Energy management and optimization method for SOFC-GT combined heat and power system

Abstract

The present application relates to the field of energy system optimization control, and specifically discloses a kind of energy management and optimization method of SOFC-GT combined heat and power system, adopts 15 minute time scale's intraday real-time rolling optimization method, in combination with prediction model, rolling optimization and feedback correction, real-time adjustment combined heat and power system equipment output, suppress control deviation;First, prediction model is established using historical data and random error;In control time domain, the system operation plan is optimized, and rolling optimization is realized;According to real-time data, the prediction error is corrected, and feedback correction is completed;Real-time simulation model is constructed, and the effect of energy management strategy is verified.The present application uses the above method, can reduce the influence of day-ahead prediction error, improve system scheduling accuracy, reduce performance loss, and give consideration to economy and environmental protection;Real-time simulation verification shows that the system stability and economy are good, and the energy utilization efficiency and operation reliability of SOFC-GT combined heat and power system are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of energy system optimization and control technology, and in particular to an energy management and optimization method for an SOFC-GT combined heat and power system. Background Technology

[0002] Energy is a vital material foundation for human survival and development. Currently, global energy consumption remains dominated by fossil fuels, leading to severe resource depletion, environmental pollution, and climate change. With increasing energy demand and growing environmental challenges, combined heat and power (CHP) systems, as a highly efficient energy utilization method, have attracted widespread attention. CHP systems offer an effective solution to current energy and environmental problems because they can improve efficiency through energy cascading and can be integrated with photovoltaic and wind power generation devices to absorb renewable energy.

[0003] Combined heat and power (CHP) systems comprehensively utilize the chemical energy of fuels, converting it into various forms of energy such as electricity and heat. This significantly improves energy efficiency, reduces dependence on fossil fuels, lowers carbon emissions, and achieves sustainable energy utilization.

[0004] However, existing energy management strategies cannot effectively cope with fluctuations in load demand and photovoltaic output during real-time operation, resulting in significant system operational deviations. Traditional energy management strategies typically schedule operations on an hourly basis, failing to respond promptly to fluctuations on finer time scales, which limits system stability and economic efficiency. For example, during peak photovoltaic power generation periods, failure to adjust system output in a timely manner may lead to curtailment, reducing energy utilization; conversely, during sudden increases in load demand, if the system cannot respond quickly, it may cause energy supply shortages, affecting normal user operations.

[0005] Furthermore, existing combined heat and power (CHP) systems also have certain limitations in terms of coordinated equipment operation. The output adjustment of individual devices within the system often relies on pre-set control logic, lacking the ability for real-time optimization and flexible allocation, making it difficult to fully utilize the overall system performance. At the same time, when faced with complex changes in operating conditions, such as sudden changes in weather conditions or equipment failures, traditional energy management strategies struggle to quickly and effectively respond, thus affecting the system's reliability and stability.

[0006] Therefore, in order to better cope with the fluctuations in load demand and photovoltaic output, improve the operating efficiency and stability of the cogeneration system, and reduce system operating costs and environmental impact, this invention proposes a more advanced and precise energy management strategy to achieve real-time optimized control of the cogeneration system. Summary of the Invention

[0007] The purpose of this invention is to provide an energy management and optimization method for SOFC-GT combined heat and power systems. By introducing rolling optimization on a 15-minute time scale, the system can adjust equipment output in real time and smooth out control deviations. This method combines predictive models, rolling optimization, and feedback correction, which can effectively reduce the impact of day-ahead prediction errors, improve the accuracy of system scheduling, and enable the system to flexibly adjust equipment output to respond to demand fluctuations and smooth out control deviations during real-time operation.

[0008] To achieve the above objectives, this invention provides an energy management and optimization method for an SOFC-GT combined heat and power system, comprising the following steps: S1. Based on the day-ahead energy management optimization results of the SOFC-GT cogeneration system, and combined with the latest load and photovoltaic data, a predictive model is established; S2. Establish a rolling optimization model and design an optimization objective function for it, taking into account the tracking day-ahead energy management plan, reducing the uncertainty of system operation, economic efficiency and environmental protection, and setting weight coefficients by weighted summation. S3. Set system operation constraints for the rolling optimization model, including power balance constraints, output constraints of each device, and electrical energy rotation reserve constraints. S4. Execute the real-time rolling optimization process, including initializing the output values ​​of each device; within each 15-minute time window, solve for the optimal control sequence based on the latest load and photovoltaic data; execute the optimal control scheme for the current time period and update the device output values ​​for the next time period to achieve feedback correction. S5. Build a simulation system based on a real-time simulation platform, and create a combined heat and power (CHP) operating environment with physical information interaction function by introducing a physical information interface and simulate its actual output characteristics. S6. Perform real-time simulation verification, run the intraday real-time rolling optimization model, and output the optimization results; verify the performance of the real-time rolling optimization model in grid-connected and off-grid modes, and compare the intraday real-time rolling optimization results with the day-ahead optimization results to verify the effectiveness of the intraday real-time rolling optimization model in responding to load fluctuations and smoothing control deviations.

[0009] Preferably, in S1, the prediction model uses historical data and random error simulation to predict fluctuations in load demand and photovoltaic output over a 15-minute timescale, and uses the predicted load and photovoltaic values ​​as input data for intraday rolling optimization, specifically: By simulating load forecast deviations through the superposition of random errors, the expression for the predicted load demand value and photovoltaic power generation output value is as follows: ; ; ; ; in, , , , These are the load and photovoltaic output values ​​during the day-ahead dispatch phase. , , , These are short-term forecasts for intraday load and photovoltaic output. , , , This is the threshold value for the fluctuation of the predicted values ​​of the day-ahead load and photovoltaic output during the time period t, which are the input variables. Let U be a random array that follows a U(-1,1) distribution.

[0010] Preferably, in S1, the established prediction model expression is as follows: ; The state variables in the prediction model are the output values ​​of all controllable equipment in the cogeneration system. The controllable variables of all controllable equipment in the cogeneration system include the output of SOFC-GT units, the charging and discharging power of energy storage batteries, the power exchange of the grid interconnection line, the thermal power of gas boilers, the thermal power of electric boilers, the cooling power of absorption chillers, and the cooling power of electric chillers. In the formula, It is the first in the t+k time period combined heat and power system i Predicted output values ​​of the adjustable equipment; For the t+k time period i Adjustment values ​​for the output of the adjustable equipment; For time period t, that is, the current control time period. i The actual output of the adjustable equipment.

[0011] Preferably, in S2, the rolling optimization model is established based on the day-ahead energy management optimization model. The optimization objectives include tracking the day-ahead energy management optimization results, reducing performance losses caused by uncertainties in system operation, and maintaining the system's economic and environmental benefits. The objective function of the rolling optimization model is: ; in, These are the weighting coefficients. , , , , , , , The following are the intraday optimization results at time t+k for SOFC-GT output, energy storage battery charging and discharging power, grid interconnection line switching power, gas boiler thermal power, electric boiler thermal power, absorption chiller cooling power, electric chiller cooling power, and energy storage battery SOC. , , , , , , , The results represent the day-ahead energy management strategy optimizations for SOFC-GT output, energy storage battery charging and discharging power, grid interconnection line switching power, gas boiler thermal power, electric boiler thermal power, absorption chiller cooling power, electric chiller cooling power, and energy storage battery SOC at time t+k.

[0012] Preferably, in S3, the power balance constraint is divided into cold, hot, and electrical power balance constraints, and the specific expressions are as follows: ; ; ; In the formula, , The output power of the photovoltaic power generation system and the SOFC-GT system are respectively. This represents the interaction power between the integrated energy system and the public power grid; under off-grid operation conditions, this value is 0. , For lithium batteries, at any given moment, at least one of the charging and discharging power must be 0. , Electricity is consumed by electric boilers and electric chillers. The electrical power required by the load; , , The output heat power of SOFC-GT system, gas boiler, and electric boiler are respectively. The absorption chiller requires heat power. The load requires thermal power; , The cooling power outputs of the absorption chiller and the electric chiller are respectively. The specific expression for the power grid output constraint is as follows: ; In the formula, , These represent the maximum and minimum constraints on the power interaction between the system and the public power grid, respectively. The specific expression for the equipment output constraint is as follows: ; ; ; ; ; ; ; In the formula, , These are the minimum output power of the photovoltaic power generation system and the SOFC-GT system, respectively. , These represent the maximum output power of the photovoltaic power generation system and the SOFC-GT system, respectively. , , These are the minimum output thermal power of SOFC-GT systems, gas boilers, and electric boilers, respectively. , , These are the maximum output thermal power of the SOFC-GT system, gas boiler, and electric boiler, respectively. , These are the minimum output cooling power of absorption chillers and electric chillers, respectively. , These are the maximum output cooling power of the absorption chiller and the electric chiller, respectively. The specific expression for the energy storage unit constraint is as follows: ; ; ; In the formula, , This represents the maximum charging and discharging power of the energy storage battery. , Minimum and maximum constraints for the state of charge of energy storage batteries; The specific expression for the electrical energy spinning reserve constraint is as follows: ; in, and These represent the maximum power exchanged between the energy storage battery and the grid interconnection line, respectively. This is the minimum electrical energy rotational reserve power that the system needs to maintain.

[0013] Preferably, in S4, the prediction error is corrected based on real-time operating data, the rolling optimization window moves forward, and during the intraday rolling optimization process, for the current control period t, the rolling optimization output result of the next period t+1 is executed; the error between the measured value and the predicted value of the equipment output in period t+1 is corrected, and the equipment output value in period t+1 is updated before entering the next round of optimization.

[0014] Preferably, in S5, a real-time simulation model of the system is established based on the mathematical model of the combined heat and power system. The model includes three parts: electronic system, thermal subsystem, and cold subsystem. The electronic system includes the SOFC-GT combined power generation system model, photovoltaic power generation system model, energy storage battery model, and system grid connection tie line model. The thermal subsystem includes the gas boiler model and the electric boiler model. The electric boiler is the load of the electronic system, and its actual output value needs to be fed back to the electronic system as the input value. The cold subsystem includes an absorption chiller model and an electric chiller model. The absorption chiller is the load of the thermal subsystem, and its actual output value needs to be fed back to the thermal subsystem as an input value. The electric chiller is the load of the electronic system, and its actual output value needs to be fed back to the electronic system as an input value. The energy storage battery model output controls the charging and discharging power through a bidirectional DC-DC converter. The SOFC-GT controller module operates as a local controller to track the power reference value.

[0015] Preferably, in S6, the real-time optimized operation of the SOFC-GT cogeneration system is simulated through a real-time simulation platform. The intraday real-time rolling optimization model is run, the optimization results are output, and the performance of the real-time rolling optimization model in grid-connected and off-grid modes is verified. The intraday real-time rolling optimization results are compared with the day-ahead optimization results to verify the effectiveness of the intraday real-time rolling optimization model in responding to load fluctuations and smoothing control deviations.

[0016] Therefore, the energy management and optimization method for the SOFC-GT combined heat and power system described above has the following beneficial effects: (1) Based on the day-ahead energy management optimization results, the present invention implements intraday rolling optimization. By eliminating the execution deviation between the plan 24 hours in advance and the real-time scheduling, and smoothing out power fluctuations at a finer time scale (15 minutes), the invention significantly reduces the impact of prediction errors and improves scheduling accuracy and load dynamic response capability while maintaining the economical and environmentally friendly operation of the system.

[0017] (2) Compared with the traditional energy management strategy based on hours, the daily real-time rolling optimization results of this invention are more accurate. By making real-time fine corrections to the equipment output, the photovoltaic output and load demand fluctuations can be controlled at the minute level, effectively reducing control deviation and solving the problem of insufficient dynamic response on a small time scale in traditional methods.

[0018] (3) This invention uses a real-time simulation platform to conduct detailed simulations of the real-time optimized operation of the SOFC-GT combined heat and power system. The results are as follows: In grid-connected mode, the system can respond promptly to load demand and photovoltaic output fluctuations, utilize energy storage batteries to quickly respond to SOFC-GT power regulation needs, and ensure a continuous and stable power supply through peak shaving and valley filling; In off-grid mode, the overall operating trend of the system is consistent with the day-ahead trend, and the SOC curve change trend is basically consistent, effectively avoiding curtailment and improving the energy utilization rate of the system. The verification shows that the system can flexibly adjust equipment output during real-time operation, smooth out control deviations, and improve the stability and economy of the system.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] Figure 1 This is an overall flowchart of the energy management and optimization method for an SOFC-GT combined heat and power system according to the present invention; Figure 2 This is a graph showing the day-ahead and intraday variation curves of load demand and photovoltaic output in an embodiment of the energy management and optimization method for an SOFC-GT combined heat and power system of the present invention. (a) is a graph of electrical load demand data, (b) is a graph of heat load demand data, (c) is a graph of cooling load demand data, and (d) is a graph of solar irradiance data. Figure 3 This is a daily rolling optimization flowchart of an embodiment of the energy management and optimization method for an SOFC-GT combined heat and power system according to the present invention; Figure 4 This is a time-domain flowchart of an embodiment of an energy management and optimization method for an SOFC-GT combined heat and power system according to the present invention. Figure 5 This is a diagram showing the real-time grid optimization operation results in grid-connected mode of an embodiment of the energy management and optimization method for an SOFC-GT combined heat and power system according to the present invention. Figure 6 This is a diagram showing the real-time optimized operation results of the cold and hot networks in grid-connected mode, according to an embodiment of the energy management and optimization method of an SOFC-GT combined heat and power system of the present invention. (a) is a diagram showing the optimized operation results of the hot network in grid-connected mode, and (b) is a diagram showing the optimized operation results of the cold network in grid-connected mode. Figure 7 This is a graph showing the SOC changes of the energy storage battery under the day-ahead and intraday optimization modes in an embodiment of the energy management and optimization method for an SOFC-GT combined heat and power system of the present invention in grid-connected mode; Figure 8This is a diagram showing the real-time grid optimization operation results in off-grid mode of an embodiment of the energy management and optimization method for an SOFC-GT combined heat and power system according to the present invention. Figure 9 This is a diagram showing the real-time optimized operation results of the cold and hot networks in off-grid mode, according to an embodiment of the energy management and optimization method of an SOFC-GT combined heat and power system of the present invention. (a) is the optimized operation result diagram of the hot network in grid-connected mode, and (b) is the optimized operation result diagram of the cold network in grid-connected mode. Figure 10 This is a graph showing the SOC changes of the energy storage battery under off-grid mode in a specific embodiment of the energy management and optimization method for an SOFC-GT combined heat and power system according to the present invention, with day-ahead and intraday optimization. Detailed Implementation

[0021] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0023] like Figure 1 As shown, an energy management and optimization method for an SOFC-GT combined heat and power system includes the following steps: S1. Based on the day-ahead energy management optimization results of the SOFC-GT cogeneration system, and combined with the latest load and photovoltaic data, a predictive model is established.

[0024] The established forecasting model uses historical data and random error simulations to predict fluctuations in load demand and photovoltaic output over a 15-minute timescale. The predicted load and photovoltaic values ​​are used as input data for intraday rolling optimization. Specifically: By simulating load forecast deviations through the superposition of random errors, the expression for the predicted load demand value and photovoltaic power generation output value is as follows: ; ; ; ; in, , , , These are the load and photovoltaic output values ​​during the day-ahead dispatch phase. , , , These are short-term forecasts for intraday load and photovoltaic output. , , , This is the threshold value for the fluctuation of the predicted values ​​of the day-ahead load and photovoltaic output during the time period t, which are the input variables. Let U be a random array that follows a U(-1,1) distribution.

[0025] The established prediction model expression is as follows: ; ; ; The rewritten, more concise, generalized expression is as follows: ; The state variables in the prediction model are the output values ​​of all controllable equipment in the cogeneration system. The controllable variables of all controllable equipment in the cogeneration system include the output of SOFC-GT units, the charging and discharging power of energy storage batteries, the power exchange power of grid interconnection lines, the thermal power of gas boilers, the thermal power of electric boilers, the cooling power of absorption chillers, and the cooling power of electric chillers.

[0026] In the formula, It is the first in the t+k time period combined heat and power system i Predicted output values ​​of the adjustable equipment; For the t+k time period i Adjustment values ​​for the output of the adjustable equipment; For time period t, that is, the current control time period. i The actual output of the adjustable equipment.

[0027] Based on the above rolling forecast model, the day-ahead and intraday variation curves of various load demands and photovoltaic output of the SOFC-GT cogeneration system can be obtained, as follows: Figure 2 As shown.

[0028] S2. Establish a rolling optimization model and design an optimization objective function for it, taking into account the tracking day-ahead energy management plan, reducing the uncertainty of system operation, economic efficiency and environmental protection, and setting weight coefficients by weighted summation.

[0029] The established rolling optimization model optimizes the system operation plan within the control time domain, making the system operation results as close as possible to the day-ahead scheduling results. The rolling optimization model is built on the day-ahead energy management optimization model, and the optimization objectives include tracking the day-ahead energy management optimization results, reducing the performance loss caused by the uncertainty of system operation, and maintaining the economic and environmental benefits of the system.

[0030] The objective function of the rolling optimization model is: ; in, These are the weighting coefficients. , , , , , , , The following are the intraday optimization results at time t+k for SOFC-GT output, energy storage battery charging and discharging power, grid interconnection line switching power, gas boiler thermal power, electric boiler thermal power, absorption chiller cooling power, electric chiller cooling power, and energy storage battery SOC. , , , , , , , The results represent the day-ahead energy management strategy optimizations for SOFC-GT output, energy storage battery charging and discharging power, grid interconnection line switching power, gas boiler thermal power, electric boiler thermal power, absorption chiller cooling power, electric chiller cooling power, and energy storage battery SOC at time t+k.

[0031] S3. Set system operation constraints for the rolling optimization model, including power balance constraints, output constraints of each device, and electrical energy rotation reserve constraints.

[0032] Power balance constraints are divided into cold, hot, and electrical power balance constraints, and their specific expressions are as follows: ; ; ; In the formula, , The output power of the photovoltaic power generation system and the SOFC-GT system are respectively. This represents the interaction power between the integrated energy system and the public power grid; under off-grid operation conditions, this value is 0. , For lithium batteries, at any given moment, at least one of the charging and discharging power must be 0. , Electricity is consumed by electric boilers and electric chillers. The electrical power required by the load; , , The output heat power of SOFC-GT system, gas boiler, and electric boiler are respectively. The absorption chiller requires heat power. The load requires thermal power; , These represent the cooling power outputs of the absorption chiller and the electric chiller, respectively.

[0033] The specific expression for the power grid output constraint is as follows: ; In the formula, , These represent the maximum and minimum constraints on the power interaction between the system and the public power grid, respectively.

[0034] The specific expression for the equipment output constraint is as follows: ; ; ; ; ; ; ; In the formula, , These are the minimum output power of the photovoltaic power generation system and the SOFC-GT system, respectively. , These represent the maximum output power of the photovoltaic power generation system and the SOFC-GT system, respectively. , , These are the minimum output thermal power of SOFC-GT systems, gas boilers, and electric boilers, respectively. , , These are the maximum output thermal power of the SOFC-GT system, gas boiler, and electric boiler, respectively. , These are the minimum output cooling power of absorption chillers and electric chillers, respectively. , These represent the maximum output cooling power of the absorption chiller and the electric chiller, respectively.

[0035] The specific expression for the energy storage unit constraint is as follows: ; ; ; In the formula, , This represents the maximum charging and discharging power of the energy storage battery. , The minimum and maximum values ​​of the state of charge of the energy storage battery are constrained.

[0036] Secondly, to ensure the reliability of the combined heat and power (CHP) system, a certain amount of spinning reserve is required. Since the inertial time constants of cold and hot energy are relatively large, short-term mismatches between energy supply and load demand will not affect the safe and stable operation of the system, and spinning reserve can be disregarded. However, the time constant of electrical energy is only on the order of milliseconds, so electrical spinning reserve must be considered. In this embodiment of the CHP system, the main devices providing electrical spinning reserve are energy storage batteries, and the power exchanged through the grid interconnect can also be adjusted in emergency situations.

[0037] The specific expression for the electrical energy spinning reserve constraint is as follows: ; in, and These represent the maximum power exchanged between the energy storage battery and the grid interconnection line, respectively. This is the minimum electrical energy rotational reserve power that the system needs to maintain.

[0038] S4. Execute the real-time rolling optimization process, including initializing the output values ​​of each device; within each 15-minute time window, solve for the optimal control sequence based on the latest load and photovoltaic data; execute the optimal control scheme for the current time period and update the device output values ​​for the next time period to achieve feedback correction.

[0039] Intraday rolling optimization process as follows: Figure 3 As shown, the prediction error is corrected based on real-time operating data, and the rolling optimization window moves forward. During the intraday rolling optimization process, for the current control period t, the rolling optimization output results for the next period t+1 are executed; however, due to the existence of load demand, photovoltaic power output fluctuations and prediction errors, it is necessary to correct the error between the measured and predicted values ​​of equipment output in period t+1 in a timely manner. Before entering the next round of optimization, the equipment output value in period t+1 is updated first.

[0040] The specific time-domain implementation process is as follows: Figure 4As shown in the figure, Nc represents the control time domain and Np represents the prediction time domain. Since optimization within a rolling cycle requires knowing all the information about the system operation within that cycle, Np ≥ Nc is required. During time period t, based on the predicted system operation data in the prediction time domain, a certain optimization method is used in the control time domain to perform control optimization, solving for the optimal control sequence for system operation within the control time domain. The first element of the control sequence is executed in this control time period. After control execution is completed, the process is rolled forward, repeating the above optimization process in time period t+1 until the optimization of the entire scheduling cycle is completed. In this invention, the daytime energy management cycle is 24 hours, with a time scale of 1 hour. To better describe the fluctuations of the system operation within a smaller time scale during the day, intraday rolling optimization is initiated every 15 minutes, with the control time domain Nc = 4 and the prediction time domain Np = 4, meaning the rolling window length is 1 hour.

[0041] S5. Build a simulation system based on a real-time simulation platform. By introducing a physical information interface, create a combined heat and power (CHP) operating environment with physical information interaction function and simulate its actual output characteristics.

[0042] Based on the mathematical model of the combined heat and power (CHP) system, a real-time simulation model of the system is established. The entire system consists of three parts: an electronic system, a thermal subsystem, and a cooling subsystem. The electronic system includes models of the SOFC-GT combined power generation system, the photovoltaic power generation system, the energy storage battery, and the system grid connection. The thermal subsystem includes models of a gas-fired boiler and an electric boiler, where the electric boiler serves as the load of the electronic system, and its actual output value needs to be fed back to the electronic system as input value.

[0043] The cold subsystem includes an absorption chiller model and an electric chiller model. The absorption chiller is the load of the thermal subsystem, and its actual output value needs to be fed back to the thermal subsystem as an input value. The electric chiller is the load of the electronic system, and its actual output value needs to be fed back to the electronic system as an input value. The energy storage battery model output controls the charging and discharging power through a bidirectional DC-DC converter. The SOFC-GT controller module operates as a local controller to track the power reference value.

[0044] S6. Simulate the real-time optimized operation of the SOFC-GT cogeneration system using a real-time simulation platform. Run the intraday real-time rolling optimization model, output the optimization results, and verify the performance of the real-time rolling optimization model in grid-connected and off-grid modes. Compare the intraday real-time rolling optimization results with the day-ahead optimization results to verify the effectiveness of the intraday real-time rolling optimization model in responding to load fluctuations and mitigating control deviations.

[0045] A detailed simulation of the real-time optimized operation of the SOFC-GT cogeneration system was conducted using a real-time simulation platform to analyze the performance of energy management strategies in response to load demand and photovoltaic output fluctuations on a smaller intraday timescale. To facilitate calculations and data analysis, the operating condition length was compressed by a factor of 10 (8640s), and the simulation step size was set to 0.1ms. The weighting coefficients in the intraday rolling optimization objective function were set to... =0.5, =0.3, =0.5, =0.1, =0.1, =0.1, =0.1, =0.1, and the simulation results will be analyzed below.

[0046] Traditional day-ahead energy management strategies operate on an hourly basis, resulting in coarse-grained scheduling and an inability to respond promptly to load fluctuations on finer time scales. In contrast, intraday real-time rolling optimization provides more precise results, and in combined heat and power (CHP) systems, each power unit tracks the day-ahead energy management plan while making subtle adjustments to its output, thus responding promptly to fluctuations in photovoltaic output and load demand and reducing control deviations.

[0047] Figure 5 The results of real-time grid optimization under grid-connected mode are shown. It can be seen that the trend of intraday real-time rolling optimization results is basically consistent with the day-ahead dispatch results, and it can respond promptly to load demand and photovoltaic output fluctuations on a 15-minute time scale. In particular, some small peaks appeared in the power curve during the operation of the energy storage battery. This is because when SOFC-GT adjusts the output power, the faster-responding energy storage battery is needed to perform peak shaving and valley filling to ensure the continuity and stability of power supply.

[0048] Figure 6 The results of real-time optimized operation of the cold and hot grids under grid-connected mode were presented. The overall trend was basically consistent with the day-ahead dispatch results, but minor adjustments were made to the equipment output to address fluctuations in intraday load demand and photovoltaic output. Especially in Figure 6 In the results of the optimized operation of the heating network in (a), the electric boiler, which serves as a backup device in the daytime energy management strategy, was activated for real-time optimization during the day to cope with the fluctuation of load demand on the 15-minute time scale.

[0049] Figure 7The comparison of SOC changes of energy storage batteries under the grid-connected mode with day-ahead optimization and intraday optimization shows that, due to the role of peak shaving and valley filling in the intraday real-time optimization operation, and the smaller capacity of the energy storage batteries under the grid-connected mode, there are certain differences in the changing trends of the two SOC curves. In addition, the SOC exceeded the set threshold in the intraday real-time optimization, which should be avoided. A certain margin should be left when designing the battery capacity.

[0050] Figure 8 The results of real-time grid optimization of the combined heat and power system in off-grid mode were demonstrated. Since the system cannot interact with the public power grid in off-grid mode, the consumption of photovoltaic power generation can only rely on energy storage batteries. At the same time, the energy storage batteries also play a role in dynamic response when the SOFC-GT power generation is adjusted. During the operation, some small peaks appeared in the power curve. Apart from that, the overall operating trend of the system is consistent with that of the daytime.

[0051] Figure 9 The results of real-time optimized operation of the cold and hot grids in off-grid mode were presented. The overall trend was basically consistent with the day-ahead dispatch results, but minor adjustments were made to the equipment output to address fluctuations in daily load demand and photovoltaic output. Especially in Figure 9 In the results of the optimized operation of the heating network shown in (a), the electric boiler, which serves as a backup device in the daytime energy management strategy, is activated in real-time optimization during the day to cope with the fluctuation of load demand on the 15-minute time scale.

[0052] Figure 10 The comparison of SOC changes of energy storage batteries under off-grid mode and intraday optimization shows that although energy storage batteries play a role in peak shaving and valley filling during intraday real-time optimization, the capacity of energy storage batteries is larger in off-grid mode, and the two SOC curves show a basically consistent trend. However, in intraday real-time optimization, the SOC exceeds the set threshold as in grid-connected mode, so a certain margin should be left when designing battery capacity.

[0053] Therefore, this invention adopts the above-mentioned energy management and optimization method for SOFC-GT cogeneration system. Based on a real-time simulation platform, a daily real-time rolling optimization system simulation model of SOFC-GT cogeneration system is constructed. By introducing a daily real-time rolling optimization method with a time scale of 15 minutes, the system can cope with the fluctuations in photovoltaic output and load demand on a finer time scale during real-time operation. Simulation results show that this method can flexibly adjust the equipment output to respond to demand fluctuations based on the day-ahead energy management optimization results, smooth out control deviations, and improve the stability and economy of the system.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An energy management and optimization method for an SOFC-GT combined heat and power system, characterized in that, Includes the following steps: S1. Based on the day-ahead energy management optimization results of the SOFC-GT cogeneration system, and combined with the latest load and photovoltaic data, a predictive model is established; S2. Establish a rolling optimization model and design an optimization objective function for it, taking into account the tracking day-ahead energy management plan, reducing the uncertainty of system operation, economic efficiency and environmental protection, and setting weight coefficients by weighted summation. S3. Set system operation constraints for the rolling optimization model, including power balance constraints, output constraints of each device, and electrical energy rotation reserve constraints. S4. Execute the real-time rolling optimization process, including initializing the output values ​​of each device; within each 15-minute time window, solve for the optimal control sequence based on the latest load and photovoltaic data; execute the optimal control scheme for the current time period and update the device output values ​​for the next time period to achieve feedback correction. S5. Build a simulation system based on a real-time simulation platform, and create a combined heat and power (CHP) operating environment with physical information interaction function by introducing a physical information interface and simulate its actual output characteristics. S6. Perform real-time simulation verification, run the intraday real-time rolling optimization model, and output the optimization results; The performance of the real-time rolling optimization model under grid-connected and off-grid modes was verified, and the intraday real-time rolling optimization results were compared with the day-ahead optimization results to verify the effectiveness of the intraday real-time rolling optimization model in responding to load fluctuations and mitigating control deviations.

2. The energy management and optimization method for an SOFC-GT combined heat and power system according to claim 1, characterized in that, In S1, the prediction model uses historical data and random error simulations to predict fluctuations in load demand and photovoltaic output over a 15-minute timescale, and uses the predicted load and photovoltaic values ​​as input data for intraday rolling optimization, specifically: By simulating load forecast deviations through the superposition of random errors, the expression for the predicted load demand value and photovoltaic power generation output value is as follows: ； ； ； ； in, , , , These are the load and photovoltaic output values ​​during the day-ahead dispatch phase. , , , These are short-term forecasts for intraday load and photovoltaic output. , , , This is the threshold value for the fluctuation of the predicted values ​​of the day-ahead load and photovoltaic output during the time period t, which are the input variables. Let U be a random array that follows a U(-1,1) distribution.

3. The energy management and optimization method for an SOFC-GT combined heat and power system according to claim 2, characterized in that, In S1, the established prediction model expression is as follows: ； The state variables in the prediction model are the output values ​​of all controllable equipment in the cogeneration system. The controllable variables of all controllable equipment in the cogeneration system include the output of SOFC-GT units, the charging and discharging power of energy storage batteries, the power exchange of the grid interconnection line, the thermal power of gas boilers, the thermal power of electric boilers, the cooling power of absorption chillers, and the cooling power of electric chillers. In the formula, It is the first in the t+k time period combined heat and power system i Predicted output values ​​of the adjustable equipment; For the t+k time period i Adjustment values ​​for the output of the adjustable equipment; For time period t, that is, the current control time period. i The actual output of the adjustable equipment.

4. The energy management and optimization method for an SOFC-GT combined heat and power system according to claim 1, characterized in that, In S2, the rolling optimization model is built upon the day-ahead energy management optimization model. The optimization objectives include tracking the day-ahead energy management optimization results, reducing performance losses caused by system operational uncertainties, and maintaining the system's economic and environmental performance. The objective function of the rolling optimization model is: ； in, These are the weighting coefficients. , , , , , , , The following are the intraday optimization results at time t+k for SOFC-GT output, energy storage battery charging and discharging power, grid interconnection line switching power, gas boiler thermal power, electric boiler thermal power, absorption chiller cooling power, electric chiller cooling power, and energy storage battery SOC. , , , , , , , The results represent the day-ahead energy management strategy optimizations for SOFC-GT output, energy storage battery charging and discharging power, grid interconnection line switching power, gas boiler thermal power, electric boiler thermal power, absorption chiller cooling power, electric chiller cooling power, and energy storage battery SOC at time t+k.

5. The energy management and optimization method for an SOFC-GT combined heat and power system according to claim 1, characterized in that, In S3, power balance constraints are divided into cold, hot, and electrical power balance constraints, and their specific expressions are as follows: ； ； ； In the formula, , The output power of the photovoltaic power generation system and the SOFC-GT system are respectively. This represents the interaction power between the integrated energy system and the public power grid; under off-grid operation conditions, this value is 0. , For lithium batteries, at any given moment, at least one of the charging and discharging power must be 0. , Electricity is consumed by electric boilers and electric chillers. The electrical power required by the load; , , The output heat power of SOFC-GT system, gas boiler, and electric boiler are respectively. The absorption chiller requires heat power. The load requires thermal power; , The cooling power outputs of the absorption chiller and the electric chiller are respectively. The specific expression for the power grid output constraint is as follows: ； In the formula, , These represent the maximum and minimum constraints on the power interaction between the system and the public power grid, respectively. The specific expression for the equipment output constraint is as follows: ； ； ； ； ； ； ； In the formula, , These are the minimum output power of the photovoltaic power generation system and the SOFC-GT system, respectively. , These represent the maximum output power of the photovoltaic power generation system and the SOFC-GT system, respectively. , , These are the minimum output thermal power of SOFC-GT systems, gas boilers, and electric boilers, respectively. , , These are the maximum output thermal power of the SOFC-GT system, gas boiler, and electric boiler, respectively. , These are the minimum output cooling power of absorption chillers and electric chillers, respectively. , These are the maximum output cooling power of the absorption chiller and the electric chiller, respectively. The specific expression for the energy storage unit constraint is as follows: ； ； ； In the formula, , This represents the maximum charging and discharging power of the energy storage battery. , Minimum and maximum constraints for the state of charge of energy storage batteries; The specific expression for the electrical energy spinning reserve constraint is as follows: ； in, and These represent the maximum power exchanged between the energy storage battery and the grid interconnection line, respectively. This is the minimum electrical energy rotational reserve power that the system needs to maintain.

6. The energy management and optimization method for an SOFC-GT combined heat and power system according to claim 1, characterized in that, In S4, the prediction error is corrected based on real-time running data, and the rolling optimization window moves forward. During the intraday rolling optimization process, for the current control period t, the rolling optimization output result of the next period t+1 is executed. Correct the error between the measured and predicted equipment output values ​​for time period t+1. Before proceeding to the next round of optimization, update the equipment output values ​​for time period t+1.

7. The energy management and optimization method for an SOFC-GT combined heat and power system according to claim 1, characterized in that, In S5, a real-time simulation model of the combined heat and power system is established based on the mathematical model of the combined heat and power system. The model consists of three parts: electronic system, thermal subsystem, and cold subsystem. The electronic system includes the SOFC-GT combined power generation system model, photovoltaic power generation system model, energy storage battery model, and system grid connection model. The thermal subsystem includes the gas boiler model and the electric boiler model. The electric boiler is the load of the electronic system, and its actual output value needs to be fed back to the electronic system as the input value. The cold subsystem includes an absorption chiller model and an electric chiller model. The absorption chiller is the load of the thermal subsystem, and its actual output value needs to be fed back to the thermal subsystem as an input value. The electric chiller is the load of the electronic system, and its actual output value needs to be fed back to the electronic system as an input value. The energy storage battery model output controls the charging and discharging power through a bidirectional DC-DC converter. The SOFC-GT controller module operates as a local controller to track the power reference value.

8. The energy management and optimization method for an SOFC-GT combined heat and power system according to claim 1, characterized in that, In S6, the real-time optimized operation of the SOFC-GT cogeneration system is simulated through a real-time simulation platform. The intraday real-time rolling optimization model is run, the optimization results are output, and the performance of the real-time rolling optimization model in grid-connected and off-grid modes is verified. The intraday real-time rolling optimization results are compared with the day-ahead optimization results to verify the effectiveness of the intraday real-time rolling optimization model in responding to load fluctuations and smoothing control deviations.

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