Power distribution network new energy consumption optimization method considering transformer life loss
By introducing Arrhenius reaction kinetics theory and event triggering mechanism into the distribution network, the economic quantification and real-time scheduling of transformer lifetime loss are optimized, solving the problem of integrating transformer lifetime loss and economic scheduling in the distribution network, reducing the risks caused by the randomness of photovoltaic output, and improving the stability and economy of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID SHANGHAI ENERGY INTERCONNECTION RES INST CO LTD
- Filing Date
- 2026-02-07
- Publication Date
- 2026-05-08
AI Technical Summary
Existing technologies struggle to effectively balance the impact of large-scale distributed renewable energy access on transformer lifespan and economic dispatch in power distribution networks. This makes it difficult to deeply integrate equipment health status with operational economics, and the prediction error risk caused by the randomness of photovoltaic power output is difficult to resolve effectively.
By introducing Arrhenius reaction kinetics theory, a quantitative relationship between transformer winding hot spot temperature and life loss is established and incorporated into the overall system optimization objective. Combined with an event triggering mechanism, a forced correction strategy is executed in real-time scheduling, including energy storage discharge, load shedding, or curtailment strategies, to optimize load transfer and reduce transformer load rate and hot spot temperature.
This approach achieves a deep integration of the economic quantification of transformer lifespan losses and equipment health status, reducing transformer load rate and hot spot temperature, improving the economy and stability of the distribution network, and reducing the prediction error risk caused by the randomness of photovoltaic output.
Smart Images
Figure CN122000961A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a technology in a specific field, namely, an optimization method for the absorption of new energy in a distribution network that takes into account transformer lifespan losses. Background Technology
[0002] With the escalating global energy crisis and increasingly prominent environmental issues, building a clean, low-carbon, safe, and efficient energy system has become a consensus among countries. As a key hub connecting energy production and consumption, the power distribution network is facing unprecedented pressure for transformation and development opportunities. It must adapt to the integration needs of large-scale distributed renewable energy sources (such as distributed photovoltaics and decentralized wind power) and improve the system's carrying capacity and flexibility. Summary of the Invention
[0003] This invention addresses the aforementioned shortcomings of existing technologies by proposing an optimization method for renewable energy consumption in distribution networks that considers transformer lifetime losses. Using Arrhenius reaction kinetics theory, a quantitative relationship between transformer winding hotspot temperature and lifetime losses is established, and lifetime losses are transformed into specific economic costs, incorporated into the overall system optimization objective. This achieves a deep integration of equipment health status and operational economy, overcoming the limitations of traditional single-timescale scheduling in balancing long-term economic efficiency and instantaneous safety. At the day-ahead scale, a global optimization model incorporating transformer lifetime loss costs is established, and a baseline plan is formulated. At the real-time scale, an event-triggered mechanism based on the transformer safety domain is introduced, implementing forced correction strategies for both forward and reverse overloads. This effectively solves the prediction error risk caused by the randomness of photovoltaic output, achieving decoupling and synergy between economic scheduling and physical safety control.
[0004] This invention is achieved through the following technical solution:
[0005] This invention relates to an optimization method for renewable energy consumption in distribution networks that takes into account transformer lifespan losses, comprising:
[0006] Step 1: Real-time status perception and ultra-short-term prediction calculation of real-time load rate and power deviation.
[0007] Step 2: Introduce event trigger flags Event trigger judgment: When the transformer is overloaded in the forward or reverse direction, the forced safety mode is triggered. When the transformer load rate is within the safety threshold, the system is in economic tracking mode, that is, the energy storage makes fine adjustments to the power deviation according to the benchmark dispatch plan, so as to maintain the economy of the day-ahead plan without adjusting the flexible load.
[0008] The mandatory security modes include:
[0009] When the transformer is positively overloaded, the economic constraints of the day-ahead plan are abandoned, and emergency control is carried out according to the principle of "energy storage discharge and load shedding". That is, the energy storage system discharges at its maximum capacity. On the basis of the day-ahead command, the minimum discharge power required to eliminate the over-limit is superimposed and limited by the maximum discharge power of the equipment. If the over-limit cannot be eliminated even when the energy storage reaches the maximum discharge power, the interruptible load is forcibly triggered to be shedding.
[0010] When the transformer is overloaded in reverse, emergency control is carried out according to the strategy of "energy storage charging + curtailment of solar power", that is, energy storage is forced to charge, and when the energy storage is full, the output of solar power is restricted.
[0011] During the real-time correction phase, transferable loads remain unchanged because they cannot complete the replanning of energy conservation throughout the day in a short period of time; only interruptible loads (IL) participate in the emergency response to positive overloads.
[0012] The aforementioned fine-tuning refers to the system being in economic tracking mode when the transformer load rate is within a safe threshold. This means that the energy storage system fine-tunes the power deviation according to the baseline dispatch plan to maintain the economy of the day-ahead plan without adjusting the flexible load.
[0013] The baseline scheduling plan is obtained in the following way:
[0014] Step i: After collecting prediction data, transformer parameters, and basic data, calculate the hot spot temperature and acceleration factor based on the Arrhenius reaction kinetic model; and quantify the life loss of the transformer to obtain the life loss cost.
[0015] Step ii: Construct a multi-objective optimization function that includes the lifetime loss cost of the transformer in step i, and solve it after applying constraints to obtain the baseline scheduling plan. Attached Figure Description
[0016] Figure 1 This is a flowchart of the present invention;
[0017] Figure 2 This is a schematic diagram of a scenario for an example embodiment;
[0018] Figures 3-8 This is a schematic diagram illustrating the effect of an example. Detailed Implementation
[0019] like Figure 2 As shown in this embodiment, a new energy consumption optimization method for distribution networks that takes into account transformer lifespan losses is included:
[0020] Step 1: Real-time status perception and ultra-short-term forecasting calculation of real-time load factor and power deviation, specifically: Due to the random fluctuations in photovoltaic output, the day-ahead dispatch plan in the real-time operation phase ( This will inevitably lead to deviations. Real-time data collection of source and load operation is achieved by intelligent sensing terminals deployed in the distribution transformer area to calculate the real-time load rate. and power deviation , where: superscript , These represent the real-time value and the current day's optimized value, respectively. , These are the real-time load and photovoltaic power, respectively. Indicates time; This refers to the rated capacity of the transformer.
[0021] Step 2: Introduce event trigger flags The event triggering judgment is based on the comparison result between the real-time load rate obtained in step 1 and the load rate threshold, specifically including:
[0022] 2.1 Calculate the event trigger flag ,in: This is the load rate threshold.
[0023] In this embodiment, the load rate threshold is set to 80%.
[0024] 2.2 Determine the event trigger flag, specifically including:
[0025] ①When The system is determined to be experiencing a forward overload on the transformer, triggering a forced safety mode. In this case, the economic constraints of the day-ahead plan are abandoned, and emergency control is implemented according to the principle of "energy storage discharge, load shedding." This means the energy storage system discharges at its maximum capacity, adding the minimum discharge power required to eliminate the overload based on the day-ahead command, and is limited by the equipment's maximum discharge power. If the energy storage reaches its maximum discharge power but still cannot eliminate the over-limit, the interruptible load will be forcibly disconnected, resulting in the corrected load command: Among them: response quantity ; , These are the real-time and day-ahead energy storage discharge powers, respectively; This represents the maximum discharge power of the energy storage. , These are the real-time and day-ahead interruptible load power, respectively.
[0026] ②When The system was determined to be in reverse overload mode, triggering a forced safety mode: at this point, photovoltaic backfeeding was severe. Emergency control was implemented using a "energy storage charging + curtailment" strategy, i.e., forced charging of energy storage. When energy storage is full, it is necessary to limit photovoltaic output, and the resulting photovoltaic output reduction command will be used. Among them: photovoltaic reduction ; , These are the real-time and day-ahead energy storage charging powers, respectively; This represents the maximum charging power for energy storage. , These are the real-time and day-ahead interruptible load power, respectively.
[0027] During the real-time correction phase, transferable loads remain unchanged because they cannot complete the replanning of energy conservation throughout the day in a short period of time; only interruptible loads (IL) participate in the emergency response to positive overloads.
[0028] ③When The system is determined to be in economic tracking mode when the transformer load rate is within the safe threshold. This means that the energy storage system makes minor adjustments to the power deviation according to the baseline dispatch plan to maintain the economy of the day-ahead plan without adjusting the flexible load. ,in: This is the smoothing coefficient, taking values [0,1]. For energy storage tracking power. ,in: , These are the real-time and day-ahead transferable loads, respectively; , These are the real-time and day-ahead interruptible loads, respectively.
[0029] The baseline scheduling plan is obtained in the following way:
[0030] Step i, after collecting prediction data, transformer parameters, and basic data, calculates the hotspot temperature and acceleration factor based on the Arrhenius reaction kinetic model, specifically as follows: The economic quantification of transformer lifespan loss yields the lifespan loss cost, specifically: ,in: Transformer investment cost, total lifespan loss , This refers to the expected insulation life of the transformer.
[0031] In this embodiment, the expected insulation life of the transformer is taken as 180,000 hours (approximately 20 years).
[0032] Step ii: Construct a multi-objective optimization function that includes the lifetime loss cost of the transformer from step i, apply constraints, and solve to obtain the baseline scheduling plan, specifically including:
[0033] a. Construct the objective function: The optimization objective is to minimize the total system cost within the scheduling cycle, including the cost of purchasing and selling electricity, the total lifecycle cost of energy storage, the cost of curtailment penalties for solar power, and the cost of transformer lifespan losses. Specifically: ,in: This represents the total number of scheduling periods; for Time-of-use electricity purchase and sale costs; for The full lifecycle cost of time-limited energy storage; for The cost of penalties for skipping light during certain time periods; for Cost of flexible load scheduling during specific time periods.
[0034] The specific costs of purchasing and selling electricity are as follows: ,in: , They are respectively Electricity purchase and sales prices for different time periods; , They are respectively Electricity purchase and sales power during specific time periods.
[0035] The specific full-cycle cost of energy storage is as follows: ,in: Cost per unit capacity; For energy storage life; This refers to the number of scheduling periods per year. Operating cost per unit power; The cost of operation and maintenance per unit capacity; This refers to the rated capacity of the energy storage.
[0036] The specific cost of the light-wasting penalty is as follows: ,in: The unit of abandoned light penalty coefficient; for Available photovoltaic power during certain time periods; for Actual photovoltaic power utilized during the time period.
[0037] The specific cost of flexible load scheduling is as follows: ,in: , These are the unit price for interruptible load compensation and the unit price for transferable load dispatching, respectively. This is the original reference power.
[0038] b. Set constraints, including: power balance constraints. Physical constraints of energy storage , , , and flexible load constraints , , ,in: for Power of rigid load during a given time period.
[0039] c. Solving via mixed-integer nonlinearity: The baseline scheduling plan is obtained by using the CPLEX solver, specifically as follows: , , and .
[0040] Based on practical application scenario experiments, the following were selected: Figure 5 and Figure 7 Two typical distribution substations are compared and analyzed, including one with severe power backfeeding and the other without. The performance of the strategy is comprehensively evaluated from both technical and economic perspectives. Taking a clustering scenario with special characteristics as an example of photovoltaic power output, the impact of different photovoltaic access capacities on transformers is analyzed. At one-hour intervals, considering various power transmissions within the distribution substation, scenarios with different photovoltaic capacities are constructed, and the daily transformer load rate is statistically analyzed.
[0041] like Figure 3 As shown, the transformer load factor variation curves and hotspot temperature variation curves are displayed for different photovoltaic (PV) grid connection capacities. The transformer load factor exhibits negative values from 7:00 to 19:00, and the negative offset intensifies with increasing PV capacity. Particularly in the 8MW scenario, the load factor approaches -1 at 13:00, indicating that the power backfeed reaches the transformer's rated capacity at this time. This reverse overload phenomenon stems from the fact that peak PV power generation far exceeds local load demand, causing excess power to be fed back to the upstream grid through the transformer.
[0042] like Figure 3 Figure 4 The figure shows the hotspot temperature variation curves of the transformer under different photovoltaic (PV) grid connection capacities. The hotspot temperature variation curves are highly positively correlated with the absolute value of the load factor. During the period from 8:00 to 10:00, as PV power generation increases and the load factor decreases, the hotspot temperature decreases accordingly. However, during the peak PV output period from 10:00 to 14:00, although the load factor is negative, its absolute value increases, causing the hotspot temperature to rise continuously, reaching its peak at 14:00. Afterward, as PV output decreases, the temperature gradually drops. Therefore, the accuracy of the transformer model proposed in this invention is verified.
[0043] With the integration of distributed photovoltaic (PV) power, the backfeeding phenomenon in power distribution networks has become increasingly serious. This invention analyzes in detail the issue of backfeeding power in power distribution networks. The invention uses actual data from a specific region where PV backfeeding is severe. This region has a 10kV power distribution network with a transformer capacity of 2MVA, a PV installed capacity of 6.5MW, and 4.5MWh of energy storage. Local time-of-use electricity prices are shown in Table 2.
[0044] Table 2 Time-of-use electricity prices
[0045] Comparing the transformer load rates before and after the adjustment, the results are as follows: Figure 6 As shown in the figure, it can be seen from the figure that when the photovoltaic power output alone cannot be fully absorbed by the distribution network, and during the peak photovoltaic power generation period of 10-15 hours, the back-feeding power of the transformer far exceeds its capacity, which can easily cause transformer damage. Therefore, this invention uses energy storage, load adjustment and other methods to reduce the load and back-feeding pressure of the transformer by absorbing photovoltaic power generation.
[0046] Table 3. Cost Comparison Before and After Adjustment (RMB)
[0047] Before the adjustment, the total cost on the user side was RMB 1526.21, mainly consisting of new energy maintenance costs of RMB 1526.21, transformer maintenance costs of RMB 1495.17, and energy storage operation and maintenance costs of RMB 456.21, with negative interaction costs. After the adjustment, the user side introduced adjustment compensation, significantly reducing interaction costs to -RMB 4908.95. Transformer and energy storage operation and maintenance costs decreased, ultimately resulting in a total cost of -RMB 2882.52, indicating that the user side achieved net profit overall after the adjustment. The operator's compensation cost was 0, the electricity purchase cost was RMB 2676.08, the electricity sales revenue was RMB 3457.82, and the net profit was RMB 781.74. After the adjustment, the operator's compensation cost is 7060.82 yuan, the electricity purchase cost slightly decreases to 2668.83 yuan, and the electricity sales revenue increases to 4448.05 yuan, resulting in a net loss of 1281.60 yuan. This indicates a significant increase in the operator's costs after the adjustment, which may be related to the user-side compensation mechanism. The adjusted cost reduction of 102.08% reflects a major change in the interaction model between users and operators.
[0048] Therefore, this embodiment selects a local power distribution network with minimal power backflow, comprising a 1.125MVA transformer, distributed photovoltaic (PV) power, wind power, and energy storage. It also includes adjustable loads such as irrigation and aquaculture. The output of the PV and wind turbines is as follows... Figure 5 As shown.
[0049] Table 4 Time-of-use electricity prices
[0050] like Figure 8As shown, before the adjustment, the load factor was close to 0.9 during the peak hours of 12:00 and 21:00, exceeding the transformer's economical operating range of 0.6-0.8, which could lead to increased maintenance costs. After the adjustment, the load factor dropped below 0.8 during the peak hours, and increased from 0.55 to around 0.6 during the off-peak hours of 3:00-5:00. The overall curve was smoother, and the load factor was concentrated in the 0.6-0.8 range, which is consistent with the transformer's optimal operating range and reduces maintenance costs.
[0051] As shown in Table 5, transformer maintenance costs decreased from RMB 29.31 to RMB 19.88. This was mainly achieved through the time-sharing of adjustable loads (irrigation, aquaculture), such as shifting non-emergency loads from peak hours to off-peak hours, thus balancing grid pressure and reducing costs.
[0052] On the user side, load adjustment strategies have altered the cost structure by optimizing load rates and resource allocation. New energy maintenance costs decreased from RMB 478.87 to RMB 232.65, a reduction of 51.41%, primarily due to improved operational stability of wind and solar power equipment. Interaction costs decreased by 29.06%, reflecting reduced load management complexity. Transformer maintenance costs decreased from RMB 29.31 to RMB 19.88, a reduction of 32.17%, due to load rates being controlled within the economic range of 0.6-0.8, validating the non-linear relationship between equipment maintenance and load rates. However, energy storage operation and maintenance costs increased by 8.97% due to increased charging and discharging frequency, and an additional adjustment compensation cost of RMB 2753.40 was added, mainly to incentivize the shifting of adjustable load periods, leading to a total user-side cost increase from RMB 4913.49 to RMB 6495.43. This change reflects the strategy of exchanging short-term compensation investments for long-term equipment maintenance and energy consumption cost-effectiveness; the economics of load adjustment need to be comprehensively evaluated in conjunction with long-term equipment lifecycle costs.
[0053] For operators, the load adjustment strategy effectively reduced system electricity purchase costs and impacted revenue structure. Electricity purchase costs decreased significantly from RMB 4176.08 to RMB 3038.64, a reduction of 27.24%, primarily due to users reducing grid purchases during peak hours and increasing energy storage charging during off-peak hours, fully utilizing peak-valley price differences to optimize electricity consumption. Electricity sales revenue decreased by RMB 277.84 due to increased renewable energy absorption capacity on the user side, but the electricity purchase cost far exceeded the sales loss. Furthermore, operators need to pay RMB 2753.40 in adjustment compensation costs to incentivize load adjustment; this expenditure can be considered a necessary investment for improving grid stability. Overall, the strategy, through load shifting in time and space, rebalances the direct electricity purchase costs for operators with indirect system reliability benefits, providing a feasible path for optimizing the economy and stability of the distribution network.
[0054] Table 5. Cost Comparison Before and After Adjustment (RMB)
[0055] Compared with existing technologies, the performance improvement of this method is as follows: by introducing the Arrhenius reaction kinetics theory to establish a quantitative model of transformer life loss and converting life loss into economic cost into day-ahead optimization objectives, a deep integration of equipment health status and operational economy is achieved; at the same time, a day-ahead-real-time dual-scale scheduling framework is adopted, and an event triggering mechanism based on the transformer safety domain is introduced at the real-time scale, which effectively solves the prediction error risk caused by the randomness of photovoltaic power output.
[0056] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.
Claims
1. A method for optimizing the absorption of new energy sources in a distribution network, taking into account transformer lifespan losses, characterized in that, include: Step 1: Real-time status perception and ultra-short-term prediction calculation of real-time load rate and power deviation; Step 2: Introduce event trigger flags Event trigger judgment: When the transformer is overloaded in the forward or reverse direction, the forced safety mode is triggered. When the transformer load rate is within the safety threshold, the system is in economic tracking mode, that is, the energy storage makes fine adjustments to the power deviation according to the benchmark dispatch plan, so as to maintain the economy of the day-ahead plan without adjusting the flexible load.
2. The method for optimizing the absorption of new energy in a distribution network, taking into account transformer lifespan losses, as described in claim 1, is characterized in that... The mandatory security modes include: When the transformer is in a forward overload, the economic constraints of the day-ahead plan are abandoned, and emergency control is carried out according to the principle of "energy storage discharge and load shedding". That is, the energy storage system discharges at its maximum capacity. On the basis of the day-ahead command, the minimum discharge power required to eliminate the over-limit is added and is limited by the maximum discharge power of the equipment. If the over-limit cannot be eliminated even when the energy storage reaches the maximum discharge power, the interruptible load is forcibly triggered to be shedding. When the transformer is overloaded in reverse, emergency control is carried out according to the "energy storage charging + curtailment" strategy, that is, energy storage is forced to charge, and when the energy storage is full, the output of photovoltaic power is restricted.
3. The method for optimizing the absorption of new energy in a distribution network, taking into account transformer lifespan losses, as described in claim 1, is characterized in that... The aforementioned fine-tuning refers to the system being in economic tracking mode when the transformer load rate is within a safe threshold. This means that the energy storage system fine-tunes the power deviation according to the baseline dispatch plan to maintain the economy of the day-ahead plan without adjusting the flexible load.
4. The method for optimizing the absorption of new energy in a distribution network, taking into account transformer lifespan losses, as described in claim 1, is characterized in that... The baseline scheduling plan is obtained in the following way: Step i: After collecting prediction data, transformer parameters, and basic data, calculate the hot spot temperature and acceleration factor based on the Arrhenius reaction kinetic model; and quantify the life loss of the transformer to obtain the life loss cost. Step ii: Construct a multi-objective optimization function that includes the lifetime loss cost of the transformer in step i, and solve it after applying constraints to obtain the baseline scheduling plan.
5. The method for optimizing the absorption of new energy in a distribution network, taking into account transformer life losses, as described in any one of claims 1-4, is characterized in that, specifically... include: Step 1: Real-time status perception and ultra-short-term forecasting calculation of real-time load factor and power deviation. Specifically: Due to the random fluctuations in photovoltaic output, the day-ahead dispatch plan will inevitably deviate during the real-time operation phase. This is achieved by using intelligent sensing terminals deployed in distribution substations to collect source and load operation data in real time and calculate the real-time load factor. and power deviation , where: superscript , These represent the real-time value and the current day's optimized value, respectively. , These are the real-time load and photovoltaic power, respectively. Indicates time; This refers to the rated capacity of the transformer. Step 2: Introduce event trigger flags The event triggering judgment is based on the comparison result between the real-time load rate obtained in step 1 and the load rate threshold, specifically including: 2.1 Calculate the event trigger flag ,in: The load rate threshold; 2.2 Determine the event trigger flag, specifically including: ①When The system is determined to be experiencing a forward overload on the transformer, triggering a forced safety mode. In this case, the economic constraints of the day-ahead plan are abandoned, and emergency control is implemented according to the principle of "energy storage discharge, load shedding." This means the energy storage system discharges at its maximum capacity, adding the minimum discharge power required to eliminate the overload based on the day-ahead command, and is limited by the equipment's maximum discharge power. If the energy storage reaches its maximum discharge power but still cannot eliminate the over-limit, the interruptible load will be forcibly disconnected, resulting in the corrected load command: Among them: response quantity ; , These are the real-time and day-ahead energy storage discharge powers, respectively; This represents the maximum discharge power of the energy storage. , These are the real-time and day-ahead interruptible load power, respectively; ②When The system was determined to be a transformer reverse overload, triggering a forced safety mode. At this point, severe backfeeding by the photovoltaic system was detected, and emergency control was implemented according to the "energy storage charging + curtailment" strategy, i.e., forced energy storage charging. When energy storage is full, it is necessary to limit photovoltaic output, and the resulting photovoltaic output reduction command will be used. Among them: photovoltaic reduction ; , These are the real-time and day-ahead energy storage charging powers, respectively; This represents the maximum charging power for energy storage. , These are the real-time and day-ahead interruptible load power, respectively; During the real-time correction phase, transferable loads remain unchanged because they cannot complete the replanning of energy conservation throughout the day in a short period of time; only interruptible loads (IL) participate in the emergency response to positive overloads. ③When The system is determined to be in economic tracking mode when the transformer load rate is within the safe threshold. This means that the energy storage system makes minor adjustments to the power deviation according to the baseline dispatch plan to maintain the economy of the day-ahead plan without adjusting the flexible load. ,in: This is the smoothing coefficient, taking values [0,1]. For energy storage to track power, ,in: , These are the real-time and day-ahead transferable loads, respectively; , These are the real-time and day-ahead interruptible loads, respectively.
6. The method for optimizing the absorption of new energy in a distribution network, taking into account transformer lifespan losses, as described in claim 5, is characterized in that... The baseline scheduling plan is obtained in the following way: Step i: After collecting prediction data, transformer parameters, and basic data, the hotspot temperature and acceleration factor are calculated based on the Arrhenius reaction kinetic model. Specifically, the lifespan loss is economically quantified to obtain the transformer's lifespan loss cost. ,in: Transformer investment cost, total lifespan loss , The expected insulation life of the transformer; Step ii: Construct a multi-objective optimization function that includes the lifetime loss cost of the transformer in step i, and solve it after applying constraints to obtain the baseline scheduling plan.
7. The method for optimizing the absorption of new energy in a distribution network, taking into account transformer lifespan losses, as described in claim 6, is characterized in that... Step ii specifically includes: a. Construct the objective function: The optimization objective is to minimize the total system cost within the scheduling cycle, including the cost of purchasing and selling electricity, the total lifecycle cost of energy storage, the cost of curtailment penalties for solar power, and the cost of transformer lifespan losses. Specifically: ,in: This represents the total number of scheduling periods; for Time-of-use electricity purchase and sale costs; for The full lifecycle cost of time-limited energy storage; for The cost of penalties for skipping light during certain time periods; for Cost of flexible load scheduling during specific time periods; b. Set constraints, including: power balance constraints. Physical constraints of energy storage , , , and flexible load constraints , , ,in: for Time-limited rigid load power; c. Solving via mixed-integer nonlinearity: The baseline scheduling plan is obtained by using the CPLEX solver, specifically as follows: , , and .
8. The method for optimizing the absorption of new energy in a distribution network, taking into account transformer lifespan losses, as described in claim 7, is characterized in that... The specific costs of purchasing and selling electricity are as follows: ,in: , They are respectively Electricity purchase and sales prices for different time periods; , They are respectively Power purchased and sold during specific time periods; The specific full-cycle cost of energy storage is as follows: ,in: Cost per unit capacity; For energy storage life; This refers to the number of scheduling periods per year. Operating cost per unit power; The cost of operation and maintenance per unit capacity; This refers to the rated capacity of the energy storage. The specific cost of the light-wasting penalty is as follows: ,in: The unit of abandoned light penalty coefficient; for Available photovoltaic power during certain time periods; for Actual photovoltaic power utilized during the time period; The specific cost of flexible load scheduling is as follows: ,in: , These are the unit price for interruptible load compensation and the unit price for transferable load dispatching, respectively. This is the original reference power.