STATCOM and ESS optimal configuration method considering network loss sensitivity of power distribution network

Abstract

The invention discloses a STATCOM (Static Synchronous Compensator) and ESS (Electronic Static Security) optimal configuration method considering the network loss sensitivity of a power distribution network, which comprises the following steps of: 1, obtaining the original network loss sensitivity through a method of injecting active / reactive power into a node by network loss to solve a partial derivative; after the result is calculated, the standard deviation of the result is calculated to obtain an improved network loss sensitivity factor, and the larger the network loss sensitivity factor is, the more sensitive the network loss is to the injected active / reactive power of the node, so that the node with the larger sensitive degree is screened out to install the STATCOM and the ESS, and the active / reactive compensation income is maximized. And 2, selecting a minimum total consumption cost and a minimum voltage stability index as an objective function, processing by adopting a linear weighting method, and then determining constraint conditions such as power balance constraint, STATCOM capacity constraint and ESS operation constraint so as to construct an optimization model. And step 3, adopting a multi-objective particle swarm optimization (MOPSO) to solve the constructed model, performing optimization iteration after determining candidate installation nodes of the STATCOM and the ESS, and finally obtaining a result with the minimum consumption cost, the minimum network loss and the minimum voltage deviation.

Description

Technical Field

[0001] The invention belongs to the technical field of power system analysis, operation and optimization. Background Art

[0002] With the proposal of the national "dual carbon" goal and the construction and promotion of new power systems, a large number of new energy sources, microgrids, and electric vehicle charging stations are continuously connected to the distribution network. This makes the operation of the distribution network more complicated, and voltage over-limit, increased load peak-to-valley difference, and low new energy consumption rate have become hot issues. In response to the above problems, energy storage systems (ESS) and static synchronous compensators (STATCOM) and other equipment have been connected to the distribution network in large quantities. Therefore, how to make the most of the newly connected ESS and STATCOM resources is of great research significance. Existing research lacks the use of relevant technical indicators (such as loss sensitivity factor) in the optimization model for the simultaneous coordinated configuration of active and reactive compensation. Therefore, there is still room for further improvement in the selection of compensation capacity and location of ESS and STATCOM equipment. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of distribution network loss; first, the traditional network loss sensitivity parameters are improved and the results are calculated; second, the compensation node priorities of ESS and STATCOM are sorted based on the size of the improved network loss sensitivity factor; finally, a mathematical model with the minimum total economic cost and the minimum static voltage stability index is constructed, and the multi-objective particle swarm algorithm (MOPSO) is used to solve the model. After verification by the simulation results, by improving the compensation position and capacity of ESS and STATCOM in the distribution network, the optimal configuration of active and reactive power can be achieved at the same time, achieving the effect of maximizing the photovoltaic absorption rate, reducing the network loss of the distribution network and reducing the node voltage fluctuation, and greatly reducing the compensation cost.

[0004] The technical solution adopted by the present invention is:

[0005] A method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of distribution network loss, comprising the following steps:

[0006] Step 1: The original network loss sensitivity is obtained by taking the partial derivative of the network loss with respect to the active / reactive power injected into the node. After the result is calculated, the standard deviation is calculated to obtain the improved network loss sensitivity factor. The larger the network loss sensitivity factor is, the more sensitive the network loss is to the active / reactive power injected into the node. In this way, nodes with higher sensitivity are selected to install STATCOM and ESS, so as to maximize the active / reactive compensation benefits.

[0007] Step 2: Select the minimum total consumption cost and voltage stability index as the objective function, and use the linear weighted method to process it. Then determine the constraints such as power balance constraints, STATCOM capacity constraints, ESS operation constraints, etc. to build an optimization model.

[0008] Step 3: The constructed model is solved by using the multi-objective particle swarm optimization algorithm (MOPSO). After the candidate installation nodes of STATCOM and ESS are determined, the optimization iteration is performed to finally obtain the result with the minimum consumption cost, the lowest network loss and the smallest voltage deviation.

[0009] In step 1, the specific expression of the calculation method of the traditional network loss sensitivity is as follows:

[0010]

[0011] Where LSIP and LSIQ represent the sensitivity of system network loss to node active / reactive load respectively; P Loss is the network loss; P i , P j are the active power injected into nodes i and j respectively; Q i , Q j are the reactive power injected into nodes i and j respectively; N is the number of system nodes. ij , β ij The calculation method is as follows:

[0012]

[0013] Where V i ∠δ i 、V j ∠δ j are the voltages at nodes i and j, respectively, and r ij +jx ij =Z ij is the i-row, j-column element of the node impedance matrix.

[0014] The traditional network loss sensitivity is improved, and the specific expression is as follows:

[0015]

[0016] Where, σ_LSIP and σ_LSIQ represent the improved network loss sensitivity factors.

[0017] In step 2, the optimization model constraints mainly include power balance constraints, STATCOM capacity constraints, and ESS operation constraints:

[0018] Power balance constraints:

[0019]

[0020] Where P Gi is the active output of the generator at node i, P E Including ESS active power and PV output power; Q Gi , Q S is the reactive power output of the generator at node i and the reactive power compensation output of STATCOM; G ij , B ij , δ ij is the conductance, susceptance and voltage phase difference between nodes i and j, and N is the number of nodes.

[0021] STATCOM capacity constraints:

[0022] Q s,min ≤Q s,i ≤Q s,max

[0023] Where: Q s,min , Q s,max is the minimum and maximum reactive power output of STATCOM when the system is running in base state, Q s,i Configure the capacity for the STATCOM at the node.

[0024] ESS operation constraints:

[0025]

[0026] Where: S ess , P ess , Q ess Respectively, apparent, active and reactive power; SOC min , SOC max are the minimum and maximum states of charge of the ESS, respectively; SOC0 is the initial state of charge; SOC e SOC is the state of charge at the end moment; t is the charge state of ESS at time t; η c , η d are the charging and discharging efficiency of ESS; α ess,c , α ess,d are the active charge and discharge states of ESS respectively; ess,c , β ess,d They are the reactive charging and discharging states of ESS respectively.

[0027] In step 2, the objective function is to minimize the total consumption cost and the voltage stability index. The specific expression is as follows:

[0028]

[0029] In the formula, λ1 and λ2 are weight coefficients of the objective function, and λ1+λ2=1. F1 is the total compensation cost of ESS and STATCOM, which is composed of economic objectives, including the power loss caused by annual line network loss f1, STATCOM investment cost f2, ESS investment and its operation cost f3, f4 and ESS arbitrage income f5; F2 is the technical goal, including the system voltage stability index L. The specific expression is as follows:

[0030] Power loss caused by annual line active network loss:

[0031]

[0032] Where: P i,j is the line active network loss corresponding to node j in period t, K t is the electricity price during period t, where T is 24 hours.

[0033] STATCOM investment cost:

[0034] f2=0.000375s 2 -0.30415s+162.4

[0035] Where: s is the STATCOM compensation capacity cost.

[0036] ESS investment cost:

[0037]

[0038] Where: C E 、E B It is divided into the capacity and configuration unit price of ESS; γ is the discount rate; Y is the life of ESS.

[0039] ESS operating costs:

[0040]

[0041] Where: C M is the unit price of ESS operation and maintenance cost; S B,t is the apparent power of ESS at time t, Δt=1h.

[0042] Profits from ESS arbitrage:

[0043]

[0044] Where: P C,t , P D,t are the charging and discharging power of ESS at time t; K t The grid electricity price.

[0045]

[0046] Voltage stability factor:

[0047] Where: R ij represents the load participation factor; Represents the voltage phasor of nodes i and j respectively. The stability coefficient represents the distance between the current state of the power system and the limit state of voltage stability, which can be obtained through power flow calculation. The value of the L index varies between [0,1] to determine the system voltage stability margin. The L index value is usually less than 1 (voltage collapse point), and the closer it is to 0 (no-load point), the greater the voltage stability margin.

[0048] In step 3, the process of solving the optimization model by the multi-objective particle swarm algorithm is as follows:

[0049] First, the standard deviation of the network loss sensitivity of each node is calculated, the candidate installation node is selected, and the ESS capacity and STATCOM configuration set are set; then the parameters of MOPSO are initialized and the fitness value of the particle is calculated; then it is determined whether the maximum number of iterations has been reached. If not, the pbest and gbest of the example are updated, and pbest is sampled. Finally, the optimal value of the sampling result is selected as gbest and the result is output.

[0050] The beneficial effects of the STATCOM and ESS optimization configuration method of the present invention taking into account the power distribution network loss sensitivity are as follows:

[0051] 1) Simultaneous active and reactive compensation in the distribution network can significantly reduce voltage deviation and network loss, and the voltage stability is also significantly improved, and the photovoltaic absorption rate is further improved.

[0052] 2) Compared with the configuration scheme that considers all nodes, improving the traditional network loss sensitivity and sorting it to select nodes with greater sensitivity as the compensation positions of STATCOM and ESS can maximize the reduction of network losses, make the voltage curve smoother and more stable, and be more conducive to increasing photovoltaic consumption; in terms of compensation cost, the scheme that takes into account network loss sensitivity is also superior. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the operating method proposed by the present invention;

[0054] Figure 2 This is a flow chart of the MOPSO algorithm proposed in the present invention;

[0055] Figure 3 It is a schematic diagram of the topological structure of a 33-node system of the present invention;

[0056] Figure 4 It is a node network loss sensitivity curve diagram of the present invention;

[0057] Figure 5 It is a graph showing the change of charging and discharging power and state of charge of the energy storage system of the present invention;

[0058] Figure 6 The photovoltaic output and time-of-use electricity price curve of the present invention;

[0059] Figure 7 This is a voltage optimization result diagram of a 33-node distribution network of the present invention; DETAILED DESCRIPTION

[0060] The present invention provides a method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of distribution network loss; first, the traditional network loss sensitivity parameters are improved and the results are calculated; second, the compensation node priorities of ESS and STATCOM are sorted based on the size of the improved network loss sensitivity factor; finally, a mathematical model with the minimum total economic cost and the minimum static voltage stability index is constructed, and the model is solved by a multi-objective particle swarm algorithm (MOPSO). After verification by the simulation results, by improving the compensation position and capacity of ESS and STATCOM in the distribution network, the optimal configuration of active and reactive power can be achieved at the same time, achieving the effect of maximizing the photovoltaic absorption rate, reducing the distribution network loss and reducing the node voltage fluctuation, and greatly reducing the compensation cost.

[0061] Figure 1 The flowchart of the method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of the distribution network loss is shown in FIG. Figure 1 As shown, the method flow is:

[0062] (1) Calculate the network loss sensitivity standard deviation of each node and sort them to select candidate installation nodes for ESS and STATCOM;

[0063] (2) Constructing multi-objective functions through economic and technical indicators;

[0064] (3) Determine system constraints: power balance constraints, STATCOM capacity constraints, and ESS operation constraints;

[0065] (4) Establish a model by combining the objective function with the set of constraints;

[0066] (5) Use MOPSO to solve the target optimization model and output the final result after iterative optimization;

[0067] Implementation Cases

[0068] For a certain distribution network and traffic network, in order to reflect the effectiveness of the method proposed in the present invention, the following four schemes are formulated for comparative analysis. By improving the compensation position and capacity of ESS and STATCOM in the distribution network, the optimal configuration of active and reactive power can be achieved at the same time, so as to maximize the photovoltaic absorption rate, reduce the distribution network loss and reduce the node voltage fluctuation, and greatly reduce the compensation cost. Figure 2 It is the flowchart of MOPSO algorithm. Figure 3 This is a schematic diagram of the topological structure of a 33-node system. Figure 4 is the node network loss sensitivity curve, Figure 5 It is the charging and discharging power and charge state change diagram of the energy storage system. Figure 6 This is the photovoltaic output and time-of-use electricity price curve. Figure 7 This is the voltage optimization result diagram of the 33-node distribution network.

[0069] Solution 1: Only install ESS in the distribution network for active power compensation without considering network loss sensitivity.

[0070] Solution 2: Only install STATCOM in the distribution network for reactive power compensation without considering the sensitivity of network loss.

[0071] Solution 3: All nodes are included in the comprehensive consideration and capacity configuration is performed. ESS regulates active power and STATCOM regulates reactive power.

[0072] Solution 4: Select nodes with high network loss sensitivity as candidate compensation locations, ESS regulates active power, and STATCOM regulates reactive power.

[0073] See also Figure 3 In this embodiment, the IEEE33 node system is used for simulation and analysis. The reference voltage is 12.66kV, the reference power is 100MV·A, and the voltage fluctuation range of each node in the system is 0.95~1.05pu. At the same time, in order to better realize the local consumption of distributed new energy, a 5MW photovoltaic power generation module is connected to the system end node 18. The figure shows the installation position of ESS and STATCOM in scheme 4. See Figure 4 The standard deviation of network loss sensitivity of each node shows that the nodes with the largest σ_LSIP are 32, 20, 19, and 21, indicating that the nodes are more sensitive when injecting active power. The nodes with the largest σ_LSIQ are 29, 28, 32, and 30, and the impact of reactive power injected by the node on node voltage and network loss is greater. The compensation node table of ESS and STATCOM of each scheme is obtained as follows:

[0074] Table 1 ESS and STATCOM compensation nodes of each scheme

[0075]

[0076] Figure 5 The optimized charging and discharging strategies and charge state changes of ESS at various times within 24 hours are given. It can be seen that ESS absorbs active power during the time periods of 00:00-08:00 and 12:00-18:00, and releases active power when the load is high at noon and in the evening, achieving the effect of peak shaving and valley filling. Figure 5 As shown in the figure, it can be seen that the photovoltaic system generates active power from 6:00 to 17:00. Figure 5 and Figure 6 It can be seen that, except for the high load at noon and in the evening when the energy storage system needs to release active power, the rest of the time is absorbing active power to consume photovoltaic power. And through the time-of-use electricity price, the ESS charging and discharging benefits are maximized, thereby reducing the ESS compensation cost.

[0077] See also Figure 7 From the comparison of the voltages of each node under the four schemes, it can be seen that when all installable nodes are considered, although active / reactive compensation alone cannot meet the needs of the distribution network, the node voltage can be more stable than when not installed; and when the network loss sensitivity is considered and nodes with high sensitivity are used as installation nodes, the system voltage stability index of scheme four is 0.13, and that of scheme three is 0.18. Therefore, it is obvious that the system voltage of scheme four is more stable, which can also be verified by the fact that the node voltage curve of scheme four is smoother than that of scheme three; therefore, in summary, the configuration strategy of scheme four is obviously more outstanding in voltage stability and economy.

[0078] The optimal solutions obtained by MOPSO for the four schemes are shown in Table 4. The results show that the reasonable optimization of the compensation position and capacity of STATCOM and ESS can not only reduce the system network loss, but also improve the system voltage stability. First of all, both schemes 1 and 2 perform active / reactive compensation separately. Although the total cost of ESS and STATCOM is lower than that of schemes 3 and 4, the voltage still fluctuates greatly, and the active network loss is slightly reduced. In the long run, its power grid loss will be greater. Scheme 3 considers all nodes and finally installs STATCOM on nodes 5, 12, and 19, and ESS on node 8; while Scheme 4 selects STATCOM compensation nodes 19, 21, and 32 and ESS compensation node 28 in consideration of network loss sensitivity; Table 4 shows that Scheme 4 reduces reactive compensation by 0.51MW·h and energy storage capacity by 0.188Mvar compared with Scheme 3, which directly reduces the compensation cost of Scheme 4 by 19% compared with Scheme 3; at the same time, Scheme 4 reduces active network loss by 24% compared with no compensation; in contrast, Scheme 3 only reduces it by 14%, and the ESS arbitrage income of Scheme 4 also increases by 18% compared with Scheme 3; in summary, Scheme 4 has the best compensation effect in consideration of network loss sensitivity factor.

[0079] Table 4 ESS and STATCOM parameters under four schemes

[0080]

[0081] The beneficial effects of the optimized configuration of STATCOM and ESS compensation of the distribution network taking into account the network loss sensitivity of the embodiment of the present invention are: the simultaneous active and reactive compensation in the distribution network can greatly reduce the voltage deviation and network loss, and the voltage stability is also significantly improved, and the photovoltaic consumption rate is further improved. And compared with the configuration scheme considering all nodes, improving the traditional network loss sensitivity and sorting it to select nodes with greater sensitivity as the STATCOM and ESS compensation positions can maximize the reduction of network losses, the voltage curve is smoother and more stable, and it is more conducive to increasing photovoltaic consumption; in terms of compensation cost, the scheme taking into account the network loss sensitivity is also more superior.

[0082] This implementation case is only a preferred specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of distribution network loss, characterized in that: The following steps are involved: Step 1: The original network loss sensitivity is obtained by taking the partial derivative of the network loss with respect to the active / reactive power injected into the node. After the result is calculated, the standard deviation is calculated to obtain the improved network loss sensitivity factor. The larger the network loss sensitivity factor is, the more sensitive the network loss is to the active / reactive power injected into the node. In this way, nodes with higher sensitivity are selected to install STATCOM and ESS, so as to maximize the active / reactive compensation benefits. Step 2: Select the minimum total consumption cost and voltage stability index as the objective function, and use the linear weighted method to process it. Then determine the constraints such as power balance constraints, STATCOM capacity constraints, ESS operation constraints, etc. to build an optimization model. Step 3: The constructed model is solved by using the multi-objective particle swarm optimization algorithm (MOPSO). After the candidate installation nodes of STATCOM and ESS are determined, the optimization iteration is performed to finally obtain the result with the minimum consumption cost, the lowest network loss and the smallest voltage deviation.

2. According to claim 1, a method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of distribution network loss, characterized in that: The implementation of step 1 is divided into the following two steps: Step 1.1: Calculate the traditional network loss sensitivity. The specific expression is as follows: In the formula, LSIP and LSIQ represent the sensitivity of system network loss to node active / reactive load respectively; P Loss is the network loss; P i , P j are the active power injected into nodes i and j respectively; Q i , Q j are the reactive power injected into nodes i and j respectively; N is the number of system nodes. ij , β ij The calculation method is as follows: Where V i ∠δ i 、V j ∠δ j are the voltages at nodes i and j, respectively, and r ij +jx ij =Z ij is the i-row, j-column element of the node impedance matrix. Step 1.2: Improve the traditional network loss sensitivity. The specific expression is as follows:

3. The method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of distribution network loss according to claim 1, characterized in that: The model constraints described in step 2 mainly include power balance constraints, STATCOM capacity constraints, and ESS operation constraints; Power balance constraints: Where P Gi is the active output of the generator at node i, P E Including ESS active power and PV output power; Q Gi , Q S is the reactive power output of the generator at node i and the reactive power compensation output of STATCOM; G ij , B ij , δ ij is the conductance, susceptance and voltage phase difference between nodes i and j, and N is the number of nodes. STATCOM capacity constraints: Q s,min ≤Q s,i ≤Q s,max Where: Q s,min , Q s,max is the minimum and maximum reactive power output of STATCOM when the system is running in base state, Q s,i Configure the capacity for the STATCOM at the node. ESS operation constraints: Where: S ess , P ess , Q ess Respectively, apparent, active and reactive power; SOC min , SOC max are the minimum and maximum states of charge of the ESS, respectively; SOC0 is the initial state of charge; SOC e SOC is the state of charge at the end moment; t is the charge state of ESS at time t; η c , η d are the charging and discharging efficiency of ESS; α ess,c , α ess,d They are respectively the active charge and discharge states of ESS; β ess,c , β ess,d They are the reactive charging and discharging states of ESS respectively.

4. The method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of distribution network loss according to claim 1, characterized in that: In step 2, the objective function is to minimize the total consumption cost and the voltage stability index. The specific expression is as follows: In the formula, λ1 and λ2 are weight coefficients of the objective function, and λ1+λ2=1. F1 is the total cost of ESS and STATCOM compensation, which consists of economic objectives, including the power loss caused by annual line network loss f1, STATCOM investment cost f2, ESS investment and its operating costs f3, f4 and ESS arbitrage income f5; F2 is the technical objective, including the system voltage stability index L. The specific expression is as follows: Power loss caused by annual line active network loss: Where: P i,j is the line active network loss corresponding to node j in period t, K t is the electricity price during period t, where T is 24 hours. STATCOM investment cost: <h2 style=";text-align:left;direction:ltr">f2 = 0.000375s<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -0.30415s+162.4 Where: s is the STATCOM compensation capacity cost. ESS investment cost: Where: C E 、E B It is divided into the capacity and configuration unit price of ESS; γ is the discount rate; Y is the life of ESS. ESS operating costs: Where: C M is the unit price of ESS operation and maintenance cost; S B,t is the apparent power of ESS at time t, Δt=1h. Profits from ESS arbitrage: Where: P C,t , P D,t are the charging and discharging power of ESS at time t; K t The grid electricity price. Voltage stability factor: Where: R ij represents the load participation factor; Represents the voltage phasor of nodes i and j respectively. The stability coefficient represents the distance between the current state of the power system and the limit state of voltage stability, which can be obtained through power flow calculation. The value of the L index varies between [0,1] to determine the system voltage stability margin. The L index value is usually less than 1 (voltage collapse point), and the closer it is to 0 (no-load point), the greater the voltage stability margin.

5. The method for optimizing the configuration of STATCOM and ESS taking into account the sensitivity of distribution network loss according to claim 1, characterized in that: In step 3, the process of solving the optimization model by the multi-objective particle swarm algorithm is as follows: First, the standard deviation of the network loss sensitivity of each node is calculated, the candidate installation node is selected, and the ESS capacity and STATCOM configuration set are set; then the parameters of MOPSO are initialized and the fitness value of the particle is calculated; then it is determined whether the maximum number of iterations has been reached. If not, the pbest and gbest of the example are updated, and pbest is sampled. Finally, the optimal value of the sampling result is selected as gbest and the result is output.

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