Method and system for calculating operating conditions of park combined cooling, heating and power system through hourly analysis throughout the year

Abstract

The application relates to the field of combined cooling, heating and power system planning, and discloses a park combined cooling, heating and power system operation condition calculation method and system based on annual hour-by-hour analysis, which comprises the following steps: constructing a park combined cooling, heating and power system model with a given configuration, and obtaining annual hour-by-hour data sequences; taking maximum local renewable energy consumption as the primary operation target, and performing hour-by-hour energy balance simulation; for each simulation period: using the renewable energy power generation in the period to directly meet the local power load demand; if there is surplus renewable energy power generation, the energy storage system is preferentially charged; if the renewable energy power generation cannot meet the load demand, the energy storage system is preferentially discharged to supplement; when the combined output of the renewable energy and the energy storage system still cannot meet the load, the power shortage is balanced by purchasing power from the power grid; and based on the energy balance simulation result, the total operation electricity fee of the system in the calculation period is calculated. The application can realize accurate operation electricity fee calculation.

Description

Technical Field

[0001] This invention relates to the field of planning for combined cooling, heating and power (CCHP) systems in industrial parks, and in particular to a method and system for calculating the operating conditions of CCHP systems in industrial parks by analyzing them hourly throughout the year. Background Technology

[0002] Industrial parks are crucial for achieving regional carbon neutrality, encompassing five key supporting elements: park infrastructure, green production, green services, ecological carbon sinks, and carbon accounting systems. These parks are typically based on distributed photovoltaic power generation, planned as multi-energy complementary smart energy systems supplemented by clean energy sources such as energy storage, small wind turbines, and air source heat pumps. While zero-carbon smart parks have been widely implemented nationwide, there is limited experience in designing and constructing integrated wind-solar-storage zero-carbon parks that comprehensively consider the park's cooling, heating, electricity, and steam loads, and an effective commercial operation model is lacking. Parks face multiple construction needs, including efficiency improvement, cost savings, and low-carbon green initiatives. Zero-carbon parks require unified management and scheduling of "source-grid-load-storage," but current system management methods are insufficient to support such multi-faceted intelligent scheduling, and zero-carbon park planning cannot fully coordinate these multi-dimensional needs, resulting in unintelligent system management.

[0003] The main approach to solving these problems is to adopt a combined cooling, heating, and power (CCHP) system to provide energy supply and management services for the entire industrial park. This system is a novel energy supply / management technology characterized by integrated generation, grid, load, and storage; multi-energy complementarity; and supply-demand coordination. It can fully utilize the temporal and spatial coupling characteristics of various energy subsystems, promoting the consumption of renewable energy, reducing fossil fuel consumption within the region, lowering greenhouse gas emission intensity, and achieving green and low-carbon development.

[0004] The combined cooling, heating, and power (CCHP) system in the industrial park integrates various energy sources such as electricity, cooling, and heating to achieve cascaded utilization and synergistic optimization of energy, serving as an important means to improve energy efficiency and promote the consumption of renewable energy. Accurate prediction of operating costs is crucial during the system's planning and operational evaluation phases. Currently, most methods for evaluating the economic viability of CCHP systems analyze typical days or typical operating scenarios, or use annual averages for static calculations. These methods have significant shortcomings: First, they lack accuracy, failing to capture intraday and seasonal fluctuations in load and renewable energy output, and ignoring changes in equipment efficiency at partial load rates, leading to significant deviations in cost estimation. Second, they lack dynamism, failing to reflect the impact of time-varying factors such as the charging and discharging behavior of energy storage equipment, time-of-use pricing, and equipment start-up and shutdown losses on economic operation. Finally, they weaken decision support; macro-level evaluation results are insufficient to guide specific operational strategy optimization and refined equipment selection. Summary of the Invention

[0005] To address the aforementioned problems, the purpose of this invention is to provide a method and system for calculating the operating conditions of a park's combined cooling, heating, and power (CCHP) system based on hourly analysis throughout the year. This method is designed for systems with fixed equipment capacity and achieves accurate calculation of operating costs by simulating hourly operating conditions aimed at maximizing green energy consumption.

[0006] To achieve the above objectives, in a first aspect, the technical solution adopted by the present invention is as follows: a method for calculating the operating conditions of a park combined cooling, heating, and power (CCHP) system based on hourly analysis throughout the year, comprising: constructing a park CCHP system model with a given configuration, wherein the park CCHP system includes at least a local renewable energy power generation unit, a storage system of a given capacity, and local electricity load; acquiring hourly data sequences for the entire year, wherein the hourly data sequences include at least: renewable energy power generation, electricity load, and grid electricity price; performing hourly energy balance simulation with maximizing the absorption of local renewable energy as the primary operating objective; for each simulation period: prioritizing the use of renewable energy power generation to directly meet local electricity load demand; if there is a surplus of renewable energy power generation, prioritizing the charging of the storage system; if renewable energy power generation cannot meet load demand, prioritizing the discharge of the storage system to supplement it; when the combined output of renewable energy and the storage system still cannot meet the load, the power deficit is balanced by purchasing electricity from the grid; and calculating the total operating electricity cost of the system within the calculation period based on the results of the energy balance simulation.

[0007] Furthermore, in conducting hourly energy balance simulations, the set of periods of electricity exemption is identified and processed, including:

[0008] Identify one or more consecutive time periods to form a time period set; Calculate the algebraic sum of the grid-connected electricity for all time periods within the set. The grid-connected electricity is the difference between the renewable energy generation power and the electrical load power. If the absolute value of the algebraic sum of the grid-connected electricity is less than or equal to the rated discharge capacity of the energy storage system, then the set of time periods is determined to be the set of time periods exempt from electricity purchase; when calculating the operating electricity cost, the purchased power of all time periods in the set of time periods exempt from electricity purchase is set to zero.

[0009] Furthermore, the set of periods exempt from electricity purchase is identified using a cyclic accumulation algorithm, including: Search for the time intervals in each group where P(t) < 0; Create an array to store the time periods where P(t) < 0 and the corresponding electricity price data for those time periods; if the sum of the grid-connected electricity for each time period in the array is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0. Create an array to store the time periods starting from P(t) < 0 and their corresponding electricity prices. f buyThe system searches for the first time period with the lowest electricity price and starts charging the energy storage from that time period. If the sum of the grid-connected electricity available in each time period within the array is greater than the charging amount of the energy storage battery, an array is created to store this information. P The array contains n data points: the time period (t) < 0 and the corresponding electricity price data for that time period. Sort by time, starting from the first P Starting from the time interval t where (t)<0, the sequence number of this time interval is denoted as i=1; Starting from i, iterate through each time period in the subsequent array, and accumulate the available electricity for the current time period i. If the sum of the available electricity for the time periods {1, i} is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0; and continue to iterate through the next time period. Update i to i+1 and restart the new accumulation loop; if the sum of the grid-connected electricity in the time period {1, i} is greater than the discharge capacity of the energy storage battery, calculate the electricity purchase cost for that time period and the electricity purchase cost for each time period when P(t) < 0. Stop iterating and finally calculate the operating electricity cost for each time period.

[0010] Furthermore, update i to i+1 and restart a new accumulation loop; if the sum of the grid-connected electricity during the {1, i} period is greater than the discharge capacity of the energy storage battery, the electricity purchase fee for that period is... for:

[0011] In the formula, Let t represent the energy storage capacity over the time period t.

[0012] Furthermore, the electricity purchase fee for each time period when P(t) < 0. The calculation is as follows: .

[0013] Furthermore, the time resolution of the hourly data series is 1 hour, the calculation period is one year, and there are a total of 8760 time periods.

[0014] Furthermore, the local renewable energy generation unit includes at least one of a photovoltaic power generation system and a wind power generation system.

[0015] Secondly, the technical solution adopted by this invention is as follows: a system for calculating the operating conditions of a park's combined cooling, heating, and power (CCHP) system based on hourly analysis throughout the year, comprising: a model building module for building a model of a park CCHP system with a given configuration, the park CCHP system including at least a local renewable energy power generation unit, a storage system of a given capacity, and local electricity load; a sequence acquisition module for acquiring hourly data sequences throughout the year, the hourly data sequences including at least: renewable energy power generation, electricity load, and grid electricity price; an energy balance simulation module for performing hourly energy balance simulation with maximizing the absorption of local renewable energy as the primary operating objective; for each simulation period: priority is given to using the renewable energy power generation of that period to directly meet the local electricity load demand; if there is a surplus of renewable energy power generation, priority is given to charging the storage system; if renewable energy power generation cannot meet the load demand, priority is given to discharging the storage system to supplement it; when the combined output of renewable energy and the storage system still cannot meet the load, the power deficit is balanced by purchasing electricity from the grid; and an electricity cost calculation module for calculating the total operating electricity cost of the system within the calculation period based on the results of the energy balance simulation.

[0016] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0017] Fourthly, the technical solution adopted by the present invention is: a computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0018] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention features fast and efficient (in seconds) and high evaluation accuracy. It fully considers the dynamic characteristics of system operation through point-by-point calculation over 8760 hours, resulting in economic evaluation results that are closer to reality and avoid the errors caused by typical daily analysis methods.

[0019] 2. This invention is specifically designed for park combined cooling, heating and power systems with "green electricity consumption" as the core objective. Its operation simulation logic is more in line with the actual operation strategy of such systems, and the calculation results are more realistic and reliable.

[0020] 3. For systems with a fixed equipment capacity (especially energy storage capacity), this invention can effectively assess the economic viability of achieving green electricity consumption targets under a given configuration, providing key data support for system operation performance evaluation and subsequent capacity upgrades. Attached Figure Description

[0021] Figure 1 This is a flowchart of the calculation method for the operating conditions of the park's combined cooling, heating and power system, analyzed hourly throughout the year, in an embodiment of the present invention. Figure 2 This is a topology diagram of the combined cooling, heating and power system in the park according to an embodiment of the present invention; The parameters in the attached diagram are explained below: 1. Summer cooling load, winter heating load, and office load of the park are calculated together. P load middle.

[0022] 2. P wind (t) represents the predicted wind power generation data for time period t. P solar (t) represents the photovoltaic power generation forecast data for time period t. f sel (t) represents the electricity price data sold to the power grid during the time period t. P load (t) represents the load data for time period t. P (t) represents the amount of electricity that can be fed into the grid during the time period t. Positive values ​​indicate the amount of electricity purchased from the grid, while negative values ​​indicate the amount of electricity discharged into the grid. P SE (t) represents the energy storage capacity during time period t. or ch and or dis These represent the energy storage charging and discharging efficiency, respectively. C op Let t be the electricity purchase fee for the time period. A positive value represents the revenue from selling electricity to the grid, and a negative value represents the revenue from selling electricity to the grid. Detailed Implementation

[0023] To address the inaccuracy of existing methods in evaluating the economic viability of fixed-configuration systems, particularly energy storage applications geared towards green energy consumption, this invention provides a method and system for calculating the operational status of a park's combined cooling, heating, and power (CCHP) system based on hourly analysis throughout the year. The method includes: constructing a system topology and equipment model; acquiring 8760 hours of meteorological, load, and electricity price data annually; conducting hourly energy balance simulations based on the principle of self-consumption and maximizing the absorption of local green energy to determine the system's operational status, including energy storage charging and discharging; and accurately calculating the total electricity cost incurred by the system during the simulation period due to the absorption of green energy and the reduction of externally purchased electricity. This invention can accurately assess the operational economics of a system with green energy consumption as its core objective, given a specific energy storage capacity, providing direct evidence for the rationale for energy storage capacity and the optimization of operational strategies.

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] In one embodiment of the present invention, a method for calculating the operating conditions of a park's combined cooling, heating, and power (CCHP) system based on hourly analysis throughout the year is provided. This method enables accurate calculation of the operating electricity costs of the CCHP system, with green electricity consumption as the core objective, providing direct evidence for the rationality demonstration of energy storage capacity and optimization of operating strategies. In this embodiment, as shown... Figure 1 As shown, the method includes the following steps: 1) Construct a model of a park cogeneration system with a given configuration. The park cogeneration system includes at least a local renewable energy power generation unit, a storage system of a given capacity, and local electrical load.

[0027] 2) Obtain hourly data sequences for the entire year. The hourly data sequences should include at least: renewable energy generation power, electrical load power, and grid electricity price.

[0028] 3) With maximizing the absorption of local renewable energy as the primary operational objective, hourly energy balance simulations are conducted; for each simulation period: Priority should be given to using renewable energy generation capacity during this period to directly meet local electricity load demand; If there is a surplus of renewable energy generation, the energy storage system will be charged first. If renewable energy generation cannot meet the load demand, the energy storage system will be the first to discharge to supplement it. When the combined output of renewable energy and energy storage systems is still insufficient to meet the load, the power deficit is balanced by purchasing electricity from the grid.

[0029] 4) Based on the results of energy balance simulation, calculate the total operating electricity cost of the system during the calculation period.

[0030] In step 1) above, a system model with a given configuration is constructed. The energy equipment composition and electrical topology of the park's combined cooling, heating and power system are clarified.

[0031] The local renewable energy generation unit includes at least one of a photovoltaic power generation system and a wind power generation system. A given capacity energy storage system, such as a lithium iron phosphate battery. Input fixed parameters for all devices, where the rated power and capacity of the energy storage system are known fixed values.

[0032] In step 2) above, the time resolution of the hourly data sequence is 1 hour, the calculation period is one year, and there are a total of 8760 time periods.

[0033] Specifically, step 2) involves obtaining hourly boundary condition data for the entire year. This includes collecting typical annual meteorological data (including at least solar irradiance and ambient temperature) for the system's location to generate hourly power generation data for the renewable energy units; obtaining hourly electricity load data for the entire park over 8760 hours; and obtaining hourly electricity price data for the local power grid. Finally, it involves obtaining data on the energy storage system's capacity, charging, and power generation efficiency.

[0034] In step 3) above, the set of periods of electricity exemption from purchase is identified and processed during the hourly energy balance simulation, including the following steps: 3.1) Identify one or more consecutive time periods to form a time period set.

[0035] 3.2) Calculate the algebraic sum of the grid-connected electricity for all time periods within the set. The grid-connected electricity is the difference between the renewable energy generation power and the electrical load power.

[0036] 3.3) If the absolute value of the algebraic sum of the grid-connected electricity is less than or equal to the rated discharge capacity of the energy storage system, then the set of time periods is determined to be the set of time periods exempt from electricity purchase; when calculating the operating electricity cost, the purchased power of all time periods in the set of time periods exempt from electricity purchase is set to zero.

[0037] Specifically, hourly operation simulations are conducted based on the principle of green electricity consumption. The hourly operating data generated in step 2) (such as hourly wind turbine power generation, hourly photovoltaic power generation, energy storage charging, and energy storage power generation) are input into the economic calculation, processed in groups of 24 data points. The grid-connected electricity volume for each time period is calculated; positive values ​​indicate electricity purchased from the grid, while negative values ​​indicate electricity discharged to the grid. Energy dispatch simulations are performed sequentially for all 8760 hours of the year, with a time step of 1 hour. For any given time t, the system's operating logic follows the following priority order: Local renewable energy generation is prioritized to directly meet the load demand. When there is surplus renewable energy generation, the energy storage system is prioritized for charging. When renewable energy generation cannot meet the load demand, the energy storage system is prioritized for discharging to supplement the load. When the combined output of renewable energy and energy storage still cannot meet the load demand, the shortfall is purchased from the grid.

[0038] In this embodiment, the operating electricity cost is calculated for periods when the available grid-connected electricity is positive. An array is created to store the time periods where P(t) > 0 and the corresponding electricity price data for those periods. If the sum of the available grid-connected electricity for each time period in this array is less than the discharge capacity of the energy storage battery, then the electricity purchase cost for these time periods is 0.

[0039] Sort the two-dimensional array (in descending order) based on the electricity price data. Search for the first time period with the lowest peak electricity price, starting from this time period t, and denote its index as i=1. Iterate through each subsequent time period starting from i, accumulating the grid-connected electricity for the current time period i. If the sum of the grid-connected electricity for periods {1, i} is less than the discharge capacity of the energy storage battery, then the electricity purchase fee for these time periods is 0. Continue iterating through the next time period. Update i to i+1 and start a new accumulation loop. If the sum of the grid-connected electricity for periods {1, i} is greater than the discharge capacity of the energy storage battery, the calculation expression for the electricity purchase fee for that time period is: (1) In the formula, P (t) represents the amount of electricity that can be fed into the grid during the time period t. Positive values ​​indicate the amount of electricity purchased from the grid, while negative values ​​indicate the amount of electricity discharged into the grid. P SE (t) represents the energy storage capacity during time period t. or ch and or dis These represent the energy storage charging and discharging efficiency, respectively. C op Let t be the electricity purchase fee for the time period. A positive value represents the revenue from selling electricity to the grid, and a negative value represents the revenue from selling electricity to the grid.

[0040] For other time periods where P(t) > 0, the formula for calculating the electricity purchase cost for each time period is as follows: (2) In the formula, f buy (t) represents the electricity price purchased from the grid during time period t. Terminate the current loop.

[0041] In this embodiment, the operating electricity cost is calculated for periods when the available electricity supply is negative. Identifying the set of periods exempt from electricity purchase is achieved through a cyclic accumulation algorithm, including the following steps: 3.3.1) Search for the time intervals in each group where P(t) < 0; 3.3.2) Create an array to store the time periods when P(t) < 0 and the corresponding electricity price data for those time periods; if the sum of the grid-connected electricity for each time period in the array is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0. 3.3.3) Create an array to store the time periods starting from the time period P(t) < 0 and the corresponding electricity price data. f buy The system searches for the first time period with the lowest electricity price and starts charging the energy storage from that time period. If the sum of the grid-connected electricity available in each time period within the array is greater than the charging amount of the energy storage battery, an array is created to store this information. P The array contains n data points: the time period (t) < 0 and the corresponding electricity price data for that time period. 3.3.4) Sort by time, starting from the first P Starting from the time interval t where (t)<0, the sequence number of this time interval is denoted as i=1; 3.3.5) Starting from i, iterate through each time period in the subsequent array, and accumulate the available electricity for the current time period i. If the sum of the available electricity for the time periods {1, i} is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0; and continue to iterate through the next time period. 3.3.6) Update i to i+1 and restart the new accumulation loop; if the sum of the grid-connected electricity in the {1, i} time period is greater than the discharge capacity of the energy storage battery, calculate the electricity purchase fee for that time period and the electricity purchase fee for each time period when P(t)<0. 3.3.7) Stop the iteration and finally calculate the operating electricity cost for each time period to obtain the annual electricity operating cost of the system.

[0042] In step 3.3.6 above, i is updated to i+1, and a new accumulation loop starts again; if the sum of the grid-connected electricity in the time periods {1, i} is greater than the discharge capacity of the energy storage battery, the electricity purchase fee for that time period is... for: (3) In the formula, Let t represent the energy storage capacity over the time period t.

[0043] In step 3.3.6 above, the electricity purchase fee for each time period when P(t) < 0 is... The calculation is as follows: (4) In one embodiment of the present invention, a system for calculating the operating conditions of a park combined cooling, heating and power system, analyzed hourly throughout the year, is provided, comprising: The model building module constructs a model of a park's combined cooling, heating and power (CCHP) system with a given configuration. The park's CCHP system includes at least a local renewable energy power generation unit, an energy storage system of a given capacity, and local electrical load. The sequence acquisition module acquires hourly data sequences for the entire year. The hourly data sequences include at least: renewable energy power generation, electrical load, and grid electricity price. The energy balance simulation module, with maximizing the absorption of local renewable energy as its primary operational objective, performs hourly energy balance simulations; for each simulation period: Priority should be given to using renewable energy generation capacity during this period to directly meet local electricity load demand; If there is a surplus of renewable energy generation, the energy storage system will be charged first. If renewable energy generation cannot meet the load demand, the energy storage system will be the first to discharge to supplement it. When the combined output of renewable energy and energy storage systems is still insufficient to meet the load, the power deficit is balanced by purchasing electricity from the grid. The electricity cost calculation module calculates the total operating electricity cost of the system within the calculation period based on the results of energy balance simulation.

[0044] In the above embodiments, the identification and processing of the set of periods of electricity exemption during hourly energy balance simulation includes: Identify one or more consecutive time periods to form a time period set; Calculate the algebraic sum of the grid-connected electricity for all time periods within the set. The grid-connected electricity is the difference between the renewable energy generation power and the electrical load power. If the absolute value of the algebraic sum of the grid-connected electricity is less than or equal to the rated discharge capacity of the energy storage system, then the set of time periods is determined to be the set of time periods exempt from electricity purchase; when calculating the operating electricity cost, the purchased power of all time periods in the set of time periods exempt from electricity purchase is set to zero.

[0045] In the above embodiments, the set of periods exempt from electricity purchase is identified through a cyclic accumulation algorithm, including: Search for the time intervals in each group where P(t) < 0; Create an array to store the time periods where P(t) < 0 and the corresponding electricity price data for those time periods; if the sum of the grid-connected electricity for each time period in the array is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0. Create an array to store the time periods starting from P(t) < 0 and their corresponding electricity prices. f buy The system searches for the first time period with the lowest electricity price and starts charging the energy storage from that time period. If the sum of the grid-connected electricity available in each time period within the array is greater than the charging amount of the energy storage battery, an array is created to store this information. P The array contains n data points: the time period (t) < 0 and the corresponding electricity price data for that time period. Sort by time, starting from the first P Starting from the time interval t where (t)<0, the sequence number of this time interval is denoted as i=1; Starting from i, iterate through each time period in the subsequent array, and accumulate the available electricity for the current time period i. If the sum of the available electricity for the time periods {1, i} is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0; and continue to iterate through the next time period. Update i to i+1 and restart the new accumulation loop; if the sum of the grid-connected electricity in the time period {1, i} is greater than the discharge capacity of the energy storage battery, calculate the electricity purchase cost for that time period and the electricity purchase cost for each time period when P(t) < 0. Stop iterating and finally calculate the operating electricity cost for each time period.

[0046] In this embodiment, i is updated to i+1, and a new accumulation cycle begins; if the sum of the grid-connected electricity during the {1, i} period is greater than the discharge capacity of the energy storage battery, the electricity purchase fee for that period is... for:

[0047] In the formula, Let t represent the energy storage capacity over the time period t.

[0048] In this embodiment, the electricity purchase fee for each time period when P(t) < 0 is... The calculation is as follows: .

[0049] In the above embodiments, the time resolution of the hourly data sequence is 1 hour, the calculation period is one year, and there are a total of 8760 time periods.

[0050] In the above embodiments, the local renewable energy power generation unit includes at least one of a photovoltaic power generation system and a wind power generation system.

[0051] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0052] In an example, taking an office park in Hebei Province as an example, the method of this invention is applied to calculate and analyze the hourly operating costs of its planned combined cooling, heating, and power system. Figure 2As shown, the system in this embodiment includes (1) a photovoltaic power generation system, (2) an energy storage system, (3) a ground source heat pump heating system, and (4) the power load of the office area. The park is closed on Saturdays and Sundays, except for national statutory holidays such as the Spring Festival. The park has 261 working days a year and 105 holidays. The working hours are 8 hours a day. The peak power consumption period is from 7 am to 5 pm. The annual power load characteristics are that the peak power consumption season is in summer, followed by winter. The power load is less in spring and autumn. The load on weekdays is significantly greater than that on holidays. The power load during the day is greater than that at night. The winter heating period is from November 15 to March 15. In November and March, the heating time is from 8 am to 6 pm for a total of 10 hours, and the heat pump unit operates at full load of 400kW. From 6 pm to 8 am the next day for a total of 14 hours, the heat pump unit operates at 25% load of 100kW. From 8:00 AM to 6:00 PM on December 2nd, the heat pump units operated at full load (450kW) for 10 hours. From 6:00 PM to 8:00 AM the following day, the heat pump units operated at 25% load (115kW) for 14 hours. When wind and solar resources are abundant, priority is given to using photovoltaic and wind power to supply the park's electricity load, adopting a "self-consumption with surplus power fed into the grid" model. The energy storage charging and discharging strategy is set based on the different electricity prices during different seasons in the local area, with charging and discharging once a day for optimal economic efficiency. If photovoltaic and wind power cannot meet the park's electricity load and the energy storage charging capacity, electricity will be purchased from the external grid, thus simultaneously considering wind and photovoltaic power integration and peak-valley arbitrage.

[0053] This case study sets time-of-use electricity prices based on the "State Grid Beijing Electric Power Company's Electricity Price List for Commercial and Industrial Users Purchasing Electricity," specifically for 10 kV voltage level time-of-use electricity consumption (January to December 2023).

[0054] Step 1: Identify the topology of the combined cooling, heating and power (CCHP) system, which includes: ground and rooftop photovoltaic power generation system (total installed capacity 1304.46 kW), ground source heat pump heating system (640 kW), and energy storage system (172 kW / 344 kWh), and construct a physical structure model.

[0055] Step 2: Obtain hourly boundary condition data for the entire year. Collect typical annual meteorological data (including at least solar irradiance and ambient temperature) for an office park in Hebei Province, and generate hourly power generation data sequences for renewable energy units; obtain hourly electricity load data for the park for 8760 hours throughout the year; obtain hourly electricity price data for the local power grid; obtain data on energy storage system capacity, charging, and power generation efficiency.

[0056] Step 3: Conduct hourly operation simulation based on the principle of green electricity consumption. Input the hourly operating data generated by S2 (such as hourly wind turbine power generation, hourly photovoltaic power generation, energy storage charging, and energy storage power generation) into the economic calculation, and process them in groups of 24 data points. Calculate the grid-connected electricity for each time period. Positive values ​​indicate electricity purchased from the grid, and negative values ​​indicate electricity discharged to the grid. Perform energy dispatch simulations for all 8760 hours of the year, with a time step of 1 hour. For any time t, the system's operating logic follows the following priority order: Prioritize using local renewable energy power generation to directly meet the electricity load demand. When there is surplus renewable energy generation, prioritize charging the energy storage system. When renewable energy generation cannot meet the load demand, prioritize the energy storage system discharging to supplement it. When the combined output of renewable energy and energy storage still cannot meet the load, the shortfall is purchased from the grid.

[0057] Step 4: First, calculate the operating electricity cost for periods when the available grid-connected electricity is positive. Create an array to store the time periods where P(t) > 0 and the corresponding electricity price data for those periods. If the sum of the available grid-connected electricity for each time period in this array is less than the discharge capacity of the energy storage battery, then the electricity purchase cost for these time periods is 0.

[0058] Step 5: Sort the two-dimensional array (descending order) according to the electricity price data. Search for the first time period with the smallest peak electricity price. Starting from this time period t, let the index of this time period be i=1. Iterate through each subsequent time period starting from i, and accumulate the grid-connected electricity for the current time period i. If the sum of the grid-connected electricity for the {1, i} time periods is less than the discharge capacity of the energy storage battery, then the electricity purchase fee for these time periods is 0. Continue to iterate through the next time period. Update i to i+1 and start a new accumulation loop; if the sum of the grid-connected electricity for the {1, i} time periods is greater than the discharge capacity of the energy storage battery, the calculation expression for the electricity purchase fee for this time period is: (1) In the formula, P (t) represents the amount of electricity that can be fed into the grid during the time period t. Positive values ​​indicate the amount of electricity purchased from the grid, while negative values ​​indicate the amount of electricity discharged into the grid. P SE (t) represents the energy storage capacity during time period t. or ch and or dis These represent the energy storage charging and discharging efficiency, respectively. C op Let t be the electricity purchase fee for the time period. A positive value represents the revenue from selling electricity to the grid, and a negative value represents the revenue from selling electricity to the grid.

[0059] For other time periods where P(t) > 0, the formula for calculating the electricity purchase cost for each time period is as follows: (2) In the formula, f buy (t) represents the electricity price purchased from the grid during time period t. Terminate the current loop.

[0060] Step 6: Calculate the operating electricity cost for periods when the available grid-connected electricity is negative. Search for time periods in each group where P(t) < 0. Create an array to store the time periods where P(t) < 0 and the corresponding electricity price data for those periods. If the sum of the available grid-connected electricity for each time period in this array is less than the charging amount of the energy storage battery, then the electricity purchase cost for these time periods is 0. Create an array to store the time periods after that point and the corresponding electricity price data. f buy The system searches for the first time period with the lowest electricity price and starts charging the energy storage battery from that time period. If the sum of the grid-connected electricity for each time period in the array is greater than the charging amount of the energy storage battery, an array is created to store the time periods where P(t) < 0 and the corresponding electricity price data for that time period. The array contains n data points. Sort by time, starting from the first time period t where P(t) < 0, and denote the index of that time period as i = 1. Starting from i, iterate through each subsequent time period in the array, accumulating the grid-connected electricity for the current time period i. If the sum of the grid-connected electricity for periods {1, i} is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0. Continue iterating through the next time period. Update i to i+1 and start a new accumulation loop; if the sum of the grid-connected electricity for periods {1, i} is greater than the discharge capacity of the energy storage battery, the calculation expression for the electricity purchase fee for that time period is: (3) In the formula, f sel P(t) represents the electricity price sold to the grid during time period t. For other time periods where P(t) < 0, the calculation expression for the electricity purchase fee for each time period is as follows: (4) Step 7: Stop the iteration and finally calculate the operating electricity cost for each time period to obtain the annual electricity operating cost of the system.

[0061] The calculation shows that the annual electricity purchase from the grid will cost 92,100 yuan.

[0062] This embodiment has at least the following technical effects: Taking an office park in northern China as an example, this invention accurately reveals the system's operational economy under different conditions, such as summer cooling, winter heating, and transitional seasons, through hourly operation simulation and cost calculation. The results show that this method quantifies the core value of energy storage of a given capacity in improving the self-consumption rate of photovoltaic power, accurately identifies numerous "zero-cost periods" throughout the year where energy storage can completely replace grid power purchases, thus achieving high-precision prediction of the park's annual operating electricity costs. This provides reliable data support for the park's energy budget formulation, operational strategy optimization, and energy storage configuration effect evaluation.

[0063] In one embodiment of the present invention, a computing device is provided. This computing device can be a terminal and may include a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. When the computer programs are executed by the processor, they implement the methods described in the above embodiments. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions stored in the memory.

[0064] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0066] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating the operating conditions of a combined cooling, heating, and power (CCHP) system in a park, analyzed hourly throughout the year, characterized in that: include: Construct a model of a combined cooling, heating and power (CCHP) system for a given park configuration. The CCHP system should include at least a local renewable energy generation unit, an energy storage system of a given capacity, and local electricity load. Obtain hourly data sequences for the entire year, which must include at least: renewable energy generation capacity, electrical load capacity, and grid electricity price; With maximizing the absorption of local renewable energy as the primary operational objective, hourly energy balance simulations were conducted; for each simulation period: Priority should be given to using renewable energy generation capacity during this period to directly meet local electricity load demand; If there is a surplus of renewable energy generation, the energy storage system will be charged first. If renewable energy generation cannot meet the load demand, the energy storage system will be the first to discharge to supplement it. When the combined output of renewable energy and energy storage systems is still insufficient to meet the load, the power deficit is balanced by purchasing electricity from the grid. Based on the results of energy balance simulation, calculate the total operating electricity cost of the system during the calculation cycle.

2. The method for calculating the operating conditions of a park's combined cooling, heating, and power system based on hourly analysis throughout the year as described in claim 1, characterized in that... In performing hourly energy balance simulations, the set of periods of electricity exemption from purchase is identified and processed, including: Identify one or more consecutive time periods to form a time period set; Calculate the algebraic sum of the grid-connected electricity for all time periods within the set. The grid-connected electricity is the difference between the renewable energy generation power and the electrical load power. If the absolute value of the algebraic sum of the grid-connected electricity is less than or equal to the rated discharge capacity of the energy storage system, then the set of time periods is determined to be a set of time periods exempt from electricity purchase; when calculating the operating electricity cost, the purchased power of all time periods in the set of time periods exempt from electricity purchase is set to zero.

3. The method for calculating the operating conditions of a park's combined cooling, heating, and power system based on hourly analysis throughout the year as described in claim 2, is characterized in that... The set of periods exempt from electricity purchase is identified using a cyclic accumulation algorithm, including: Search for the time intervals in each group where P(t) < 0; Create an array to store the time periods where P(t) < 0 and the corresponding electricity price data for those time periods; if the sum of the grid-connected electricity for each time period in the array is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0. Create an array to store the time periods starting from P(t) < 0 and their corresponding electricity prices. f buy The system searches for the first time period with the lowest electricity price and starts charging the energy storage from that time period. If the sum of the grid-connected electricity available in each time period within the array is greater than the charging amount of the energy storage battery, an array is created to store this information. P The array contains n data points: the time period (t) < 0 and the corresponding electricity price data for that time period. Sort by time, starting from the first P Starting from the time interval t where (t)<0, the sequence number of this time interval is denoted as i=1; Starting from i, iterate through each time period in the subsequent array, and accumulate the available electricity for the current time period i. If the sum of the available electricity for the time periods {1, i} is less than the charging amount of the energy storage battery, then the electricity purchase fee for these time periods is 0; and continue to iterate through the next time period. Update i to i+1 and restart the new accumulation loop; if the sum of the grid-connected electricity in the time periods {1, i} is greater than the discharge capacity of the energy storage battery, calculate the electricity purchase cost for that time period and the electricity purchase cost for each time period when P(t) < 0. Stop iterating and finally calculate the operating electricity cost for each time period.

4. The method for calculating the operating conditions of a park's combined cooling, heating, and power system based on hourly analysis throughout the year, as described in claim 3, is characterized in that... Update i to i+1 and restart the new accumulation loop; If the sum of the grid-connected electricity during periods {1, i} is greater than the discharge capacity of the energy storage battery, the electricity purchase fee for that period will be [not specified]. for: In the formula, Let t represent the energy storage capacity over the time period t.

5. The method for calculating the operating conditions of a park's combined cooling, heating, and power system based on hourly analysis throughout the year as described in claim 3, characterized in that... Electricity purchase fee for each time period when P(t) < 0 The calculation is as follows: 。 6. The method for calculating the operating conditions of a park's combined cooling, heating, and power system based on hourly analysis throughout the year as described in claim 1, characterized in that... The time resolution of the hourly data series is 1 hour, the calculation period is one year, and there are a total of 8760 time periods.

7. The method for calculating the operating conditions of a park's combined cooling, heating, and power system based on hourly analysis throughout the year as described in claim 1, characterized in that... Local renewable energy generation units include at least one of photovoltaic power generation systems and wind power generation systems.

8. A system for calculating the operating conditions of a combined cooling, heating, and power (CCHP) system in a park, analyzed hourly throughout the year, characterized in that: include: The model building module constructs a model of a park's combined cooling, heating and power (CCHP) system with a given configuration. The park's CCHP system includes at least a local renewable energy power generation unit, an energy storage system of a given capacity, and local electrical load. The sequence acquisition module acquires hourly data sequences for the entire year. The hourly data sequences include at least: renewable energy power generation, electrical load, and grid electricity price. The energy balance simulation module, with maximizing the absorption of local renewable energy as its primary operational objective, performs hourly energy balance simulations; for each simulation period: Priority should be given to using renewable energy generation capacity during this period to directly meet local electricity load demand; If there is a surplus of renewable energy generation, the energy storage system will be charged first. If renewable energy generation cannot meet the load demand, the energy storage system will be the first to discharge to supplement it. When the combined output of renewable energy and energy storage systems is still insufficient to meet the load, the power deficit is balanced by purchasing electricity from the grid. The electricity cost calculation module calculates the total operating electricity cost of the system within the calculation period based on the results of energy balance simulation.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 7.

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