Multi-objective optimization energy scheduling method for active power distribution network

Abstract

The invention discloses a multi-objective optimization energy scheduling method for an active power distribution network, and the method comprises the following steps: building an index evaluation system including scheduling cost, credit and power supply guarantee, and carrying out the evaluation of the schedulable potential of a new energy vehicle, a flexible load, an energy storage device and distributed energy during different scheduling periods. Determining the dispatching priority of the power distribution network by adopting a method of combining an analytic hierarchy process and an entropy weight method; a multi-target scheduling optimization model and constraint conditions are established in the aspects of scheduling economy, renewable energy consumption capacity and user satisfaction, and the convergence optimization capacity and speed of a multi-target optimization algorithm are improved based on an improved particle swarm optimization algorithm. According to the method, the scheduling priority can be searched in the global active power distribution network to ensure that the optimal vector solution is obtained by the multi-objective functions of economy, absorption rate and satisfaction, so that the scheduling reasonability of the active power distribution network under the condition of encountering load demand response is improved, and the method is simple, efficient, easy to implement and good in practicability and economy.

Description

Technical Field

[0001] The invention relates to a multi-objective optimization energy dispatching method for an active power distribution network. Background Art

[0002] With the continuous growth of human demand for electricity, improving the quality of electricity and reasonably distributing electricity have become the main responsibilities of the distribution network. With the increasing dispatch scale of the active distribution network architecture year by year, using existing dispatchable equipment to solve the load demand has become the focus of research in recent years. Many scholars at home and abroad have conducted in-depth research on the energy dispatching methods of distribution networks. Although this type of improvement method can improve the accuracy and efficiency of dispatching, it only optimizes from the perspective of dispatching economy, while ignoring other target optimizations such as user satisfaction. At the same time, there is a lack of indicator evaluation of dispatching equipment, which makes it impossible for the distribution network to accurately judge whether each dispatching equipment is in the best utilization position, that is, the best priority. Therefore, it is necessary to establish priorities based on the evaluation of the dispatching capacity indicators of the distribution network to improve the rationality of the energy of the distribution network. Summary of the invention

[0003] The purpose of the present invention is to provide a multi-objective optimization energy scheduling method for an active distribution network. By evaluating the scheduling capability of the distribution network, the scheduling order of energy supply equipment is established. At the same time, by optimizing the objectives from multiple angles, the adaptability of energy distribution during demand response of the distribution network is improved to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a multi-objective optimization energy scheduling method for an active distribution network, comprising the following steps:

[0005] Step (A): Establish a multi-angle indicator evaluation system based on the various requirements of the distribution network dispatching capacity, and determine the dispatching potential of the dispatchable devices during the dispatching period;

[0006] Step (B), using a combination of analytic hierarchy process and entropy weight method to determine the priority of distribution network dispatching for the dispatchable resources in step (A);

[0007] Step (C), establishing a multi-objective optimization scheduling model and constraint conditions that comprehensively consider the energy capacity of the distribution network;

[0008] Step (D): using an improved particle swarm algorithm to improve the optimization capability and convergence speed of multi-objective optimization scheduling.

[0009] Preferably, in step (A), the priority scheduling evaluation in the indicator evaluation system includes three indicators: scheduling cost, credit and power supply security; the dispatchable devices include new energy vehicles, flexible loads, energy storage devices, and distributed energy.

[0010] Preferably, in step (B), the schedulable energy equipment is subjected to a hierarchical analysis method and an entropy weight method to combine subjective and objective weights to determine priority weights, so as to help reduce the amount of calculation for subsequent multi-objective optimization scheduling.

[0011] Preferably, in step (C), the multi-objective optimization scheduling function includes scheduling economy, renewable energy consumption rate and user satisfaction. Constraints include power balance and voltage constraints.

[0012] Preferably, in step (D), the disturbance term and quadratic nonlinearity in the particle swarm algorithm are improved to enhance the global optimization capability, and improve the weight and speed.

[0013] Compared with the prior art, the advantages and benefits of the present invention are:

[0014] 1. Evaluate the dispatching capacity of the distribution network and predict the dispatching potential of dispatchable equipment;

[0015] 2. The dispatching priority of each dispatchable unit is established by combining subjective and objective mixed weights, so that the distribution network can cope with load fluctuations more targeted and practical;

[0016] 3. Implement multi-objective optimization for each energy device, so that the distribution network changes from a single economic cost optimization dispatch to a multi-dimensional optimization dispatch, which greatly improves the service quality of the client and the distribution network;

[0017] 4. The introduction of the improved optimal particle swarm optimization algorithm effectively saves the calculation process of scheduling target implementation and improves scheduling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the scheduling method of the present invention;

[0019] Figure 2 This is a flow chart of the particle swarm optimization algorithm of the present invention. DETAILED DESCRIPTION

[0020] 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 only 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.

[0021] See also Figure 1-2 , the present invention provides a technical solution: a multi-objective optimization energy scheduling method for an active distribution network.

[0022] like Figure 1As shown, in step (A), the evaluation system is established by considering the selection of indicators of dispatching cost, credit, and power supply security, wherein the dispatchable potential cost model of each dispatching device is calculated step by step using equations (1) to (5);

[0023] Scheduling cost indicators:

[0024] Flexible load dispatching cost:

[0025]

[0026] In formula (1): C1 to C k To compensate for the cost of multiple levels, each level will be added on top of the previous level; Reduce compensation costs for users, P D,x The load reduction compensation amount for the xth reporting user; To reduce the power of the k-th load; C k The load reduction compensation amount at the kth level;

[0027] New energy vehicle dispatching costs:

[0028] In formula (2): Compensate the cost of the i-th new energy vehicle; is the discharge amount of the i-th car in period t; I EV,sub is the unit compensation price of new energy vehicles; H is the time period within a scheduling cycle;

[0029] Energy storage device dispatch cost:

[0030]

[0031] In formula (3): C ESS,j The dispatch cost of the energy storage equipment; is the charging and discharging power of the energy storage device during the scheduling period; C ins,j Installation capital; L j is the usage time; Δt is the cycle interval; C eo,j

[0032] The operation and maintenance cost of energy storage equipment;

[0033] Distributed energy dispatch costs:

[0034] C CDG,m =C fuel,m +C om,m +C dp,m +C en,m (5);

[0035] In formula (4)-(5): C fuel,m, C om,m , C dp,m , C en,m They are fuel, operation and maintenance, depreciation, and environmental costs, which together constitute the distributed energy dispatching cost C CDG,m , C f,m The fuel cost per unit power consumed by m equipment; is the power generated by device m in period t; η is the power supply efficiency; C o,m L is the unit operation and maintenance capital; m C is the useful life; ins,m is the installation cost of equipment m; A is the number of pollutants generated by equipment m; C a is the environmental cost of pollutant a; E m,a is the emission of pollutant a;

[0036]

[0037] The above is the total cost of dispatchable resources. The four dispatchable resource costs are dimensionlessly normalized to obtain the exploitable potential under the same benchmark value, i.e., the dispatchable capacity:

[0038]

[0039] In formula (7): from top to bottom, they represent dimensionless values ​​of new energy vehicles, flexible loads, energy storage devices, and distributed energy. The above dispatchable capacities are compared within the same confidence interval, and the power supply security under the same confidence scale is calculated, which is the dispatchable capacity adjustment boundary allowed by the active distribution network;

[0040] Creditworthiness

[0041] In formula (8), R EV is the credit rating of new energy vehicles; Z i Dispatch times for new energy vehicles; The actual online time. is the estimated Internet connection time; R FL G is the user's credit rating; x Scheduling times for users; It is the real power shortage; For the expected power shortage;

[0042] Power supply security

[0043] In formula (9): D S,n The actual power supply capacity of the dispatching equipment; ΔT L,n , T L,n is the networking cycle and the total networking cycle; ΔP S,n , P S is the transferable power and the maximum operating power.

[0044] Step (B), using the analytic hierarchy process and entropy weight method to determine the priority of distribution network dispatching for the dispatchable resources in step (A):

[0045] The analytic hierarchy process includes the following steps:

[0046] Step (B1), decision matrix

[0047] The value in formula (10) is given by the expert score, aij = 1 / aj i;

[0048] Step (B2), weight value

[0049]

[0050] In formulas (11)-(13): M i The accumulation of elements in the i-th row of the matrix; v i M i nth root; w i is the final normalized indicator matrix;

[0051] Step (B3), consistency check In formula (14), CI is the consistency index, and the rationality of the value is obtained by comparing the consistency, CR = CI / RI (CR < 0.1), and the values ​​of RI in the 1-9 level are 0, 0, 0.58, 0.9, 1.12, 1.24, 1.32, 1.41, and 1.45;

[0052] Among them, the entropy weight method includes the following steps:

[0053] Step (S1), data matrix

[0054]

[0055] In formula (15): r ij is the evaluation value of the jth indicator of the i project;

[0056] Step (S2), weight value

[0057]

[0058] In formulas (16)-(18): p ij is the evaluation weight; j is the entropy weight; w j is the entropy weight, and then substitute it into equations (11)-(13) to get the weight values ​​of each item, all represented by λ;

[0059] Step (S3), comprehensive evaluation of dynamic scheduling priority:

[0060]

[0061] In formula (19): cm,q , AHP,q , EM,q The weights are evaluated by hybrid evaluation, analytic hierarchy process evaluation, and entropy weight method respectively; thereby evaluating the priority order; the priority weights are determined by combining subjective and objective weights to reduce the amount of calculation for subsequent multi-objective optimization scheduling.

[0062] In step (C), a multi-objective optimization scheduling model is constructed:

[0063] Optimal dispatch economy:

[0064] C T =C DS +C EX +C R,om -(C R,sub +C EV,cd ) (20);

[0065]

[0066] In formula (21): represents the charge and discharge amount of the i-th vehicle during period t; C WT,sub , C PV,sub The wind and solar subsidy price; represents the interactive power and electricity price of the distribution grid; ρ wI , pv Indicates the operation and maintenance coefficient of wind and solar power units;

[0067] Renewable energy consumption rate:

[0068] In formula (22): It represents the total energy sent and received during time period t;

[0069] And user satisfaction:

[0070]

[0071] In formula (23): the above model is, from top to bottom, the satisfaction of flexible load electricity consumption, the satisfaction of electricity consumption duration, and the satisfaction of new energy vehicles; are the number of users with power shortage in period t; ΔL′ D,x , ΔL′ S,x Indicates the reduction of transfer volume in period t; L′ DT,x , L′ ST,x Indicates reduction of power consumption of transfer load; S LE,i Indicates the SOC under off-grid condition;

[0072] Constraints:

[0073] Power Balance: In formula (24): The flexible load is short of power; is the charging cluster power;

[0074] Voltage Constraint: V i,min ≤V i ≤V i,max (25), where: V i,max 、V i,min It is the upper and lower limits of the voltage value of the distribution network node.

[0075] In step (D), the improved particle swarm algorithm is used to optimize the multi-objective scheduling energy system. The workflow is as follows: Figure 2 As shown:

[0076] V id (T+1)=ωV id (T)+c1r1(p id -x id (T))+c2r2(p gd -x id (T)) (26);

[0077] x id (T+1)=x id (T)+v id (T+1) (27);

[0078] In formula (26), ω is the inertia weight; c1 and c2 are learning coefficients; T is the number of iterations; r1 and r2 are random numbers with values ​​greater than 0 and less than 1;

[0079] The particle swarm algorithm is used to improve the disturbance term and the quadratic nonlinear term, increase the convergence speed, and achieve global optimization.

[0080]

[0081] In formula (28): V id is the particle speed; r follows a random independent uniform distribution; p id To search for the optimal solution; p gd Search for the optimal solution for all particles; ω S is the initial weight; ω E is the minimum weight; x id The current iteration position of the particle.

[0082] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-objective optimization energy scheduling method for an active distribution network, characterized in that: The following steps are involved: Step (A): Establish a multi-angle indicator evaluation system based on the various requirements of the distribution network dispatching capacity, and determine the dispatching potential of the dispatchable devices during the dispatching period; Step (B), using a combination of analytic hierarchy process and entropy weight method to determine the priority of distribution network dispatching for the dispatchable resources in step (A); Step (C), establishing a multi-objective optimization scheduling model and constraint conditions that comprehensively consider the energy capacity of the distribution network; Step (D): using an improved particle swarm algorithm to improve the optimization capability and convergence speed of multi-objective optimization scheduling.

2. The multi-objective optimization energy scheduling method for active distribution network according to claim 1 is characterized by: In step (A), the priority dispatch evaluation in the indicator evaluation system includes three indicators: dispatch cost, credit and power supply security; the dispatchable devices include new energy vehicles, flexible loads, energy storage devices and distributed energy; Among them, the dispatchable potential cost model of each dispatching device is calculated step by step using equations (1) to (5); Flexible load dispatching cost: In formula (1): C1 to C k To compensate for the cost of multiple levels, each level will be added on top of the previous level; Reduce compensation costs for users, P D,x The load reduction compensation amount for the xth reporting user; To reduce the power of the k-th load; C k The amount of compensation for load reduction at level k; New energy vehicle dispatching costs: In formula (2): Compensate the cost of the i-th new energy vehicle; is the discharge amount of the i-th car in the t period; I EV,sub is the unit compensation price of new energy vehicles; H is the time period within a scheduling cycle; Energy storage device dispatch cost: In formula (3): C ESS,j is the dispatch cost of the energy storage equipment; is the charging and discharging power of the energy storage device during the scheduling period; C ins,j Installation capital; L j is the usage time; Δt is the cycle interval; C eo,j The operation and maintenance cost of energy storage equipment; Distributed energy dispatch costs: C CDG,m =C fuel,m +C om,m +C dp,m +C en,m (5); In formula (4)-(5): C fuel,m , C om,m , C dp,m , C en,m They are fuel, operation and maintenance, depreciation, and environmental costs, which together constitute the distributed energy dispatching cost C CDG,m , C f,m The fuel cost per unit power consumed by m equipment; is the power generated by device m in period t; η is the power supply efficiency; C o,m L is the unit operation and maintenance capital; m C is the useful life; ins,m is the installation cost of equipment m; A is the number of pollutants generated by equipment m; C a is the environmental cost of pollutant a; E m,a is the emission of pollutant a; The above is the total cost of dispatchable resources. The four dispatchable resource costs are dimensionlessly normalized to obtain the potential that can be tapped under the same benchmark value, that is, the dispatchable capacity: In formula (7): from top to bottom, they represent dimensionless values ​​of new energy vehicles, flexible loads, energy storage devices, and distributed energy. The above dispatchable capacities are compared within the same confidence interval, and the power supply security under the same confidence scale is calculated, which is the dispatchable capacity adjustment boundary allowed by the active distribution network; Creditworthiness In formula (8), R EV is the credit rating of new energy vehicles; Z i Dispatch times for new energy vehicles; The actual online time. is the estimated Internet connection time; R FL G is the user's credit rating; x Scheduling times for users; It is the real power shortage; For the expected power shortage; Power supply security In formula (9): D S,n The actual power supply capacity of the dispatching equipment; ΔT L,n , T L,n is the networking cycle and the total networking cycle; ΔP S,n , P S is the transferable power and the maximum operating power.

3. The multi-objective optimization energy scheduling method for active distribution network according to claim 1 is characterized by: In step (B), a combination of analytic hierarchy process and entropy weight method is used to construct dynamic scheduling priority evaluation; Among them: The hierarchical analysis method includes the following steps: Step (B1), decision matrix The value in formula (10) is given by the expert score, aij = 1 / aji; Step (B2), weight value In formulas (11)-(13): M i The accumulation of elements in the i-th row of the matrix; v i M i nth root; w i is the final normalized indicator matrix; Step (B3), consistency check In formula (14), CI is the consistency index, and the rationality of the value is obtained by comparing the consistency, CR = CI / RI (CR < 0.1), and the values ​​of RI in the 1-9 level are 0, 0, 0.58, 0.9, 1.12, 1.24, 1.32, 1.41, and 1.45; Among them: The entropy weight method includes the following steps: Step (S1), data matrix In formula (15): r ij is the evaluation value of the jth indicator of the i project; Step (S2), weight value In formulas (16)-(18): p ij is the evaluation weight; j is the entropy weight; w j is the entropy weight, and then substitute it into equations (11)-(13) to get the weight values ​​of each item, all represented by λ; Step (S3), comprehensive evaluation of dynamic scheduling priority: In formula (19): cm,q , AHP,q , EM,q The weights are respectively hybrid evaluation, analytic hierarchy process evaluation, and entropy weight method evaluation; thereby evaluating the priority order.

4. The multi-objective optimization energy scheduling method for active distribution network according to claim 1 is characterized by: In step (C), the multi-objective optimization scheduling function includes: Scheduling Economy: C T =C DS +C EX +C R,om -(C R,sub +C EV,cd ) (20); In formula (21): represents the charge and discharge amount of the i-th vehicle during period t; C WT,sub , C PV,sub The wind and solar subsidy price; represents the interactive power and electricity price of the distribution grid; ρ wI , pv Indicates the operation and maintenance coefficient of wind and solar power units; Renewable energy consumption rate: In formula (22): It represents the total energy sent and received during time period t; And user satisfaction: In formula (23): the above model is, from top to bottom, the satisfaction of flexible load electricity consumption, the satisfaction of electricity consumption duration, and the satisfaction of new energy vehicles; are the number of users with power shortage in period t; ΔL′ D,x , ΔL′ S,x Indicates the reduction of transfer volume in period t; L′ DT,x , L′ ST,x Indicates reduction of power consumption of transfer load; S LE,i Indicates the SOC under off-grid condition; Constraints: Power Balance: In formula (24): The flexible load is short of power; is the charging cluster power; Voltage Constraint: V i,min ≤V i ≤V i,max (25), where: V i,max 、V i,min It is the upper and lower limits of the voltage value of the distribution network node.

5. The multi-objective optimization energy scheduling method for active distribution network according to claim 1, characterized in that: In step (D), V id (T+1)=ωV id (T)+c1r1(p id -x id (T))+c2r2(p gd -x id (T)) (26); x id (T+1)=x id (T)+v id (T+1)(27); In formula (26), ω is the inertia weight; c1 and c2 are learning coefficients; T is the number of iterations; r1 and r2 are random numbers with values ​​greater than 0 and less than 1; The particle swarm algorithm is used to improve the disturbance term and the quadratic nonlinear term, increase the convergence speed, and achieve global optimization. In formula (28): V id is the particle speed; r follows a random independent uniform distribution; p id To search for the optimal solution; p gd Search for the optimal solution for all particles; ω S is the initial weight; ω E is the minimum weight; x id The current iteration position of the particle.

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