A method for optimizing voltage quality and network loss in distribution network with energy storage system

Abstract

The present invention discloses a method for optimizing and controlling the voltage quality and network loss of a distribution network containing an energy storage system. It is mainly aimed at the development status and challenges of the continuous improvement of energy storage technology, increasingly stringent requirements for grid voltage quality, and large-scale access of distribution networks to distributed energy storage systems. The present invention mainly focuses on the impact of the energy storage system on the grid voltage quality and network loss after it is connected to the grid, constructs a two-layer optimization model for the energy storage system, and focuses on the node voltage quality of the distribution network. Further based on the new war strategy optimization algorithm, the two-layer optimization model of the energy storage system is solved. Finally, the voltage and network loss control effect on the IEEE33 node containing a distributed energy storage system is verified through example analysis. The results show that this method effectively improves the voltage quality and economic benefits of the distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage of distribution networks connected to distributed energy, and specifically relates to a method for optimizing voltage quality and network loss of a distribution network containing an energy storage system based on a war strategy optimization algorithm. Background Art

[0002] Since the 21st century, the world's environmental and energy problems have become increasingly serious. Traditional fossil energy can no longer meet the needs of development, and various clean energy sources have emerged. With the rapid development of science and technology, social electricity consumption has increased, and the quality of grid voltage has become more stringent. The large-scale access of distributed energy and distributed energy storage systems to the distribution network has become a development trend. Distributed energy storage systems have small capacity, are decentralized, have rapid response and high efficiency, and can effectively suppress the fluctuation of distributed energy output, thereby improving the quality of node voltage, smoothing load fluctuations, and providing strong support for maintaining the power balance and safe and reliable operation of the distribution network. Summary of the invention

[0003] The purpose of the present invention is to use the war strategy optimization method to construct an optimal configuration model of a distributed energy storage system that mainly takes into account the voltage quality of the distribution network and the network loss and operating losses and meets the economic operation requirements of the system, and solves the model based on the improved war strategy optimization algorithm, and finally obtains a distributed energy storage control strategy for the distribution network that mainly controls the node voltage and supplements the network loss under the condition of frequent distributed output fluctuations, and proves that the optimal scheduling model solved based on the war strategy optimization algorithm has good optimization and convergence performance.

[0004] In order to achieve the above-mentioned invention object, the present invention provides the following technical solutions:

[0005] A node voltage and network loss optimization control strategy for a distribution network containing an energy storage system based on a war strategy optimization algorithm, the method comprising the following steps:

[0006] Step 1: Construct an energy storage system optimization configuration model that mainly controls the voltage of distribution network nodes and supplements network losses;

[0007] Step 2: Construct an energy storage system control optimization objective function that mainly controls the voltage of distribution network nodes and supplements the network loss;

[0008] Step 3: Apply the war strategy optimization algorithm to solve the upper layer fragmentation model and lower layer operation model of the energy storage system, including:

[0009] Step (3-1) initializing the parameters and optimization target problem of the war strategy optimization algorithm;

[0010] Step (3-2) initializes the size of the army, the ranking of each soldier, the size of the problem, and calculates the initial fitness of each soldier;

[0011] Step (3-3) defines the attack strategy: Soldiers update their positions based on the positions of the king and commander;

[0012] Step (3-4) calculates and ranks the attack power of soldiers on the battlefield;

[0013] Steps (3-5) define the defense strategy; soldiers change their positions based on the positions of nearby soldiers and the king's position, and protect the king as much as possible without losing the battle;

[0014] Steps (3-6) replace or place weak soldiers;

[0015] Step (3-7) uses the objective function to update the fitness value of each individual, and records the individual with the best fitness value and its corresponding related attributes;

[0016] Step (4): Output the optimal charging and discharging control instructions of the energy storage system for improving the voltage quality and network loss of the distribution network nodes.

[0017] In some embodiments, step 1 specifically includes:

[0018] Step (1-1) constructs an upper-level planning model for the energy storage system; the upper-level planning model includes an investment cost model and a line loss model for the energy storage system;

[0019] Step (1-2) constructs the constraints of the upper-level planning model of the energy storage system. The constraints of the upper-level planning model include node power balance constraints, energy storage system power constraints, energy storage system capacity constraints, node voltage constraints, and energy storage system installation node constraints.

[0020] Step (1-3) constructs a lower-level operation model of the energy storage system, wherein the lower-level operation model includes energy storage system charge and discharge power constraints, energy storage system charge state constraints, and energy storage system charge and discharge state conversion constraints;

[0021] Steps (1-4) construct the objective functions of the upper-level planning model and the lower-level operation model of the energy storage system.

[0022] In some implementations, the investment cost model and the line loss model in step (1-1) are respectively:

[0023] Investment cost model

[0024]

[0025] Line loss cost model

[0026]

[0027] Where: G 1 is the investment cost; G 2is the line loss cost; k is the number of energy storage devices installed; τ is the annual interest rate; y is the service life of the energy storage device; μ is the discount factor, C Pess_k is the unit power factor cost of the energy storage device; P essk is the rated power of the kth energy storage device; C Eess_k is the rated power of the energy storage device; E esk is the rated capacity of the kth energy storage device.

[0028] In some implementations, the relational expressions of the constraints of the upper-level planning model in step (1-2) are:

[0029] Power balance constraints

[0030]

[0031] Energy storage system power constraints

[0032] P ess,min ≤P essk ≤P ess,max ;

[0033] Energy storage system capacity constraints

[0034] E ess,min ≤E essk ≤E ess,max ;

[0035] Node Voltage Constraints

[0036] U min ≤U m ≤U max ;

[0037] Energy storage node installation constraints

[0038]

[0039] Where: P zm , Q zm are the active and reactive power injected into node m, R m-1 , X m-1 are the resistance and reactance between nodes m-1 and m, respectively, P m , Q m are the active load and reactive load of node m respectively, U m-1 is the voltage at node m-1, λ m If it is 1, it means that there is an energy storage system connected to the point, otherwise there is no energy storage system connected. ess is the total number of energy storage system nodes, E ess,max is the upper limit of energy storage system capacity, E ess,min It is the lower limit of energy storage system capacity.

[0040] In some implementations, the relationship of the lower layer operation model in steps (1-3) includes:

[0041] Energy storage system charging and discharging power constraints

[0042] -P essk ≤P essk,t ≤P essk ,

[0043] Energy storage system state of charge (SOC) constraints

[0044] S SOC,min ≤S soc (t)≤S SOC,max ,

[0045] Energy storage system charging and discharging state transition constraints

[0046]

[0047] Where: S SOC,min , S SOC,max are the upper and lower limits of the energy storage system’s state of charge, E ess,t-1 and E ess,t are the capacities of the energy storage system at time t-1 and time t, respectively, P ess,t is the charging and discharging power of the energy storage system at time t, η ch and η dis are the charging and discharging power of the energy storage system, respectively, essk is the lower limit of charge and discharge power, P essk It is the upper limit of charge and discharge power.

[0048] In some embodiments, step 2 specifically includes:

[0049] Step (2-1) configures an upper-level planning function for the upper-level planning model of the energy storage system, where the upper-level planning function includes the investment cost of the energy storage system and the line loss cost;

[0050] Step (2-2) configures a lower-level operation function for the lower-level operation model of the energy storage system, wherein the lower-level operation function mainly includes minimizing node voltage deviation and minimizing branch losses within the line;

[0051] Step (2-3) configures the objective function weights mainly by controlling the voltage of the distribution network nodes and supplemented by network losses.

[0052] In some embodiments, the attack strategy defined in step (3-3) is:

[0053] X i (t+1)=X i (t)+2×ρ×(CK)+rand×(W i ×KXi (t))

[0054] Where, X i (t+1) is the new position, X i is the commander's position, K is the king's position, W is the i is the ranking weight.

[0055] In some implementations, the process of calculating and ranking the attack power of soldiers on the battlefield in step (3-4) specifically includes:

[0056] If the attack power (fitness) of the new position (Fn) is less than the attack power of the previous position (Fp), the soldier occupies the previous position. i The relationship between (t+1) is

[0057] X i (t+1)=(X i (t+1))×(F n ≥F p )+(X i (t))×(F n <F p );

[0058] If the soldier successfully updates his position, the soldier's level R i The relationship is

[0059] R i =(R i +1)×(F n ≥F p )+(R i )×(F n <F p );

[0060] According to the ranking new weight W i The calculation formula is as follows

[0061]

[0062] In some implementations, the relationship between the defense strategy defined in steps (3-5) is:

[0063] X i (t+1)=X i (t)+2×ρ×(KX rand (t))+rand×W i ×(cX i (t))

[0064] The beneficial effects of the present invention are as follows:

[0065] This method can effectively cope with the node voltage and network loss optimization control of the distribution network containing energy storage system in the scenario of distributed power output fluctuation. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flowchart of a method for optimizing voltage quality and network loss of a distribution network including an energy storage system in some embodiments of the present invention;

[0067] Figures 2 to 5 This is the result of the example analysis based on the IEEE-33 node system. The base voltage of the distribution network is 12.66kV, and the total load is (3.715+j2.3)MVA. According to my country's national standard for power quality, the upper and lower limits of the node voltage are defined as 1.05UN and 0.95UN (UN is the base voltage of the grid). Referring to the actual construction of the distribution network and the operation of distributed power sources, a 200kW photovoltaic power source is configured at nodes 7 and 24 respectively.

[0068] Figure 2 and Figure 3 They are the node voltage amplitude and node voltage deviation rate respectively. The ordinate is the actual voltage amplitude and the abscissa is time.

[0069] 7. The charging and discharging power curve and SOC curve of the energy storage system at node 24 are as follows: Figure 4 Figure 5 As shown in the figure, the vertical axis represents SOC and charge / discharge power value, and the horizontal axis represents time. DETAILED DESCRIPTION

[0070] The present invention will be further described below in conjunction with the accompanying drawings.

[0071] Combination Figure 1 The present embodiment provides a method for optimizing voltage quality and network loss of a distribution network including an energy storage system, the method comprising the following steps:

[0072] Step 1: Construct an energy storage system optimization configuration model that mainly controls the voltage of distribution network nodes and supplements the network loss, including:

[0073] Step (1-1) constructs an upper-level planning model for the energy storage system. The upper-level planning model mainly includes an investment cost model and a line loss model for the energy storage system. The investment cost model and the line loss model are:

[0074] (1) Investment cost model

[0075]

[0076] (2) Line loss cost model

[0077]

[0078] Where: G 1 is the investment cost; G 2 is the line loss cost; k is the number of energy storage devices installed; τ is the annual interest rate; y is the service life of the energy storage device; μ is the discount factor, C Pess_k is the unit power factor cost of the energy storage device; P essk is the rated power of the kth energy storage device; C Eess_k is the rated power of the energy storage device; E esk is the rated capacity of the kth energy storage device, P lossjt For power loss.

[0079] Step (1-2) constructs the upper model constraint conditions of the energy storage system. The constraint conditions mainly include node power balance constraint, energy storage system power constraint, energy storage system capacity constraint, node voltage constraint, and energy storage system installation node constraint. The relationship between the constraint conditions is:

[0080] (1) Power balance constraints

[0081]

[0082] (2) Energy storage system power constraints

[0083] P ess,min ≤P essk ≤P ess,max

[0084] (3) Energy storage system capacity constraints

[0085] E ess,min ≤E essk ≤E ess,max

[0086] (4) Node voltage constraints

[0087] U min ≤U m ≤U max

[0088] (5) Energy storage node installation constraints

[0089]

[0090] Where: P zm , Q zm are the active and reactive power injected into node m, P zm-1 , Q zm-1 are the active and reactive power injected into node m-1, R m-1 , X m-1 are the resistance and reactance between nodes m-1 and m, respectively, P m , Q mare the active load and reactive load of node m respectively, U m-1 is the voltage at node m-1, λ m If it is 1, it means that there is an energy storage system connected to the point, otherwise there is no energy storage system connected. ess is the total number of energy storage system nodes, E ess,max is the upper limit of energy storage system capacity, E ess,min It is the lower limit of energy storage system capacity.

[0091] Step (1-3) constructs the lower-level operation model of the energy storage system. The lower-level operation model mainly includes the energy storage system charge and discharge power constraints, the energy storage system charge state constraints, and the energy storage system charge and discharge state conversion constraints. The relationship equations of the lower-level operation model are:

[0092] (1) Energy storage system charging and discharging power constraints

[0093] -P essk ≤P essk,t ≤P essk

[0094] (2) Energy storage system state of charge (SOC) constraints

[0095] S SOC,min ≤S soc (t)≤S SOC,max

[0096] (3) Energy storage system charge and discharge state transition constraints

[0097] E ess,t =E ess,t-1 +P ess,t η ch

[0098] E ess,t =E ess,t-1 -P ess,t / η dis

[0099] Where: S SOC,min , S SOC,max are the upper and lower limits of the energy storage system’s state of charge, E ess,t-1 and E ess,t are the capacities of the energy storage system at time t-1 and time t, respectively, P ess,t is the charging and discharging power of the energy storage system at time t, η ch and η dis are the charging and discharging power of the energy storage system, respectively, essk is the lower limit of charge and discharge power, P essk It is the upper limit of charge and discharge power.

[0100] Step (2-1) configures the upper-level planning function for the upper-level planning model of the energy storage system. The upper-level planning function mainly includes the investment cost of the energy storage system and the line loss cost. The specific relationship is:

[0101] minf up =αG 1 +βG 2

[0102] α, β are weights respectively;

[0103] Step (2-2) configures the lower-level operation function for the lower-level operation model of the energy storage system. The lower-level operation function mainly includes minimizing the node voltage deviation and minimizing the branch loss in the line. The specific relationship is:

[0104]

[0105] Step (2-3) considers the weight configuration of the objective function with the control of distribution network node voltage as the main factor and network loss as the auxiliary factor.

[0106] Step 3: Apply the war strategy optimization algorithm to optimize the upper-level planning model and the lower-level operation model, including:

[0107] Step (3-1) initializes the parameters and optimization target of the war strategy optimization algorithm based on the original optimization algorithm;

[0108] Step (3-3) defines the attack strategy: Soldiers update their positions according to the positions of the king and commander, and the corresponding relationship of the strategy is:

[0109] X i (t+1)=X i (t)+2×ρ×(CK)+rand×(W i ×KX i (t))

[0110] Among them, X i (t+1) is the new position, X i is the commander's position, K is the king's position, W is the i is the weight, C and K are the positions of the king and commander respectively.

[0111] Step (3-4) calculates and sorts the attack power of the soldiers on the battlefield.

[0112] If the attack power (fitness) of the new position (Fn) is less than the attack power of the previous position (Fp), the soldier occupies the previous position, and the corresponding relationship can be:

[0113] X i (t+1)=(X i (t+1))×(Fn ≥F p )+(X i (t))×(F n <F p );

[0114] If the soldier successfully updates his position, the soldier's level R i , and the corresponding relationship can be:

[0115] R i =(R i +1)×(F n ≥F p )+(R i )×(F n <F p );

[0116] According to the ranking new weight W i The calculation is as follows:

[0117]

[0118] Where Max_iter is the maximum number of iterations;

[0119] Steps (3-5) define the defense strategy. Soldiers change their positions according to the positions of nearby soldiers and the king, and protect the king to the maximum extent without losing the battle. The corresponding relationship of the defense strategy is:

[0120] X i (t+1)=X i (t)+2×ρ×(KX rand (t))+rand×W i ×(cX i (t))

[0121] This war strategy explores more of the search space than the previous strategy because it involves random soldier positions. For larger values ​​of Wi calculated, soldiers take larger parameters and update their positions, while Wi soldiers take small parameters when updating their positions.

[0122] (1) Exploration stage 1, the corresponding relationship can be:

[0123] X i (t+1)=X i (t)+2×ρ×(CK)+r×(W i ×KX i (t))

[0124] (2) Exploration stage 2, the corresponding relationship can be:

[0125] Xi (t+1)=X i (t)+2×ρ×(KX rand (t))+r×W i ×(CX i (t))

[0126] Step (3-6) replaces or places weak soldiers, and the corresponding relationship can be:

[0127] X w (t+1)=Lb+rand×(Ub-Lb)

[0128] The second approach is to relocate the weak soldiers on the battlefield closer to the median of the entire army as shown in step (3-6). This approach improves the convergence of the algorithm.

[0129] X w (t+1)=-(1-randn)×(X w (t)-median(X))+K

[0130] Steps (3-7) use the objective function to update the fitness value of each individual and record the individual with the best fitness value and its corresponding related attributes.

[0131] Figure 2 , Figure 3 It can be seen that when neither the photovoltaic power source nor the energy storage system is configured, the voltage deviation of the distribution network system nodes is large. Node 18 is located at the end of the system, and the lowest voltage is 11.56kV, which exceeds the national standard requirements (the voltage deviation shall not exceed ±5% of the rated voltage); when the energy storage system is configured at nodes 7 and 24, both near the photovoltaic power source nodes, the voltage deviation is further reduced. It can be seen that the energy storage system configured near the photovoltaic power source node can effectively smooth the output fluctuation of the photovoltaic power source, improve the penetration rate and utilization rate of the photovoltaic power source, and improve the voltage quality of the distribution network.

[0132] The charging and discharging power curve and SOC curve of the energy storage system are as follows: Figure 4 and Figure 5 As shown. Figure 4 and Figure 5 It can be seen that the energy storage system is charged during the low load period of 0:00-9:00, and the SOC of the energy storage system gradually increases; it is mainly discharged during the peak period of 10:00-21:00, and the SOC of the energy storage system gradually decreases. The energy storage devices connected to the two nodes are basically in operation all day, and the utilization rate is very high. In order to extend the service life and prevent the energy storage device from overcharging and over-discharging, the SOC variation range is always maintained at 10% to 90%, and the SOC at the beginning and end of the day is maintained at 50% of the initial value to ensure the normal operation of the energy storage system on the second day.

[0133] It can be concluded that the energy storage system decision-making strategy obtained by using the war strategy optimization algorithm has a good node voltage network loss control effect.

[0134] The above undisclosed matters can all be implemented using existing technologies, so they will not be elaborated here.

[0135] The above-mentioned example analysis is an example analysis of the patent protection content, and does not impose any form of limitation on the present invention. Any modification, equivalent substitution, improvement, etc. made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for optimizing voltage quality and network loss of a distribution network including an energy storage system, characterized in that: The method for optimizing the voltage quality and network loss of a distribution network including an energy storage system comprises the following steps: Step 1: Construct an energy storage system optimization configuration model that mainly controls the voltage of distribution network nodes and supplements network losses; Step 2: Construct an energy storage system control optimization objective function that mainly controls the voltage of distribution network nodes and supplements the network loss; Step 3: Apply the war strategy optimization algorithm to solve the upper layer fragmentation model and lower layer operation model of the energy storage system, including: Step (3-1) initializing the parameters and optimization target problem of the war strategy optimization algorithm; Step (3-2) initializes the size of the army, the ranking of each soldier, the size of the problem, and calculates the initial fitness of each soldier; Step (3-3) defines the attack strategy: Soldiers update their positions based on the positions of the king and commander; Step (3-4) calculates and ranks the attack power of soldiers on the battlefield; Steps (3-5) define the defense strategy; soldiers change their positions based on the positions of nearby soldiers and the king's position, and protect the king as much as possible without losing the battle; Steps (3-6) replace or place weak soldiers; Step (3-7) uses the objective function to update the fitness value of each individual, and records the individual with the best fitness value and its corresponding related attributes; Step (4): Output the optimal charging and discharging control instructions of the energy storage system for improving the voltage quality and network loss of the distribution network nodes.

2. According to claim 1, a method for optimizing voltage quality and network loss of a distribution network including an energy storage system is characterized in that: Step 1 specifically includes: Step (1-1) constructs an upper-level planning model for the energy storage system; the upper-level planning model includes an investment cost model and a line loss model for the energy storage system; Step (1-2) constructs the constraints of the upper-level planning model of the energy storage system. The constraints of the upper-level planning model include node power balance constraints, energy storage system power constraints, energy storage system capacity constraints, node voltage constraints, and energy storage system installation node constraints. Step (1-3) constructs a lower-level operation model of the energy storage system, wherein the lower-level operation model includes a charge and discharge power constraint of the energy storage system, a charge state constraint of the energy storage system, and a charge and discharge state conversion constraint of the energy storage system; Steps (1-4) construct the objective functions of the upper-level planning model and the lower-level operation model of the energy storage system.

3. According to claim 2, a method for optimizing voltage quality and network loss of a distribution network including an energy storage system is characterized in that: The investment cost model and line loss model in step (1-1) are: Investment cost model Line loss cost model Where: G1 is the investment cost; G2 is the line loss cost; k is the number of energy storage devices installed; τ is the annual interest rate; y is the service life of the energy storage device; μ is the discount factor, C Pess_k is the unit power factor cost of the energy storage device; P essk is the rated power of the kth energy storage device; C Eess_k is the rated power of the energy storage device; E esk is the rated capacity of the kth energy storage device.

4. According to claim 2, a method for optimizing voltage quality and network loss of a distribution network including an energy storage system is characterized in that: The relational expressions of the constraints of the upper-level planning model in step (1-2) are: Power balance constraints Energy storage system power constraints P ess,min ≤P essk ≤P ess,max ; Energy storage system capacity constraints AND ess,min ≤E essk ≤E ess,max ; Node Voltage Constraints IN min ≤U m ≤U max ; Energy storage node installation constraints Where: P zm , Q zm are the active and reactive power injected into node m, R m-1 , X m-1 are the resistance and reactance between nodes m-1 and m, respectively, P m , Q m are the active load and reactive load of node m respectively, U m-1 is the voltage at node m-1, λ m If it is 1, it means that there is an energy storage system connected to the point, otherwise there is no energy storage system connected. ess is the total number of energy storage system nodes, E ess,max is the upper limit of energy storage system capacity, E ess,min It is the lower limit of energy storage system capacity.

5. According to claim 2, a method for optimizing voltage quality and network loss of a distribution network including an energy storage system is characterized in that: The relationship between the lower-level operation model in steps (1-3) includes: Energy storage system charging and discharging power constraints -P essk ≤P essk,t ≤P essk , Energy storage system state of charge (SOC) constraints S SOC,min ≤S soc (t)≤S SOC,max , Energy storage system charging and discharging state transition constraints Where: S SOC,min , S SOC,max are the upper and lower limits of the energy storage system’s state of charge, E ess,t-1 and E ess,t are the capacities of the energy storage system at time t-1 and time t, respectively, P ess,t is the charging and discharging power of the energy storage system at time t, η ch and η dis are the charging and discharging power of the energy storage system, respectively, essk is the lower limit of charge and discharge power, P essk It is the upper limit of charge and discharge power.

6. According to claim 2, a method for optimizing voltage quality and network loss of a distribution network including an energy storage system is characterized in that: Step 2 specifically includes: Step (2-1) configures an upper-level planning function for the upper-level planning model of the energy storage system, where the upper-level planning function includes the investment cost of the energy storage system and the line loss cost; Step (2-2) configures a lower-level operation function for the lower-level operation model of the energy storage system, wherein the lower-level operation function mainly includes minimizing node voltage deviation and minimizing branch losses within the line; Step (2-3) configures the objective function weights mainly by controlling the voltage of the distribution network nodes and supplemented by network losses.

7. The method for optimizing voltage quality and network loss of a distribution network including an energy storage system according to claim 1, characterized in that: The relational expression of the attack strategy defined in step (3-3) is: X i (t+1)=X i (t)+2×ρ×(C-K)+rand×(W i ×K-X i (t)) Where, X i (t+1) is the new position, X i is the commander's position, K is the king's position, W is the i is the ranking weight.

8. A method for optimizing voltage quality and network loss of a distribution network including an energy storage system according to claim 7, characterized in that: The process of calculating and ranking the attack power of soldiers on the battlefield in step (3-4) specifically includes: If the attack power (fitness) of the new position (Fn) is less than the attack power of the previous position (Fp), the soldier occupies the previous position. i The relationship between (t+1) is X i (t+1)=(X i (t+1))×(F n ≥F p )+(X i (t))×(F n <F p ); If the soldier successfully updates his position, the soldier's level R i The relationship is R i =(R i +1)×(F n ≥F p )+(R i )×(F n <F p ); According to the ranking new weight W i The calculation formula is as follows 9. A method for optimizing voltage quality and network loss of a distribution network including an energy storage system according to claim 8, characterized in that: The relationship between the defense strategy defined in step (3-5) is: X i (t+1)=X i (t)+2×ρ×(K-X rand (t))+rand×W i ×(c-X i (t))。

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