A virtual cloud power plant industrial user peak load optimization dispatching system

Abstract

The present invention proposes a virtual cloud power plant industrial user peak load optimization dispatching system, which includes: a collection module obtains the historical load demand of industrial users to obtain the load forecast Dx of industrial users; collects the real-time power generation of the virtual cloud power plant, and collects the current environmental information, and obtains the expected power generation of the virtual cloud power plant in combination with meteorological data and environmental information; the processing module is configured to formulate a load plan of the power dispatching center according to the expected power generation and the load forecast Dx; the judgment module is configured to correct the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator, and obtain the corrected load forecast Dxy; when Dxy≤0.8Dx or Dxy≥1.2Dx, the judgment module adjusts the load plan according to the corrected load forecast Dxy. The present invention improves the operating efficiency and reliability of the power grid, and by considering economic incentives, guarantees the power demand of industrial users and enhances the enthusiasm of industrial users to participate in peak load regulation.

Description

Technical Field

[0001] The present invention relates to the technical field of power peak regulation, and in particular to a peak regulation optimization and dispatching system for industrial users of a virtual cloud power plant. Background Art

[0002] With the deepening of the market-oriented reform of the power industry, the two-way interaction between the supply side and the demand side has gradually received attention, and the user side has come into being to participate in peak load response. Among them, large industrial users have large power consumption and a high degree of automation, which has become an effective way to deal with the problem of economically and efficiently absorbing a high proportion of renewable energy. However, due to restrictions in production planning, personnel management and other aspects, it is very difficult for industrial users to participate in demand response. It is to formulate the optimal dispatching plan for industrial users' dispatchable equipment, realize the efficient use of renewable energy, thereby smoothing out the fluctuations of new energy and reducing the impact of new energy access on the power grid, which is of great significance to promoting the stable operation of the power grid and low-carbon development.

[0003] However, when industrial users participate in peak load response, the subsidies given by load aggregators have a significant impact on their cost structure. This means that the peak load optimization scheduling strategy based solely on the technical perspective may ignore the important role of economic incentives in the decision-making of industrial users. If the impact of the load aggregator's subsidies on the cost of industrial users is not taken into account, the peak load optimization strategy may not be effective in actual application and cannot fully stimulate the enthusiasm of industrial users to participate in peak load response.

[0004] Therefore, it is necessary to consider peak-shaving subsidies based on the actual production needs of industrial users in order to form the optimal peak-shaving scheduling strategy for industrial users and meet industrial electricity demand. Summary of the invention

[0005] In view of this, the present invention proposes a virtual cloud power plant industrial user peak-shaving optimization scheduling system, which aims to solve the problem that the current peak-shaving strategy is imperfect due to insufficient consideration of actual production needs and the impact of load aggregator subsidies on industrial electricity consumption.

[0006] The present invention proposes a virtual cloud power plant industrial user peak load optimization scheduling system, comprising:

[0007] A collection module is configured to obtain historical load demands of industrial users, and obtain a load forecast Dx of the industrial users according to the historical load demands;

[0008] The acquisition module is further configured to acquire real-time power generation of the virtual cloud power plant and current environmental information, and process the real-time power generation in combination with meteorological data and the environmental information to obtain an estimated power generation of the virtual cloud power plant;

[0009] A processing module, configured to formulate a load plan for a power dispatching center according to the expected power generation and the load forecast Dx;

[0010] A judgment module is configured to collect the load growth information of the industrial users after formulating the load plan, and to correct the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator to obtain a corrected load forecast Dxy;

[0011] When 0.8Dx<Dxy<1.2Dx, the judgment module does not adjust the load plan;

[0012] When Dxy≤0.8Dx or Dxy≥1.2Dx, the judgment module adjusts the load plan according to the corrected load forecast Dxy.

[0013] Furthermore, the acquisition module processes the real-time power generation in combination with the meteorological data and the environmental information to obtain the estimated power generation of the virtual cloud power plant, including:

[0014] The environmental information collected by the collection module includes real-time illumination area △G and real-time water level difference △H;

[0015] The acquisition module is also used to obtain the sunny day ratio △Q and precipitation △J within the future preset time S1 according to the meteorological data;

[0016] The acquisition module is also used to pre-set a first preset proportion Q1, a second preset proportion Q2, a third preset proportion Q3 and a fourth preset proportion Q4, and Q1<Q2<Q3<Q4; pre-set a first adjustment coefficient A1, a second adjustment coefficient A2, a third adjustment coefficient A3 and a fourth adjustment coefficient A4, and 0<A1<A2<A3<A4<1.2;

[0017] The acquisition module is also used to select an adjustment coefficient to adjust the real-time illumination area △G according to the relationship between the sunny day proportion △Q and each preset proportion Qi, and obtain the power generation of photovoltaic power generation within the future preset time S1 according to the adjusted illumination area.

[0018] Further, when the acquisition module selects an adjustment coefficient to adjust the real-time illumination area △G according to the relationship between the sunny day proportion △Q and each preset proportion Qi, i=1, 2, 3, 4, including:

[0019] When Q1≤△Q<Q2, the first adjustment coefficient A1 is selected to adjust the real-time illumination area △G, and the adjusted illumination area is obtained as △G×A1;

[0020] When Q2≤△Q<Q3, the second adjustment coefficient A2 is selected to adjust the real-time illumination area △G, and the adjusted illumination area is obtained as △G×A2;

[0021] When Q3≤△Q<Q4, the third adjustment coefficient A3 is selected to adjust the real-time illumination area △G, and the adjusted illumination area is obtained as △G×A3;

[0022] When Q4≤ΔQ, the fourth adjustment coefficient A4 is selected to adjust the real-time illumination area ΔG, and the adjusted illumination area is obtained as ΔG×A4.

[0023] Furthermore, the acquisition module is also used to pre-set a first preset precipitation amount J1, a second preset precipitation amount J2, a third preset precipitation amount J3 and a fourth preset precipitation amount J4, and J1<J2<J3<J4;

[0024] The acquisition module is used to select an adjustment coefficient to adjust the real-time water level difference △H according to the relationship between the precipitation △J and each preset precipitation Ji, and obtain the power generation of hydropower within a future preset time S1 according to the adjusted water level difference.

[0025] Furthermore, the acquisition module selects an adjustment coefficient to adjust the real-time water level difference △H according to the relationship between the precipitation △J and each preset precipitation Ji, i=1, 2, 3, 4, including:

[0026] When J1≤△J<J2, the first adjustment coefficient A1 is selected to adjust the real-time water level difference △H, and the adjusted water level difference is obtained as △H×A1;

[0027] When J2≤△J<J3, the second adjustment coefficient A2 is selected to adjust the real-time water level difference △H, and the adjusted water level difference is △H×A2;

[0028] When J3≤△J<J4, the third adjustment coefficient A3 is selected to adjust the real-time water level difference △H, and the adjusted water level difference is obtained as △H×A3;

[0029] When J4≤ΔJ, the fourth adjustment coefficient A4 is selected to adjust the real-time water level difference ΔH, and the adjusted water level difference is ΔH×A4.

[0030] Furthermore, when the processing module formulates a load plan of the power dispatching center according to the expected power generation and load forecast Dx, it includes:

[0031] The processing module formulates a load plan for the power dispatching center according to the relationship between the photovoltaic power generation D1 and the hydropower generation D2 within the future preset time S1 and the load forecast Dx;

[0032] when When the power consumption of the power supply ...

[0033] when When D1+D2-Dx is stored in the first storage device, the electric energy of the second storage device is transferred to the first storage device so that the electric energy storage capacity in the first storage device reaches 0.3×Dx;

[0034] when When the electric energy of the second storage device is taken out and charged into the first storage device, the electric energy storage capacity in the first storage device reaches 0.3×Dx.

[0035] Furthermore, the judgment module modifies the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator to obtain the modified load forecast Dxy, including:

[0036] The judgment module obtains the growth rate △Z of the load demand of the industrial user within the past preset time S2, and pre-sets a first preset growth rate Z1, a second preset growth rate Z2, a third preset growth rate Z3 and a fourth preset growth rate Z4, and Z1<Z2<Z3<Z4;

[0037] Selecting a load correction coefficient according to the relationship between the growth rate △Z and each preset growth rate to correct the load forecast Dx, and obtaining a corrected load forecast Dxy;

[0038] When Z1≤△Z<Z2, the acquisition module selects the first load correction coefficient T1 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T1;

[0039] When Z2≤△Z<Z3, the acquisition module selects the second load correction coefficient T2 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T2;

[0040] When Z3≤△Z<Z4, the acquisition module selects the third load correction coefficient T3 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T3;

[0041] When Z4≤△Z, the acquisition module selects the fourth load correction coefficient T4 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T4;

[0042] Among them, 0<T1<T2<T3<T4.

[0043] Furthermore, the judgment module modifies the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator, and when obtaining the modified load forecast Dxy, it also includes:

[0044] After selecting the i-th load correction coefficient Ti to correct the load forecast Dx and obtain the corrected load forecast Dxy, Dxy=Dx×Ti, i=1, 2, 3, 4, the judgment module is also used to obtain the subsidy intensity difference △T between the real-time subsidy intensity T0 of the load aggregator and the preset subsidy intensity threshold Tmin, △T=T0-Tmin, pre-set the first preset correction coefficient B1, the second preset correction coefficient B2 and the third preset correction coefficient B3, and 0<B1<B2<B3<1.2; pre-set the first preset difference △T1, the second preset difference △T2 and the third preset difference △T3, and △T1<△T2<△T3; the judgment module selects the correction coefficient according to the relationship between the subsidy intensity difference △T and each preset difference to perform secondary correction on the corrected load forecast Dxy;

[0045] When △T1≤△T<△T2, the judgment module selects the first preset correction coefficient B1 to perform secondary correction on the corrected load forecast Dxy to obtain the secondary corrected predicted load forecast Dxy, Dxy=Dx×T i×B1;

[0046] When △T2≤△T<△T3, the judgment module selects the second preset correction coefficient B2 to perform secondary correction on the corrected load forecast Dxy to obtain the secondary corrected predicted load forecast Dxy, Dxy=Dx×T i×B2;

[0047] When ΔT3≤ΔT, the judgment module selects the third preset correction coefficient B3 to perform secondary correction on the corrected load forecast Dxy to obtain the secondary corrected predicted load forecast Dxy, Dxy=Dx×T i×B3.

[0048] Furthermore, when the processing module adjusts the load plan according to the revised load forecast, it includes:

[0049] After selecting the nth preset correction coefficient Bn, the corrected load forecast Dxy is corrected twice to obtain the second corrected predicted load forecast Dxy, Dxy = Dx × T i × Bn, i = 1, 2, 3, 4, n = 1, 2, 3, the judgment module adjusts the load plan according to the relationship between the corrected load forecast Dxy and the load forecast Dx.

[0050] Furthermore, when the judgment module adjusts the load plan according to the magnitude relationship between the corrected load forecast Dxy and the load forecast Dx, it includes:

[0051] When Dxy≥1.2Dx, the amount of electric energy stored in the first storage device is adjusted to 0.5×Dx, and the second storage device is connected in series with the first storage device. When the electric energy in the first storage device is exhausted, the second storage device is used to continue to supply power;

[0052] When Dxy≤0.8Dx, the electric energy storage capacity in the first storage device is adjusted to 0.2×Dx, and the surplus electric energy is stored in the second storage device.

[0053] Compared with the prior art, the beneficial effect of the present invention is that by collecting the historical load demand of industrial users and the real-time power generation of virtual cloud power plants through the acquisition module, combined with current environmental information and meteorological data, future power demand and power generation can be predicted more accurately. The processing module formulates a power dispatching plan based on these predictions to ensure the efficiency and stability of power grid operation. The judgment module monitors the load growth and the subsidy strategy of the load aggregator in real time, and dynamically adjusts the load forecast according to actual conditions. It realizes flexible adjustment of load plans under different power demand and supply conditions, helps to balance the power grid load, fully meets industrial power demand, and ensures the stability and reliability of the dispatching plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0055] Figure 1 A functional block diagram of a peak load optimization and dispatching system for industrial users of a virtual cloud power plant provided by an embodiment of the present invention;

[0056] Figure 2 A flow chart of a peak-shaving optimization scheduling system for industrial users in a virtual cloud power plant provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0058] A virtual power plant is a power coordination management system that uses advanced information and communication technologies and software systems to achieve aggregation and coordinated optimization of DER (Distributed Energy Resource) such as DG (distributed generator), energy storage systems, controllable loads, and electric vehicles, so as to participate in the power market and power grid operation as a special power plant. The core of the concept of virtual power plants can be summarized as "communication" and "aggregation". The key technologies of virtual power plants mainly include coordinated control technology, smart metering technology, and information and communication technology. The most attractive function of virtual power plants is that they can aggregate DER to participate in the operation of the power market and ancillary service market, and provide management and ancillary services for distribution and transmission networks.

[0059] Load aggregators refer to the behavior of aggregating the loads of electricity users in the electricity market and then trading them. The purpose of load aggregators is to improve the resource utilization and price stability of the electricity market by aggregating the loads of electricity users. In the electricity market, load aggregators can form regional markets by aggregating the loads of electricity users in different regions. Usually, industrial users who participate in load aggregators will receive certain price subsidies. Therefore, industrial users tend to participate in load aggregation to reduce costs. However, if the power supply side does not take into account the impact of the load aggregator's subsidies on industry, it may lead to poor results of peak-shaving optimization strategies in actual applications, because the actual electricity consumption obtained by industrial users after participating in peak-shaving may not meet industrial demand, causing industrial users to be forced to give up peak-shaving.

[0060] Therefore, it is necessary to propose a virtual cloud power plant industrial user peak-shaving optimization scheduling system to solve the problem that the current peak-shaving strategy is imperfect due to the failure to fully consider the actual production needs and the impact of load aggregator subsidies on industrial electricity consumption.

[0061] In some embodiments of the present application, see Figure 1As shown, a virtual cloud power plant industrial user peak load optimization scheduling system includes: a collection module is configured to obtain the historical load demand of industrial users, and obtain the load forecast Dx of industrial users according to the historical load demand. The collection module is also configured to collect the real-time power generation of the virtual cloud power plant, and collect the current environmental information, and process the real-time power generation in combination with meteorological data and environmental information to obtain the expected power generation of the virtual cloud power plant. The processing module is configured to formulate a load plan for the power dispatching center according to the expected power generation and the load forecast Dx. The judgment module is configured to collect the load growth information of industrial users after formulating the load plan, and correct the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator, and obtain the corrected load forecast Dxy. When 0.8Dx<Dxy<1.2Dx, the judgment module does not adjust the load plan. When Dxy≤0.8Dx or Dxy≥1.2Dx, the judgment module adjusts the load plan according to the corrected load forecast Dxy.

[0062] Specifically, see Figure 2 As shown, the system operation process is:

[0063] Step S100, obtaining historical load demands of industrial users, and obtaining load forecast Dx of industrial users according to the historical load demands.

[0064] Step S200, collect the real-time power generation of the virtual cloud power plant, collect the current environmental information, process the real-time power generation in combination with the meteorological data and the environmental information, and obtain the estimated power generation of the virtual cloud power plant.

[0065] Step S300, formulating a load plan for the power dispatching center according to the expected power generation and load forecast Dx.

[0066] Step S400, collect the load growth information of industrial users, correct the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator, and obtain the corrected load forecast Dxy.

[0067] Step S500, determining whether to adjust the load plan based on the corrected load forecast Dxy.

[0068] Specifically, the historical load demand of industrial users is obtained, and the future load forecast Dx is predicted based on it. This includes collecting the past power usage data of industrial users. This may include daily, weekly or monthly power consumption, peak and valley time of power consumption, etc. The average load data is calculated based on the historical load of industrial users, and the average load data is used as the future load forecast Dx. The real-time power generation of the virtual cloud power plant is collected, and combined with the current environmental information and meteorological data, this information is processed to predict the future power generation of the virtual cloud power plant. It helps to understand the available power resources more accurately. According to the predicted power generation and the load demand of industrial users, the power dispatching center formulates the corresponding load plan. It ensures the efficient allocation and use of power resources. Monitor the load growth of industrial users, and combine the real-time subsidy strength of load aggregators to revise the initial load forecast Dx to obtain the revised load forecast Dxy. Make the industrial power load forecast closer to the actual situation Based on the revised load forecast Dxy, determine whether the original load plan needs to be adjusted. It ensures that the dispatching plan can respond to changes in actual demand in a timely manner.

[0069] It is understandable that accurate load forecasting and real-time adjustment can more effectively allocate and utilize power resources, especially when there is a high proportion of renewable energy. By flexibly adjusting the load plan, it is possible to cope with fluctuations in load and power generation, thereby maintaining the stable operation of the power grid. By considering the subsidy impact of load aggregators, it stimulates the enthusiasm of industrial users to participate in peak load regulation, which helps to reduce the operating costs of industrial users.

[0070] In some embodiments of the present application, the acquisition module processes the real-time power generation in combination with meteorological data and environmental information, and when obtaining the expected power generation of the virtual cloud power plant, it includes: the environmental information collected by the acquisition module includes the real-time illumination area △G and the real-time water level difference △H. The acquisition module is also used to obtain the sunny day proportion △Q and precipitation △J in the future preset time S1 according to the meteorological data. The acquisition module is also used to pre-set the first preset proportion Q1, the second preset proportion Q2, the third preset proportion Q3 and the fourth preset proportion Q4, and Q1<Q2<Q3<Q4. The first adjustment coefficient A1, the second adjustment coefficient A2, the third adjustment coefficient A3 and the fourth adjustment coefficient A4 are pre-set, and 0<A1<A2<A3<A4<1.2. The acquisition module is also used to select the adjustment coefficient to adjust the real-time illumination area △G according to the size relationship between the sunny day proportion △Q and each preset proportion Qi, and obtain the power generation of photovoltaic power generation in the future preset time S1 according to the adjusted illumination area.

[0071] In some embodiments of the present application, when the acquisition module selects an adjustment coefficient to adjust the real-time illumination area △G according to the relationship between the sunny day proportion △Q and each preset proportion Qi, i=1, 2, 3, 4, including: when Q1≤△Q<Q2, select the first adjustment coefficient A1 to adjust the real-time illumination area △G, and the adjusted illumination area is △G×A1. When Q2≤△Q<Q3, select the second adjustment coefficient A2 to adjust the real-time illumination area △G, and the adjusted illumination area is △G×A2. When Q3≤△Q<Q4, select the third adjustment coefficient A3 to adjust the real-time illumination area △G, and the adjusted illumination area is △G×A3. When Q4≤△Q, select the fourth adjustment coefficient A4 to adjust the real-time illumination area △G, and the adjusted illumination area is △G×A4.

[0072] Specifically, measure the current solar radiation area, which is an important factor affecting photovoltaic power generation. Predict the proportion of sunny days in the future, which directly affects the intensity and duration of solar radiation. On sunny days, the intensity of solar radiation is high and the duration is long, while on cloudy or overcast days, the intensity of radiation will weaken and the duration will be shortened. Adjusting the radiation area can more accurately reflect the impact of this natural change on solar power generation. Based on the accurate illumination area after adjustment, the photovoltaic power generation in the future can be calculated.

[0073] In some embodiments of the present application, the acquisition module is further used to preset a first preset precipitation J1, a second preset precipitation J2, a third preset precipitation J3, and a fourth preset precipitation J4, and J1<J2<J3<J4. The acquisition module is used to select an adjustment coefficient according to the relationship between the precipitation △J and each preset precipitation Ji to adjust the real-time water level difference △H, and obtain the power generation of hydropower generation within the future preset time S1 according to the adjusted water level difference.

[0074] In some embodiments of the present application, the acquisition module selects an adjustment coefficient to adjust the real-time water level difference △H according to the relationship between the precipitation △J and each preset precipitation Ji, i=1, 2, 3, 4, including: when J1≤△J<J2, select the first adjustment coefficient A1 to adjust the real-time water level difference △H, and obtain the adjusted water level difference as △H×A1. When J2≤△J<J3, select the second adjustment coefficient A2 to adjust the real-time water level difference △H, and obtain the adjusted water level difference as △H×A2. When J3≤△J<J4, select the third adjustment coefficient A3 to adjust the real-time water level difference △H, and obtain the adjusted water level difference as △H×A3. When J4≤△J, select the fourth adjustment coefficient A4 to adjust the real-time water level difference △H, and obtain the adjusted water level difference as △H×A4.

[0075] Specifically, the power generation capacity of a hydroelectric power station depends largely on the water level of the reservoir. Increased precipitation will cause the water level of the reservoir to rise, and the greater the water level difference, the higher the potential energy available for power generation, thereby increasing the potential for power generation.

[0076] It is understandable that by integrating environmental and meteorological data, the power generation of renewable energy sources such as photovoltaics and hydropower can be predicted more accurately, which helps the supply side of the power market make more accurate scheduling decisions. Accurate power generation forecasts enable power dispatch centers to better arrange load plans for industrial users, ensure the balance of power supply and demand, and improve the overall efficiency of the system. By monitoring and predicting power generation in real time, grid operators can better cope with the volatility of renewable energy and reduce the risk of grid instability and power outages.

[0077] In some embodiments of the present application, when the processing module formulates a load plan for the power dispatching center based on the expected power generation and load forecast Dx, it includes: the processing module formulates a load plan for the power dispatching center based on the relationship between the photovoltaic power generation D1 and the hydropower generation D2 and the load forecast Dx within a future preset time S1.

[0078] when When the power consumption is high, 0.3×Dx of electricity is stored in the first storage device to meet the power demand of industrial users during peak hours, and D1+D2-Dx-0.3Dx of electricity is stored in the second storage device.

[0079] when When D1+D2-Dx is charged, the electric energy of D1+D2-Dx is stored in the first storage device, and the electric energy of the second storage device is transferred to charge the first storage device so that the electric energy storage capacity in the first storage device reaches 0.3×Dx.

[0080] when When the electric energy of the second storage device is taken out and charged into the first storage device, the electric energy storage capacity in the first storage device reaches 0.3×Dx.

[0081] Specifically, through intelligent power dispatching and energy storage management, power resources can be allocated and stored more efficiently to cope with changes in supply and demand. During peak power demand, the first storage device provides stable power support, which helps prevent power outages and stabilize the power grid. In some embodiments of the present application, the first storage device may be a device that quickly stores and releases electrical energy, such as a battery, and the second energy storage device may be a flywheel energy storage system to fully preserve excess power.

[0082] It is understandable that by storing the renewable energy generation that exceeds the immediate demand, the overall utilization rate of these energy sources is improved. Flexibility to respond to various power supply and demand situations improves the grid's ability to adapt to the volatility of renewable energy and the volatility of electricity demand.

[0083] In some embodiments of the present application, the judgment module corrects the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator, and obtains the corrected load forecast Dxy, including: the judgment module obtains the growth rate △Z of the load demand of industrial users in the past preset time S2, and pre-sets the first preset growth rate Z1, the second preset growth rate Z2, the third preset growth rate Z3 and the fourth preset growth rate Z4, and Z1<Z2<Z3<Z4. According to the relationship between the growth rate △Z and each preset growth rate, the load correction coefficient is selected to correct the load forecast Dx to obtain the corrected load forecast Dxy.

[0084] Specifically, when Z1≤△Z<Z2, the acquisition module selects the first load correction coefficient T1 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T1. When Z2≤△Z<Z3, the acquisition module selects the second load correction coefficient T2 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T2. When Z3≤△Z<Z4, the acquisition module selects the third load correction coefficient T3 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T3. When Z4≤△Z, the acquisition module selects the fourth load correction coefficient T4 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T4. Among them, 0<T1<T2<T3<T4.

[0085] In some embodiments of the present application, when the judgment module corrects the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator, and obtains the corrected load forecast Dxy, it also includes: after selecting the i-th load correction coefficient Ti to correct the load forecast Dx, and obtain the corrected load forecast Dxy, Dxy=Dx×Ti, i=1, 2, 3, 4, the judgment module is also used to obtain the subsidy strength difference △T between the real-time subsidy strength T0 of the load aggregator and the preset subsidy strength threshold Tmin, △T=T0-Tmin, and pre-set the first preset correction coefficient B1, the second preset correction coefficient B2 and the third preset correction coefficient B3, and 0<B1<B2<B3<1.2. Pre-set the first preset difference △T1, the second preset difference △T2 and the third preset difference △T3, and △T1<△T2<△T3. The judgment module selects the correction coefficient according to the relationship between the subsidy strength difference △T and each preset difference to perform a secondary correction on the corrected load forecast Dxy.

[0086] Specifically, when △T1≤△T<△T2, the judgment module selects the first preset correction coefficient B1 to perform secondary correction on the corrected load forecast Dxy, and obtains the secondary corrected predicted load forecast Dxy, Dxy=Dx×T i×B1. When △T2≤△T<△T3, the judgment module selects the second preset correction coefficient B2 to perform secondary correction on the corrected load forecast Dxy, and obtains the secondary corrected predicted load forecast Dxy, Dxy=Dx×T i×B2. When △T3≤△T, the judgment module selects the third preset correction coefficient B3 to perform secondary correction on the corrected load forecast Dxy, and obtains the secondary corrected predicted load forecast Dxy, Dxy=Dx×T i×B3.

[0087] It is understandable that by considering the load growth rate and the subsidy intensity of load aggregators, this dual correction mechanism can improve the accuracy of load forecasting, thereby providing more reliable data support for power dispatching. Considering the impact of load aggregator subsidy adjustment on load forecasting enables the power system to better adapt to changes in market dynamics and economic factors, and improves the flexibility and responsiveness of the power market. More accurate load forecasting and power supply planning can better meet the needs of industrial users.

[0088] In some embodiments of the present application, when the processing module adjusts the load plan according to the corrected load forecast, it includes: selecting the nth preset correction coefficient Bn, performing a secondary correction on the corrected load forecast Dxy, and obtaining the secondary corrected predicted load forecast Dxy, Dxy = Dx × Ti × Bn, i = 1, 2, 3, 4, n = 1, 2, 3, and the judgment module adjusts the load plan according to the size relationship between the corrected load forecast Dxy and the load forecast Dx.

[0089] In some embodiments of the present application, when the judgment module adjusts the load plan according to the magnitude relationship between the modified load forecast Dxy and the load forecast Dx, it includes: when Dxy≥1.2Dx, adjusting the electric energy storage capacity in the first storage device to 0.5×Dx, and connecting the second storage device in series with the first storage device, and when the electric energy in the first storage device is exhausted, using the second storage device to continue to supply power. When Dxy≤0.8Dx, adjusting the electric energy storage capacity in the first storage device to 0.2×Dx, and storing the excess electricity in the second storage device.

[0090] It is understandable that by dynamically adjusting the energy storage strategy based on real-time load forecasts, the power grid can respond to load fluctuations more flexibly and improve adaptability to different power demand situations. Reasonable allocation and utilization of electrical energy in storage devices ensures that sufficient power support can be provided during high-load periods, while avoiding waste of resources during low-load periods. By precisely controlling the charging and discharging of energy storage systems, reliance on expensive peak power can be reduced, thereby reducing energy costs and improving overall economic benefits. At peak times of power demand, ensure that sufficient backup power is available to reduce the risk of power outages caused by demand exceeding supply. Through effective power dispatch and energy storage management, better utilization of renewable energy is achieved and reliance on fossil fuels is reduced.

[0091] In the above-mentioned embodiment, the peak load optimization and dispatching system for industrial users of the virtual cloud power plant can more accurately predict future power demand and power generation by collecting the historical load demand of industrial users and the real-time power generation of the virtual cloud power plant through the acquisition module, and combining the current environmental information and meteorological data. The processing module formulates a power dispatching plan based on these predictions to ensure the efficiency and stability of the power grid operation. The judgment module monitors the load growth and the subsidy strategy of the load aggregator in real time, and dynamically adjusts the load forecast according to the actual situation. It realizes the flexible adjustment of the load plan under different power demand and supply conditions, which helps to balance the power grid load, fully meet the industrial power demand, and ensure the stability and reliability of the dispatching plan.

[0092] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A virtual cloud power plant industrial user peak load optimization scheduling system, characterized in that: include: A collection module is configured to obtain historical load demands of industrial users, and obtain a load forecast Dx of the industrial users according to the historical load demands; The acquisition module is further configured to acquire real-time power generation of the virtual cloud power plant and current environmental information, and process the real-time power generation in combination with meteorological data and the environmental information to obtain an estimated power generation of the virtual cloud power plant; A processing module, configured to formulate a load plan for a power dispatching center according to the expected power generation and the load forecast Dx; A judgment module is configured to collect the load growth information of the industrial users after formulating the load plan, and to correct the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator to obtain a corrected load forecast Dxy; When 0.8Dx<Dxy<1.2Dx, the judgment module does not adjust the load plan; When Dxy≤0.8Dx or Dxy≥1.2Dx, the judgment module adjusts the load plan according to the corrected load forecast Dxy; The judgment module corrects the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator to obtain the corrected load forecast Dxy, including: The judgment module obtains the growth rate △Z of the load demand of the industrial user within the past preset time S2, and pre-sets a first preset growth rate Z1, a second preset growth rate Z2, a third preset growth rate Z3 and a fourth preset growth rate Z4, and Z1<Z2<Z3<Z4; Selecting a load correction coefficient according to the relationship between the growth rate △Z and each preset growth rate to correct the load forecast Dx, and obtaining a corrected load forecast Dxy; When Z1≤△Z<Z2, the acquisition module selects the first load correction coefficient T1 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T1; When Z2≤△Z<Z3, the acquisition module selects the second load correction coefficient T2 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T2; When Z3≤△Z<Z4, the acquisition module selects the third load correction coefficient T3 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T3; When Z4≤△Z, the acquisition module selects the fourth load correction coefficient T4 to correct the load forecast Dx, and obtains the corrected load forecast Dxy, Dxy=Dx×T4; Among them, 0<T1<T2<T3<T4; The judgment module corrects the load forecast Dx according to the load growth information and the real-time subsidy strength of the load aggregator, and obtains the corrected load forecast Dxy, further comprising: After selecting the i-th load correction coefficient Ti to correct the load forecast Dx and obtain the corrected load forecast Dxy, Dxy=Dx×Ti, i=1, 2, 3, 4, the judgment module is also used to obtain the subsidy intensity difference △T between the real-time subsidy intensity T0 of the load aggregator and the preset subsidy intensity threshold Tmin, △T=T0-Tmin, pre-set the first preset correction coefficient B1, the second preset correction coefficient B2 and the third preset correction coefficient B3, and 0<B1<B2<B3<1.2; pre-set the first preset difference △T1, the second preset difference △T2 and the third preset difference △T3, and △T1<△T2<△T3; the judgment module selects the correction coefficient according to the relationship between the subsidy intensity difference △T and each preset difference to perform secondary correction on the corrected load forecast Dxy; When △T1≤△T<△T2, the judgment module selects the first preset correction coefficient B1 to perform secondary correction on the corrected load forecast Dxy to obtain the secondary corrected predicted load forecast Dxy, Dxy=Dx×Ti×B1; When △T2≤△T<△T3, the judgment module selects the second preset correction coefficient B2 to perform secondary correction on the corrected load forecast Dxy to obtain the secondary corrected predicted load forecast Dxy, Dxy=Dx×Ti×B2; When ΔT3≤ΔT, the judgment module selects the third preset correction coefficient B3 to perform secondary correction on the corrected load forecast Dxy to obtain the secondary corrected predicted load forecast Dxy, Dxy=Dx×Ti×B3.

2. The virtual cloud power plant industrial user peak load optimization scheduling system according to claim 1 is characterized in that: The acquisition module processes the real-time power generation in combination with the meteorological data and the environmental information to obtain the estimated power generation of the virtual cloud power plant, including: The environmental information collected by the collection module includes real-time illumination area △G and real-time water level difference △H; The acquisition module is also used to obtain the sunny day ratio △Q and precipitation △J within the future preset time S1 according to the meteorological data; The acquisition module is also used to pre-set a first preset proportion Q1, a second preset proportion Q2, a third preset proportion Q3 and a fourth preset proportion Q4, and Q1<Q2<Q3<Q4; pre-set a first adjustment coefficient A1, a second adjustment coefficient A2, a third adjustment coefficient A3 and a fourth adjustment coefficient A4, and 0<A1<A2<A3<A4<1.2; The acquisition module is also used to select an adjustment coefficient to adjust the real-time illumination area △G according to the relationship between the sunny day proportion △Q and each preset proportion Qi, and obtain the power generation of photovoltaic power generation within the future preset time S1 according to the adjusted illumination area.

3. The virtual cloud power plant industrial user peak load optimization scheduling system according to claim 2 is characterized in that: When the acquisition module selects an adjustment coefficient to adjust the real-time illumination area △G according to the relationship between the sunny day proportion △Q and each preset proportion Qi, i=1, 2, 3, 4, including: When Q1≤△Q<Q2, the first adjustment coefficient A1 is selected to adjust the real-time illumination area △G, and the adjusted illumination area is obtained as △G×A1; When Q2≤△Q<Q3, the second adjustment coefficient A2 is selected to adjust the real-time illumination area △G, and the adjusted illumination area is obtained as △G×A2; When Q3≤△Q<Q4, the third adjustment coefficient A3 is selected to adjust the real-time illumination area △G, and the adjusted illumination area is obtained as △G×A3; When Q4≤ΔQ, the fourth adjustment coefficient A4 is selected to adjust the real-time illumination area ΔG, and the adjusted illumination area is obtained as ΔG×A4.

4. The virtual cloud power plant industrial user peak load optimization scheduling system according to claim 3 is characterized in that: The acquisition module is also used to preset a first preset precipitation amount J1, a second preset precipitation amount J2, a third preset precipitation amount J3 and a fourth preset precipitation amount J4, and J1<J2<J3<J4; The acquisition module is used to select an adjustment coefficient to adjust the real-time water level difference △H according to the size relationship between the precipitation △J and each preset precipitation Ji, and obtain the power generation of hydropower generation within the future preset time S1 according to the adjusted water level difference.

5. The virtual cloud power plant industrial user peak load optimization scheduling system according to claim 4 is characterized in that: The acquisition module selects an adjustment coefficient to adjust the real-time water level difference △H according to the relationship between the precipitation △J and each preset precipitation Ji, i=1, 2, 3, 4, including: When J1≤△J<J2, the first adjustment coefficient A1 is selected to adjust the real-time water level difference △H, and the adjusted water level difference is obtained as △H×A1; When J2≤△J<J3, the second adjustment coefficient A2 is selected to adjust the real-time water level difference △H, and the adjusted water level difference is △H×A2; When J3≤△J<J4, the third adjustment coefficient A3 is selected to adjust the real-time water level difference △H, and the adjusted water level difference is obtained as △H×A3; When J4≤ΔJ, the fourth adjustment coefficient A4 is selected to adjust the real-time water level difference ΔH, and the adjusted water level difference is ΔH×A4.

6. The virtual cloud power plant industrial user peak load optimization scheduling system according to claim 5 is characterized in that: When the processing module formulates a load plan for the power dispatching center according to the expected power generation and load forecast Dx, it includes: The processing module formulates a load plan for the power dispatching center according to the relationship between the photovoltaic power generation D1 and the hydropower generation D2 within the future preset time S1 and the load forecast Dx; when When the power consumption of the power supply ... when When D1+D2-Dx is stored in the first storage device, the electric energy of the second storage device is transferred to the first storage device so that the electric energy storage capacity in the first storage device reaches 0.3×Dx; when When the electric energy of the second storage device is taken out and charged into the first storage device, the electric energy storage capacity in the first storage device reaches 0.3×Dx.

7. The virtual cloud power plant industrial user peak load optimization scheduling system according to claim 6 is characterized in that: When the judgment module adjusts the load plan according to the revised load forecast, it includes: After selecting the nth preset correction coefficient Bn, the corrected load forecast Dxy is corrected for the second time to obtain the second corrected predicted load forecast Dxy, Dxy = Dx × Ti × Bn, i = 1, 2, 3, 4, n = 1, 2, 3, the judgment module adjusts the load plan according to the relationship between the corrected load forecast Dxy and the load forecast Dx.

8. The virtual cloud power plant industrial user peak load optimization scheduling system according to claim 7 is characterized in that: When the judgment module adjusts the load plan according to the magnitude relationship between the corrected load forecast Dxy and the load forecast Dx, it includes: When Dxy≥1.2Dx, the amount of electric energy stored in the first storage device is adjusted to 0.5×Dx, and the second storage device is connected in series with the first storage device. When the electric energy in the first storage device is exhausted, the second storage device is used to continue to supply power; When Dxy≤0.8Dx, the electric energy storage capacity in the first storage device is adjusted to 0.2×Dx, and the surplus electric energy is stored in the second storage device.

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