A grid-forming converter and electric heat cold energy storage system collaborative optimization control method

Abstract

The application discloses a kind of network type converter and electric heat cold energy storage system collaborative optimization control method, contain steps as follows: construct "network type converter-electric heat cold energy storage-flexible load" multi-objective optimization model;Establish the collaborative constraint of network type converter and energy storage system;Establish demand response and network state linkage mechanism;Solve the multi-objective optimization problem by NNC algorithm and combine TOPSIS method to filter optimal operation scheme;According to optimal operation scheme, dynamically adjust the virtual inertia and droop coefficient of network type converter, synchronously dispatch electric energy storage charge-discharge power and cold energy storage and / or heat energy storage electric drive equipment operating state.The method can quickly and accurately suppress new energy output fluctuation and load mutation, strictly constrain system voltage deviation and frequency deviation within a more optimal operating range, fundamentally enhancing the transient stability and dynamic power quality of the grid with high proportion of new energy access.

Description

Technical Field

[0001] This invention belongs to the field of grid-type power electronics technology, and specifically relates to a collaborative optimization control method for grid-type converters and electrothermal energy storage systems. Background Technology

[0002] As the proportion of new energy sources (photovoltaics, wind power, etc.) in the energy system continues to increase, grid-connected converters are gradually becoming core equipment for maintaining system voltage and frequency stability. However, in actual operation, grid-connected converters still face two key problems that urgently need to be solved:

[0003] 1. Grid-type converters mostly operate independently and do not form an effective coordination mechanism with the electric, thermal, and cold energy storage system. When the output of new energy sources fluctuates significantly (such as a sudden drop in photovoltaic output due to cloud cover or a sudden change in wind power output due to wind speed changes) or there is a sudden increase in load, relying solely on the virtual inertia regulation and droop control functions of the grid-type converter itself has obvious limitations in its regulation capability. This can easily lead to problems such as excessive voltage sag and excessive frequency fluctuation range in the system, and in severe cases, it may even affect the safe operation of the entire energy system.

[0004] 2. Traditional integrated energy system optimization strategies often focus on the dual objectives of "operating costs and pollutant emissions," failing to incorporate the operating status of grid-connected converters into the optimization system. This leads to a complete disconnect between energy storage charging and discharging scheduling, load adjustment, and grid requirements. For example, during peak renewable energy output periods, if the energy storage system blindly charges, it will further increase the active power output pressure on the grid-connected converters, causing the system frequency to be too high. Conversely, during peak load periods, the lack of effective adjustment for flexible loads will force the grid-connected converters to operate at full load for extended periods, shortening equipment lifespan and reducing system stability. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a collaborative optimization control method for grid-type converters and electric thermal energy storage systems. By linking the electric thermal energy storage system, the operating status of the grid-type converter is deeply coupled with energy storage scheduling and load adjustment, thereby solving the problems of poor independent operation stability of the equipment and insufficient collaboration with the electric thermal energy storage system.

[0006] The technical solution of the present invention is as follows:

[0007] A method for coordinated optimization control of a grid-type converter and an electric thermal energy storage system includes the following steps:

[0008] S1. Construct a multi-objective optimization model of "grid-type converter - electric heating and cooling energy storage - flexible load" with the optimization objectives of minimizing system operating costs, minimizing pollutant emissions, and minimizing the state objectives of the grid-type converter. The state objectives of the grid-type converter are characterized by voltage deviation and frequency deviation. The operating costs include the operation and maintenance costs of the grid-type converter, the charging and discharging loss costs of energy storage, the costs of new energy power generation, and the grid interaction costs. The grid-type converter includes photovoltaic inverters, wind power converters, and energy storage converters, all of which adopt grid-based control strategies.

[0009] S2. Establish coordinated constraints between the grid-type converter and the energy storage system. Set the voltage regulation range of the grid-type converter to ±2% of the rated voltage and the frequency regulation dead zone to a frequency deviation of <0.1Hz. Define the power response delay of the grid-type converter and the new energy power generation equipment to ≤50ms. Establish the coupling relationship between the grid-type converter and the electric energy storage, thermal energy storage, and cold energy storage. When a voltage deviation >1% is detected, trigger the rapid charging and discharging of the electric energy storage, with a response time ≤100ms. When the frequency deviation >0.05Hz, link the electric drive equipment of the cold energy storage and / or thermal energy storage to adjust the power demand.

[0010] S3. Establish a demand response and grid status linkage mechanism to monitor electrical, thermal, and cooling loads, and establish a "grid status - load adjustment" mapping rule for flexible loads. The flexible loads include interruptible cooling loads and transferable electrical loads. When the grid-type converter is in a voltage sag and the deviation is 1%-2%, 30% of the interruptible cooling load is reduced. When the system frequency is higher than 50.1Hz, 50% of the transferable electrical load is transferred to off-peak hours. The load flexibility adjustment reduces the regulation pressure on the grid-type converter.

[0011] S4. Solve the multi-objective optimization problem using the NNC algorithm and combine it with the TOPSIS method to select the optimal running scheme;

[0012] S5. Using the grid-type converter as the core control node, real-time data on new energy power generation output, energy storage capacity, and load demand are collected. The virtual inertia and droop coefficient of the grid-type converter are dynamically adjusted according to the optimal operation scheme. The charging and discharging power of electric energy storage and the operating status of electric drive equipment for cold energy storage and / or thermal energy storage are simultaneously scheduled to achieve synergistic optimization of grid stability, system economy, and environmental protection.

[0013] A further improvement to the technical solution of this invention lies in the fact that the coupling relationship between the electrical energy storage and the grid-type converter in S2 satisfies the formula:

[0014] ;

[0015] In the formula, This represents the active power output of the grid-type converter at time t. This represents the active power output of the photovoltaic inverter at time t. This represents the active power output of the wind turbine converter at time t. Represents the charging power of the stored electrical energy at time t. This represents the discharge power of the stored electrical energy at time t. This represents the amount of active power compensation required by the grid-type converter at time t to maintain frequency stability; and satisfy: ;

[0016] In the formula, This is the droop factor, with a value range of 0.05-0.1 pu / Hz; Let t be the system frequency deviation at time t.

[0017] A further improvement of the technical solution of the present invention is that the coupling relationship between the thermal energy storage and the grid-type converter in S2 is as follows: when the voltage deviation of the grid-type converter is less than or equal to 1% and the frequency deviation is less than or equal to 0.05Hz, the thermal energy storage is preferentially charged by driving the gas turbine; when the grid-type converter requires reactive power compensation and the reactive power reserve is less than 20% of the rated capacity, the power of the electric drive heat pump is reduced to reduce reactive power consumption, and the gas boiler is switched to auxiliary heating simultaneously to ensure that the reactive power reserve of the grid-type converter is restored to greater than or equal to 20% of the rated capacity.

[0018] A further improvement to the technical solution of this invention is as follows: the coupling relationship between cold energy storage and the grid-type converter in S2 is as follows: when the system voltage deviation ΔU(t) is greater than 1%, the starting power of the electric chiller associated with the cold energy storage is adjusted. By adjusting the operating power of the chiller unit in response to the system voltage change, the power response delay is less than or equal to 100ms; when the system frequency deviation Δf(t) is greater than 0.05Hz, the cold energy storage enters the linkage control mode, and the cold energy storage rate is increased on the basis of the existing operation of the electric chiller to improve the cooling power and convert excess electrical energy into cold energy for storage in the cold energy storage device, thereby consuming the system's surplus active power and providing downlink support for the system frequency.

[0019] A further improvement to the technical solution of this invention lies in the following: the mapping rule of "grid status - load adjustment" in S3 satisfies the following quantitative relationship: when the voltage deviation of the grid-type converter... At that time, the amount of cooling load that can be interrupted is:

[0020] ;

[0021] In the formula, Let t be the total cooling load at time t, and the load execution response time after the reduction command is sent be ≤60s; when the frequency deviation At that time, the transferable electrical load transfer amount is:

[0022] ;

[0023] In the formula, Let t be the maximum transferable electrical load. The transfer process adopts a "start first, then stop" switching method, and the switching interval is less than or equal to 10s.

[0024] A further improvement to the technical solution of this invention is as follows: The specific steps of S4 are as follows: The multi-objective optimization problem is transformed into a single-objective sequential optimization using the NNC algorithm. First, the single-objective optimal solution that minimizes the state objective of the grid converter, the system operating cost, and the pollutant emission is obtained as the Pareto front anchor point. After normalizing the objective function, the Utopian line is equally divided to generate segment points. Voltage and frequency threshold constraints of the grid converter are added to solve the corresponding single-objective optimal solution, forming a uniform Pareto front. The weighted proximity of each solution is calculated using the TOPSIS method, where the voltage / frequency deviation of the grid converter has a weight of 30%, and the operating cost and pollutant emission each have a weight of 35%, and the optimal operating scheme is selected.

[0025] A further improvement of the technical solution of the present invention is that the normalization process of the objective function in S4 satisfies the following rules:

[0026] The normalization formula for operating costs is:

[0027] ;

[0028] In the formula, For actual operating costs, To minimize operating costs, This represents the maximum operating cost.

[0029] The normalization formula for pollutant emissions is:

[0030] ;

[0031] In the formula, This represents the actual amount of pollutants emitted. To minimize emissions, This represents the maximum emission value.

[0032] The state normalization formula for a grid-type converter is:

[0033] ;

[0034] In the formula, This represents the target state value for an actual grid-type converter. The minimum value of the state objective. The objective is the maximum value of the state objective; and the normalized values ​​of the three objective functions are all within the interval [0,1].

[0035] A further improvement to the technical solution of this invention is as follows: the specific rules for dynamically adjusting the virtual inertia and droop coefficient of the grid-type converter in S5 are as follows: when the system frequency change rate is greater than 0.2Hz / s, the virtual inertia of the grid-type converter is increased to 4-5s; when the frequency change rate is less than 0.1Hz / s, the virtual inertia is decreased to 2-3s; when the system active power reserve is greater than 30% of the rated power, the droop coefficient is 0.05-0.07pu / Hz; when the system active power reserve is less than 20% of the rated power, the droop coefficient is 0.08-0.1pu / Hz.

[0036] A further improvement to the technical solution of the present invention lies in: the The calculation satisfies the formula:

[0037] ;

[0038] In the formula, The rated power of the photovoltaic inverter. Let t be the real-time solar irradiance. Solar irradiance under standard test conditions. For power temperature coefficient, Let t be the actual temperature of the photovoltaic module. The component temperature is under standard test conditions.

[0039] A further improvement to the technical solution of the present invention lies in: the The calculation satisfies the following classification rules: when the real-time wind speed (Cut-in wind speed) or (When cutting out wind speed), ;when At (rated wind speed), , The rated power of the wind power converter; when hour, .

[0040] The beneficial effects of this invention are:

[0041] 1. This invention breaks through the limitations of independent control of traditional grid-connected equipment. By establishing a collaborative response mechanism with the multi-energy storage system (electricity, heat, and cold) and flexible loads, a multi-timescale stability defense line is constructed. This method can quickly and accurately suppress fluctuations in new energy output and sudden load changes, and strictly constrain system voltage and frequency deviations within a better operating range, fundamentally enhancing the transient stability and dynamic power quality of the power grid under high-proportion new energy access.

[0042] 2. This invention pioneered a multi-objective optimization model centered on "operating cost - pollutant emissions - grid-type converter status" and employed the NNC algorithm for accurate and uniform solution of the Pareto front. It overcomes the one-sidedness of traditional single-objective or dual-objective optimization strategies and can intelligently find the optimal balance between economy and environmental protection under the rigid constraint of ensuring stable system operation. This achieves global optimization of the overall system performance and avoids other performance degradation problems caused by pursuing the extreme of a single objective.

[0043] 3. This invention achieves differentiated synergy between grid-type converters and heterogeneous energy storage (electric, thermal, and cold) through a refined coupling constraint model. It not only utilizes the rapid power response characteristics of electrical energy storage, but also deeply explores and schedules the energy time shift and power regulation potential of thermal and cold energy storage, forming an integrated control pattern "with electricity as the core and thermal and cold as support". This effectively expands the system's flexible adjustment resources and improves the overall energy utilization efficiency.

[0044] 4. This invention achieves full-process intelligentization from "optimization solution" to "scheme selection" by combining the NNC algorithm with the TOPSIS method; the generated control scheme set can uniformly cover the Pareto front, ensuring the diversity and representativeness of the schemes; and the TOPSIS decision based on weight preference enables the final scheme to flexibly adapt to the differentiated needs of different application scenarios, greatly enhancing the practicality and universality of the method. Attached Figure Description

[0045] Figure 1 Diagram of the coordinated control architecture of grid-type converter and electric-thermal-cooling energy storage.

[0046] Figure 2 This is a flowchart of a collaborative optimization control method for a grid-type converter and an electric thermal energy storage system based on the NNC algorithm.

[0047] Figure 3 A comparison chart showing the voltage / frequency coordinated control effect of a grid-type converter. Detailed Implementation

[0048] The present invention will now be described in further detail with reference to specific embodiments and the accompanying drawings.

[0049] Example 1

[0050] like Figures 1-2 As shown, the present invention relates to a method for coordinated optimization control of a grid-type converter and an electric thermal energy storage system, comprising the following steps:

[0051] 1. Collect data on new energy power generation output, energy storage capacity, and load demand to construct a multi-objective optimization model of "grid-type converter - electric thermal energy storage - flexible load". The optimization objectives are to minimize system operating costs, minimize pollutant emissions, and minimize the state objectives of the grid-type converter. The state objectives of the grid-type converter are characterized by voltage deviation and frequency deviation. The operating costs include the operation and maintenance costs of the grid-type converter, the energy storage charging and discharging loss costs, the new energy power generation costs, and the grid interaction costs. The grid-type converter includes photovoltaic inverters, wind power converters, and energy storage converters, all of which adopt grid-based control strategies.

[0052] Regarding the operating cost objective, the objective function expression for the operating cost is as follows:

[0053] ;

[0054] in, The operation and maintenance costs of grid-connected converters are calculated linearly based on the rated power and operating time of the equipment, covering three categories: photovoltaic inverters, wind power converters, and energy storage converters. The charging and discharging loss cost of the electric thermal energy storage system, including electrical energy storage (BAT), thermal energy storage (TES) and cold energy storage (CES), is dynamically modeled based on the charging and discharging power and loss coefficient. The cost of renewable energy generation mainly includes the unit generation cost of photovoltaic and wind power and the cost of abandonment penalties; The interaction costs with the upper-level power grid, which include electricity purchase costs and electricity sales revenue, are subject to time-of-use pricing mechanisms.

[0055] Regarding pollutant emission targets, the objective function expression for the pollutant emission amount is as follows:

[0056] ;

[0057] in, , and These represent the emissions of carbon dioxide, nitrogen oxides, and sulfur dioxide during system operation. The emissions of each pollutant are calculated based on the emission coefficients of the corresponding equipment and energy consumption. Grid-type converters themselves do not generate direct pollutant emissions during operation; therefore, only indirect emissions from the energy consumed by the converter in upstream production processes are considered. The pollutant emissions are mainly generated by gas turbine operation and electricity purchase from the grid. Calculated based on the consumption of various energy sources and their corresponding carbon emission coefficients. Based on the consumption of sulfur-containing fuels and desulfurization efficiency, the calculation is as follows: Emission factors are quantified based on combustion process and equipment type.

[0058] Regarding the state objectives of grid-connected converters, to quantitatively evaluate their operating state, a state objective function is constructed using voltage deviation and frequency deviation as the core evaluation indicators. Its expression is as follows: ;

[0059] in: This is a comprehensive indicator of the operating status of network equipment; the smaller the value, the better the operating status of the equipment. The deviation between the actual voltage and the rated voltage of the system at time t is expressed in per-unit value or as a percentage. The deviation between the actual frequency and the rated frequency of the system at time t, in Hz; , These are the weighting coefficients for voltage deviation and frequency deviation, reflecting their relative importance in equipment condition assessment. The condition target is used to directly incorporate the stability support capability of grid-connected converters into the multi-objective optimization process, ensuring that energy storage scheduling and load adjustment revolve around grid requirements.

[0060] 2. Establish coordinated constraints between the grid-type converter and the energy storage system. Set the voltage regulation range of the grid-type converter to ±2% of the rated voltage, the frequency regulation dead zone to a frequency deviation <0.1Hz, and trigger emergency frequency regulation when the frequency deviation ≥0.1Hz. Also, specify that the power response delay of the grid-type converter and the new energy power generation equipment is ≤50ms. Establish the coupling relationship between the electric energy storage, thermal energy storage, and cold energy storage and the grid-type converter respectively. When a voltage deviation >1% is detected, trigger the rapid charging and discharging of the electric energy storage, with a response time ≤100ms. When the frequency deviation >0.05Hz, link the electric drive equipment of the cold energy storage and / or thermal energy storage to adjust the power demand.

[0061] The coupling relationship between the energy storage and the grid-type converter satisfies the following formula:

[0062] ;

[0063] In the formula, This represents the active power output of the grid-type converter at time t. This represents the active power output of the photovoltaic inverter at time t. This represents the active power output of the wind turbine converter at time t. This represents the charging power of the stored electrical energy at time t (the value is non-negative and is only valid during charging). This represents the discharge power of the stored electrical energy at time t (the value is non-negative and is only valid during discharge). This represents the amount of active power compensation required by the grid-type converter at time t to maintain frequency stability; and satisfy: ;

[0064] In the formula, This is the droop factor, with a value range of 0.05-0.1 pu / Hz; Let t be the system frequency deviation at time t.

[0065] The The calculation satisfies the formula:

[0066] ;

[0067] In the formula, The rated power of the photovoltaic inverter. Let t be the real-time solar irradiance. Solar irradiance under standard test conditions. For power temperature coefficient, Let t be the actual temperature of the photovoltaic module. The component temperature is under standard test conditions.

[0068] The The calculation satisfies the following classification rules: when the real-time wind speed (Cut-in wind speed) or (When cutting out wind speed), ;when At (rated wind speed), , The rated power of the wind power converter; when hour, .

[0069] The coupling relationship between the thermal energy storage and the grid-type converter is as follows: when the voltage deviation of the grid-type converter is less than or equal to 1% and the frequency deviation is less than or equal to 0.05Hz, the thermal energy storage is preferentially charged by driving the gas turbine; when the grid-type converter requires reactive power compensation and the reactive power reserve is less than 20% of the rated capacity, the power of the electric-driven heat pump is reduced to reduce reactive power consumption, and the gas boiler is switched to auxiliary heating simultaneously to ensure that the reactive power reserve of the grid-type converter is restored to greater than or equal to 20% of the rated capacity.

[0070] The coupling relationship between the cold energy storage and the grid-connected converter is as follows: When the system voltage deviation ΔU(t) is greater than 1%, the starting power of the electric chiller associated with the cold energy storage is adjusted. The power response delay is less than or equal to 100ms, responding to system voltage changes by adjusting the chiller's operating power. When the system frequency deviation Δf(t) is greater than 0.05Hz, the cold energy storage enters a linkage control mode, increasing the cold energy storage rate based on the existing operation of the electric chiller. This enhances the cooling power, converting excess electrical energy into cold energy stored in the cold energy storage device, thereby consuming the system's surplus active power and providing downlink support for the system frequency. The power adjustment range of the cold energy storage is constrained by the rated capacity of the electric chiller, the cooling load state of the cold energy storage device, and the cold energy demand. Through cross-energy synergy between the grid-connected converter and the cold energy storage, the system achieves electricity-cold synergy support, further improving operational resilience and adjustment flexibility in multi-energy interconnection scenarios.

[0071] 3. Establish a demand response and grid status linkage mechanism to monitor electrical, heating, and cooling loads, and establish a "grid status - load adjustment" mapping rule for flexible loads. The flexible loads include interruptible cooling loads and transferable electrical loads. When the grid-type converter is in a voltage sag and the deviation is 1%-2%, reduce the interruptible cooling load by 30%. When the system frequency is higher than 50.1Hz, transfer 50% of the transferable electrical load to the off-peak period. The load flexibility adjustment reduces the regulation pressure of the grid-type converter.

[0072] The mapping rule for "grid status - load adjustment" satisfies the following quantitative relationship: when the voltage deviation of the grid-type converter... At that time, the amount of cooling load reduction C can be interrupted. cut (t) is:

[0073] ;

[0074] In the formula, Let t be the total cooling load at time t, and the load execution response time after the reduction command is sent be ≤60s; when the frequency deviation At that time, the transferable electrical load transfer amount E trans (t) is:

[0075] ;

[0076] In the formula, Let t be the maximum transferable electrical load. The transfer process adopts a "start first, then stop" switching method, and the switching interval is less than or equal to 10s.

[0077] 4. Solve the multi-objective optimization problem using the NNC algorithm and combine it with the TOPSIS method to select the optimal operating scheme. The specific steps are as follows: The multi-objective optimization problem is transformed into a single-objective sequential optimization using the NNC algorithm. First, the single-objective optimal solution that minimizes the state objective of the grid converter, the system operating cost, and the pollutant emission is solved as the Pareto front anchor point. After normalizing the objective function, the Utopian line is equally divided to generate segment points. Voltage and frequency threshold constraints of the grid converter are added to solve the corresponding single-objective optimal solutions, forming a uniform Pareto front. The weighted proximity of each solution is calculated using the TOPSIS method, where the voltage / frequency deviation of the grid converter has a weight of 30%, and the operating cost and pollutant emission each have a weight of 35%, thus selecting the optimal operating scheme.

[0078] The normalization process for the objective function satisfies the following rules:

[0079] The normalization formula for operating costs is:

[0080] ;

[0081] In the formula, For actual operating costs, To minimize operating costs, This represents the maximum operating cost.

[0082] The normalization formula for pollutant emissions is:

[0083] ;

[0084] In the formula, This represents the actual amount of pollutants emitted. To minimize emissions, This represents the maximum emission value.

[0085] The state normalization formula for a grid-type converter is:

[0086] ;

[0087] In the formula, This represents the target state value for an actual grid-type converter. The minimum value of the state objective. The objective is the maximum value of the state objective; and the normalized values ​​of the three objective functions are all within the interval [0,1].

[0088] 5. Using the grid-type converter as the core control node, real-time data on new energy power generation output, energy storage capacity, and load demand are collected. The virtual inertia and droop coefficient of the grid-type converter are dynamically adjusted according to the optimal operation scheme. The charging and discharging power of electric energy storage and the operating status of electric drive equipment for cold energy storage and / or thermal energy storage are simultaneously scheduled to achieve synergistic optimization of grid stability, system economy, and environmental protection.

[0089] Specifically, the control system collects real-time data on new energy power generation output, energy storage capacity, and electricity, heat, and cooling load demand in each control cycle. It calculates the system voltage and frequency deviations and determines whether the system meets the operating requirements based on the constraints of the grid-type converter's voltage regulation range of ±2% of the rated voltage and the frequency regulation dead zone deviation of less than 0.1Hz. If the operating requirements are met, the virtual inertia and droop coefficient of the grid-type converter are adaptively tuned according to the optimal solution obtained in the current optimization cycle, and energy storage and flexible loads are coordinated and scheduled. If any deviation does not meet the constraints, the multi-objective optimization model considering voltage / frequency safety constraints is re-solved, the optimal control scheme is updated and distributed to each execution unit until the system returns to the allowable range of constraints.

[0090] The specific rules for dynamically adjusting the virtual inertia and droop coefficient of the grid-type converter are as follows: when the system frequency change rate is greater than 0.2 Hz / s, the virtual inertia of the grid-type converter is increased to 4-5 s; when the frequency change rate is less than 0.1 Hz / s, the virtual inertia is decreased to 2-3 s; when the system active power reserve is greater than 30% of the rated power, the droop coefficient is 0.05-0.07 pu / Hz; when the system active power reserve is less than 20% of the rated power, the droop coefficient is 0.08-0.1 pu / Hz.

[0091] Case Analysis

[0092] (A) Case Introduction

[0093] This invention selects a comprehensive energy system in an office park as the research object. The park includes office buildings, conference areas, and supporting service areas, exhibiting typical characteristics of multi-energy loads (electricity, heat, and cooling): significant electricity load increases in the mornings of weekdays, cooling load is higher from noon to afternoon, and heating load accounts for a larger proportion in winter. The system is equipped with photovoltaic power generation units, wind power generation units, battery energy storage devices, thermal energy storage devices, cold energy storage devices, as well as various types of energy-consuming equipment such as electric chillers and heat pumps. A grid-connected converter enables coordinated control of renewable energy sources, energy storage devices, and flexible loads. The system operates in parallel with the public power grid; when internal energy supply is insufficient, it can be supplemented by the public grid; when renewable energy output is surplus, it prioritizes charging through electric energy storage and coordinating the use of thermal and cooling energy storage to absorb the surplus energy.

[0094] To verify the effectiveness of the method described in this invention, a typical day during the transitional season was selected as the analysis object, with a scheduling cycle of 24 hours and a time interval of 1 hour. A typical day during the transitional season was chosen because the electricity, heat, and cooling loads are relatively balanced during this period, without extreme high cooling loads in summer or extreme high heating loads in winter, thus better reflecting the system's control performance under normal comprehensive operating conditions. The following two comparative operating conditions were constructed in the calculation:

[0095] One is the traditional independent control mode, in which the grid-type converter mainly relies on its own virtual inertia and droop control to maintain voltage and frequency stability, and the electric energy storage, thermal energy storage, cold energy storage and flexible loads have not established a deep linkage with the grid state.

[0096] Secondly, the present invention provides a collaborative optimization control mode. Based on the real-time output of new energy sources, the state of charge of energy storage, and the demand for electric, heat, and cold loads, a multi-objective optimization model is established to minimize operating costs, pollutant emissions, and the state target of the grid-type converter. The Pareto front is obtained using the NNC algorithm, and the optimal operating scheme is selected by combining it with the TOPSIS method. On this basis, according to the aforementioned coupling constraint relationship and linkage rules, the virtual inertia, droop coefficient, electric energy storage charging and discharging power, thermal / cold energy storage operating status, and flexible load transfer strategy of the grid-type converter are dynamically adjusted, thereby forming a collaborative control mechanism of "grid-type converter - electric, heat, and cold energy storage - flexible load".

[0097] Figure 3 The variation curves of system voltage deviation and frequency deviation under two typical daily operating conditions are presented to characterize the improvement effect of the present invention on grid stability. Simulation results show that under traditional independent control, the maximum system voltage deviation is 1.83%, the average voltage deviation is 0.64%, the maximum frequency deviation is 0.118Hz, and the average frequency deviation is 0.041Hz. After adopting the collaborative optimization control of the present invention, the maximum system voltage deviation is reduced to 0.76%, the average voltage deviation to 0.27%, the maximum frequency deviation to 0.043Hz, and the average frequency deviation to 0.016Hz. After obtaining the Pareto front using the NNC algorithm and selecting the optimal compromise scheme using the TOPSIS method, typical daily operating results further demonstrate that the system operating cost is reduced by 10.8% compared to traditional independent control, the comprehensive pollutant emissions are reduced by 12.3%, and the wind and solar renewable energy absorption rate is increased by approximately 11.6%.

[0098] (B) Results Analysis

[0099] Depend on Figure 3It can be seen that under traditional independent control conditions, the system voltage and frequency deviations fluctuate significantly during the morning rapid load rise period, the afternoon cold load peak period, and the evening secondary load rise period. This is because, under the combined effect of fluctuations in renewable energy output and load disturbances, the grid-type converter mainly relies on its own inertia support and droop adjustment. Energy storage and flexible loads have not yet responded in coordination with the grid requirements, resulting in excessive adjustment pressure on the grid-type converter in certain periods, thus exhibiting problems such as insufficient voltage support and delayed frequency recovery. Especially during the periods of concentrated office load (8:00-10:00, 17:00-20:00) and rapid changes in photovoltaic output (11:00-14:00), the system deviation peaks are more prominent; in some periods, the voltage deviation exceeds 1%, and the frequency deviation is also higher than 0.05Hz in several periods, indicating that the system under traditional control has entered an operating range that requires enhanced coordinated adjustment.

[0100] After adopting the collaborative optimization control method described in this invention, the voltage deviation curve and frequency deviation curve converge significantly, indicating that by directly incorporating the state target of the grid-type converter into the optimization model, this invention enables energy storage scheduling and load adjustment to revolve around the system stability requirements. When the system voltage deviation exceeds 1%, the energy storage rapidly adjusts its charge and discharge according to the aforementioned collaborative constraints to provide rapid voltage support for the grid-type converter. When the system frequency deviation approaches or exceeds 0.05Hz, the electric drive equipment associated with cold energy storage and / or hot energy storage adjusts its power demand in tandem, converting some surplus energy into cold or hot energy storage, thereby absorbing excess active power and reducing the frequency regulation pressure on the grid-type converter. When the system is in a voltage sag range, the cold load can be interrupted and reduced according to a preset mapping rule. When a high frequency trend appears, the load can be transferred to off-peak periods, further reducing the impact on the grid-type equipment from the load side. Thus, the system transforms from the traditional "single-point support for the grid-type converter" to "multi-link collaborative support of source-storage-load," significantly improving dynamic operation quality.

[0101] From the voltage performance perspective, under the operating conditions of this invention, the maximum voltage deviation decreased from 1.83% to 0.76%, a reduction of approximately 58.5%; the average voltage deviation decreased from 0.64% to 0.27%, a reduction of approximately 57.8%. This indicates that this invention can effectively suppress bus voltage fluctuations caused by new energy source fluctuations and sudden load changes, keeping the system voltage more stably within the allowable range. Combined with the aforementioned voltage regulation constraints, it is known that under traditional operating conditions, when load disturbances are superimposed at noon and in the evening, grid-type converters are more likely to approach greater voltage regulation pressure. However, this invention, through rapid response of energy storage and flexible load-assisted regulation, suppresses the voltage deviation to a lower level, thereby improving the system's voltage support capability.

[0102] From the perspective of frequency indicators, under the operating conditions of this invention, the maximum frequency deviation decreased from 0.118Hz to 0.043Hz, a reduction of approximately 63.6%; the average frequency deviation decreased from 0.041Hz to 0.016Hz, a reduction of approximately 61.0%. Under the traditional independent control conditions, the frequency deviation exceeded 0.05Hz at multiple times, indicating that the system had entered the range where cold energy storage and / or thermal energy storage should participate in the coordinated regulation. However, after adopting the collaborative optimization control of this invention, the peak frequency deviation was effectively suppressed to below 0.05Hz, demonstrating that the joint regulation of thermal, cold, and electrical energy storage played a significant auxiliary supporting role for the grid-type converter. Meanwhile, under traditional operating conditions, the maximum frequency deviation reaches 0.118Hz, which exceeds the set upper limit of the 0.1Hz frequency modulation dead zone, indicating that the system is at risk of triggering emergency frequency modulation; while under the operating conditions of the present invention, the maximum frequency deviation is only 0.043Hz, which does not reach the severe fluctuation range above the 0.05Hz linkage threshold, and is also far below the 0.1Hz emergency frequency modulation boundary, indicating that the present invention can significantly improve the system frequency stability and anti-disturbance recovery capability.

[0103] Further analysis of the system's overall operation results reveals that, under the collaborative optimization control of this invention, the system operating cost decreases by 10.8% compared to traditional independent control, and the overall pollutant emissions decrease by 12.3%. This demonstrates that the invention does not sacrifice economic efficiency and environmental friendliness by introducing a grid stability objective; on the contrary, it improves overall operating efficiency through multi-energy synergy. Firstly, energy storage absorbs low-cost surplus energy during off-peak hours and releases it during peak hours, reducing the need for high-priced electricity purchases and the start-up and shutdown of high-emission conventional units. Secondly, thermal and cold energy storage, through cross-energy conversion, undertakes part of the power fluctuation absorption function, improving the local consumption capacity of new energy sources and increasing the wind and solar new energy consumption rate by approximately 11.6%. This aligns with the design concept of this invention, which aims for coordinated optimization of three objectives: minimum operating cost, minimum pollutant emissions, and minimum grid-connected converter state target.

[0104] In summary, this invention achieves unified coordination between the grid stability objective and the system's economic and environmental objectives by constructing a collaborative optimization control framework of "grid-type converter - electric heating and cooling energy storage - flexible load". Figure 3 The results show that the present invention can significantly reduce system voltage and frequency deviations, enhance the system's adaptability to new energy fluctuations and load disturbances, and simultaneously reduce operating costs, reduce pollutant emissions, and improve the capacity for new energy absorption, thus verifying the feasibility and superiority of the method of the present invention.

[0105] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for coordinated optimization control of a grid-type converter and an electric thermal energy storage system, characterized in that, The steps are as follows: S1. Construct a multi-objective optimization model of "grid-type converter - electric heating and cooling energy storage - flexible load" with the optimization objectives of minimizing system operating costs, minimizing pollutant emissions, and minimizing the state objectives of the grid-type converter. The state objectives of the grid-type converter are characterized by voltage deviation and frequency deviation. The operating costs include the operation and maintenance costs of the grid-type converter, the charging and discharging loss costs of energy storage, the costs of new energy power generation, and the grid interaction costs. The grid-type converter includes photovoltaic inverters, wind power converters, and energy storage converters, all of which adopt grid-based control strategies. S2. Establish coordinated constraints between the grid-type converter and the energy storage system. Set the voltage regulation range of the grid-type converter to ±2% of the rated voltage and the frequency regulation dead zone to a frequency deviation of <0.1Hz. Define the power response delay of the grid-type converter and the new energy power generation equipment to ≤50ms. Establish the coupling relationship between the grid-type converter and the electric energy storage, thermal energy storage, and cold energy storage. When a voltage deviation >1% is detected, trigger the rapid charging and discharging of the electric energy storage, with a response time ≤100ms. When the frequency deviation >0.05Hz, link the electric drive equipment of the cold energy storage and / or thermal energy storage to adjust the power demand. S3. Establish a demand response and grid status linkage mechanism to monitor electrical, heating, and cooling loads, and establish a "grid status - load adjustment" mapping rule for flexible loads. The flexible loads include interruptible cooling loads and transferable electrical loads. When the grid-type converter is in a voltage sag and the deviation is 1%-2%, reduce the interruptible cooling load by 30%. When the system frequency is higher than 50.1Hz, transfer 50% of the transferable electrical load to off-peak hours. The load flexibility adjustment reduces the regulation pressure on the grid-type converter. S4. Solve the multi-objective optimization problem using the NNC algorithm and combine it with the TOPSIS method to select the optimal running scheme; S5. Using the grid-type converter as the core control node, real-time data on new energy power generation output, energy storage capacity, and load demand are collected. The virtual inertia and droop coefficient of the grid-type converter are dynamically adjusted according to the optimal operation scheme. The charging and discharging power of electric energy storage and the operating status of electric drive equipment for cold energy storage and / or thermal energy storage are simultaneously scheduled to achieve synergistic optimization of grid stability, system economy, and environmental protection.

2. The collaborative optimization control method according to claim 1, characterized in that: The coupling relationship between the energy storage and the grid-type converter in S2 satisfies the following formula: ； In the formula, This represents the active power output of the grid-type converter at time t. This represents the active power output of the photovoltaic inverter at time t. This represents the active power output of the wind turbine converter at time t. Represents the charging power of the stored electrical energy at time t. This represents the discharge power of the stored electrical energy at time t. This represents the amount of active power compensation required by the grid-type converter at time t to maintain frequency stability; and satisfy: ; In the formula, This is the droop factor, with a value range of 0.05-0.1 pu / Hz; Let t be the system frequency deviation at time t.

3. The collaborative optimization control method according to claim 1, characterized in that: The coupling relationship between thermal energy storage and grid-type converter in S2 is as follows: when the voltage deviation of the grid-type converter is less than or equal to 1% and the frequency deviation is less than or equal to 0.05Hz, the thermal energy storage is preferentially charged by the gas turbine; when the grid-type converter requires reactive power compensation and the reactive power reserve is less than 20% of the rated capacity, the power of the electric drive heat pump is reduced to reduce reactive power consumption, and the auxiliary heating of the gas boiler is switched synchronously to ensure that the reactive power reserve of the grid-type converter is restored to greater than or equal to 20% of the rated capacity.

4. The collaborative optimization control method according to claim 1, characterized in that: The coupling relationship between cold energy storage and grid-type converter in S2 is as follows: When the system voltage deviation ΔU(t) is greater than 1%, the starting power of the electric chiller associated with the cold energy storage is adjusted. By adjusting the operating power of the chiller unit in response to the system voltage change, the power response delay is less than or equal to 100ms. When the system frequency deviation Δf(t) is greater than 0.05Hz, the cold energy storage enters the linkage control mode. On the basis of the existing operation of the electric chiller, the cold energy storage rate is increased to improve the cooling power and convert the excess electrical energy into cold energy for storage in the cold energy storage device, thereby consuming the system's surplus active power and providing downlink support for the system frequency.

5. The collaborative optimization control method according to claim 1, characterized in that: The mapping rule for "grid status - load adjustment" in S3 satisfies the following quantitative relationship: when the voltage deviation of the grid-type converter... At that time, the amount of cooling load that can be interrupted is: ； In the formula, Let t be the total cooling load at time t, and the load execution response time after the reduction command is sent be ≤60s; when the frequency deviation At that time, the transferable electrical load transfer amount is: ； In the formula, Let t be the maximum transferable electrical load. The transfer process adopts a "start first, then stop" switching method, and the switching interval is less than or equal to 10s.

6. The collaborative optimization control method according to claim 1, characterized in that: The specific steps of S4 are as follows: The multi-objective optimization problem is transformed into a single-objective sequential optimization using the NNC algorithm. First, the single-objective optimal solution that minimizes the state objective of the grid converter, the system operating cost, and the pollutant emission is obtained as the Pareto front anchor point. After normalizing the objective function, the Utopian line is equally divided to generate segment points. Voltage and frequency threshold constraints of the grid converter are added to solve the corresponding single-objective optimal solutions, forming a uniform Pareto front. The weighted proximity of each solution is calculated using the TOPSIS method, where the voltage / frequency deviation of the grid converter has a weight of 30%, and the operating cost and pollutant emission each have a weight of 35%, and the optimal operating scheme is selected.

7. The collaborative optimization control method according to claim 6, characterized in that: The normalization process of the objective function in S4 satisfies the following rules: The normalization formula for operating costs is: ； In the formula, For actual operating costs, To minimize operating costs, This represents the maximum operating cost. The normalization formula for pollutant emissions is: ； In the formula, This represents the actual amount of pollutants emitted. To minimize emissions, This represents the maximum emission value. The state normalization formula for a grid-type converter is: ； In the formula, This represents the target state value for an actual grid-type converter. The minimum value of the state objective. The objective is the maximum value of the state objective; and the normalized values ​​of the three objective functions are all within the interval [0,1].

8. The collaborative optimization control method according to claim 1, characterized in that: The specific rules for dynamically adjusting the virtual inertia and droop coefficient of the grid-type converter in S5 are as follows: when the system frequency change rate is greater than 0.2 Hz / s, the virtual inertia of the grid-type converter is increased to 4-5 s; when the frequency change rate is less than 0.1 Hz / s, the virtual inertia is decreased to 2-3 s. When the system's active power reserve is greater than 30% of the rated power, the droop factor is taken as 0.05-0.07 pu / Hz; when the system's active power reserve is less than 20% of the rated power, the droop factor is taken as 0.08-0.1 pu / Hz.

9. The collaborative optimization control method according to claim 2, characterized in that: The The calculation satisfies the formula: ； In the formula, The rated power of the photovoltaic inverter. Let t be the real-time solar irradiance. Solar irradiance under standard test conditions. For power temperature coefficient, Let t be the actual temperature of the photovoltaic module. The component temperature is under standard test conditions.

10. The collaborative optimization control method according to claim 2, characterized in that: The The calculation satisfies the following classification rules: when the real-time wind speed (Cut-in wind speed) or (When cutting out wind speed), ;when At (rated wind speed), , The rated power of the wind power converter; when hour, .

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