Power distribution network project cost method considering cross-regional power purchase

Abstract

The invention discloses a power distribution network project cost method considering cross-regional power purchase. The method comprises the steps of obtaining power data of a target region and a plurality of adjacent regions adjacent to the target region; based on the power data, a power distribution network project cost model considering cross-regional power purchase is constructed, and the power distribution network cost model takes the lowest total cost of a target region and a neighbor region as a target. Adjusting by taking a neighbor area on-grid electricity price range constraint, a target area landing electricity price range constraint and an available maximum power value constraint of the target area and each neighbor area as constraints, and taking an electricity purchasing scheme of the power distribution network as a decision variable; and solving the power distribution network engineering cost model to obtain an optimal power purchase scheme of the power distribution network. According to the method, both economical efficiency and resource allocation efficiency are considered, and by constructing the accurate power distribution network cost model, on the basis of comprehensively considering various constraint conditions, the total cost can be reduced, and the electric charge burden of a user can be relieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution networks, and in particular relates to a power distribution network engineering cost method taking into account cross-regional power purchase. Background Art

[0002] With the rapid development of renewable energy and the continuous reform of the power market, the cost optimization problem of power distribution network has become more and more prominent. Traditional distribution network cost analysis methods often ignore inter-regional power transactions and market price fluctuations, resulting in low distribution network operation efficiency and waste of power resources. Therefore, it is particularly important to develop a new distribution network cost assembly database system that can achieve cross-regional power purchase optimization.

[0003] The system aims to comprehensively improve the economic efficiency of the power distribution network through three modules: data acquisition, model construction, and analysis and processing. The data acquisition module is responsible for collecting power data in the target area and its neighboring areas, including the market cost of thermal power and hydropower, transaction volume, etc., to form basic data support. Based on the acquired data, the data model construction module constructs a distribution network cost model with the lowest total cost as the goal. The model comprehensively considers the regional electricity price constraints and the available power value restrictions, and achieves the optimal configuration of the power purchase strategy by optimizing the decision variables. Finally, the data analysis and processing module provides scientific power purchase strategy recommendations for distribution network operations by solving the model, helping to maximize economic benefits.

[0004] In addition, as competition in the electricity market intensifies and price volatility increases, the system ensures the flexibility and adaptability of power procurement decisions by dynamically monitoring and adjusting the electricity price range. This not only optimizes the allocation of power resources, but also enhances the overall economy and security of the power grid. Therefore, building such a new distribution network cost assembly database system has important practical significance and application value for promoting the healthy development of the power market and improving the efficiency of power services. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention proposes a distribution network engineering cost method taking into account cross-regional power purchase.

[0006] The technical solution of the present invention is as follows:

[0007] A method for cost estimation of distribution network engineering considering cross-regional power purchase, comprising:

[0008] Acquire power data of a target area and several neighboring areas adjacent to the target area;

[0009] Based on the power data, a distribution network engineering cost model considering cross-regional power purchase is constructed, wherein the distribution network cost model aims to minimize the total cost of the target area and the neighboring areas, and is constrained by the on-grid power price range constraints of the neighboring areas, the on-site power price range constraints of the target area, and the maximum power value constraints of the target area and each neighboring area, and the power purchase plan of the distribution network is used as a decision variable;

[0010] The distribution network engineering cost model is solved to obtain the optimal power purchasing plan for the distribution network.

[0011] Furthermore, the electricity data includes the total construction cost of the thermal power market, the trading electricity of thermal power plants, the total construction cost of the hydropower market, the trading electricity of the hydropower market, the cost of other power generation markets, i.e. the cost of new energy power generation markets, and the trading electricity of other power markets, i.e. the trading electricity of new energy power generation markets.

[0012] Furthermore, the objectives of the distribution network cost model are:

[0013]

[0014] Where P li is the on-grid electricity price of neighboring region i; Q 外i is the amount of electricity purchased from neighboring area i; P h is the on-site electricity price in the target area; Q 内 is the amount of electricity purchased from the target area; i is the serial number of the neighboring area, i∈[1,n], and n is the total number of neighboring areas.

[0015] Furthermore, the expression of the grid-connected electricity price range constraint of the neighboring area is:

[0016] P lmin,i ≤P li ≤P lmax,i

[0017] Where P lmin,i is the minimum on-grid electricity price of neighboring area i; P lmax,i is the maximum on-grid electricity price of the neighboring area i; the expression of the on-grid electricity price range constraint of the target area is:

[0018] P hmin ≤P h ≤P hmax

[0019] Where P hmin is the minimum landed electricity price in the target area; P hmax The maximum landed electricity price in the target area;

[0020] Furthermore, the minimum landed electricity price P in the target area hminand the maximum electricity price in the target area P hmax The method to obtain is as follows:

[0021]

[0022] Where P h is the electricity price in the target area; P p represents the base electricity price of the target area p; Q p represents the available power value of the target area p; s is the set of neighboring areas adjacent to the target area.

[0023] Furthermore, the minimum total cost target minP of the distribution network cost model satisfies the following formula:

[0024] C H内 +C S内 +C O内 +C H外 +C S外 +C O外 ≤minP

[0025] Where minP is the minimum target of total construction cost; P is the total construction cost; C H内 is the total cost of thermal power market in the target area; C S内 is the total cost of hydropower market in the target area; C O内 is the cost of other electricity markets in the target area; C H外 is the total cost of thermal power market in the neighboring region; C S外 is the total cost of hydropower market in the neighboring region; C O外 is the market cost of other power plants in the neighboring area.

[0026] Furthermore, the expression for the maximum power value constraint available in the target area is as follows:

[0027]

[0028] In the formula, is the availability factor of thermal power units; is the availability factor of the hydropower plant; is the new energy availability coefficient; P 内max is the maximum power available in the target area; P H内,max P is the maximum power value of the thermal power units in the target area in this cycle; S内,max P is the maximum power value that the hydropower plant in the target area can provide in this cycle; O内,max is the maximum power value of the new energy available in the target area in this cycle;

[0029] Furthermore, the expression for the maximum power value constraint of the neighboring area is as follows:

[0030]

[0031] In the formula, is the availability factor of thermal power units in the neighboring area; is the availability factor of hydropower plants in the neighboring area; is the available coefficient of new energy in the neighboring area; P 外max is the maximum power available in the neighboring area; P H外,max P is the maximum power value of the thermal power units in the neighboring area in this cycle; S外,max P is the maximum power value that the hydropower plant in the neighboring area can provide in this cycle; O外,max is the maximum power value of renewable energy available in the neighboring area in this period;

[0032] Furthermore, the method for obtaining the maximum available power value of the target area and each neighboring area is as follows:

[0033] Obtain the total power loss and daily average power loss of the target area and each neighboring area in the previous cycle, and predict the total power loss and daily power loss of the target area and each neighboring area in the current cycle;

[0034] Based on the predicted total power loss and daily power loss of the target area and each neighboring area in this period, the maximum power available from the hydropower plant, the maximum power available from the thermal power unit and the maximum power available from new energy in the target area and each neighboring area in this period are analyzed, and the maximum power available for the target area and each neighboring area is further obtained.

[0035] Furthermore, the specific calculation formula of the total power consumption is as follows:

[0036] The total power consumption during this period is Q m-1 =Total power supply Q in the target area of ​​this cycle m-1 +Total electricity purchased in this period Q m-1 ;

[0037] The total power consumption during this period is Q m =Total power supply Q in the target area of ​​this cycle m +Total electricity purchased in this period Q m .

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention proposes a distribution network engineering cost method that takes into account cross-regional power purchases. The method takes into account both economy and resource allocation efficiency. By constructing an accurate distribution network cost model, it can reduce the overall cost and alleviate the user's electricity bill burden on the basis of comprehensive consideration of multiple constraints. At the same time, it has the ability to monitor changes in the power market in real time, can flexibly respond to market fluctuations, and enhance the adaptability and flexibility of the distribution network in competition. In addition, it also effectively supports the development of renewable energy, promotes the optimization of the power structure and promotes the use of green energy by rationally integrating new energy and traditional energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a method for cost estimation of a distribution network project taking into account cross-regional power purchase in an embodiment. DETAILED DESCRIPTION

[0041] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0042] Embodiment 1:

[0043] A distribution network engineering cost method considering cross-regional power purchase is provided in the present invention. Figure 1 As shown, including:

[0044] S1, obtaining power data of a target area and several neighboring areas adjacent to the target area;

[0045] S2. Based on power data, a distribution network engineering cost model considering cross-regional power purchase is constructed. The distribution network cost model aims to minimize the total cost of the target area and the neighboring areas, and is constrained by the on-grid electricity price range constraints of the neighboring areas, the on-site electricity price range constraints of the target area, and the maximum power value constraints of the target area and each neighboring area, and the power purchase plan of the distribution network is used as the decision variable;

[0046] S3. Solve the distribution network engineering cost model to obtain the optimal power purchasing plan for the distribution network.

[0047] Embodiment 2:

[0048] This embodiment is further designed on the basis of the first embodiment in that the power data in this embodiment includes the total cost of the thermal power market, the amount of electricity traded by the thermal power plant, the total cost of the hydropower market, the amount of electricity traded by the hydropower market, the cost of other power generation markets, i.e., the cost of the new energy power generation market, and the amount of electricity traded by other power markets, i.e., the amount of electricity traded by the new energy power generation market. The total cost of the thermal power and hydropower markets includes fuel costs, equipment maintenance fees, depreciation fees, etc. The amount of electricity traded by the thermal power and hydropower plants includes water resource fees, equipment maintenance fees, and depreciation fees; the maximum power available includes the maximum power available by the hydropower plant this month, the maximum power available by the thermal power unit, and the maximum power available by the new energy.

[0049] Embodiment three:

[0050] This embodiment is further designed on the basis of the first embodiment in that the objectives of the distribution network cost model in this embodiment are:

[0051]

[0052] Where P li is the on-grid electricity price of neighboring region i; Q 外i is the amount of electricity purchased from neighboring area i; P h is the on-site electricity price in the target area; Q 内 is the amount of electricity purchased from the target area; i is the serial number of the neighboring area, i∈[1,n], and n is the total number of neighboring areas.

[0053] Embodiment 4:

[0054] This embodiment is further designed on the basis of the first embodiment in that the expression of the grid-connected electricity price range constraint of the neighboring area in this embodiment is:

[0055] P lmin,i ≤P li ≤P lmax,i

[0056] Where P lmin,i is the minimum on-grid electricity price of neighboring area i; P lmax,i is the maximum on-grid electricity price of neighboring area i;

[0057] The expression of the target area grid-connected electricity price range constraint is:

[0058] P hmin ≤P h ≤P hmax

[0059] Where P hmin is the minimum landed electricity price in the target area; P hmax The maximum landed electricity price in the target area;

[0060] The expression for the maximum power value constraint of the target area and each neighboring area is:

[0061] Embodiment five:

[0062] This embodiment is further designed on the basis of the fourth embodiment in that the minimum landed electricity price P in the target area hmin and the maximum electricity price in the target area P hmax The method to obtain is as follows:

[0063]

[0064] Where P h is the electricity price in the target area; P p represents the base electricity price of the target area p; Q p represents the available power value of the target area p; s is the set of neighboring areas adjacent to the target area.

[0065] Embodiment six:

[0066] This embodiment is further designed on the basis of the fourth embodiment in that the minimum target minP of the total cost of the distribution network cost model in this example satisfies the following formula:

[0067] C H内 +C S内 +C O内 +C H外 +C S外 +C O外 ≤minP

[0068] Where minP is the minimum target of total construction cost; P is the total construction cost; C H内 is the total cost of the thermal power market in the target area, including fuel cost, equipment maintenance fee, depreciation fee, etc.; H is the transaction power of the thermal power plant in the target area; C S内 is the total cost of the hydropower market in the target area, including water resource fees, equipment maintenance fees, depreciation fees, etc.; S is the amount of electricity traded in the hydropower market within the province; C O内 is the cost of other power markets in the target area, such as renewable energy power generation; O is the amount of electricity traded in other power markets in the province; C H外 is the total cost of the thermal power market in the neighboring region, including fuel cost, equipment maintenance fee, depreciation fee, etc.; H is the traded electricity of thermal power plants outside the province; C S外 is the total cost of the hydropower market in the neighboring region, including water resource fees, equipment maintenance fees, depreciation fees, etc.; S is the amount of electricity traded in the hydropower market outside the province; C O外 is the market cost of other power plants in the neighboring area, and O is the electricity volume traded in other power markets outside the province.

[0069] Embodiment seven:

[0070] This embodiment is further designed on the basis of the first embodiment in that the expression for the maximum power value constraint available in the target area in this embodiment is as follows:

[0071]

[0072] In the formula, is the availability factor of thermal power units; is the availability factor of the hydropower plant; is the new energy availability coefficient; P 内max is the maximum power available in the target area; P H内,max P is the maximum power value of the thermal power units in the target area in this cycle; S内,max P is the maximum power value that the hydropower plant in the target area can provide in this cycle; O内,max is the maximum power value of the new energy available in the target area in this cycle; Taking into account the availability coefficients of different energy sources at different times (for example, the maximum power available from a hydropower plant will be reduced during the dry season, thermal power units may be affected by factors such as equipment failure and fail to reach maximum power generation, and new energy sources may be affected by factors such as weather), these coefficients can be dynamically adjusted according to actual operating conditions, equipment status, natural conditions and other factors. For example, in the rainy season, Close to 1; during equipment maintenance, will decrease; on cloudy days, for new energy such as solar energy, Will decrease.

[0073] Embodiment eight:

[0074] This embodiment is further designed on the basis of the first embodiment in that the expression for the constraint of the maximum power value available in the neighboring area in this embodiment is as follows:

[0075]

[0076] In the formula, is the availability factor of thermal power units in the neighboring area; is the availability factor of hydropower plants in the neighboring area; is the available coefficient of new energy in the neighboring area; P 外max is the maximum power available in the neighboring area; P H外,max P is the maximum power value of the thermal power units in the neighboring area in this cycle; S外,max P is the maximum power value that the hydropower plant in the neighboring area can provide in this cycle; O外,max is the maximum power value of renewable energy available in the neighboring area in this period; These coefficients can be adjusted dynamically according to actual operating conditions, equipment status, natural conditions and other factors. For example, in the rainy season, Close to 1; during equipment maintenance, will decrease; on cloudy days, for new energy such as solar energy, Will decrease.

[0077] Embodiment nine:

[0078] This embodiment is further designed on the basis of the eighth embodiment in that the method for obtaining the maximum available power value of the target area and each neighboring area in this embodiment is as follows:

[0079] Obtain the total power loss and daily average power loss of the target area and each neighboring area in the previous cycle, and predict the total power loss and daily power loss of the target area and each neighboring area in the current cycle;

[0080] Based on the predicted total power loss and daily power loss of the target area and each neighboring area in this period, the maximum power available from the hydropower plant, the maximum power available from the thermal power unit and the maximum power available from new energy in the target area and each neighboring area in this period are analyzed, and the maximum power available for the target area and each neighboring area is further obtained.

[0081] Embodiment ten:

[0082] This embodiment is further designed on the basis of the ninth embodiment in that the specific calculation formula of the total power consumption in this embodiment is as follows:

[0083] The total power consumption during this period is Q m-1 =Total power supply Q in the target area of ​​this cycle m-1 +Total electricity purchased in this period Q m-1 ;

[0084] The total power consumption during this period is Q m =Total power supply Q in the target area of ​​this cycle m +Total electricity purchased in this period Q m .

[0085] Among them, the total power consumption Q in the previous cycle m-1 = Last month's H+S+O; the total amount of electricity purchased in the previous cycle is Q m-1 =Hout+Sout+Oout in the previous cycle; total power consumption in this cycle is Qout m = H+S+O in this period, the total amount of electricity purchased in this period is Q m It is H outside + S outside + O outside this month.

[0086] Embodiment nine:

[0087] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in any of the above embodiments.

[0088] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0089] The above description is only a 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 conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for cost estimation of distribution network engineering considering cross-regional power purchase, characterized in that: include: Acquire power data of a target area and several neighboring areas adjacent to the target area; Based on the power data, a distribution network engineering cost model considering cross-regional power purchase is constructed, wherein the distribution network cost model aims to minimize the total cost of the target area and the neighboring areas, and is constrained by the on-grid power price range constraints of the neighboring areas, the on-site power price range constraints of the target area, and the maximum power value constraints of the target area and each neighboring area, and the power purchase plan of the distribution network is used as a decision variable; The distribution network engineering cost model is solved to obtain the optimal power purchasing plan for the distribution network.

2. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 1 is characterized in that: The electricity data include the total construction cost of the thermal power market, the trading volume of thermal power plants, the total construction cost of the hydropower market, the trading volume of the hydropower market, the cost of other power generation markets, namely the cost of new energy power generation markets, and the trading volume of other power markets, namely the trading volume of new energy power generation markets.

3. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 1 is characterized in that: The objectives of the distribution network cost model are: Where P li is the on-grid electricity price of neighboring region i; Q 外i is the amount of electricity purchased from neighboring area i; P h is the on-site electricity price in the target area; Q 内 is the amount of electricity purchased from the target area; i is the serial number of the neighboring area, i∈[1,n], and n is the total number of neighboring areas.

4. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 1 is characterized in that: The expression of the grid-connected electricity price range constraint of the neighboring area is: P lmin,i ≤P li ≤P lmax,i Where P lmin,i is the minimum on-grid electricity price of neighboring area i; P lmax,i is the maximum on-grid electricity price of neighboring area i; The expression of the target area on-grid electricity price range constraint is: P hmin ≤P h ≤P hmax Where P hmin is the minimum landed electricity price in the target area; P hmax It is the maximum value of the on-site electricity price in the target area.

5. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 4 is characterized in that: The minimum landed electricity price in the target area P hmin and the maximum electricity price in the target area P hmax The method to obtain is as follows: Where P h is the electricity price in the target area; P p represents the base electricity price of the target area p; Q p represents the available power value of the target area p; s is the set of neighboring regions adjacent to the target region.

6. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 4 is characterized in that: The minimum total cost target minP of the distribution network cost model satisfies the following formula: C H内 +C S内 +C O内 +C H外 +C S外 +C O外 ≤minP Where minP is the minimum target of total construction cost; P is the total construction cost; C H内 is the total cost of thermal power market in the target area; C S内 is the total cost of hydropower market in the target area; C O内 is the cost of other electricity markets in the target area; C H外 is the total cost of thermal power market in the neighboring region; C S外 is the total cost of hydropower market in the neighboring region; C O外 is the market cost of other power plants in the neighboring area.

7. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 1 is characterized in that: The expression for the maximum power value constraint of the target area is as follows: In the formula, is the availability factor of thermal power units; is the availability factor of the hydropower plant; is the new energy availability coefficient; P 内max is the maximum power available in the target area; P H内,max P is the maximum power value of the thermal power units in the target area in this cycle; S内,max P is the maximum power value that the hydropower plant in the target area can provide in this cycle; O内,max is the maximum power value of the new energy available in the target area in this cycle; 8. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 1 is characterized in that: The expression for the maximum power value constraint of the neighboring area is as follows: In the formula, is the availability factor of thermal power units in the neighboring area; is the availability factor of hydropower plants in the neighboring area; is the available coefficient of new energy in the neighboring area; P 外max is the maximum power available in the neighboring area; P H外,max P is the maximum power value of the thermal power units in the neighboring area in this cycle; S外,max P is the maximum power value that the hydropower plant in the neighboring area can provide in this cycle; O外,max is the maximum power value of renewable energy available in the neighboring area in this period; 9. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 8, characterized in that: The method for obtaining the maximum available power value of the target area and each neighboring area is as follows: Obtain the total power loss and daily average power loss of the target area and each neighboring area in the previous cycle, and predict the total power loss and daily power loss of the target area and each neighboring area in the current cycle; Based on the predicted total power loss and daily power loss of the target area and each neighboring area in this period, the maximum power available from the hydropower plant, the maximum power available from the thermal power unit and the maximum power available from new energy in the target area and each neighboring area in this period are analyzed, and the maximum power available for the target area and each neighboring area is further obtained.

10. The method for cost estimation of distribution network engineering considering cross-regional power purchase according to claim 9, characterized in that: The specific calculation formula of the total power consumption is as follows: The total power consumption during this period is Q m-1 =Total power supply Q in the target area of ​​this cycle m-1 +Total electricity purchased in this period Q m-1 ; The total power consumption during this period is Q m =Total power supply Q in the target area of ​​this cycle m +Total electricity purchased in this period Q m .

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