Day-day two-stage optimization scheduling method and system for RSOC integrated energy system

Abstract

The invention discloses a day-ahead-day two-stage optimization scheduling method and system for an RSOC integrated energy system. The method comprises the steps that the RSOC integrated energy system is established based on running characteristics of the RSOC; respectively establishing an RSOC variable working condition operation model, a combined heat and power generation model and a multi-element load demand response model based on the RSOC comprehensive energy system; based on the RSOC variable working condition operation model, the combined heat and power generation model and the multi-element load demand response model, jointly establishing a day-ahead-intra-day two-stage optimization scheduling model; optimizing the day-ahead-intra-day two-stage optimization scheduling model based on a day-ahead plan scheduling target and a day-ahead constraint condition to obtain optimal operation state data of system equipment; and based on the optimal operation state data, the intra-day rolling optimization target and the intra-day constraint condition, performing rolling adjustment on the day-ahead-intra-day two-stage optimization scheduling model to obtain an optimal scheduling operation scheme. Under the condition of ensuring stable and reliable operation of the comprehensive energy system, the operation cost of the system is reduced, and the optimization control precision of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system optimization, and more specifically to a two-stage day-ahead and intra-day optimization scheduling method and system for an RSOC integrated energy system. Background Art

[0002] At present, under the background of "dual carbon" goals, my country's clean energy installed capacity has increased rapidly. The output characteristics of clean energy are significantly affected by the natural resource endowment of the region, and the impact of large-scale grid connection of clean energy on the safe and stable operation of the power system has become increasingly prominent. With the increasingly close coupling of heterogeneous energy such as electricity, heat, and hydrogen in the integrated energy system, and the increase in the penetration rate of clean energy in the system, the types and number of equipment in the system are also increasing rapidly, and the problem of system source and load uncertainty is becoming more and more prominent. How to operate the integrated energy system safely and stably has become a problem that plagues the academic and engineering communities.

[0003] From the current development status at home and abroad. Many studies rarely consider the variable operating characteristics of RSOC, the model results have certain deviations, and most of them only consider the single load demand response. With the development of integrated energy system technology, the connection and coupling between energy subsystems such as electricity, heat, hydrogen, and gas in the system are becoming closer. With the increase in the penetration rate of clean energy in the integrated energy system and the application of flexible and adjustable resources such as demand response, the coupling degree of multiple energy subsystems in the system is becoming closer and closer, and the operation of the integrated energy system is becoming more and more complex and changeable. The traditional day-ahead optimization scheduling leads to a significant imbalance between supply and demand in the system, and the day-ahead scheduling can no longer meet the basic requirements for stable operation of the system.

[0004] Therefore, how to reduce the system operating cost while ensuring the stable and reliable operation of the integrated energy system and improve the system optimization control accuracy are problems that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention provides a two-stage optimization scheduling method and system for the RSOC integrated energy system, which reduces the operating cost of the system and improves the system optimization control accuracy while ensuring the stable and reliable operation of the integrated energy system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A two-stage optimization scheduling method for a RSOC integrated energy system, day-ahead and intraday, comprising:

[0008] Establish RSOC integrated energy system based on RSOC operation characteristics;

[0009] Based on the RSOC integrated energy system, an RSOC variable operating condition operation model, a cogeneration model and a multi-load demand response model are respectively established;

[0010] A two-stage day-ahead and intraday optimization scheduling model is jointly established based on the RSOC variable operating condition operation model, the cogeneration model and the multi-load demand response model;

[0011] The day-ahead and day-intraday two-stage optimization scheduling model is optimized based on the day-ahead planning scheduling target and the day-ahead constraint conditions to obtain the optimal operating status data of the system equipment;

[0012] Based on the optimal operating status data, the intraday rolling optimization target and the intraday constraints, the day-ahead and intraday two-stage optimization scheduling model is rolled over to obtain the optimal scheduling operation plan.

[0013] Preferably, the RSOC variable operating condition operation model includes a SOFC operating condition operation model and a SOEC operating condition operation model;

[0014] The SOFC operating model is:

[0015]

[0016] in, represents the electrical load rate of SOFC at time t, represents the output power of SOFC at time t, P RSOC Indicates RSOC rated power, represents the output thermal power of SOFC at time t, f1, f2, f3, F1, F2 and F3 all represent the variable operating parameters of SOFC, represents the hydrogen consumption power of RSOC at time t, L hv Indicates the high heating value of hydrogen;

[0017] The SOEC operating model is:

[0018]

[0019] in, represents the electrical load rate of SOEC at time t, represents the electric power consumed by SOEC at time t, represents the thermal power consumed by SOEC at time t, represents the hydrogen production power of SOEC at time t, and e1, e2, e3, E1, E2 and E3 represent the variable operating parameters of SOEC.

[0020] Preferably, the cogeneration model is specifically:

[0021]

[0022] in, represents the electrical power generated by the cogeneration at time t, η GT,pe represents the gas turbine electrical efficiency, represents the gas power input of CHP at time t, represents the thermal power output of the cogeneration at time t, η l,th represents the gas turbine heat loss coefficient, represents the load rate of the cogeneration unit at time t, P CHP,pe,0 It represents the rated electric power output of the cogeneration unit. c1, c2, c3 and e4 represent the variable operating parameters of the cogeneration unit.

[0023] Preferably, the multi-load demand response model includes: a basic load demand response model, a transferable load demand response model and a curtailable load demand response model;

[0024] The base load demand response model is:

[0025]

[0026] in, It represents the base load power of energy source j at time t of the system; represents the total load power of energy source j in the system at time t; η j,foun represents the basic load ratio of energy j, j∈(pe,th,hy) corresponds to the set of electricity, heat and hydrogen energy, pe represents electricity, th represents heat and hy represents hydrogen energy;

[0027] The transferable load demand response model is:

[0028]

[0029] in, represents the load power that can be transferred from energy source j to the system at time t; η j,IDR,A represents the transferable load proportion of energy source j;

[0030] The curtailable load demand response model is:

[0031]

[0032] in, Indicates the load power that can be reduced by energy j at time t; η j,IDR,B It indicates the load reduction ratio of energy source j.

[0033] Preferably, the day-ahead planned scheduling target is: the total scheduling operation cost of the system the day before is the lowest; the intra-day rolling optimization target is: the total scheduling operation cost of the system within the day is the lowest.

[0034] Preferably, the total scheduling operation cost includes: system energy purchase cost, system operation and maintenance cost, system environmental cost and system demand response cost.

[0035] Preferably, the intraday constraints include: equipment operating status constraints, intraday demand response constraints and the day-ahead constraints.

[0036] Preferably, the day-ahead constraints include: energy storage device charging and discharging constraints, RSOC operation constraints, energy interaction constraints, day-ahead demand response constraints and equipment ramping constraints.

[0037] Preferably, the RSOC integrated energy system includes: a reversible solid oxide fuel cell RSOC, a photovoltaic power generation device, a wind power generation device, a cogeneration device, an electric heating device, a gas boiler, a heat storage device and a hydrogen storage device.

[0038] A two-stage optimization scheduling system for a RSOC integrated energy system in the day-ahead and day-intraday stages, comprising: a system construction module, a first model construction module, a second model construction module, a model optimization module and a scheduling scheme output module;

[0039] The system building module is used to establish an RSOC integrated energy system based on the operating characteristics of the RSOC;

[0040] The first model building module is used to respectively establish an RSOC variable operating condition operation model, a cogeneration model and a multi-load demand response model based on the RSOC integrated energy system;

[0041] The second model building module is used to jointly establish a day-ahead and intraday two-stage optimization scheduling model based on the RSOC variable operating condition operation model, the cogeneration model and the multi-load demand response model;

[0042] The model optimization module is used to optimize the day-ahead and intra-day two-stage optimization scheduling model based on the day-ahead planning scheduling target and the day-ahead constraint conditions to obtain the optimal operating status data of the system equipment;

[0043] The scheduling scheme output module is used to perform rolling adjustments on the day-ahead and day-intraday two-stage optimization scheduling model based on the optimal operating status data, the intraday rolling optimization target and the intraday constraints to obtain the optimal scheduling operation scheme.

[0044] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a two-stage optimization scheduling method and system for a RSOC integrated energy system, which has the following beneficial effects:

[0045] 1. While ensuring the stable and reliable operation of the integrated energy system, the operating cost of the system is reduced. At the same time, reasonable optimization and scheduling improves the flexibility of the system, coordinates the balance of supply and demand, and improves the optimization control accuracy of the system, reduces the impact of errors, and improves the overall efficiency of the system.

[0046] 2. The day-ahead and intraday optimization scheduling method of the present invention alleviates the source-load volatility according to the electricity, heat and hydrogen coupling characteristics of the integrated energy system.

[0047] 3. Through intraday optimization scheduling, the output of each device in the integrated energy system can be adjusted in the day-ahead planning and scheduling, reducing the deviation between the system energy supply and load demand, thereby reducing the system's operating costs.

[0048] 4. The uncertainty brought by clean energy and load forecasting to the integrated energy system can be alleviated through the multi-energy coupling mechanism of the integrated energy system to meet the load demand as much as possible.

[0049] 5. Demand response strategies and parameters have a significant impact on the operation of the integrated energy system. In the actual system, the present invention needs to adapt to local conditions, reasonably formulate demand response strategies, and guide the healthy development of the demand response mechanism.

[0050] 6. The IES-RSOC day-ahead and day-intraday two-stage optimization scheduling method of the present invention, which comprehensively considers the variable operating conditions of the equipment, can effectively alleviate the problem of insufficient accuracy of the efficiency scheduling scheme, improve the accuracy of the scheduling model, and make the scheduling results more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0052] Figure 1 A flow chart of a two-stage optimization scheduling method for a RSOC integrated energy system provided by the present invention, namely, day-ahead and day-intraday.

[0053] Figure 2 This is a framework diagram of the RSOC integrated energy system provided by the present invention.

[0054] Figure 3 A schematic diagram of the RSOC-based integrated energy system model provided by the present invention.

[0055] Figure 4 This is a schematic diagram of the day-ahead and intra-day optimization scheduling solution provided by the present invention.

[0056] Figure 5 A schematic diagram of the structure of a two-stage optimization scheduling system for a day-ahead and intra-day RSOC integrated energy system provided by the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] like Figure 1 As shown, the embodiment of the present invention discloses a two-stage optimization scheduling method for a RSOC integrated energy system, including:

[0060] Establish RSOC integrated energy system based on RSOC operation characteristics;

[0061] Based on the RSOC integrated energy system, RSOC variable operating condition model, cogeneration model and multi-load demand response model are established respectively;

[0062] A two-stage day-ahead and intraday optimization scheduling model is jointly established based on the RSOC variable operating condition model, cogeneration model and multi-load demand response model.

[0063] Based on the day-ahead planning scheduling target and day-ahead constraints, the day-ahead and intraday two-stage optimization scheduling model is optimized to obtain the optimal operating status data of the system equipment;

[0064] Based on the optimal operating status data, intraday rolling optimization objectives and intraday constraints, the day-ahead and intraday two-stage optimization scheduling model is rolled over to obtain the optimal scheduling operation plan.

[0065] Example 2

[0066] The embodiment of the present invention discloses a two-stage optimization scheduling method for a RSOC integrated energy system, including:

[0067] Establish RSOC comprehensive energy system based on RSOC operation characteristics:

[0068] Preferably, in order to solve the problems of insufficient clean energy consumption and inflexible load demand response in traditional integrated energy systems, the present invention establishes a reversible solid oxide fuel cell (RSOC) integrated energy system based on the operating characteristics of reversible solid oxide fuel cells (RSOC) that can flexibly switch between solid oxide fuel cell (SOFC) and solid oxide electrolysis cell (SOEC) modes.

[0069] Preferably, Figure 2 As shown, the RSOC integrated energy system includes: a reversible solid oxide fuel cell RSOC, a photovoltaic power generation device (photovoltaic, PV), a wind power generation device (wind generation, WG), a hydrogen methanation device (methanation, MH), a combined heat and power device (combined heat and power, CHP), an electric heat device (electric heat, EH), a gas fired boiler (gas fired boiler, GB), a thermal energy storage device (thermal energy storage, TES) and a hydrogen energy storage device (hydrogen energy storage, HES).

[0070] Preferably, RSOC can be used to couple the three energy sources of electricity, heat and hydrogen in the system, thereby increasing the clean energy consumption rate of the integrated energy system and further improving the energy supply quality of the system.

[0071] Based on the RSOC integrated energy system, RSOC variable operating condition operation model, cogeneration model and multi-load demand response model are established respectively:

[0072] Preferably, in the field of integrated energy system optimization and scheduling, some studies have used constant efficiency coefficients to characterize the operating characteristics of equipment. However, when the system equipment deviates from the rated operating point, the operating characteristics such as efficiency coefficients and consumption characteristics may change accordingly. The conversion efficiency of CHP units in the RSOC integrated energy system and the charging and discharging efficiency of RSOC are greatly affected by the state of charge. In order to make the integrated energy system more in line with actual operating conditions, a variable operating condition operation model of RSOC and cogeneration equipment is established.

[0073] Preferably, the RSOC variable operating condition operation model includes a SOFC operating condition operation model and a SOEC operating condition operation model;

[0074] The SOFC operating model is:

[0075]

[0076] in, represents the electrical load rate of SOFC at time t, represents the output power of SOFC at time t, P RSOC Indicates RSOC rated power, represents the output thermal power of SOFC at time t, f1, f2, f3, F1, F2 and F3 all represent the variable operating parameters of SOFC, represents the hydrogen consumption power of RSOC at time t, L hv Indicates the high heating value of hydrogen;

[0077] The SOEC operating model is:

[0078]

[0079] in, represents the electrical load rate of SOEC at time t, represents the electric power consumed by SOEC at time t, represents the thermal power consumed by SOEC at time t, represents the hydrogen production power of SOEC at time t, and e1, e2, e3, E1, E2 and E3 represent the variable operating parameters of SOEC.

[0080] Preferably, the cogeneration device can efficiently convert fossil fuels into electricity and heat. The power generation power and efficiency of the cogeneration unit are significantly affected by the load rate. In order to minimize the impact of load rate changes on the cogeneration output, a cogeneration model is established.

[0081] Preferably, the cogeneration model is specifically:

[0082]

[0083] in, represents the electrical power generated by the cogeneration at time t, η GT,pe represents the gas turbine electrical efficiency, represents the gas power input of CHP at time t, represents the thermal power output of the cogeneration at time t, η l,th represents the gas turbine heat loss coefficient, represents the load rate of the cogeneration unit at time t, P CHP,pe,0 It represents the rated electric power output of the cogeneration unit. c1, c2, c3 and e4 represent the variable operating parameters of the cogeneration unit.

[0084] Preferably, in this embodiment, the variable operating parameters of SOFC, the variable operating parameters of SOEC and the variable operating parameters of the cogeneration unit are shown in Table 1:

[0085] Table 1 Variable operating parameters of key equipment

[0086]

[0087]

[0088] Preferably, different demand response models are adopted according to the demand elasticity of different types of loads, so that the integrated energy system can adapt to users with different energy usage characteristics and establish a multi-load demand response of basic load, transferable load and curtailable load.

[0089] Preferably, base load refers to the basic stable load level required for the integrated energy system, which is closely related to the system operation safety and user economic benefits, and is not affected by factors such as electricity price signals; transferable load refers to part of the load that users use to respond to dispatch signals based on market energy prices or incentive mechanisms, but the total energy consumption of users remains unchanged; reducible load refers to the user's adjustment and optimization of the total energy demand based on the market incentive mechanism. After reducing the load in some time periods, the total energy consumption of users during the dispatch cycle will decrease.

[0090] Preferably, the multi-load demand response model includes: a basic load demand response model, a transferable load demand response model and a curtailable load demand response model;

[0091] The base load demand response model is:

[0092]

[0093] in, It represents the base load power of energy source j at time t of the system; represents the total load power of energy source j in the system at time t; η j,foun represents the basic load ratio of energy j, j∈(pe,th,hy) corresponds to the set of electricity, heat and hydrogen energy, pe represents electricity, th represents heat and hy represents hydrogen energy;

[0094] The transferable load demand response model is:

[0095]

[0096] in, represents the load power that can be transferred from energy source j to the system at time t; η j,IDR,A represents the transferable load proportion of energy source j;

[0097] The curtailable load demand response model is:

[0098]

[0099] in, Indicates the load power that can be reduced by energy j at time t; η j,IDR,B It indicates the load reduction ratio of energy source j.

[0100] A two-stage day-ahead and intraday optimization scheduling model is established based on the RSOC variable operating condition model, cogeneration model and multi-load demand response model:

[0101] Preferably, the upper layer of the two-stage day-ahead and intraday optimization scheduling model aims to minimize the total operating cost of the system day-ahead scheduling, and solves the information such as the equipment operation plan and demand response scheduling in the day-ahead scheduling. The lower layer aims to minimize the total operating cost of the system intraday scheduling, and performs rolling optimization scheduling with a smaller sampling accuracy based on the upper layer scheduling results.

[0102] Preferably, the day-ahead and day-intraday two-stage optimization scheduling model is specifically constructed as follows: Figure 3 As shown in the figure: the integrated energy system based on RSOC consists of energy layer, equipment layer and load layer, among which wind energy, solar energy and gas energy in the energy layer provide energy to the equipment layer; the equipment layer includes electric heating device, wind power generation, photovoltaic cells, heat storage device, hydrogen storage device, gas boiler, hydrogen methanation device, RSOC, cogeneration device, and the variable operating condition model of RSOC and cogeneration value is considered at the same time; the load layer includes electric load, heat load and hydrogen load. Among them, wind energy, solar energy and gas energy are converted into electric energy through wind turbines, photovoltaic cells and cogeneration devices respectively, and the electric energy is transmitted to electric heating devices, RSOC and electric loads; gas energy is converted into heat energy through gas boilers and cogeneration devices, and the heat energy generated by electric heating devices and RSOC is transmitted to the heat storage device for storage. Electricity, heat and hydrogen loads establish multi-load demand response, and adopt different response modes for users with different energy usage characteristics.

[0103] Based on the day-ahead planning scheduling objectives and day-ahead constraints, the day-ahead and intraday two-stage optimization scheduling model is optimized to obtain the optimal operating status data of the system equipment:

[0104] Preferably, the day-ahead scheduling target is: the total scheduling operation cost of the system the day before is the lowest, and the scheduling is optimized with an accuracy of 1 hour.

[0105] Preferably, the total dispatching operation cost includes: system energy purchase cost, system operation and maintenance cost, system environmental cost and system demand response cost.

[0106] Preferably, the day-ahead scheduling target is specifically expressed as:

[0107]

[0108] Among them, G uprepresents the total operation cost of the system's day-ahead scheduling, represents the energy purchase cost of the system at time t, represents the operation and maintenance cost of the system at time t; represents the environmental cost of the system at time t; represents the demand response cost of the system at time t.

[0109] Preferably, the system energy purchase cost at time t is Specifically:

[0110]

[0111] Among them, k∈(pe,hy,ga), pe,hy and ga represent electric energy, hydrogen energy and gas energy respectively. They represent the electricity purchase price of the system at time t, the hydrogen purchase price of the system at time t, and the gas purchase price of the system at time t respectively; They respectively represent the electricity power purchased by the system at time t, the hydrogen power purchased by the system at time t, and the gas power purchased by the system at time t.

[0112] Preferably, the system operation and maintenance cost at time t is Specifically:

[0113]

[0114] Among them, q∈(RSOC, PV, WG, CHP, GB, EH) represents the set of system energy conversion devices, It represents the hydrogen storage capacity of the hydrogen storage equipment at time t of the system, It represents the heat storage capacity of the heat storage equipment at time t of the system; represents the output power of device q at time t in the system, c q,ma represents the equipment q operation and maintenance cost, c TES,ma represents the unit cost of operation and maintenance of thermal storage equipment, c HES,ma Represents the unit cost of operation and maintenance of hydrogen storage equipment.

[0115] Preferably, the environmental cost of the system at time t is Specifically:

[0116]

[0117] in, represents the environmental cost of the gas boiler at time t, represents the output power of the gas boiler at time t, b∈(CO2, NO2, SO2), e GB,b 、e CHP,b Represent the environmental cost coefficients of carbon dioxide, nitrogen dioxide and sulfur dioxide of gas boilers and cogeneration units, represents the environmental cost of cogeneration of heat and power at time t, Represents the output power of the cogeneration system at time t.

[0118] Preferably, the system demand response cost at time t is Specifically:

[0119]

[0120] Among them, c IDR,A represents the load transfer incentive price, represents the load power that can be transferred from energy source j to the system at time t, c IDR,B represents the load reduction incentive price, It indicates that energy j can reduce load power at time t in the system.

[0121] Preferably, the day-ahead constraints include: energy storage device charging and discharging constraints, RSOC operation constraints, energy interaction constraints, day-ahead demand response constraints, and equipment ramping constraints.

[0122] Preferably, the energy storage device charge and discharge energy constraints are specifically:

[0123]

[0124] in, represents the charging constraint of energy storage device s, represents the energy release constraint of energy storage device s, P s,ch,max , P s,dis,max They represent the maximum charging and discharging power of the energy storage device s, Indicates the charging and discharging parameters of the energy storage device s. When it is 0, it means the device is in the charging state, and when it is 1, it means the device is in the discharging state. s,dis represents the energy release efficiency of energy storage device s at time t, η s,ch Represents the charging efficiency of energy storage device s at time t.

[0125] Preferably, when the RSOC load rate is low, its efficiency is low. In order to fully ensure the economy and efficiency of the RSOC system, the RSOC operating power is limited. The RSOC operating constraints are specifically:

[0126]

[0127] in, Indicates the maximum discharge power of SOFC; Indicates the maximum power consumption of SOEC; Indicates RSOC operating status constraint parameters.

[0128] Preferably, the energy interaction constraints between the integrated energy system and the external system are as follows:

[0129]

[0130] Among them, P buy,j,max and P buy,j,min They represent the upper and lower limits of the system’s purchased power, Represents the energy purchased by the system at time t.

[0131] Preferably, the integrated energy system achieves flexible regulation and response to energy demand by adjusting the matching relationship between energy supply and demand, and restricts system load demand such as day-ahead demand response constraints:

[0132]

[0133] Among them, η j,IDR,A,min represents the minimum transferable ratio of load j, η j,IDR,A,max represents the maximum transferable ratio of load j, η j,IDR,B,min represents the minimum reduction ratio of load j, η j,IDR,B,max Indicates the maximum reduction ratio of load j.

[0134] Preferably, the output power or input power change rate of the equipment in the integrated energy system is limited within a certain time interval, which is usually used to limit the rate of equipment startup, shutdown or power adjustment to protect the equipment and ensure system stability. Based on this, the equipment ramp constraint is specifically:

[0135]

[0136] Where ΔP q,max and ΔP q,min They represent the upper and lower limits of the ramp power of device q, ΔP s,ch,max and ΔP s,ch,min They represent the upper and lower limits of the charging power of the energy storage device s, ΔP s,dis,max and ΔP s,dis,min They respectively represent the upper and lower limits of the energy storage device s’s discharge power.

[0137] Based on the optimal operating status data, intraday rolling optimization objectives and intraday constraints, the day-ahead and intraday two-stage optimization scheduling model is rolled over to obtain the optimal scheduling operation plan.

[0138] Preferably, the intraday rolling optimization goal is: the total operation cost of the system's scheduling within the day is the lowest, and the optimized scheduling is performed with 1 hour as the control time domain and 15 minutes as the sampling accuracy.

[0139] Preferably, the intraday rolling optimization target is specifically expressed as:

[0140]

[0141] Among them, G downIt is the total operation cost of system scheduling within a day.

[0142] Preferably, the intraday optimal scheduling complies with the day-ahead optimal operating equipment status and the demand response adjustment, and the intraday constraints include: equipment operating status constraints, intraday demand response constraints and day-ahead constraints.

[0143] Preferably, the day-ahead equipment operation status constraints are scaled and used for intraday scheduling. The equipment operation status constraints are specifically:

[0144]

[0145] in, represents the running variable of device q, P q,min represents the minimum operating power of device q, P q,max represents the maximum operating power of device q, Indicates the variable of the hydrogen storage device discharging operation. Indicates the charging operation variable of the hydrogen storage device, Indicates the variable of the heat storage device discharging energy. Indicates the variable of the heat storage device charging operation.

[0146] Preferably, due to the different time scales within the day before the day, one time scale of the day before is expanded to four time scales within the day, and the load transfer amount within the day is the demand response constraint within the day, which is specifically:

[0147]

[0148] in, represents the load transfer amount during the optimal scheduling time t within the day, represents the load transfer amount within the day-ahead scheduled scheduling time round(T / 4), where T∈

[124] is the day-ahead scheduled scheduling time; round(·) is rounded up.

[0149] Preferably, the intraday constraints include the above-mentioned day-ahead constraints. Due to the expansion of the time scale, parameters such as equipment ramp speed in the intraday optimization scheduling are taken as one-fourth of the day-ahead scheduling.

[0150] Preferably, Figure 4 As shown, the day-ahead optimization scheduling makes short-term forecasts for clean energy output and electricity, heat, and hydrogen loads, and optimizes scheduling in the solver with an accuracy of 1 hour with the goal of minimizing the day-ahead and intra-day operating costs, obtaining data such as the optimal operating status of system equipment.

[0151] Intraday scheduling is based on basic information such as the operating status of the day-ahead scheduling equipment and the demand response scheduling volume, with 1 hour as the control time domain and 15 minutes as the sampling accuracy, and performs rolling scheduling. Intraday scheduling starts from the first control time domain, and the clean energy output and load demand of the time domain are predicted based on historical data, and then the intraday optimization scheduling is performed. According to the predicted clean energy output and load demand, the output and load of each device in the system are adjusted through double-layer optimization scheduling, and then the control time domain is adjusted. Repeat the above steps. When the cost of the two-stage optimization scheduling model of the day-ahead and intraday reaches the minimum and the scheduling decision is completed in the last period of the day, the intraday rolling optimization is completed, and the optimal scheduling operation plan is obtained.

[0152] Example 3

[0153] In conjunction with the examples, the specific steps of the present invention are as follows:

[0154] The output and load data of clean energy in Sichuan and Chongqing in 2020 were selected, and optimized operation simulation scheduling was carried out based on the constructed RSOC integrated energy system.

[0155] The established RSOC integrated energy system takes into account the variable operating characteristics of key equipment, making its scheduling results more scientific and reasonable.

[0156] The RSOC integrated energy system model constructed involves time-of-use electricity prices, gas purchase unit prices, hydrogen purchase unit prices, electricity sales unit prices, local electricity sales, hydrogen sales, and heat sales prices as shown in Table 2:

[0157] Table 2 Energy prices on typical days

[0158]

[0159]

[0160] The control variable method is adopted, based on the two-stage optimization dispatch model of day-ahead and intraday, and the multiple load demand response is considered respectively. The clean energy output and load demand data of Sichuan and Chongqing are selected and brought into the solution program to obtain the optimized operation system cost of different typical days. The results show that the intraday optimization dispatch cost of three typical days in summer, transition season and winter is 3.8%, 4.6% and 1.9% lower than the day-ahead optimization dispatch cost respectively when only the load can be reduced is considered. The intraday optimization dispatch cost is 3.1%, 4.7% and 2.0% lower than the day-ahead optimization dispatch cost respectively when the complete demand response is considered.

[0161] A typical day in the transition season was selected to simulate the day-ahead and intraday operation of the RSOC integrated energy system. The supply of daily electricity load, heat load, and hydrogen load was obtained respectively. The results show that the electricity load in the early stage of the typical day in the transition season is mainly supplied by clean energy output, and the clean energy output is insufficient in the later stage. The shortfall of electricity load is supplied by SOFC hydrogen-consuming power generation, cogeneration devices, and purchased electricity; the heat load is mainly supplied by electric heating devices, and the shortfall of heat load is coordinated by SOFC thermal power, gas boilers, and cogeneration devices; the hydrogen load is mainly supplied by SOEC electrolysis hydrogen production. In the early stage, the system has sufficient hydrogen supply capacity. In addition to supplying hydrogen load, the system also stores part of the hydrogen energy in the hydrogen storage device. In the later stage, the hydrogen energy supply capacity is insufficient. In order to avoid cutting part of the hydrogen load, the system purchases a small amount of hydrogen from the outside.

[0162] Different hydrogen prices were set to analyze their impact on the optimized operation of the integrated energy system on a typical day during the transition season. The results showed that with the increase in hydrogen prices, the total operating cost of the integrated energy system gradually increased, the gas purchase cost and the electricity purchase cost also gradually increased, while the hydrogen purchase cost gradually decreased. When the hydrogen price increased to 1.2 yuan / kW·h, the system stopped purchasing hydrogen, and the total cost of the system also tended to stabilize.

[0163] The variable operating characteristics of RSOC and CHP equipment were controlled separately, and the optimized operation of the typical day in the transition season of the integrated energy system under different conditions was observed. The results showed that under the variable operating characteristics, the system's power purchase cost, environmental cost, and operation and maintenance cost increased compared with the rated conditions. Considering the variable operating characteristics of the equipment will increase the cost to a certain extent, but it can make the configuration results more reasonable and accurate, and in line with the actual project.

[0164] Example 4

[0165] like Figure 5 As shown, a two-stage optimization scheduling system of the day-ahead and intraday of the RSOC integrated energy system includes: a system construction module, a first model construction module, a second model construction module, a model optimization module and a scheduling scheme output module;

[0166] System building module for building RSOC integrated energy system based on RSOC operation characteristics;

[0167] The first model building module is used to establish the RSOC variable operating condition operation model, the cogeneration model and the multi-load demand response model based on the RSOC integrated energy system;

[0168] The second model building module is used to jointly establish a day-ahead and intraday two-stage optimization scheduling model based on the RSOC variable operating condition operation model, the cogeneration model and the multi-load demand response model;

[0169] The model optimization module is used to optimize the day-ahead and intraday two-stage optimization scheduling model based on the day-ahead planning scheduling target and the day-ahead constraints to obtain the optimal operating status data of the system equipment;

[0170] The scheduling plan output module is used to make rolling adjustments to the day-ahead and day-intraday two-stage optimization scheduling model based on the optimal operating status data, intraday rolling optimization objectives and intraday constraints to obtain the optimal scheduling operation plan.

[0171] Preferably, the function implementation methods of each functional module in the system of this embodiment correspond to the above methods one by one, and will not be repeated here.

[0172] Example 5

[0173] Based on the same inventive concept, the present invention also provides a computer device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0174] Memory, used to store computer programs;

[0175] The processor, when used to execute the program stored in the memory, can implement a two-stage optimization scheduling method for the RSOC integrated energy system in the day-ahead and day-intraday as in Example 1 or 2.

[0176] The electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The processor may call the logic instructions in the memory to execute a two-stage optimization scheduling method for a day-ahead and day-intraday of an RSOC integrated energy system in Embodiment 1 or 2.

[0177] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0178] Through the above technical solutions, it can be known that the present invention discloses a two-stage optimization scheduling method and system for the day-ahead and intra-day of RSOC integrated energy system, which has the following beneficial effects:

[0179] 1. While ensuring the stable and reliable operation of the integrated energy system, the operating cost of the system is reduced. At the same time, reasonable optimization and scheduling improves the flexibility of the system, coordinates the balance of supply and demand, and improves the optimization control accuracy of the system, reduces the impact of errors, and improves the overall efficiency of the system.

[0180] 2. The day-ahead and intraday optimization scheduling method of the present invention alleviates the source-load volatility according to the electricity, heat and hydrogen coupling characteristics of the integrated energy system.

[0181] 3. Through intraday optimization scheduling, the output of each device in the integrated energy system can be adjusted in the day-ahead planning and scheduling, reducing the deviation between the system energy supply and load demand, thereby reducing the system's operating costs.

[0182] 4. The uncertainty brought by clean energy and load forecasting to the integrated energy system can be alleviated through the multi-energy coupling mechanism of the integrated energy system to meet the load demand as much as possible.

[0183] 5. Demand response strategies and parameters have a significant impact on the operation of the integrated energy system. In the actual system, the present invention needs to adapt to local conditions, reasonably formulate demand response strategies, and guide the healthy development of the demand response mechanism.

[0184] 6. The IES-RSOC day-ahead and day-intraday two-stage optimization scheduling method of the present invention, which comprehensively considers the variable operating conditions of the equipment, can effectively alleviate the problem of insufficient accuracy of the efficiency scheduling scheme, improve the accuracy of the scheduling model, and make the scheduling results more scientific and reasonable.

[0185] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0186] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A two-stage optimization scheduling method for RSOC integrated energy system, characterized in that: include: Establish RSOC integrated energy system based on RSOC operation characteristics; Based on the RSOC integrated energy system, an RSOC variable operating condition operation model, a cogeneration model and a multi-load demand response model are respectively established; A two-stage day-ahead and intraday optimization scheduling model is jointly established based on the RSOC variable operating condition operation model, the cogeneration model and the multi-load demand response model; The day-ahead and day-intraday two-stage optimization scheduling model is optimized based on the day-ahead planning scheduling target and the day-ahead constraint conditions to obtain the optimal operating status data of the system equipment; Based on the optimal operating status data, the intraday rolling optimization target and the intraday constraints, the day-ahead and intraday two-stage optimization scheduling model is rolled over to obtain the optimal scheduling operation plan.

2. The RSOC integrated energy system two-stage optimization scheduling method of day-ahead and day-intraday according to claim 1 is characterized in that: The RSOC variable operating condition operation model includes a SOFC operating condition operation model and a SOEC operating condition operation model; The SOFC operating model is: in, represents the electrical load rate of SOFC at time t, represents the output power of SOFC at time t, P RSOC Indicates RSOC rated power, represents the output thermal power of SOFC at time t, f1, f2, f3, F1, F2 and F3 all represent the variable operating parameters of SOFC, represents the hydrogen consumption power of RSOC at time t, L hv Indicates the high heating value of hydrogen; The SOEC operating model is: in, represents the electrical load rate of SOEC at time t, represents the electric power consumed by SOEC at time t, represents the thermal power consumed by SOEC at time t, represents the hydrogen production power of SOEC at time t, and e1, e2, e3, E1, E2 and E3 represent the variable operating parameters of SOEC.

3. The RSOC integrated energy system two-stage optimization scheduling method of day-ahead and day-intraday according to claim 1 is characterized in that: The cogeneration model is specifically: in, represents the electrical power generated by the cogeneration at time t, η GT,pe represents the gas turbine electrical efficiency, represents the gas power input of the cogeneration at time t, represents the thermal power output of the cogeneration at time t, η l,th represents the gas turbine heat loss coefficient, represents the load rate of the cogeneration unit at time t, P CHP,pe,0 It represents the rated electric power output of the cogeneration unit. c1, c2, c3 and e4 represent the variable operating parameters of the cogeneration unit.

4. The RSOC integrated energy system two-stage day-ahead and day-intraday optimization scheduling method according to claim 1 is characterized in that: The multi-load demand response model includes: a basic load demand response model, a transferable load demand response model and a curtailable load demand response model; The base load demand response model is: in, It represents the base load power of energy source j at time t of the system; represents the total load power of energy source j in the system at time t; η j,foun represents the basic load ratio of energy j, j∈(pe,th,hy) corresponds to the set of electricity, heat and hydrogen energy, pe represents electricity, th represents heat and hy represents hydrogen energy; The transferable load demand response model is: in, represents the load power that can be transferred from energy source j at time t; η j,IDR,A represents the transferable load proportion of energy source j; The curtailable load demand response model is: in, Indicates the load power that can be reduced by energy j at time t; η j,IDR,B It indicates the load reduction ratio of energy source j.

5. The RSOC integrated energy system two-stage optimization scheduling method of day-ahead and day-intraday according to claim 1 is characterized in that: The day-ahead planning scheduling target is: the total scheduling operation cost of the system the day before is the lowest; the intra-day rolling optimization target is: the total scheduling operation cost of the system within the day is the lowest.

6. A two-stage optimization scheduling method for RSOC integrated energy system before and after the day according to claim 5, characterized in that: The total dispatching operation cost includes: system energy purchase cost, system operation and maintenance cost, system environmental cost and system demand response cost.

7. The RSOC integrated energy system two-stage optimization scheduling method of day-ahead and day-intraday according to claim 1 is characterized in that: The intraday constraints include: equipment operating status constraints, intraday demand response constraints and the day-ahead constraints.

8. The RSOC integrated energy system two-stage day-ahead and day-intraday optimization scheduling method according to claim 7 is characterized in that: The day-ahead constraints include: energy storage device charging and discharging constraints, RSOC operation constraints, energy interaction constraints, day-ahead demand response constraints and equipment ramp constraints.

9. The RSOC integrated energy system two-stage day-ahead and day-intraday optimization scheduling method according to claim 1 is characterized in that: The RSOC comprehensive energy system includes: a reversible solid oxide fuel cell RSOC, a photovoltaic power generation device, a wind power generation device, a cogeneration device, an electric heating device, a gas boiler, a heat storage device and a hydrogen storage device.

10. A two-stage day-ahead and day-intraday optimization scheduling system for an RSOC integrated energy system, applied to a two-stage day-ahead and day-intraday optimization scheduling method for an RSOC integrated energy system as claimed in any one of claims 1 to 9, characterized in that: include: A system construction module, a first model construction module, a second model construction module, a model optimization module and a scheduling scheme output module; The system building module is used to establish an RSOC integrated energy system based on the operating characteristics of the RSOC; The first model building module is used to respectively establish an RSOC variable operating condition operation model, a cogeneration model and a multi-load demand response model based on the RSOC integrated energy system; The second model building module is used to jointly establish a day-ahead and intraday two-stage optimization scheduling model based on the RSOC variable operating condition operation model, the cogeneration model and the multi-load demand response model; The model optimization module is used to optimize the day-ahead and intra-day two-stage optimization scheduling model based on the day-ahead planning scheduling target and the day-ahead constraint conditions to obtain the optimal operating status data of the system equipment; The scheduling scheme output module is used to perform rolling adjustments on the day-ahead and day-intraday two-stage optimization scheduling model based on the optimal operating status data, the intraday rolling optimization target and the intraday constraints to obtain the optimal scheduling operation scheme.

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