represents the load shedding penalty cost, and P PV (t) is the photovoltaic output during period t, P is the photovoltaic power prediction during period t; all (t) is the total output power of the well group; is the average value of the total output power of the well group; N is the number of scheduling optimization periods.

[0029] In step 3, the dimensions of the established multi-objective optimization model are different. First, the objective functions of different dimensions are normalized into membership functions of the same dimension through fuzzy theory, and then the multi-objective function is processed into a single objective function using the linear weighted summation method. Finally, the weight of the objective function is determined through the hierarchical analysis method.

[0030] In step 3, referring to the fuzzy set theory, the membership function F is used to describe the optimization results of each objective function. F is defined by the following formula:

[0031]

[0032] In the formula, F i (X) is the target f i Membership function of (X); X′ i For the target f i(X) The optimal strategy for transformation, f i * (X′ i ) is the target f i (X) The optimal strategy X′ for each target i The worst value in .

[0033] In step 3, the multi-objective function is converted into a single objective function using the linear weighted summation method:

[0034]

[0035] In the formula, ω i is the weight coefficient and satisfies ω i ≥0.

[0036] In step 3, the analytic hierarchy process is used to determine the weights of multiple objectives. The analytic hierarchy process is a method of comparing the importance of elements at the same level two by two and calculating the relative weights.

[0037] In step 4, the improved particle swarm optimization external point algorithm IPSO-EPM is used to solve the planning problem. While incorporating the constraints into the objective function, the problem of falling into local extreme values ​​when solving the planning is avoided as much as possible, so that the algorithm can find the global optimal solution and optimal value of the objective function as much as possible.

[0038] In step 4, in order to solve the problem that local extreme values ​​are prone to exist when the external point method is used for optimization, the PSO algorithm is used to optimize the initial value of the external point method, and the LM algorithm is used as the nonlinear optimization algorithm of the external point method, so that the algorithm can find the global optimal solution of the objective function as much as possible.

[0039] In step 4, the IPSO-EPM algorithm includes:

[0040] Step 41: Set the maximum number of iterations of the particle swarm algorithm, the number of individuals in the population, and initialize the population particles;

[0041] Step 42: Substitute the coordinates of each particle as the initial value of the external point method variable into the external point method to find the optimal solution position and optimal value, and use this optimal value as the fitness value Z of the particle i , and replace the particle position with the optimal solution position;

[0042] Step 43: Compare the fitness value Z of each particle i and the particle's historical optimal value Z pbi , decide whether to replace the historical optimal point of the particle, find the optimal point of the population and compare it with the historical optimal point Z of the population gb Compare and decide whether to replace;

[0043] Step 44: Calculate the inertia weight and learning factor value, update the speed and position of each particle, repeat steps 42 and 43 until the number of iterations reaches the maximum number of iterations, and output the optimal particle and its fitness value.

[0044] In step 42, the steps of optimizing by the external point method are as follows: 1) select the initial point x0 of the variable and the initial value M0 of M; 2) j-1 As the initial point, use the LM algorithm to solve M as M j-1 When the problem is solved, find the optimal solution position x j ; 3) Increase the value of M. The value of M in each iteration should satisfy: 4) Take the position of the optimal solution obtained in the previous iteration as the starting point of the next iteration, and repeat 2) and 3) until |f(x k )-f(x k-1 )|<ξ0, where ξ0 is the algorithm iteration termination accuracy, f(x k )、f(x k-1 ) are the function values ​​of the kth and k-1th iterations respectively.

[0045] In step 5, when performing simulation calculations, based on the predicted data of photovoltaic power generation and multi-pumping unit loads, distributed power supply capacity, maximum and minimum outputs, ramp rate and unit power generation cost coefficient, energy storage equipment capacity, charging and discharging power, efficiency and unit power generation cost, a multi-objective optimization scheduling method is used to obtain the optimal scheduling plan for each micro-source in the oilfield microgrid.

[0046] The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method also includes, after step 5, defining microgrid operation benefit evaluation indicators, including load power fluctuation indicators and photovoltaic absorption rate indicators, which are:

[0047]

[0048]

[0049] Where S is the power fluctuation variance; is the variance function; K is the photovoltaic absorption rate; is the actual photovoltaic power; Predicting performance for photovoltaics.

[0050] The multi-objective optimization scheduling method for oilfield microgrids based on IPSO-EPM in the present invention includes the establishment of mathematical models of each unit in the oilfield microgrid, the establishment and solution of the multi-objective optimization scheduling model, the proposed IPSO-EPM algorithm, etc. Compared with the existing single-objective research considering the oilfield microgrid, the scheduling scheme formulated by the present invention can well meet the operation requirements of the oilfield microgrid, improve various indicators, and provide a scientific basis for the comprehensive decision-making of microgrid optimization scheduling. It has the advantages of clear physical meaning, scientific and reasonable methods, strong applicability, and reasonable and effective scheduling strategies. The multi-objective optimization scheduling method for oilfield microgrids based on the improved particle swarm optimization external point algorithm of the present invention has clear physical meaning, comprehensively considers the economy of the oilfield microgrid operation, load stability and renewable energy absorption rate, and has the advantages of scientific and reasonable methods, strong applicability, and effective scheduling strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the oilfield microgrid structure in a specific embodiment of the present invention;

[0052] Figure 2 It is a flow chart of IPSO-EPM algorithm in a specific embodiment of the present invention;

[0053] Figure 3 This is a measured power curve of four oil pumping units in a specific embodiment of the present invention;

[0054] Figure 4 A comparison diagram of total power curves of different schemes in a specific embodiment of the present invention;

[0055] Figure 5 A comparison diagram of light abandonment in different schemes in a specific embodiment of the present invention;

[0056] Figure 6 It is a comparison diagram of the simulation results of PSO, IPSO and IPSO-EPM in a specific embodiment of the present invention;

[0057] Figure 7 It is a flow chart of a specific embodiment of the oilfield microgrid multi-objective optimization scheduling method based on IPSO-EPM of the present invention. DETAILED DESCRIPTION

[0058] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0059] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0060] like Figure 7 As shown, Figure 7 The flowchart of the oilfield microgrid multi-objective optimization scheduling method based on IPSO-EPM of the present invention. The oilfield microgrid multi-objective optimization scheduling method based on IPSO-EPM includes:

[0061] Step 101: Build a mathematical model of the oilfield microgrid

[0062] Figure 1 This is a schematic diagram of the oilfield microgrid structure, which includes distributed power sources such as photovoltaics and gas turbines, controllable and uncontrollable pumping loads, energy storage devices, etc. Each part needs to be controlled by a central controller to formulate a scheduling plan for the micro-source, where the power flow represents the direction of source-load power flow, and the information flow indicates that the central controller collects source-load information and transmits control instructions. Its main decision variables are:

[0063] a) Power forecast curves of renewable energy and oil pumping unit loads for the next day;

[0064] b) The capacity, maximum and minimum power, ramp rate and unit dispatch cost of distributed generation;

[0065] c) Capacity, charging and discharging power and efficiency of the energy storage unit.

[0066] Based on the above information, the central controller of the oilfield microgrid needs to take into account the economy of the scheduling strategy, the stability of the load and the absorption rate of renewable energy, and formulate an effective scheduling strategy on the basis of ensuring the safe and stable operation of the microgrid.

[0067] Step 102: Constructing a multi-objective optimization scheduling model for oilfield microgrids

[0068] A multi-objective optimization scheduling model is established with the goal of minimizing the operating cost of the photovoltaic microgrid, minimizing the abandonment rate, and minimizing the variance of the total output power curve of the well group. Its objective function is as follows:

[0069] minF1=C1+C2+C3+C4 1)

[0071]

[0072]

[0073] Where C1 is the photovoltaic operation and maintenance cost, C2 is the gas turbine fuel and operation and maintenance cost, C3 is the energy storage operation and maintenance cost, C4 is the load shedding penalty cost, and P PV (t) is the photovoltaic output during period t, P is the photovoltaic power prediction during period t; all (t) is the total output power of the well group; is the average value of the total output power of the well group; N is the number of scheduling optimization periods.

[0074] Step 103: solving multi-objective planning

[0075] The dimensions of the established multi-objective optimization model are different. Firstly, the objective functions of different dimensions are normalized into membership functions of the same dimension through fuzzy theory. Then, the linear weighted summation method is used to process the multi-objective function into a single objective function. Finally, the weight of the objective function is determined through the hierarchical analysis method.

[0076] Referring to the fuzzy set theory, the membership function F is used to describe the optimization results of each objective function. F is defined by the following formula:

[0077]

[0078] In the formula, F i (X) is the target f i Membership function of (X); X′ i For the target f i (X) The optimal strategy for transformation, f i * (X′ i ) is the target f i (X) The optimal strategy X′ for each target i The worst value in .

[0079] The multi-objective function is converted into a single objective function using the linear weighted summation method:

[0080]

[0081] In the formula, ω i is the weight coefficient and satisfies ω i ≥0.

[0082] The analytic hierarchy process is used to determine the weights of multiple objectives. The analytic hierarchy process is a method of comparing the importance of elements at the same level two by two and calculating relative weights. It has a fast calculation speed, does not require the construction of a judgment matrix, and does not require consistency testing.

[0083] Step 104: IPSO-EPM algorithm

[0084] The problem solved by the established multi-objective model is a constrained nonlinear programming problem. If the initial value is not selected properly, it is easy to fall into the local extreme value. Therefore, an improved particle swarm optimization-exterior point method (IPSO-EPM) is proposed to solve the planning problem. While incorporating the constraints into the objective function, it tries to avoid falling into the local extreme value when solving the planning problem, so that the algorithm can find the global optimal solution and optimal value of the objective function as much as possible.

[0085] In order to solve the problem that local extreme values ​​are prone to exist when the exterior point method is used for optimization, the PSO algorithm is used to optimize the initial value of the exterior point method, and the (Levenberg-Marquard, LM) algorithm is used as the nonlinear optimization algorithm of the exterior point method, so that the algorithm can find the global optimal solution of the objective function as much as possible. Figure 2 This is the flow chart of IPSO-EPM algorithm. pbi is the historical optimal value of the i-th particle; Z gb is the historical optimal value of the group; a is the growth coefficient of M. The specific steps of the algorithm are as follows:

[0086] Step 1: Set the maximum number of iterations of the particle swarm algorithm, the number of individuals in the population, and initialize the population particles;

[0087] Step 2: Use the coordinates of each particle as the initial value of the external point method variable to find the optimal solution position and optimal value, and use this optimal value as the fitness value Z of the particle. i , and replace the particle position with the optimal solution position; the optimization steps of the external point method are as follows: 1) select the initial point x0 of the variable and the initial value M0 of M; 2) j-1 As the initial point, use the LM algorithm to solve M as M j-1 When the problem is solved, find the optimal solution position x j ; 3) Increase the value of M. The value of M in each iteration should satisfy: 4) Take the position of the optimal solution obtained in the previous iteration as the starting point of the next iteration, and repeat 2) and 3) until |f(x k )-f(x k-1 )|<ξ0, where ξ0 is the algorithm iteration termination accuracy, f(x k )、f(x k-1 ) are the function values ​​of the kth and k-1th iterations respectively.

[0088] Step 3: Compare the fitness value Z of each particle i and the particle's historical optimal value Z pbi , decide whether to replace the historical optimal point of the particle, find the optimal point of the population and compare it with the historical optimal point Z of the populationgb Compare and decide whether to replace;

[0089] Step 4: Calculate the inertia weight and learning factor value, update the speed and position of each particle, repeat step 2 and step 3 until the number of iterations reaches the maximum number of iterations, and output the optimal particle and its fitness value.

[0090] Step 105: Simulation calculation

[0091] Simulation input: predicted data of photovoltaic power generation and multi-pumping unit load; distributed power supply capacity, maximum and minimum output, climbing rate and unit power generation cost coefficient; energy storage equipment capacity, charging and discharging power, efficiency and unit power generation cost, etc.; based on the proposed multi-objective optimization scheduling method, the optimal scheduling plan for each micro-source of the oilfield microgrid is obtained.

[0092] Step 106: Evaluation indicators

[0093] The microgrid operation benefit evaluation indicators are defined as follows:

[0094]

[0095]

[0096] Where S is the power fluctuation variance; is the variance function; K is the photovoltaic absorption rate; is the actual photovoltaic power; Predicting performance for photovoltaics.

[0097] Microgrid evaluation indicators are used to evaluate the excellent indicators of microgrid research results. The model established in the present invention is a multi-objective optimization model with economic efficiency, renewable energy consumption rate and minimum load output power as the goals. The optimization results can be evaluated by the defined power fluctuation variance and renewable energy consumption rate, and the above-mentioned evaluation indicators are defined for this purpose.

[0098] The following are several specific embodiments of the present invention.

[0099] Example 1

[0100] Figure 3 It is the measured power curve of 4 pumping units in the well group, wherein the horizontal axis represents time (unit: s), and the vertical axis represents the measured power of the pumping unit (unit: kW). In a specific embodiment 1 of the application of the present invention, the present invention sets four scheduling strategies to verify the effectiveness of the proposed method, Scheme 1: the optimal economic scheme; Scheme 2: the minimum abandoned light rate scheme; Scheme 3: the minimum total power curve fluctuation scheme; Scheme 4: the multi-objective optimal scheme.

[0101] The single-objective optimization results are shown in Table 1. Figure 4 As shown, the total power curves of different schemes fluctuate as follows Figure 5 As shown in the figure, compared with Schemes 1 and 3, Scheme 2 is better able to track the predicted photovoltaic power trend, and the total load output power fluctuation is the largest, but the abandonment rate is the smallest; Scheme 3 has the smallest total power curve fluctuation, but its abandonment rate is large and its economy is poor. The membership of different optimization objective functions is shown in Table 2. As can be seen from the table, the comprehensive membership of multiple objectives is smaller than that of a single objective, and the overall effect is better. Decision makers can choose different schemes to arrange scheduling plans according to different scenarios, which is more conducive to the safe and reliable operation of the microgrid, and verifies the effectiveness of the proposed multi-objective optimization scheduling method.

[0102] Table 1 Single objective optimization results

[0103]

[0104] Table 2 Membership table of different optimization objectives

[0105]

[0106] A simulation comparison experiment of three algorithms was set up. To ensure the fairness of the comparison, the parameter settings and experimental data of each algorithm in the experiment were the same. The simulation comparison results of PSO, IPSO and IPSO-EPM are shown in Table 3 and Figure 6 The proposed IPSO-EPM shows good optimization ability in solving the above problems, which is characterized by fast convergence speed, small number of iterations, not easy to fall into local optimality, and the ability to quickly and accurately find the optimal solution, which verifies the effectiveness of the proposed algorithm.

[0107] Table 3 Comparison of IPSO, PSO and IPSO-EPM experimental results

[0108]

[0109] Example 2

[0110] In the specific embodiment 2 of the application of the present invention, two strategies are set up for comparison and verification of the effectiveness of the present invention. Strategy 1: adopt the multi-objective planning method proposed in the present invention, that is, use fuzzy theory to process the multi-objective function into a single objective function, and determine the weight of the objective function through the hierarchical analysis method; Strategy 2: Use the entropy weight method to process the multi-objective function into a single objective function, and determine the weight of the objective function through the hierarchical analysis method.

[0111] The membership of different optimization objectives of Strategy 2 is shown in Table 4. Compared with Strategy 1 (i.e., Table 2), it is not difficult to find that the comprehensive membership of Scheme 3 under the two strategies is the same, indicating that when an optimization model with the goal of minimizing the fluctuation of the total power curve of the load is established, the scheduling results obtained by the two strategies are the same and the optimization effect of the fluctuation of the total power curve of the load is the same. However, by comparing Scheme 1, Scheme 3 and Scheme 4, it can be found that the comprehensive membership value of Strategy 1 is smaller than that of Strategy 2, indicating that the optimization effect of Strategy 1 is better, which verifies the effectiveness of the present invention.

[0112] Table 4 Membership table of different optimization objectives of strategy 2

[0113]

[0114] Example 3

[0115] In the specific embodiment 3 of the application of the present invention, three optimization algorithms are set to compare and verify the effectiveness of the present invention. Algorithm 1: adopts the IPSO-EPM algorithm proposed by the present invention; Algorithm 2: adopts the genetic algorithm; Algorithm 3: adopts the improved genetic algorithm.

[0116] The above three algorithms are used to solve the model respectively, and 50 tests are performed to obtain the average fitness value. The experimental results are shown in Table 5. It can be seen that the fitness function value of the algorithm proposed in the present invention, that is, the IPSO-EPM algorithm, is the smallest, and it shows a good optimization ability in solving the above problems. Compared with the genetic algorithm and the improved genetic algorithm, it has the advantages of fast calculation speed and good convergence, which verifies the effectiveness of the proposed algorithm.

[0117] Table 5 Comparison of experimental results of different algorithms

[0118]

[0119] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0120] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. The multi-objective optimization scheduling method of oilfield microgrid based on IPSO-EPM is characterized by: The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method includes: Step 1, build a mathematical model of oilfield microgrid; Step 2: construct a multi-objective optimization scheduling model for oilfield microgrid; Step 3, solving multi-objective planning; Step 4, using IPSO-EPM algorithm to solve the planning problem; Step 5: Perform simulation calculations to obtain the optimal scheduling plan for each micro-source in the oilfield microgrid.

2. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 1 is characterized in that: In step 1, the following parameter information needs to be collected: a) Power forecast curves of renewable energy and oil pumping unit loads for the next day; b) The capacity, maximum and minimum power, ramp rate and unit dispatch cost of distributed generation; c) Capacity, charging and discharging power and efficiency of the energy storage unit.

3. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 2 is characterized in that: In step 1, based on the collected parameter information, the oilfield microgrid central controller takes into account the economy of the scheduling strategy, the stability of the load and the absorption rate of renewable energy, and formulates an effective scheduling strategy on the basis of ensuring the safe and stable operation of the microgrid.

4. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 1 is characterized in that: In step 2, a multi-objective optimization scheduling model is established with the goals of minimizing the operating cost of the photovoltaic microgrid, minimizing the abandonment rate, and minimizing the variance of the total output power curve of the well group.

5. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 4 is characterized in that: In step 2, the objective function of the multi-objective optimization scheduling model is established as follows: minF1=C1+C2+C3+C4 1) Where F1 represents the microgrid operation cost, F2 represents the variance of the total output power curve of the well group, F3 represents the photovoltaic absorption rate, C1 represents the photovoltaic operation and maintenance cost, C2 represents the gas turbine fuel and operation and maintenance cost, C3 represents the energy storage operation and maintenance cost, C4 represents the load shedding penalty cost, and P PV (t) is the photovoltaic output during period t, P is the photovoltaic power prediction during period t; all (t) is the total output power of the well group; is the average value of the total output power of the well group; N is the number of scheduling optimization periods.

6. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 1 is characterized in that: In step 3, the dimensions of the established multi-objective optimization model are different. First, the objective functions of different dimensions are normalized into membership functions of the same dimension through fuzzy theory, and then the multi-objective function is processed into a single objective function using the linear weighted summation method. Finally, the weight of the objective function is determined through the hierarchical analysis method.

7. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 6 is characterized in that: In step 3, referring to the fuzzy set theory, the membership function F is used to describe the optimization results of each objective function. F is defined by the following formula: In the formula, F i (X) is the target f i Membership function of (X); X′ i For the target f i (X) The optimal strategy for transformation, f i * (X′ i ) is the target f i (X) The optimal strategy X′ for each target i The worst value in .

8. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 7 is characterized in that: In step 3, the multi-objective function is converted into a single objective function using the linear weighted summation method: In the formula, ω i is the weight coefficient and satisfies ω i ≥0.

9. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 8, characterized in that: In step 3, the analytic hierarchy process is used to determine the weights of multiple objectives. The analytic hierarchy process is a method of comparing the importance of elements at the same level two by two and calculating the relative weights.

10. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 1, characterized in that: In step 4, the improved particle swarm optimization external point algorithm IPSO-EPM is used to solve the planning problem. While incorporating the constraints into the objective function, the problem of falling into local extreme values ​​when solving the planning is avoided as much as possible, so that the algorithm can find the global optimal solution and optimal value of the objective function as much as possible.

11. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 10, characterized in that: In step 4, in order to solve the problem that local extreme values ​​are prone to exist when the external point method is used for optimization, the PSO algorithm is used to optimize the initial value of the external point method, and the LM algorithm is used as the nonlinear optimization algorithm of the external point method, so that the algorithm can find the global optimal solution of the objective function as much as possible.

12. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 11, characterized in that: In step 4, the IPSO-EPM algorithm includes: Step 41: Set the maximum number of iterations of the particle swarm algorithm, the number of individuals in the population, and initialize the population particles; Step 42: Substitute the coordinates of each particle as the initial value of the external point method variable into the external point method to find the optimal solution position and optimal value, and use this optimal value as the fitness value Z of the particle i , and replace the particle position with the optimal solution position; Step 43: Compare the fitness value Z of each particle i and the particle's historical optimal value Z pbi , decide whether to replace the historical optimal point of the particle, find the optimal point of the population and compare it with the historical optimal point Z of the population gb Compare and decide whether to replace; Step 44: Calculate the inertia weight and learning factor value, update the speed and position of each particle, repeat steps 42 and 43 until the number of iterations reaches the maximum number of iterations, and output the optimal particle and its fitness value.

13. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 12, characterized in that: In step 42, the steps of optimizing by the external point method are as follows: 1) select the initial point x0 of the variable and the initial value M0 of M; 2) j-1 As the initial point, use the LM algorithm to solve M as M j-1 When the problem is solved, find the optimal solution position x j ; 3) Increase the value of M. The value of M in each iteration should satisfy: The position of the optimal solution obtained in the previous iteration is used as the starting point of the next iteration, and 2) and 3) are repeated until |f(x k )-f(x k-1 )|<ξ0, where ξ0 is the algorithm iteration termination accuracy, f(x k )、f(x k-1 ) are the function values ​​of the kth and k-1th iterations respectively.

14. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 1, characterized in that: In step 5, when performing simulation calculations, based on the predicted data of photovoltaic power generation and multi-pumping unit loads, distributed power supply capacity, maximum and minimum outputs, ramp rate and unit power generation cost coefficient, energy storage equipment capacity, charging and discharging power, efficiency and unit power generation cost, a multi-objective optimization scheduling method is used to obtain the optimal scheduling plan for each micro-source in the oilfield microgrid.

15. The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method according to claim 1, characterized in that: The IPSO-EPM-based oilfield microgrid multi-objective optimization scheduling method also includes, after step 5, defining microgrid operation benefit evaluation indicators, including load power fluctuation indicators and photovoltaic absorption rate indicators, which are: Where S is the power fluctuation variance; is the variance function; K is the photovoltaic absorption rate; is the actual photovoltaic power; Predict power for photovoltaics.

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