A building microgrid control method and system based on time-series zero-carbon balance

Abstract

The present invention provides a building microgrid control method and system based on time-series zero-carbon balance, including: predicting the operating day load and distributed energy output based on building load data and meteorological data, dividing the operating day into no fewer than two time periods, with the time scale set as T, and predicting the operating day load and distributed energy output to obtain long-term prediction results; during actual operation, short-term prediction of the average power load forecast and distributed energy output within each time period to obtain short-term prediction results; judging the energy storage battery's energy storage charge and discharge state pattern based on the long-term prediction results to maintain the energy storage battery's charge and discharge state over a long period of time, and processing the short-term prediction results to formulate a time-series zero-carbon balance control strategy to achieve zero carbon. The present invention solves the technical problems of intermittent and fluctuating, safety hazards caused by frequent battery charging and discharging, cycle life degradation, and poor distributed collaborative balance.

Description

Technical Field

[0001] The present invention relates to the field of energy control, and in particular to a building microgrid control method and system based on time-series zero-carbon balance. Background Art

[0002] Carbon emission reduction has garnered widespread public attention and in-depth research. Currently, carbon emissions from the construction industry account for a significant portion of total CO2 emissions. Therefore, building carbon reduction offers the greatest potential, is the most direct and effective approach to energy conservation and emission reduction, and low-carbon buildings have gradually become a future development trend and practical standard. The realization of zero-carbon buildings primarily involves the utilization of renewable energy technologies and improving building energy efficiency. Renewable energy generation technologies such as photovoltaic and wind power generation are key zero-carbon energy technologies because they do not produce CO2. By rationally deploying photovoltaic, wind power, and energy storage systems in building power systems, carbon emissions from building energy use can be reduced.

[0003] Due to the intermittent and volatile characteristics of renewable energy generation technologies such as photovoltaics and wind power, existing zero-carbon buildings may experience a situation where the supply of renewable energy cannot meet demand during actual operation, thus failing to achieve the zero-carbon goal.

[0004] The existing patent application document, "A Multi-Time-Scale Optimization Method for a Combined Wind, Solar, and Storage System," with publication number CN113937796A, consists of a 24-hour-ahead scale, a 1-hour-ahead scale, and a 15-minute-ahead scale. Based on the output of the thermal power units determined at the 24-hour-ahead scale, the optimal output of the pumped-storage units is determined at the 1-hour-ahead scale. Based on the optimal outputs of the thermal power units and pumped-storage units, and taking into account battery safety constraints, the optimal charge and discharge power of the battery pack over the next 15 minutes is determined by leveraging the battery's fast response speed, with the objective function being to minimize system operating costs. The technical solution disclosed in this existing document determines the output of each energy source at a preset time scale and predicts the optimal charge and discharge rate of the battery pack to optimize its output smoothness. However, this existing solution cannot control the specific output of the battery pack or the number of charge and discharge cycles, which is detrimental to the safety of the grid output and storage.

[0005] The existing patent application document, "A Two-Tier Dispatch and Control Method for Microgrids Considering the State of Charge of Hybrid Energy Storage," with publication number CN114492209A, includes the following steps: obtaining historical data on photovoltaic and wind power generation, load power demand, hybrid energy storage power limits, and hybrid energy storage state of charge limits; establishing an upper-level MPC model for the microgrid using a combined prediction method of a gray GM(1,N) and BP neural network in the upper-level control layer; estimating the initial state of charge of the hybrid energy storage under day-ahead scheduling and applying it to the lower-level control layer; optimizing the hybrid energy storage charge and discharge power control using a dynamic programming algorithm in the lower-level control layer, outputting the hybrid energy storage state of charge to the upper-level control layer as a state variable; and solving the upper-level MPC model of the microgrid based on the objective function and constraints. This prior art utilizes different time scales for each layer, but the upper-level MPC model still uses the HESS total power PC(k) to guide the hybrid energy storage charge and discharge power control in the lower-level control layer. This results in limitations in the effectiveness of the hierarchical optimization and comprehensive allocation of loads and energy, hindering the coordination of distributed energy resources, batteries, and controllable loads at each layer.

[0006] In summary, the existing technology has technical problems such as intermittent and fluctuating, frequent charging and discharging of batteries causing safety hazards, cycle life degradation, and poor distributed collaborative balance. Summary of the Invention

[0007] The technical problem to be solved by the present invention is how to solve the technical problems in the prior art of intermittent and fluctuating, safety hazards caused by frequent charging and discharging of batteries, cycle life degradation, and poor distributed collaborative balance.

[0008] The present invention solves the above technical problems by adopting the following technical solutions: A building microgrid control method based on time-series zero-carbon balance includes:

[0009] S1. Collect historical load data from the building user side and data from nearby meteorological stations to predict the load and distributed energy output on the operating day. Divide the operating day into at least two time periods, with the time scale set as T. Use this time period to predict the load and distributed energy output on the operating day, and obtain the predicted mean value of the electricity load P'ld(i) and the predicted output data of the new energy source P'dg(i) for each time period on the operating day.

[0010] S2. The average power load forecast value P'ld(i) and the new energy output forecast data P'dg(i) in each period of the operation day are used as the long-term forecast results;

[0011] S3. During the actual operation on the operation day, the short-term forecast of the average power load forecast value P'ld(i) and the output of distributed energy in each period is made, and the short-term time scale forecast result is obtained based on this;

[0012] S4. Determine the energy storage charge and discharge state mode of the energy storage battery based on the long-term prediction results, thereby maintaining the charge and discharge state of the energy storage battery over a long period of time, and process the short-term prediction results to formulate a time-series zero-carbon balance control strategy. Step S4 includes:

[0013] S41. When the energy storage battery is identified as being in a charging state mode, if the short-term power load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j), the controllable load is reduced, the energy storage battery is discharged, and the mains power is output in sequence to balance the power of the microgrid;

[0014] S42. If the short-term electricity load forecast Pld(j) is less than the short-term renewable energy output forecast Pdg(j), the energy storage battery is charged, the energy storage charging output is adjusted, the controllable load is increased, the photovoltaic output is reduced, and the wind power output is reduced.

[0015] S43. When the energy storage battery is identified as being in a discharge state mode, if the short-term electricity load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j), the energy storage battery is sequentially engaged in discharge, the energy storage discharge output is adjusted, and the controllable load and utility power output are reduced.

[0016] S44. If the short-term electricity load forecast Pld(j) is less than the short-term new energy output forecast Pdg(j), the controllable load is increased, the photovoltaic output is reduced, and the wind power output is reduced in sequence, thereby reducing the number of charge and discharge switching times of the energy storage battery.

[0017] Based on load and output forecasts at different time scales, this method optimizes the hierarchical allocation of loads and energy units. This method can effectively guide the coordination of distributed energy resources, energy storage devices, and controllable loads, mitigate the impact of renewable energy, improve supply-side and demand-side responses, and provide a path to achieving zero-carbon buildings based on historical results.

[0018] S5. Obtain the building microgrid operation data results according to the time-series zero-carbon balance control strategy, process them to obtain the mains power usage period and mains power usage, and quantify the building microgrid operation data results to iteratively obtain no less than 2 zero-carbon building implementation paths, optimize the building microgrid according to the zero-carbon building implementation path, and achieve zero carbon.

[0019] In a more specific technical solution, step S2 includes:

[0020] S21. When the predicted average value of the power load in each time period P'ld(i) is less than the predicted data of the new energy output P'dg(i), the energy storage battery is identified as being in a charging state mode and the energy storage battery is charged accordingly until the energy storage battery reaches a preset SOC charging upper limit;

[0021] S22. When the predicted average value of the power load P'ld(i) in each time period is less than the predicted data of the new energy output P'dg(i), the energy storage battery is identified as a discharge state mode to reduce the number of charge and discharge switching times of the energy storage battery.

[0022] The microgrid balancing strategy adopted in the present invention ensures that the battery is charged as much as possible until the set SOC charging upper limit. When P'ld(i) is less than P'dg(i), the energy storage battery is identified as a discharge state mode. During the discharge period, the number of charge and discharge switching should be minimized to protect the energy storage battery.

[0023] In a more specific technical solution, step S3 includes:

[0024] S31, dividing the i-th period within the operating day into m small time periods;

[0025] S32. Assume that the short-term prediction time scale is t;

[0026] S33: Process the historical load data on the building user side corresponding to the small time period and the short-time prediction time scale t and the data from the nearby weather stations to obtain a short-time scale prediction result.

[0027] The short-time scale control of the present invention is mainly based on the control layer optimization control and the timing adjustment constraints of each unit. On this basis, a reasonable control strategy is adopted to achieve system energy management and balance through the coordination of distributed power sources, energy storage systems, and controllable loads.

[0028] In a more specific technical solution, the short-term prediction time scale in step S32 includes: minute-level scale.

[0029] In a more specific technical solution, the short-time scale prediction result in step S33 includes a short-term electricity load prediction Pld(j) and a short-term new energy output prediction Pdg(j).

[0030] In a more specific technical solution, step S41 includes:

[0031] S411. When the load reduction conditions are met, the load reduction is obtained by the following logic processing:

[0032] ΔPc=min(Pld-Pdg,ΔPc1)

[0033] Among them, ΔPc1 is the maximum downward load;

[0034] S412, when the load ΔPc is reduced to ΔPc1, the energy storage battery is discharged;

[0035] S413: When the energy storage state of charge (SOC) is greater than the minimum allowable energy storage state of charge (SOCmin), the energy storage battery is controlled to adjust the discharge output using the following logic:

[0036] PESS=min(Pld-Pdg-ΔPc,PESSd)

[0037] Among them, PESSd is the maximum discharge output;

[0038] S414: When the output energy storage discharge output PESS is PESSd, the mains power output is controlled by the following logic:

[0039] Pg=Pld-Pdg-PESSd-ΔPc,

[0040] And output the mains power-time curve to the database.

[0041] In a more specific technical solution, step S42 includes:

[0042] S421. Charge the energy storage battery. When the energy storage state of charge (SOC) is less than the maximum allowable energy storage state of charge (SOCmax), the energy storage battery is controlled by the following logic to adjust the charging output:

[0043] PESS = min(Pdg - Pld, PESSc)

[0044] Among them, PESSc is the maximum charging output of energy storage;

[0045] S422: When the output energy storage charging output PESS reaches the maximum energy storage charging output PESSc, the controllable load is increased using the following logic:

[0046] ΔPc=min(Pdg-Pld-PESSc,ΔPc2)

[0047] Among them, ΔPc2 is the maximum upward load;

[0048] S423. When the controllable load ΔPc is increased to a maximum increase in load ΔPc2, the output of renewable energy is reduced. The output of renewable energy from photovoltaic and wind turbines is reduced in sequence, and the photovoltaic output is reduced according to the following logic:

[0049] ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv)

[0050] Among them, Ppv is the actual photovoltaic output;

[0051] S424: When the output ΔPpv of the photovoltaic output is equal to the actual photovoltaic output Ppv, the wind power output is reduced according to the following logic:

[0052] ΔPw=Pdg-Pld-PESSc-ΔPc-ΔPpv.

[0053] The present invention addresses the uncertainty of output from wind power, photovoltaic power, etc., and reduces the uncertainty of intermittent energy to a certain extent through wind and light power prediction. The present invention adopts multi-layer optimization control at different time scales. The long-time scale control is mainly based on global layer optimization management, taking into account the time transfer characteristics of energy storage equipment, ensuring the reasonable distribution of overall energy storage charging and discharging, and reducing the prediction error of intermittent energy power generation.

[0054] In a more specific technical solution, step S43 includes:

[0055] S431: When the energy storage state of charge (SOC) is greater than the minimum allowable energy storage state of charge (SOCmin), it is determined that the energy storage battery is in a discharge condition, and the energy storage battery is controlled to adjust the discharge output using the following logic:

[0056] PESS=min(Pld-Pdg,PESSd);

[0057] S432: When the energy storage battery discharge output PESS is PESSd, the controllable load is adjusted downward according to the following logic:

[0058] ΔPc=min(Pld-Pdg-PESS,ΔPc1);

[0059] S432: When the energy storage load ΔPc is reduced to ΔPc1, the following logic is used to control the mains output.

[0060] Pg=Pld-Pdg-PESSd-ΔPc.

[0061] In a more specific technical solution, step S44 includes:

[0062] S441. Increase the controllable load using the following logic:

[0063] ΔPc=min(Pdg-Pld,ΔPc2);

[0064] S442: When the controllable load ΔPc is increased to ΔPc2, the photovoltaic output is decreased according to the following logic:

[0065] ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv)

[0066] Among them, Ppv is the actual photovoltaic output;

[0067] S443: When the output ΔPpv of the photovoltaic output is adjusted downward to the actual photovoltaic output Ppv, the wind power output is adjusted downward according to the following logic:

[0068] ΔPw=Pdg-Pld-ΔPc-ΔPpv.

[0069] In a more specific technical solution, a building microgrid control system based on time-series zero-carbon balance includes:

[0070] The operating day load and renewable energy output forecasting module is used to collect historical load data on the building user side and data from nearby meteorological stations to predict the operating day load and distributed energy output. The operating day is divided into no less than two time periods, with the time scale set as T. The operating day load and distributed energy output are predicted based on this to obtain the predicted mean value of the electricity load P'ld(i) and the renewable energy output forecast data P'dg(i) in each time period of the operating day.

[0071] The long-term prediction result module is used to use the average power load forecast value P'ld(i) and the new energy output forecast data P'dg(i) in each period of the operation day as the long-term prediction result. The long-term prediction result module is connected to the operating daily load and new energy output prediction module;

[0072] The short-time scale prediction module is used to predict the average power load P'ld(i) and distributed energy output in each period during the actual operation of the operation day, and obtain the short-time scale prediction results based on this;

[0073] The time-series zero-carbon balance control strategy formulation module is used to determine the energy storage battery's energy storage charge and discharge state mode based on the long-term prediction results, thereby maintaining the energy storage battery's charge and discharge state over a long period of time, and processing the short-term prediction results to formulate a time-series zero-carbon balance control strategy. The time-series zero-carbon balance control strategy formulation module is connected to the long-term prediction result module and the short-term prediction module. The time-series zero-carbon balance control strategy formulation module includes:

[0074] The microgrid balancing module is used to balance the microgrid power by sequentially reducing the controllable load, discharging the energy storage battery, and generating the mains power when the short-term power load forecast Pld(j) is greater than the short-term renewable energy output forecast Pdg(j) when the energy storage battery is identified as being in the charging state.

[0075] The photovoltaic and wind power output reduction module is used to sequentially charge the energy storage battery, adjust the energy storage charging output, increase the controllable load, reduce the photovoltaic output, and reduce the wind power output when the short-term power load forecast Pld(j) is less than the short-term new energy output forecast Pdg(j);

[0076] The controllable load and utility power output reduction module is used to sequentially activate the energy storage battery to discharge, adjust the energy storage discharge output, and reduce the controllable load and utility power output when the energy storage battery is identified as being in discharge mode and the short-term power load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j);

[0077] The energy storage charge and discharge frequency reduction module is used to sequentially increase the controllable load, reduce the photovoltaic output, and reduce the wind power output when the short-term power load forecast Pld(j) is less than the short-term renewable energy output forecast Pdg(j), thereby reducing the charge and discharge switching frequency of the energy storage battery;

[0078] The building microgrid zero-carbon implementation module is used to obtain the building microgrid operation data results according to the time-series zero-carbon balance control strategy, process them to obtain the mains power usage period and mains power usage, and quantify the building microgrid operation data results to iteratively obtain no less than two zero-carbon building implementation paths, optimize the building microgrid according to the zero-carbon building implementation path, and achieve zero carbon. The building microgrid zero-carbon implementation module is connected to the time-series zero-carbon balance control strategy formulation module.

[0079] Compared with the existing technology, the present invention has the following advantages: the present invention optimizes the allocation of various loads and energy units in a hierarchical manner based on load and output forecasts at different time scales. This method can effectively guide the coordination of distributed energy, energy storage devices and controllable loads, mitigate the impact of renewable energy, improve supply-side and demand-side responses, and provide a path to achieve zero-carbon buildings based on historical results.

[0080] The microgrid balancing strategy adopted in the present invention ensures that the battery is charged as much as possible until the set SOC charging upper limit. When P'ld(i) is less than P'dg(i), the energy storage battery is identified as a discharge state mode. During the discharge period, the number of charge and discharge switching should be minimized to protect the energy storage battery.

[0081] The short-time scale control of the present invention is mainly based on the control layer optimization control and the timing adjustment constraints of each unit. On this basis, a reasonable control strategy is adopted to achieve system energy management and balance through the coordination of distributed power sources, energy storage systems, and controllable loads.

[0082] This invention addresses the uncertainty of wind power, photovoltaic power, and other sources of output by predicting wind and solar power, reducing the uncertainty of intermittent energy sources to a certain extent. It employs multi-layer optimization control at different time scales. Long-term control is primarily based on global optimization management, taking into account the time-shifting characteristics of energy storage devices to ensure a reasonable distribution of overall energy storage charge and discharge, thereby reducing prediction errors for intermittent energy generation. This invention addresses the technical issues of intermittent and fluctuating power generation, safety hazards caused by frequent battery charging and discharging, cycle life degradation, and poor distributed collaborative balance in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 This is a schematic diagram of the basic steps of a building microgrid control method based on time-series zero-carbon balance according to Example 1 of the present invention;

[0084] Figure 2This is a schematic diagram of the first formulation process of the time-sequential zero-carbon balance control strategy of Example 1 of the present invention;

[0085] Figure 3 This is a schematic diagram of the second formulation process of the time-sequential zero-carbon balance control strategy of Example 1 of the present invention;

[0086] Figure 4 This is a schematic diagram of a building microgrid control system based on time-series zero-carbon balance connected to a microgrid according to Example 2 of the present invention. DETAILED DESCRIPTION

[0087] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0088] Example 1

[0089] like Figure 1 As shown, the present invention provides a building microgrid control method based on time-series zero-carbon balance, which includes the following steps:

[0090] S1. Obtain historical database and weather station data;

[0091] S2, long-term load forecasting, to obtain the average power load forecast P'ld(i), new energy processing forecast P'dg(i), and time scale T within n periods of operation day;

[0092] S3, execute loop operation for i=1 to n step T according to the time scale;

[0093] S4. Determine whether the average power load forecast P'ld(i) is greater than or equal to the new energy processing forecast P'dg(i);

[0094] S5. Determine whether the energy storage battery is in a charging mode;

[0095] S5, determining that the energy storage battery is in a charge and discharge mode;

[0096] S6, short-term load forecast: electricity load forecast Pld(j), new energy output forecast Pdg(j) within the n-hour period of the i-th period, and time scale t;

[0097] S7, execute loop operation for j=1 to m step t according to the time scale;

[0098] S8. Determine whether the power load forecast Pld(j) is equal to the new energy output forecast Pdg(j);

[0099] S9. Determine whether the electricity load forecast Pld(j) is greater than the new energy output forecast Pdg(j) to formulate a time-series zero-carbon balance control strategy.

[0100] like Figure 2 As shown in the figure, the process of formulating the time-series zero-carbon balance control strategy includes:

[0101] S101. When the power load forecast Pld(j) is not equal to the new energy output forecast Pdg(j), determine whether the power load forecast Pld(j) is greater than the new energy output forecast Pdg(j);

[0102] S102: If the power load forecast Pld(j) is greater than the new energy output forecast Pdg(j), determine whether the energy storage state of charge (SOC) is greater than the minimum allowable energy storage state of charge (SOCmin);

[0103] S103, if the energy storage state of charge SOC is greater than the minimum allowable energy storage state of charge SOCmin, use the logic: PESS=min(Pld-Pdg-ΔPc, PESSd) to process the maximum discharge output PESSd to adjust the energy storage discharge output PESS;

[0104] S104, if the energy storage state of charge SOC is less than or equal to the energy storage minimum allowable state of charge SOCmin, adjust the energy storage discharge output PESS = 0;

[0105] S105, determining whether the output energy storage discharge output PESS is equal to the maximum discharge output PESSd;

[0106] S106 , if the output energy storage discharge output PESS is equal to the maximum discharge output PESSd, then determine whether the maximum load reduction ΔPc1 is 0;

[0107] S107, if the maximum load reduction amount ΔPc1 is 0, then make the load reduction ΔPc=0;

[0108] S108. If the maximum load reduction amount ΔPc1 is not 0, the load is reduced according to the following logic: load reduction ΔPc=min(Pld-Pdg-PESS, ΔPc1);

[0109] S109, determining whether the load reduction ΔPc output is the maximum load reduction ΔPc1;

[0110] S1010, if the load reduction ΔPc output is the maximum load reduction ΔPc1, the mains power output is calculated using the following logic: Pg = Pld - Pdg - PESSd - ΔPc, and the process jumps to step S1018;

[0111] S1011. If the power load forecast Pld(j) is less than or equal to the new energy output forecast Pdg(j), determine whether the maximum load increase ΔPc2 is 0;

[0112] S1012, if the maximum load increase ΔPc2 is 0, set the load increase ΔPc=0;

[0113] S1013. If the maximum load increase ΔPc2 is not 0, increase the load according to the following logic: ΔPc = min(Pdg - Pld, ΔPc2);

[0114] S1014, determining whether the increased load ΔPc is equal to the maximum increased load amount ΔPc2;

[0115] S1015. If the load increase ΔPc is equal to the maximum load increase ΔPc2, the PV output decrease is obtained using the following logic: ΔPpv = min(Pdg - Pld - ΔPc, Ppv). Otherwise, the process jumps to step S1018.

[0116] S1016, determining whether the photovoltaic output reduction amount ΔPpv is the actual photovoltaic output Ppv;

[0117] S1017. If the photovoltaic power output reduction amount ΔPpv is the actual photovoltaic power output Ppv, the wind power output reduction amount ΔPw = Pdg - Pld - ΔPc - ΔPpv is obtained according to the following logic;

[0118] S1018, determining whether the short-scale loop variable j is equal to m, if so, ending the short-time-scale processing step;

[0119] S1019: Determine whether the long-scale loop variable i is equal to n. If so, end the long-scale processing step.

[0120] like Figure 3 As shown, the process of formulating the time-series zero-carbon balance control strategy also includes:

[0121] S101′: when the power load forecast Pld(j) is not equal to the new energy output forecast Pdg(j), determine whether the power load forecast Pld(j) is greater than the new energy output forecast Pdg(j);

[0122] S102', if the power load forecast Pld(j) is greater than the new energy output forecast Pdg(j), then determine whether the maximum load reduction ΔPc1 is 0;

[0123] S103', if yes, reduce the load ΔPc to 0;

[0124] S104', if not, reduce the load according to the following logic: ΔPc=min(Pld-Pdg, ΔPc1);

[0125] S105', determining whether the load reduction ΔPc is the maximum load reduction ΔPc1;

[0126] S106', if yes, determine whether the energy storage state of charge SOC is greater than the energy storage minimum allowable state of charge SOCmin, if not, jump to step S1022';

[0127] S107', if the energy storage state of charge SOC is less than or equal to the energy storage minimum allowable state of charge SOCmin, then adjust the energy storage discharge output PESS = 0;

[0128] S108', if the energy storage state of charge SOC is greater than the minimum allowable energy storage state of charge SOCmin, the maximum discharge output PESSd is processed using the logic: PESS=min(Pld-Pdg-ΔPc, PESSd) to adjust the energy storage discharge output PESS;

[0129] S109', determining whether the energy storage discharge output PESS is equal to the maximum discharge output PESSd;

[0130] S1010', if yes, use the logic to calculate the mains power output: Pg = Pld - Pdg - PESSd - ΔPc; if no, jump to step S1022';

[0131] S1011′: If the electric load forecast Pld(j) is less than or equal to the new energy output forecast Pdg(j), determine whether the energy storage state of charge SOC is less than the maximum allowable energy storage state of charge SOCmax;

[0132] S1012′: If yes, process the maximum energy storage charging output PESSc using the following logic: PESS=min(Pdg-Pld, PESSc) to adjust and obtain the energy storage charging output PESS;

[0133] S1013', making the energy storage discharge output PESS = 0;

[0134] S1014', determining whether the output energy storage charging output PESS is equal to the maximum energy storage charging output PESSc;

[0135] S1015': If yes, determine whether the maximum load increase ΔPc2 is 0;

[0136] S1016', if yes, then reduce the load ΔPc=0;

[0137] S1017', if not, then use this logic to increase the load ΔPc=min(Pdg-Pld-PESSc, ΔPc2);

[0138] S1018', determining whether the increased load ΔPc is equal to the maximum increased load amount ΔPc2;

[0139] S1019', if yes, then obtain the photovoltaic output reduction amount using the following logic: ΔPpv = min(Pdg-Pld-PESSc-ΔPc, Ppv); if no, then jump to step 1022';

[0140] S1020', determining whether the photovoltaic output reduction amount ΔPpv is equal to the actual photovoltaic output Ppv;

[0141] S1021', if yes, then obtain the wind power output reduction amount according to the following logic: ΔPw = Pdg - Pld - PESSc - ΔPc - ΔPpv;

[0142] S1022′, if not, determine whether the short-scale loop variable j is equal to m, and if so, end the short-time-scale strategy formulation step;

[0143] S1023', determine whether the long-scale loop variable i is equal to n, and if so, end the long-scale strategy formulation step.

[0144] In this embodiment, the load and distributed energy output of the operating day are predicted based on the historical load data on the building user side and the data from the nearby meteorological station. The operating day is divided into n time periods with a time scale of T. This is a long-term prediction. Taking into account the wind and solar power prediction system and prediction accuracy, it is generally at the hourly level. The average power load prediction P'ld(i) and the new energy output prediction P'dg(i) in each time period of the operating day are obtained.

[0145] Due to the current high cost of energy storage batteries, to ensure their lifespan, it is important to minimize the frequency of charge and discharge cycles. Therefore, based on long-term prediction results, when P'ld(i) is less than P'dg(i), the energy storage battery is considered to be in charging mode, and the strategy should ensure that the battery is charged to the set SOC upper limit. When P'ld(i) is less than P'dg(i), the energy storage battery is considered to be in discharging mode, and the number of charge and discharge cycles should be minimized during discharge to protect the energy storage battery.

[0146] During the actual operation of the operation day, a short-term forecast of the load and distributed energy output is performed. The i-th period is divided into m small time periods with a time scale of t. This is a short-time scale forecast, generally at the minute level, and the power load forecast Pld(j), the new energy output forecast Pdg(j), and the time scale t are obtained.

[0147] In this embodiment, based on long-term predictions, the energy storage charging and discharging state mode is judged, and the charging (discharging) state should be maintained as much as possible over a long period of time. Based on short-term predictions, a time-series zero-carbon balance control strategy is formulated.

[0148] 1) When the energy storage is identified as being in charging state mode, if Pld(j)>Pdg(j), to ensure power balance, the controllable load is first considered to be reduced. If the reduction conditions are met, the load is reduced by ΔPc=min(Pld-Pdg,ΔPc1), where ΔPc1 is the maximum load reduction. When ΔPc output is ΔPc1, it means that Pld(j) is still greater than or equal to Pdg(j) at this time, and the energy storage is considered to participate in discharge. When the energy storage state of charge SOC>the minimum allowable energy storage state of charge SOCmin, the energy storage meets the discharge conditions, and the energy storage discharge output PESS=min(Pld-Pdg-ΔPc, PESSd), where PESSd is the maximum discharge output. When the output energy storage discharge output PESS is PESSd, the mains power should be considered, and the mains power output Pg=Pld-Pdg-PESSd-ΔPc, and the mains power-time curve is output to the database.

[0149] If Pld(j)<Pdg(j), give priority to charging the energy storage battery. When the energy storage state of charge SOC<the maximum allowable energy storage state of charge SOCmax, the energy storage has the charging conditions, and the energy storage charging output PESS=min(Pdg-Pld, PESSc), where PESSc is the maximum energy storage charging output. When the output energy storage charging output PESS is PESSc, the controllable load should be increased. If the increase conditions are met, increase the load ΔPc=min(Pdg-Pld-PESSc, ΔPc2), where ΔPc2 is the maximum increase. Load adjustment; when ΔPc output is ΔPc2, consideration should be given to reducing the output of new energy, and the photovoltaic order should take precedence over the wind turbine; photovoltaic power generation has a simple structure and fast start and stop speed, while wind power generation has a rotating device and a longer start and stop time. Therefore, priority should be given to reducing the photovoltaic output, and the photovoltaic output reduction amount ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv), Ppv is the actual photovoltaic output, and when the output ΔPpv is Ppv, the wind power output should be reduced, and the wind power output reduction amount ΔPw=Pdg-Pld-PESSc-ΔPc-ΔPpv.

[0150] When the energy storage is identified as a discharge state mode, if Pld(j)>Pdg(j), the energy storage is first considered to participate in the discharge. When the energy storage state of charge SOC>the minimum allowable energy storage state of charge SOCmin, the energy storage meets the discharge conditions, and the energy storage discharge output PESS is adjusted to min(Pld-Pdg, PESSd). When the output energy storage discharge output PESS is PESSd, the controllable load should be considered to be reduced. If the reduction conditions are met, the load is reduced by ΔPc=min(Pld-Pdg-PESS,ΔPc1). When the ΔPc output is ΔPc1, the mains power should be considered to be used, and the mains power output Pg=Pld-Pdg-PESSd-ΔPc.

[0151] If Pld(j) < Pdg(j), in order to avoid frequent charging and discharging switching of the energy storage battery, the controllable load should be increased first. If the conditions for increase are met, the load should be increased by ΔPc = min(Pdg-Pld, ΔPc2). When the ΔPc output is ΔPc2, the photovoltaic output should be reduced. The photovoltaic output reduction amount ΔPpv = min(Pdg-Pld-PESSc-ΔPc, Ppv), where Ppv is the actual photovoltaic output. When the output ΔPpv is Ppv, the wind power output should be reduced. The wind power output reduction amount ΔPw = Pdg-Pld-ΔPc- ΔPpv.

[0152] Example 2

[0153] like Figure 4 As shown, the building microgrid control system based on time-series zero-carbon balance provided by the present invention includes: a microgrid energy management system 1, including: a microgrid controller 101, an AC / DC energy router 102, wherein the microgrid controller 101 is connected to the AC / DC energy router 102 for bidirectional interaction of monitoring data and microgrid control data;

[0154] In this embodiment, the microgrid energy management system 1 is connected to the weather station 2 to obtain climate and environmental data collected by the weather station 2; the inverter 3, the converter 5, and the bidirectional converter 7 are respectively connected to the AC / DC energy router 102 to upload real-time grid data, and receive microgrid control signals sent by the microgrid controller 101 through the AC / DC energy router;

[0155] In this embodiment, distributed photovoltaic panels 4 are connected to inverters 3, wind turbines 6 are connected to converters 5, and energy storage batteries 8 are connected to bidirectional converters 7. These components generate power and change charge and discharge states according to microgrid control signals. In this embodiment, AC / DC energy routers 102 implement source-storage-load operating states and power control by connecting to photovoltaic, wind power, energy storage, and controllable loads. In this embodiment, microgrid controllers 101 primarily collect actual data from each unit, control AC / DC energy routers 102, and implement bidirectional communication.

[0156] The system categorizes loads into controllable loads 10 and uncontrollable loads 11. Controllable loads 10 can be adjusted and controlled according to system policies to optimize power balance. Building load forecasts are based on historical load data from the previous day or similar days.

[0157] In summary, the present invention optimizes the allocation of loads and energy units in a hierarchical manner based on load and output forecasts at different time scales. This method can effectively guide the coordination of distributed energy, energy storage devices, and controllable loads, mitigate the impact of renewable energy, improve supply-side and demand-side responses, and provide a path to achieving zero-carbon buildings based on historical results.

[0158] The microgrid balancing strategy adopted in the present invention ensures that the battery is charged as much as possible until the set SOC charging upper limit. When P'ld(i) is less than P'dg(i), the energy storage battery is identified as a discharge state mode. During the discharge period, the number of charge and discharge switching should be minimized to protect the energy storage battery.

[0159] The short-time scale control of the present invention is mainly based on the control layer optimization control and the timing adjustment constraints of each unit. On this basis, a reasonable control strategy is adopted to achieve system energy management and balance through the coordination of distributed power sources, energy storage systems, and controllable loads.

[0160] This invention addresses the uncertainty of wind power, photovoltaic power, and other sources of output by predicting wind and solar power, reducing the uncertainty of intermittent energy sources to a certain extent. It employs multi-layer optimization control at different time scales. Long-term control is primarily based on global optimization management, taking into account the time-shifting characteristics of energy storage devices to ensure a reasonable distribution of overall energy storage charge and discharge, thereby reducing prediction errors for intermittent energy generation. This invention addresses the technical issues of intermittent and fluctuating power generation, safety hazards caused by frequent battery charging and discharging, cycle life degradation, and poor distributed collaborative balance in the prior art.

[0161] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A building microgrid control method based on time-series zero-carbon balance, characterized in that: The method comprises: S1. Collect and obtain historical load data on the building user side and data from nearby meteorological stations to predict the load and distributed energy output on the operating day. Divide the operating day into at least two time periods, assuming the time scale is T, and predict the load and distributed energy output on the operating day to obtain the predicted average power load P'ld(i) and the predicted new energy output data P'dg(i) for each time period of the operating day; S2. Taking the predicted average value of power load P'ld(i) and the predicted data of new energy output P'dg(i) in each time period of the operation day as the long-term prediction result; S3. During the actual operation on the operation day, short-term forecast the average power load forecast value P'ld(i) and the distributed energy output in each time period, thereby obtaining a short-time scale forecast result; S4. Determine the energy storage charge and discharge state mode of the energy storage battery based on the long-time scale prediction results, thereby maintaining the charge and discharge state of the energy storage battery over a long period of time, and process the short-time scale prediction results to formulate a time-series zero-carbon balance control strategy, wherein step S4 includes: S41. When the energy storage battery is identified as being in a charging state mode, if the short-term power load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j), the controllable load is reduced, the energy storage battery is discharged, and the mains power is output in sequence to balance the power of the microgrid; S42. If the short-term electricity load forecast Pld(j) is less than the short-term new energy output forecast Pdg(j), charging the energy storage battery, adjusting the energy storage charging output, increasing the controllable load, reducing the photovoltaic output, and reducing the wind power output are sequentially performed; S43. When the energy storage battery is identified as being in the discharge state mode, if the short-term power load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j), the energy storage battery is sequentially engaged in discharge, the energy storage discharge output is adjusted, and the controllable load and utility power output are reduced. S44. If the short-term electricity load forecast Pld(j) is less than the short-term renewable energy output forecast Pdg(j), sequentially increase the controllable load, reduce the photovoltaic output, and reduce the wind power output, thereby reducing the number of charge and discharge switching times of the energy storage battery; S5. Obtain the building microgrid operation data results according to the time-series zero-carbon balance control strategy, and process them to obtain the mains power usage period and mains power usage, and quantify the building microgrid operation data results to iteratively obtain no less than 2 zero-carbon building implementation paths, and optimize the building microgrid according to the zero-carbon building implementation path to achieve zero carbon.

2. A building microgrid control method based on time-series zero-carbon balance according to claim 1, characterized in that: The step S2 comprises: S21. When the predicted average value of the power load in each time period P'ld(i) is less than the predicted new energy output data P'dg(i), the energy storage battery is identified as being in a charging state mode, and the energy storage battery is charged accordingly until the energy storage battery reaches a preset SOC charging upper limit; S22. When the predicted average value of the power load P'ld(i) in each time period is less than the new energy output prediction data P'dg(i), the energy storage battery is identified as a discharge state mode to reduce the number of charge and discharge switching times of the energy storage battery.

3. A building microgrid control method based on time-series zero-carbon balance according to claim 1, characterized in that: The step S3 comprises: S31, dividing the i-th time period within the operating day into m small time periods; S32. Assume that the short-term prediction time scale is t; S33: Process the building user-side historical load data and the nearby weather station data corresponding to the small time period and the short-time prediction time scale t to obtain a short-time scale prediction result.

4. A building microgrid control method based on time-series zero-carbon balance according to claim 3, characterized in that: The short-term prediction time scale in step S32 includes: a minute-level scale.

5. A building microgrid control method based on time-series zero-carbon balance according to claim 3, characterized in that: The short-time scale prediction result in step S33 includes a short-term electricity load prediction Pld(j) and a short-term new energy output prediction Pdg(j).

6. A building microgrid control method based on time-series zero-carbon balance according to claim 1, characterized in that: The step S41 includes: S411. When the load reduction conditions are met, the load reduction is obtained by the following logic processing: ΔPc=min(Pld-Pdg,ΔPc1) Among them, ΔPc1 is the maximum downward load; S412, when the load reduction ΔPc output is ΔPc1, enabling the energy storage battery to participate in discharge; S413: When the energy storage state of charge (SOC) is greater than the minimum allowable energy storage state of charge (SOCmin), the energy storage battery is controlled to adjust the discharge output using the following logic: PESS=min(Pld-Pdg-ΔPc,PESSd) Among them, PESSd is the maximum discharge output; S414: When the output energy storage discharge output PESS is PESSd, the mains power output is controlled by the following logic: Pg=Pld-Pdg-PESSd-ΔPc, And output the mains power-time curve to the database.

7. The building microgrid control method based on time-series zero-carbon balance according to claim 1 is characterized in that: The step S42 includes: S421: Charge the energy storage battery. When the energy storage state of charge (SOC) is less than the maximum allowable energy storage state of charge (SOCmax), control the energy storage battery to adjust the charging output using the following logic: PESS = min(Pdg - Pld, PESSc) Among them, PESSc is the maximum charging output of energy storage; S422: When the output energy storage charging output PESS is the maximum energy storage charging output PESSc, the controllable load is increased using the following logic: ΔPc=min(Pdg-Pld-PESSc,ΔPc2) Among them, ΔPc2 is the maximum upward load; S423: When the output of the increased controllable load ΔPc reaches the maximum increased load ΔPc2, the output of renewable energy is reduced. The output of renewable energy of photovoltaic and wind turbines is reduced in sequence, and the output of photovoltaic is reduced according to the following logic: ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv) Among them, Ppv is the actual photovoltaic output; S424: When the photovoltaic output ΔPpv is equal to the actual photovoltaic output Ppv, reduce the wind power output according to the following logic: ΔPw=Pdg-Pld-PESSc-ΔPc-ΔPpv.

8. The building microgrid control method based on time-series zero-carbon balance according to claim 1 is characterized in that: The step S43 includes: S431: When the energy storage state of charge (SOC) is greater than the minimum allowable energy storage state of charge (SOCmin), it is determined that the energy storage battery meets the discharge conditions, and the energy storage battery discharge output is controlled according to the following logic: PESS=min(Pld-Pdg,PESSd); S432: When the energy storage battery discharge output PESS is PESSd, the controllable load is reduced according to the following logic: ΔPc=min(Pld-Pdg-PESS,ΔPc1); S432 , when the energy storage load ΔPc is output as ΔPc1 , the utility power output Pg is controlled by the following logic: Pg=Pld-Pdg-PESSd-ΔPc.

9. The building microgrid control method based on time-series zero-carbon balance according to claim 1 is characterized in that: The step S44 includes: S441. Increase the controllable load using the following logic: ΔPc=min(Pdg-Pld,ΔPc2); S442: When the output of the controllable load ΔPc is ΔPc2, the photovoltaic output is reduced according to the following logic: ΔPpv=min(Pdg-Pld-PESSc-ΔPc,Ppv) Among them, Ppv is the actual photovoltaic output; S443: When the output ΔPpv of the photovoltaic output to be adjusted downward is equal to the actual photovoltaic output Ppv, the wind power output is adjusted downward according to the following logic: ΔPw=Pdg-Pld-ΔPc-ΔPpv.

10. A building microgrid control system based on time-series zero-carbon balance, characterized in that: The system comprises: The operating daily load and renewable energy output forecasting module is used to collect and obtain historical load data on the building user side and data from nearby meteorological stations to predict the operating day load and distributed energy output. The operating day is divided into no less than two time periods, with the time scale set as T. The operating day load and distributed energy output are predicted based on this to obtain the predicted mean value of the electricity load P'ld(i) and the renewable energy output forecast data P'dg(i) in each time period of the operating day; A long-time scale prediction result module, configured to use the predicted mean value of the power load P'ld(i) and the new energy output prediction data P'dg(i) in each time period of the operating day as the long-time scale prediction result, the long-time scale prediction result module being connected to the operating daily load and new energy output prediction module; A short-time scale prediction module is used to predict the average power load prediction value P'ld(i) and the output of distributed energy in each time period during the actual operation of the operation day, thereby obtaining a short-time scale prediction result; A time-series zero-carbon balance control strategy formulation module is used to determine the energy storage charge and discharge state mode of the energy storage battery based on the long-time scale prediction results, thereby maintaining the charge and discharge state of the energy storage battery over a long period of time, and processing the short-time scale prediction results to formulate a time-series zero-carbon balance control strategy. The time-series zero-carbon balance control strategy formulation module is connected to the long-time scale prediction result module and the short-time scale prediction module, wherein the time-series zero-carbon balance control strategy formulation module includes: a microgrid balancing module configured to, when the energy storage battery is identified as being in a charging state, sequentially reduce the controllable load, discharge the energy storage battery, and generate utility power if the short-term power load forecast Pld(j) is greater than the short-term renewable energy output forecast Pdg(j), thereby balancing the microgrid power; a photovoltaic and wind power output reduction module, configured to sequentially charge the energy storage battery, adjust the energy storage charging output, increase the controllable load, reduce the photovoltaic output, and reduce the wind power output when the short-term power load forecast Pld(j) is less than the short-term new energy output forecast Pdg(j); a controllable load and utility power output reduction module, configured to, when the energy storage battery is identified as being in the discharge state mode, sequentially enable the energy storage battery to participate in discharge, adjust the energy storage discharge output, and reduce the controllable load and utility power output if the short-term power load forecast Pld(j) is greater than the short-term new energy output forecast Pdg(j); an energy storage charge and discharge frequency reduction module, configured to sequentially increase the controllable load, reduce the photovoltaic output, and reduce the wind power output when the short-term power load forecast Pld(j) is less than the short-term new energy output forecast Pdg(j), thereby reducing the charge and discharge switching frequency of the energy storage battery; The building microgrid zero-carbon implementation module is used to obtain the building microgrid operation data results according to the time-series zero-carbon balance control strategy, process them to obtain the mains power usage period and mains power usage, and quantify the building microgrid operation data results to iteratively obtain no less than two zero-carbon building implementation paths, optimize the building microgrid according to the zero-carbon building implementation path, and achieve zero carbon. The building microgrid zero-carbon implementation module is connected to the time-series zero-carbon balance control strategy formulation module.

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