Networking architecture optimization method based on demand response optimal pricing model

Abstract

The invention discloses a networking architecture optimization method based on a demand response optimal pricing model, belongs to the technical field of power system design, and solves the problem that unnecessary transformation cost may be increased in order to reduce existing network loss. The method is based on power consumption, electricity price, power distribution network operation parameters and the like of a regional power grid; constructing a target function by minimizing economic loss caused by network loss; optimizing the electricity prices at different time stages; solving the model based on an improved particle swarm algorithm; and judging whether the optimized network loss reaches the standard or not according to a solving result, and further reducing the network loss by changing a networking structure if the optimized network loss does not reach the standard. The demand response power utilization pricing strategy can directly influence the power utilization behavior and the power consumption of the user, and the change of the user behavior can reduce the network loss, so that the investment transformation cost can be saved.

Description

Technical Field

[0001] The present invention relates to a networking architecture optimization method, in particular to a networking architecture optimization method based on a demand response optimal pricing model, and belongs to the technical field of power systems. Background Art

[0002] Demand response refers to when the electricity market price rises or the system reliability is threatened, the electricity user receives the direct compensation notice of induced load reduction or the electricity price adjustment signal from the power supplier, and changes its inherent habitual electricity consumption mode to reduce or shift the electricity load in a certain period of time to respond to the power supply, thereby ensuring the stability of the power grid. The pricing strategy of demand response will directly affect the user's electricity consumption behavior and electricity consumption. Through a reasonable pricing strategy, users can be guided to reduce electricity consumption or shift electricity consumption time during peak hours, thereby balancing the supply and demand relationship and reducing the pressure on the power system.

[0003] At present, many studies are mainly based on power flow calculation to see whether the flow exceeds the limit. If it exceeds the limit, the power grid architecture needs to be upgraded, or based on network loss calculation. When the network loss is large, the power grid architecture also needs to be upgraded. Although reliability and economy are guaranteed, the transformation investment cost is often large. At the same time, considering that the demand response electricity pricing strategy will directly affect the user's electricity consumption behavior and power consumption, it is equivalent to changing the power distribution in the network and thus changing the size of the network loss. Therefore, the present invention proposes a networking architecture optimization method based on the demand response optimal pricing model, with the minimum economic loss of network loss as the goal, and the safe operation of the power grid as the constraint condition, combining economy and reliability, and proposing the optimal pricing of demand response to ensure the minimum economic loss of network loss. According to the optimized network loss results, it is further proposed whether the power grid architecture needs to be changed, which effectively saves the economic loss of network loss and reduces the investment cost. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a networking architecture optimization method based on a demand response optimal pricing model, which is used to solve the technical problem that the existing power grid operation calculation results will increase unnecessary construction costs.

[0005] To achieve the above object, the present invention provides a method for optimizing a network architecture based on a demand response optimal pricing model, comprising the following steps:

[0006] Step 1: Construct an objective function. From an economic perspective, the objective function is to minimize the economic loss caused by network loss.

[0007] Step 2: Optimize the demand response peak, normal and valley electricity prices in the objective function by building an optimal demand response pricing model and constraining the electricity consumption and total electricity purchase cost before and after the demand response pricing optimization; the optimal pricing in the model includes peak, normal and valley electricity prices;

[0008] Step 3: Improve the particle swarm algorithm to solve the optimal peak, flat and valley electricity prices for demand response;

[0009] Step 4: Analyze the network loss optimization results, obtain the network loss under different time-of-use electricity prices, use the objective function formula to obtain the minimum network loss economic loss, and determine whether the optimized results meet local needs. If so, output the results; if not, further reduce the network loss by changing the networking architecture until local needs are met.

[0010] Preferably, the goal of the network loss optimization is to minimize the economic loss caused by network loss. Therefore, the network loss in different time periods is multiplied by the electricity price in each time period to obtain the lost electricity fee. Minimizing this value is the optimization goal. The objective function used is:

[0011]

[0012] In the formula, p p is the peak electricity price in the demand response optimal pricing model, p s is the normal electricity price, p v The off-peak electricity price; is the network loss under different time-of-use electricity prices, and the formula is as follows:

[0013]

[0014] G ij is the conductivity of the distribution line ij, U i , U j Corresponding to the voltage amplitude of nodes i and j, θ i,j is the voltage phase angle difference between nodes i and j.

[0015] Preferably, the demand response optimal pricing model minC formula is as follows:

[0016]

[0017]

[0018] T p +T s +T v =24

[0019] In the formula, p t is the price of electricity purchased by demand response in period t, Eit is the power consumption of the i-th load in period t; T p is the peak period, T s For normal period, T v It is the valley period;

[0020] The constraints of the optimal pricing model for demand response include:

[0021] A. Power Consumption Constraints

[0022] In the demand response optimal pricing model, the total electricity consumption before and after price optimization is equal;

[0023] ΔE it =0

[0024] Among them, ΔE it It represents the difference between total electricity consumption before and after price optimization;

[0025] B. Power purchase cost constraints

[0026] The electricity purchase cost paid after demand response optimization is no higher than the electricity purchase cost before optimization;

[0027] C≤C'

[0028] Among them, C represents the electricity purchase cost paid after demand response optimization, and C' represents the electricity purchase cost before optimization;

[0029] C. Electricity price constraints

[0030] Peak electricity prices, normal electricity prices and off-peak electricity prices should not be higher than the electricity prices set by the market;

[0031] p t ≤p tmax

[0032] Among them, p t represents the peak electricity price, normal electricity price and valley electricity price, p tmax Indicates the electricity price set by the market.

[0033] D. Voltage Constraint

[0034] The voltage at the load point is within the allowable range;

[0035] V j,in ≤V j ≤V j,max

[0036] Among them, V j is the voltage at the load point, V j,in is the minimum voltage allowed at the load point, V j,max The maximum voltage allowed at the load point.

[0037] E. Power Balance Constraints

[0038] The power balance at the load point is an equality constraint;

[0039] P j +Q j =V j |I j | * ,j=1,2,3,…,N

[0040] Where P j , Q j is the active and reactive power at the load point, V j is the voltage at the load point, I j is the current flowing into the load point.

[0041] Preferably, the velocity update formula of particles in the improved particle swarm algorithm is expressed as:

[0042]

[0043] in, represents the velocity of the ith particle in the dth dimension at the kth iteration, represents the position of the ith particle in the dth dimension in the kth iteration, w represents the inertia coefficient, c1 and c2 are acceleration factors, and they are not equal, r1 and r2 represent random numbers between 0 and 1, and p id represents the position of the dth dimension in the optimal historical position of the i-th particle, p gd Represents the position on the dth dimension in the historical optimal position of the group.

[0044] Preferably, the network loss is further reduced by changing the networking architecture, and the changes in the networking architecture include: distribution lines, distribution transformers, and reactive power compensation; among them, the distribution lines are specifically: replacing aging lines, adopting a ring power supply method, and reasonably connecting to distributed photovoltaics; the distribution transformers are specifically: replacing severely aged transformers, using low-loss distribution transformers, and adjusting transformer taps; the reactive power compensation is specifically: centralized compensation on the low-voltage side of the distribution transformer, reactive power compensation on the poles, and decentralized compensation for user terminals.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The network architecture optimization method proposed in the present invention takes the minimum economic loss of network loss as the goal and the safe operation of the power grid as the constraint condition, combines economy and reliability, proposes the optimal pricing of demand response to ensure the minimum economic loss of network loss, and further proposes whether the power grid architecture needs to be changed according to the optimized network loss results, effectively saving the economic loss of network loss and reducing investment costs;

[0047] 2. Changing user behavior through demand response pricing can guide users to reduce non-critical loads or shift electricity usage time through electricity prices during periods of tight electricity supply or peak demand, thereby avoiding power shortages, improving the flexibility of grid operation, and helping to improve the efficiency and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 The figure is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0051] Step 1: Construct the objective function. From an economic perspective, the objective function is to minimize the economic loss caused by network loss.

[0052] The goal of network loss optimization is to minimize the economic loss caused by network loss. Therefore, the network loss in different time periods is multiplied by the electricity price in each time period to obtain the lost electricity fee. Minimizing this value is the optimization goal. The objective function used is:

[0053]

[0054] In the formula, p p is the peak electricity price in the demand response optimal pricing model, p s is the normal electricity price, p v The off-peak electricity price; is the network loss under different time-of-use electricity prices, and the formula is as follows:

[0055]

[0056] G ij is the conductivity of the distribution line ij, U i , U j Corresponding to the voltage amplitude of nodes i and j, θ i,j is the voltage phase angle difference between nodes i and j.

[0057] Step 2: Optimize the demand response peak, normal and valley electricity prices in the objective function by building an optimal demand response pricing model and constraining the electricity consumption and total electricity purchase cost before and after the demand response pricing optimization; the optimal pricing in the model includes peak, normal and valley electricity prices.

[0058] The demand response optimal pricing model minC formula is as follows:

[0059]

[0060] T p +T s +T v =24

[0061] In the formula, p t is the price of electricity purchased by demand response in period t, E it is the power consumption of the i-th load in period t; T p is the peak period, T s For normal period, T v It is the valley period;

[0062] The constraints of the optimal pricing model for demand response include:

[0063] A. Power Consumption Constraints

[0064] In the demand response optimal pricing model, the total electricity consumption before and after price optimization is equal;

[0065] ΔE it =0

[0066] Among them, ΔE it It represents the difference between total electricity consumption before and after price optimization;

[0067] B. Power purchase cost constraints

[0068] The electricity purchase cost paid after demand response optimization is no higher than the electricity purchase cost before optimization;

[0069] C≤C'

[0070] Among them, C represents the electricity purchase cost paid after demand response optimization, and C' represents the electricity purchase cost before optimization;

[0071] C. Electricity price constraints

[0072] Peak electricity prices, normal electricity prices and off-peak electricity prices should not be higher than the electricity prices set by the market;

[0073] p t ≤p tmax

[0074] Among them, p trepresents the peak electricity price, normal electricity price and valley electricity price, p tmax Indicates the electricity price set by the market.

[0075] D. Voltage Constraint

[0076] The voltage at the load point is within the allowable range;

[0077] V j,in ≤V j ≤V j,max

[0078] Among them, V j is the voltage at the load point, V j,in is the minimum voltage allowed at the load point, V j,max The maximum voltage allowed at the load point.

[0079] E. Power Balance Constraints

[0080] The power balance at the load point is an equality constraint;

[0081] P j +Q j =V j |I j | * ,j=1,2,3,…,N

[0082] Where P j , Q j is the active and reactive power at the load point, V j is the voltage at the load point, I j is the current flowing into the load point.

[0083] Step 3: Improve the particle swarm algorithm to solve the optimal peak, flat and valley electricity prices for demand response.

[0084] The velocity update formula of particles in the improved particle swarm algorithm is expressed as:

[0085]

[0086]

[0087] in, represents the velocity of the ith particle in the dth dimension at the kth iteration, represents the position of the ith particle in the dth dimension in the kth iteration, w represents the inertia coefficient, c1 and c2 are acceleration factors, and they are not equal, r1 and r2 represent random numbers between 0 and 1, and p id represents the position of the dth dimension in the optimal historical position of the i-th particle, p gd Represents the position on the dth dimension in the historical optimal position of the group.

[0088] Step 4: Analyze the network loss optimization results, obtain the network loss under different time-of-use electricity prices, use the objective function formula to obtain the minimum network loss economic loss, and determine whether the optimized results meet local needs. If so, output the results; if not, further reduce the network loss by changing the networking architecture until local needs are met.

[0089] The network loss can be further reduced by changing the network architecture. The changes in the network architecture include three aspects: distribution lines, distribution transformers, and reactive power compensation. Among them, the distribution lines are specifically: replacing aging lines, adopting a ring power supply method, and reasonably connecting to distributed photovoltaics; the distribution transformers are specifically: replacing severely aged transformers, using low-loss distribution transformers, and adjusting transformer taps; the reactive power compensation is specifically: centralized compensation on the low-voltage side of the distribution transformer, reactive power compensation on the poles, and decentralized compensation for user terminals.

[0090] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A network architecture optimization method based on a demand response optimal pricing model, characterized in that: The architecture optimization method comprises the following steps: Step 1: Construct an objective function. From an economic perspective, the objective function is to minimize the economic loss caused by network loss. Step 2: Optimize the demand response peak, normal and valley electricity prices in the objective function by building an optimal demand response pricing model and constraining the electricity consumption and total electricity purchase cost before and after the demand response pricing optimization; the optimal pricing in the model includes peak, normal and valley electricity prices; Step 3: Improve the particle swarm algorithm to solve the optimal peak, flat and valley electricity prices for demand response; Step 4: Analyze the network loss optimization results, obtain the network loss under different time-of-use electricity prices, use the objective function formula to obtain the minimum network loss economic loss, and determine whether the optimized results meet local needs. If so, output the results; if not, further reduce the network loss by changing the networking architecture until local needs are met.

2. The method for optimizing the network architecture according to claim 1, characterized in that: In step 1, the goal of network loss optimization is to minimize the economic loss caused by network loss. Therefore, the network loss in different time periods is multiplied by the electricity price in each time period to obtain the lost electricity fee. Minimizing this value is the optimization goal. The objective function used is: In the formula, p p is the peak electricity price in the demand response optimal pricing model, p s is the normal electricity price, p v The off-peak electricity price; is the network loss under different time-of-use electricity prices, and the formula is as follows: G ij is the conductivity of the distribution line ij, U i , U j Corresponding to the voltage amplitude of nodes i and j, θ i,j is the voltage phase angle difference between nodes i and j.

3. The method for optimizing the network architecture according to claim 1, characterized in that: In step 2, the formula of the optimal pricing model minC for demand response is as follows: T p +T s +T v =24 In the formula, p t is the price of electricity purchased by demand response in period t, E it is the power consumption of the i-th load in period t; T p is the peak period, T s For normal period, T v It is the valley period.

4. The demand response optimal pricing model according to claim 3, characterized in that: The constraints include: A. Power Consumption Constraints In the demand response optimal pricing model, the total electricity consumption before and after price optimization is equal; ΔE it =0 Where, ΔE it It represents the difference between total electricity consumption before and after price optimization; B. Power purchase cost constraints The electricity purchase cost paid after demand response optimization is no higher than the electricity purchase cost before optimization; C≤C' Among them, C represents the electricity purchase cost paid after demand response optimization, and C' represents the electricity purchase cost before optimization; C. Electricity price constraints Peak electricity prices, normal electricity prices and off-peak electricity prices should not be higher than the electricity prices set by the market; p t ≤p tmax Among them, p t represents the peak electricity price, normal electricity price and valley electricity price, p tmax Indicates the electricity price set by the market. D. Voltage Constraint The voltage at the load point is within the allowable range; In j,in ≤V j ≤V j,max Among them, V j is the voltage at the load point, V j,in is the minimum voltage allowed at the load point, V j,max The maximum voltage allowed at the load point. E. Power Balance Constraints The power balance at the load point is an equality constraint; P j +Q j =V j |I j | * ,j=1,2,3,…,N Where P j , Q j is the active and reactive power at the load point, V j is the voltage at the load point, I j is the current flowing into the load point.

5. The method for optimizing the network architecture according to claim 1, characterized in that: In the step 3, the improved particle swarm algorithm is used to solve the established optimization model, and an asymmetric acceleration factor is used to replace the acceleration factor in the original particle swarm algorithm; Therefore, the velocity update formula of particles in the improved particle swarm algorithm is expressed as: in, represents the velocity of the ith particle in the dth dimension at the kth iteration, represents the position of the ith particle in the dth dimension in the kth iteration, w represents the inertia coefficient, c1 and c2 are acceleration factors, and they are not equal, r1 and r2 represent random numbers between 0 and 1, and p id represents the position of the dth dimension in the optimal historical position of the i-th particle, p gd Represents the position on the dth dimension in the historical optimal position of the group.

6. The method for optimizing the network architecture according to claim 1, characterized in that: In the step 4, the network loss is further reduced by changing the network architecture, and the changes in the network architecture include: distribution lines, distribution transformers, and reactive power compensation; among them, the distribution lines are specifically: replacing aging lines, adopting a ring power supply method, and reasonably connecting to distributed photovoltaics; the distribution transformers are specifically: replacing severely aged transformers, using low-loss distribution transformers, and adjusting transformer taps; the reactive power compensation is specifically: centralized compensation on the low-voltage side of the distribution transformer, reactive power compensation on the poles, and decentralized compensation for user terminals.

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