Energy management optimization method for energy storage system of building integrated photovoltaic cell electric vehicle

Abstract

The invention relates to the technical field of energy management optimization, and provides an energy management optimization method for an energy storage system of a building integrated photovoltaic cell electric vehicle, and the method is characterized in that the method comprises the steps: planning a power grid to output a supply building load; judging whether the total building load is greater than planned power grid output; in the electricity consumption trough period of the building, the remaining planned power grid outputs electricity to the electric vehicle and the energy storage battery in sequence for charging; in the peak period of building power consumption, photovoltaic power generation supplies unsatisfied building loads, whether the photovoltaic power generation amount is larger than the unsatisfied building loads or not is judged, if yes, the remaining photovoltaic power generation charges the electric vehicle and the energy storage battery in sequence, the photovoltaic power generation allowance is calculated, and if not, the unsatisfied building loads are covered with the electric vehicle and the energy storage battery in sequence; through intelligent dispatching of power grid output, photovoltaic power generation, the electric vehicle and the energy storage battery, the building load requirement is effectively balanced, and energy utilization is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy management optimization, and particularly relates to an energy management optimization method for a building integrated photovoltaic cell electric vehicle energy storage system. Background Art

[0002] In the current global energy consumption pattern, building energy consumption occupies a crucial position. In the building field, the application of photovoltaic technology is particularly critical, which can not only significantly improve the building's energy self-sufficiency ability but also effectively reduce the building's operating costs. Building integrated photovoltaic (BIPV) technology, as an innovative concept of the deep integration of photovoltaics and building materials, can not only meet the building's energy needs but also has good decorative properties, showing great development potential.

[0003] Building integrated photovoltaic technology shows significant advantages in providing sustainable electricity and improving the building indoor environment. However, due to the limitations of the installation orientation and area of the building facade for solar photovoltaic power generation, it is difficult to achieve dynamic matching of photovoltaic power generation with building loads in terms of time and power. After photovoltaic power generation is stabilized and inverted, it is connected to the building power grid to meet part of the building load, which can reduce building electricity bills and operating carbon emissions. When the traditional energy management strategy of maximizing photovoltaic utilization directly consumes photovoltaic window power generation to preferentially meet building loads, since the photovoltaic power generation is relatively small compared to the building load, for example, the annual total power generation of semi-transparent vacuum photovoltaic windows installed on all four sides of a typical high-rise office building in Guangzhou accounts for about 15.82% of the building's annual total load. The building loads consumed are mainly stable building loads that exist throughout the day. This not only cannot improve the current situation of the public power grid bearing peak and valley loads but may instead increase the pressure on the power grid to bear greater load peak and valley fluctuations, resulting in low robustness and low economy of the energy system for the application of semi-transparent photovoltaic window buildings, and causing obstacles to the engineering promotion of the application of solar photovoltaic technology in buildings.

[0004] Secondly, solar photovoltaic power generation not only affects building loads, but also affects the net power consumption of buildings when its photovoltaic power generation capacity is connected to the building power system. Currently, some studies have proposed a technical-economic-environmental optimization decision-making method for building integrated photovoltaic systems with hybrid batteries and electric vehicle energy storage based on actual household data to improve the energy autonomy of integrated photovoltaic and battery storage units and achieve self-generation and self-consumption. The research found that there is great potential in the economy of household photovoltaic battery energy storage systems. Some studies have established an optimization model of neural network genetic algorithm for building integrated photovoltaic and energy storage systems for net-zero energy consumption buildings, with the loss of power supply rate, carbon emissions and installation cost as the optimization objectives, and pointed out the compatibility of battery energy storage with net-zero energy consumption buildings. Another study has carried out multi-objective optimization for building integrated photovoltaic and energy storage buildings to optimize power supply reliability, levelized cost of energy, carbon emissions and human development index, indicating that there is obvious decarbonization potential by exporting surplus energy to the power grid. However, there are few current studies considering the robustness and economy of building power grids to explore the optimization strategy of energy system management for solar photovoltaic building applications and clarify the flexible scheduling mechanism between solar photovoltaic power generation and building power systems.

[0005] Therefore, the development of building integrated photovoltaics is of great significance for the development of renewable power supply and the reduction of carbon emissions in the construction industry. Summary of the Invention

[0006] In view of the above-mentioned defects of the prior art, the present invention proposes an energy management optimization method for a building integrated photovoltaic battery electric vehicle energy storage system, which is characterized by including the following steps:

[0007] S10: Plan the power grid output to supply building loads;

[0008] S20: Determine whether the total building load is greater than the planned power grid output. If it is, it is determined as the peak building electricity consumption period and S40 is executed; otherwise, it is determined as the off-peak building electricity consumption period and S30 is executed;

[0009] S30: During the off-peak building electricity consumption period, the remaining planned power grid output is used to charge the electric vehicle and the energy storage battery in sequence, and the remaining amount of the planned power grid output is calculated, and the process ends;

[0010] S40: During the peak building electricity consumption period, the photovoltaic power generation supplies the building load that is not satisfied;

[0011] S50: Determine whether the photovoltaic power generation amount is greater than the unsatisfied building load. If it is, S60 is executed; otherwise, S70 is executed;

[0012] S60: The remaining photovoltaic power generation is used to charge the electric vehicle and the energy storage battery in sequence, and the remaining amount of the photovoltaic power generation is calculated, and the process ends;

[0013] S70: Cover the unmet building load with an electric vehicle and an energy storage battery in sequence. If the load is still not met, use additional grid output to meet the building load.

[0014] Preferably, the S60 further includes:

[0015] After the remaining photovoltaic power generation charges the electric vehicle and the energy storage battery in sequence, when there is still remaining photovoltaic power generation, it is fed back to the public grid to support the power supply demand of the grid during peak periods.

[0016] Preferably, the method further includes:

[0017] During the peak period of building electricity consumption, evaluate the peak shaving performance of the building load by defining a peak shaving factor. The formula is as follows:

[0018]

[0019] In the formula, Peak is the peak shaving factor, P load is the building load, GO is the actual grid output, and PGO is the planned grid output.

[0020] Preferably, the method further includes:

[0021] During the off-peak period of building electricity consumption, evaluate the valley filling performance of the building load by defining a valley filling factor. The formula is as follows:

[0022]

[0023] In the formula, Valley is the valley filling factor, P load is the building load, GO is the actual grid output, and PGO is the planned grid output.

[0024] Preferably, the method further includes:

[0025] During the annual operation of the building, divide it into a cooling season and a non-cooling season to calculate the planned grid output. The formula is as follows:

[0026]

[0027] In the formula, PGO is the planned grid output, PGO C is the planned grid output in the cooling season, PGO NC is the planned grid output in the non-cooling season;

[0028] Evaluate the average peak shaving and valley filling performance of the photovoltaic energy storage system during the annual operation of the building under the proposed energy management method by defining a peak shaving and valley filling factor LSF. The formula is as follows:

[0029]

[0030] In the formula, LSF is the peak shaving and valley filling factor, P load is the building load, GO is the actual power grid output, PGO is the planned power grid output, T C is the refrigeration season, T NC is the non-refrigeration season.

[0031] Preferably, the method further includes:

[0032] Evaluating the power grid output stability of the system by defining the power grid robustness factor GRF, and the formula is as follows:

[0033]

[0034] In the formula, GRF is the power grid robustness factor, CV GO with BIPV is the coefficient of variation of the power grid output in the case of integrating a photovoltaic and energy storage system, CV load with out BIPV is the coefficient of variation of the power grid output in the baseline case without photovoltaic and energy storage, and BIPV is the building integrated photovoltaic system.

[0035] Beneficial effects:

[0036] The present application proposes an energy management optimization method for a building integrated photovoltaic battery electric vehicle energy storage system, which adopts various decision-making methods for multi-objective optimization design, evaluates system performance indicators, including the peak shaving and valley filling factor and the power grid robustness factor, and conducts sensitivity analysis to explore the interaction effects of battery capacity and power grid configuration on the performance of the energy system. Finally, an optimal energy system design scheme that meets peak shaving and valley filling, power grid robustness, and economy is proposed, which can provide a reference for decision-makers. Brief Description of the Drawings

[0037] Figure 1 is a schematic flowchart of a preferred embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of the optimized design scheme of a preferred embodiment of the present invention. Detailed Embodiments

[0039] The following describes the embodiments of the present invention in detail. The following embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given, but the protection scope of the present invention is not limited to the following embodiments.

[0040] As Figure 1 shown, the present invention proposes an energy management optimization method for a building integrated photovoltaic battery electric vehicle energy storage system, which is characterized by including the following steps:

[0041] S10: Supply the building load with the planned power grid output;

[0042] S20: Determine whether the total building load is greater than the planned grid output. If so, it is determined as the peak period of building electricity consumption and S40 is executed; otherwise, it is determined as the low period of building electricity consumption and S30 is executed.

[0043] S30: During the low period of building electricity consumption, the remaining planned grid output is used to charge the electric vehicle and the energy storage battery in sequence, and the remaining output of the planned grid is calculated, and the process ends.

[0044] S40: During the peak period of building electricity consumption, the photovoltaic power generation supplies the building load that is not satisfied.

[0045] S50: Determine whether the photovoltaic power generation amount is greater than the unsatisfied building load. If so, execute S60; otherwise, execute S70.

[0046] S60: The remaining photovoltaic power generation is used to charge the electric vehicle and the energy storage battery in sequence. After the remaining photovoltaic power generation is calculated, the process ends.

[0047] S70: Use the electric vehicle and the energy storage battery to cover the unsatisfied building load in sequence. If it is still not satisfied, use the additional grid output to satisfy the building load.

[0048] Preferably, S60 further includes:

[0049] After the remaining photovoltaic power generation is used to charge the electric vehicle and the energy storage battery in sequence, when there is still remaining photovoltaic power generation, it is fed back to the public grid to support the power supply demand of the grid during the peak period.

[0050] Preferably, the method further includes:

[0051] During the peak period of building electricity consumption, evaluate the peak shaving performance of the building load by defining the peak shaving factor. The formula is as follows:

[0052]

[0053] In the formula, Peak is the peak shaving factor, P load is the building load, GO is the actual grid output, and PGO is the planned grid output.

[0054] Preferably, the method further includes:

[0055] During the low period of building electricity consumption, evaluate the valley filling performance of the building load by defining the valley filling factor. The formula is as follows:

[0056]

[0057] In the formula, Valley is the valley filling factor, P load is the building load, GO is the actual grid output, and PGO is the planned grid output.

[0058] Specifically, the peak shaving performance refers to a performance indicator in the power system that reduces the peak of electricity consumption load or energy demand, thereby reducing system pressure, optimizing resource allocation, and reducing costs. When the peak shaving factor is equal to 1, it means that the actual grid output during the peak electricity consumption period of the building is equal to the planned grid output, and the peak shaving performance is the best; when the peak shaving factor is equal to 0, it means that the actual grid output during the peak electricity consumption period of the building is equal to the building load, and the peak shaving performance is the worst, that is, the electric energy generated by photovoltaic power generation, electric vehicles, and battery storage cannot supply power.

[0059] The valley filling performance refers to a performance in the power system that increases the electricity consumption during the low-demand period (valley period) of electricity demand by adjusting electricity consumption behavior or using energy storage devices, thereby balancing the load curve of the power system and improving energy utilization efficiency. When the valley filling factor is equal to 1, it means that the actual grid output during the low electricity consumption period of the building is equal to the planned grid output, and the valley filling performance is the best; when the valley filling factor is equal to 0, it means that the actual grid output during the low electricity consumption period of the building is equal to the building load, and the valley filling performance is the worst, that is, the electric vehicles and batteries do not store electricity.

[0060] Preferably, the method further includes:

[0061] During the annual operation of the building, it is divided into the cooling season and the non-cooling season to calculate the planned grid output, and the formula is as follows:

[0062]

[0063] In the formula, PGO is the planned grid output, PGO C is the planned grid output in the cooling season, PGO NC is the planned grid output in the non-cooling season;

[0064] The average peak shaving and valley filling performance of the photovoltaic energy storage system during the annual operation of the building under the proposed energy management method is evaluated by defining the peak shaving and valley filling factor LSF, and the formula is as follows:

[0065]

[0066] In the formula, LSF is the peak shaving and valley filling factor, P load is the building load, GO is the actual grid output, PGO is the planned grid output, T C is the cooling season, T NC is the non-cooling season.

[0067] Preferably, the method further includes:

[0068] The grid output stability of the system is evaluated by defining the grid robustness factor GRF, and the formula is as follows:

[0069]

[0070] In the formula, GRF is the grid robustness factor, and CV GO with BIPV is the coefficient of variation of the grid output in the case of an integrated photovoltaic and energy storage system, and CV load with out BlPV is the coefficient of variation of the grid output in the reference case without photovoltaic and energy storage. BIPV is the building-integrated photovoltaic system.

[0071] Specifically, when GRF is less than 1, it indicates that the output stability of the grid is improved after integrating the building-integrated photovoltaic and energy storage system, the volatility of the grid output is reduced, and the stability is increased; when GRF is greater than 1, it indicates that the output stability of the grid is not improved after integrating the building-integrated photovoltaic and energy storage system, the volatility of the grid output is increased, and the stability is reduced.

[0072] In addition, the coefficient of variation is a relative index used in statistics to measure the degree of data dispersion. The smaller the coefficient of variation, the smaller the relative volatility of the data and the higher the stability; the larger the coefficient of variation, the larger the relative volatility of the data and the lower the stability.

[0073] As Figure 2 shown, an energy management optimization method for a building-integrated photovoltaic battery electric vehicle energy storage system proposed by the present invention. Among them, the building-integrated photovoltaic battery electric vehicle energy storage system includes a rooftop photovoltaic, a semi-transparent photovoltaic window, a battery energy storage module, an electric vehicle module, and is connected to the public grid; a simulation of the rooftop photovoltaic, the semi-transparent photovoltaic window, the battery energy storage module, and the electric vehicle module is constructed based on the TRNSYS platform and the System Advisor Model tool, including simulating the rooftop photovoltaic system with Type 103b components and predicting the current-voltage performance of the rooftop photovoltaic module using the maximum power point tracking technology; simulating the dynamic state of charge of the energy storage lithium-ion battery with Type 47a components, considering the charging and discharging efficiency, the state of health of the battery, and the battery life; quantifying through the chi-square distribution and the normal distribution to simulate the actual usage of electric vehicles, considering the random arrival time and parking time, and evaluating the impact of the degradation of the vehicle battery on the vehicle performance; simulating the energy flow and distribution in high-rise buildings under different weather conditions and times, inputting local climate parameters (such as Guangzhou), and setting the building system model including modules such as building envelopes, air conditioners, lighting, and equipment to obtain the building load.

[0074] In addition, through the coupled simulation optimization design platform TRNSYS and jEplus+EA, a multi-objective optimization design of the building-integrated photovoltaic battery electric vehicle energy storage system is carried out. The multi-objective optimization design parameters are set, including the planned grid output and static battery storage capacity in the cooling season and non-cooling season. Global optimization calculations are carried out with the peak shaving and valley filling - grid robustness indicators of the system as multi-objectives, including the peak shaving and valley filling factor and the grid robustness factor, to obtain the Pareto solution set of the multi-objective optimization design. Based on the Pareto solution set, the decision-making method of the non-dominated sorting genetic algorithm NSGA-II is used to determine the optimal design scheme. Based on the principle of the minimum distance to the ideal point, a solution that achieves the best balance between the peak shaving and valley filling factor and the grid robustness factor is selected to determine the optimal system configuration. Further, through sensitivity analysis, the effects of battery capacity, planned grid output in the cooling season and non-cooling season on the peak shaving and valley filling factor and the grid robustness factor are evaluated, and the variables that have the most significant impact on the optimization objectives are determined, so as to provide theoretical guidance with technical - economic feasibility for decision-makers.

[0075] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations according to the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. An energy management optimization method for a building-integrated photovoltaic battery electric vehicle energy storage system, characterized in that It includes the following steps: S10: Plan the grid output to supply the building load; S20: Determine whether the total building load is greater than the planned grid output. If so, it is determined as the peak building electricity consumption period and S40 is executed; otherwise, it is determined as the off-peak building electricity consumption period and S30 is executed; S30: During the off-peak building electricity consumption period, the remaining planned grid output is used to charge the electric vehicle and the energy storage battery in sequence, and the remaining amount of the planned grid output is calculated, and the process ends; S40: During the peak building electricity consumption period, the photovoltaic power generation supplies the unmet building load; S50: Determine whether the photovoltaic power generation amount is greater than the unmet building load. If so, S60 is executed; otherwise, S70 is executed; S60: The remaining photovoltaic power generation is used to charge the electric vehicle and the energy storage battery in sequence, and the remaining amount of the photovoltaic power generation is calculated, and the process ends; S70: Use the electric vehicle and the energy storage battery to cover the unmet building load in sequence. If it is still not met, use the additional grid output to meet the building load.

2. The energy management optimization method of an integrated building photovoltaic cell electric vehicle energy storage system according to claim 1, characterized in that S60 further includes: After the remaining photovoltaic power generation is used to charge the electric vehicle and the energy storage battery in sequence, when there is still remaining photovoltaic power generation, it is fed back to the public grid to support the power supply demand of the grid during the peak period.

3. The energy management optimization method for an integrated building photovoltaic cell electric vehicle energy storage system according to claim 1, characterized in that, The method further includes: During the peak building electricity consumption period, evaluate the peak shaving performance of the building load by defining a peak shaving factor, and the formula is as follows: where Peak is the peak shaving factor, P load is the building load, GO is the actual grid output, and PGO is the planned grid output.

4. The energy management optimization method for an integrated building photovoltaic battery electric vehicle energy storage system according to claim 1, characterized in that, The method further includes: During the off-peak building electricity consumption period, evaluate the valley filling performance of the building load by defining a valley filling factor, and the formula is as follows: Wherein, Valley is the valley period filling factor, P load is the building load, GO is the actual power grid output, and PGO is the planned power grid output.

5. The energy management optimization method of an integrated building photovoltaic cell electric vehicle energy storage system according to claim 1, characterized in that, The method further includes: During the annual operation of the building, it is divided into the cooling season and the non-cooling season to calculate the planned grid output, and the formula is as follows: In the formula, PGO is the planned grid output, and PGO C is the planned grid output in the refrigeration season, and PGO NC is the planned grid output in the non-refrigeration season; Evaluate the average peak shaving and valley filling performance of the photovoltaic energy storage system during the annual operation of the building under the proposed energy management method by defining a peak shaving and valley filling factor LSF, and the formula is as follows: where LSF is the peak shaving and valley filling factor, P load is the building load, GO is the actual power grid output, PGO is the planned power grid output, T C is the refrigeration season, T NC is the non-refrigeration season.

6. The energy management optimization method of an integrated building photovoltaic cell electric vehicle energy storage system according to claim 1, characterized in that The method further includes: Evaluate the grid output stability of the system by defining a grid robustness factor GRF, and the formula is as follows: where GRF is the grid robustness factor, and CV GOwithBIPV is the coefficient of variation of the grid output in the case of an integrated photovoltaic and energy storage system, and CV loadwithoutBIPV is the coefficient of variation of the grid output in the reference case without photovoltaic and energy storage, and BIPV is the building integrated photovoltaic system.

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