A two-stage optimization scheduling method and system for RSOC comprehensive energy system

Abstract

The application discloses a kind of RSOC comprehensive energy system day-to-day two-stage optimization scheduling method and system, wherein the method includes: based on the operation characteristics of RSOC, establish RSOC comprehensive energy system;Based on RSOC comprehensive energy system, respectively establish RSOC variable working condition operation model, cogeneration model and multi-element load demand response model;Based on RSOC variable working condition operation model, cogeneration model and multi-element load demand response model, jointly establish day-to-day two-stage optimization scheduling model;Based on day-to-day planning scheduling target and day-to-day constraint condition, the day-to-day two-stage optimization scheduling model is optimized, and the optimal operating state data of system equipment is obtained;Based on optimal operating state data, day-to-day rolling optimization target and day-to-day constraint condition, the day-to-day two-stage optimization scheduling model is adjusted, and the optimal scheduling operation scheme is obtained.Guarantee the stable and reliable operation of comprehensive energy system, reduce the operation cost of system, improve the optimization control precision of system.

Description

Technical Field

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

[0002] Currently, under the backdrop of the "dual carbon" goals, my country's installed clean energy capacity is rapidly increasing. The output characteristics of clean energy are significantly influenced by regional natural resource endowments, and the large-scale grid connection of clean energy has an increasingly significant impact on the safe and stable operation of the power system. With the increasingly close coupling of heterogeneous energy sources such as electricity, heat, and hydrogen in integrated energy systems, and the increasing penetration of clean energy in these systems, the types and number of devices in the system are also rapidly increasing. The uncertainty of system sources and loads is becoming increasingly prominent, and ensuring the safe and stable operation of integrated energy systems has become a difficult problem that plagues both academic and engineering communities.

[0003] From the perspective of the current development status at home and abroad. Many studies have rarely considered the variable operating characteristics of RSOC, and the model results have certain deviations. Most of them only consider the response to a single load demand. However, 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 increasingly close. 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 degree of coupling between multiple energy subsystems in the system is becoming increasingly close, and the operation of the integrated energy system is becoming more complex and variable. Traditional day-ahead optimization scheduling has led to significant imbalances in system supply and demand, and day-ahead scheduling can no longer meet the basic requirements for stable system operation.

[0004] Therefore, how to reduce the system operating costs 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 system's operating costs and improves the system's 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 solutions:

[0007] A two-stage optimization scheduling method for 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 established respectively;

[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] Optimizing the day-ahead and intraday two-stage optimization scheduling model based on the day-ahead scheduling target and the day-ahead constraints 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 adjusted 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 SOFC variable operating parameters, 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 operating parameters of SOEC under variable conditions.

[0020] Preferably, the cogeneration model is specifically:

[0021]

[0022] in, represents the electric power generated by the cogeneration at time t, η GT,pe represents the gas turbine electrical efficiency, represents the gas power input to the 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, and c1, c2, c3 and e4 represent the variable operating parameters of the cogeneration unit.

[0023] Preferably, the multi-element 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, represents the base load power of energy source j at time t of the system; represents the total load power of energy source j 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 transferable load power of energy source j at time t; η j,IDR,A represents the transferable load ratio 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 represents 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 on 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, day-ahead and intraday, 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 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-element load demand response model;

[0042] 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 constraint conditions to obtain the optimal operating status data of the system equipment;

[0043] The scheduling plan 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 plan.

[0044] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a two-stage optimization scheduling method and system for the 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 supply and demand balance, 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 based on 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 operating cost of the system.

[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 actual systems, the present invention needs to be adapted 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 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

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

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

[0054] Figure 3 This is 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 intraday optimization scheduling solution provided by the present invention.

[0056] Figure 5 This is a structural diagram of a two-stage day-ahead and intraday optimization scheduling system for an RSOC integrated energy system provided by the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts 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 the RSOC integrated energy system, including:

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

[0061] Based on the RSOC integrated energy system, the RSOC variable operating condition model, combined heat and power model and multi-load demand response model were established.

[0062] 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.

[0063] Based on the day-ahead 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;

[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 integrated energy system based on RSOC operating characteristics:

[0068] Preferably, in order to solve the problems of insufficient clean energy absorption 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 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 in the figure, the RSOC integrated energy system includes: a reversible solid oxide fuel cell (RSOC), a photovoltaic power generation device (PV), a wind power generation device (WG), a hydrogen methanation device (MH), a combined heat and power (CHP) device, an electric heat device (EH), a gas-fired boiler (GB), a thermal energy storage device (TES) and a hydrogen energy storage device (HES).

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

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

[0072] 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 system equipment deviates from the rated operating point, operating characteristics such as the efficiency coefficient and consumption characteristics may change accordingly. The conversion efficiency of CHP units in the RSOC integrated energy system and the charge and discharge efficiency of RSOC are significantly affected by the state of charge. To make the integrated energy system more consistent with actual operating conditions, a variable operating condition model for RSOC and cogeneration equipment was 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 SOFC variable operating parameters, 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 operating parameters of SOEC under variable conditions.

[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 as follows:

[0082]

[0083] in, represents the electric power generated by the cogeneration at time t, η GT,pe represents the gas turbine electrical efficiency, represents the gas power input to the 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, and c1, c2, c3 and e4 represent the variable operating parameters of the cogeneration unit.

[0084] Preferably, in this embodiment, the variable operating parameters of the SOFC, the variable operating parameters of the 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 for 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 the 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 part of the time period, the total energy consumption of the user during the dispatch cycle will decrease.

[0090] Preferably, the multi-element 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, represents the base load power of energy source j at time t of the system; represents the total load power of energy source j 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 transferable load power of energy source j at time t; η j,IDR,A represents the transferable load ratio 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 represents 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's day-ahead scheduling, solving for information such as equipment operation plans and demand response scheduling quantities in the day-ahead scheduling. The lower layer aims to minimize the total operating cost of the system's intraday scheduling, and performs rolling optimization scheduling based on the upper layer scheduling results with a finer sampling accuracy.

[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 an energy layer, a device layer, and a load layer. Wind, solar, and gas energy from the energy layer provide energy to the device layer. The device layer includes electric heating devices, wind power generation, photovoltaic cells, heat storage devices, hydrogen storage devices, gas boilers, hydrogen methanation devices, RSOC, and combined heat and power (CHP) devices, and considers a variable operating condition model for RSOC and CHP values. The load layer includes electric loads, thermal loads, and hydrogen loads. Wind, solar, and gas energy are converted into electricity through wind turbines, photovoltaic cells, and CHP devices, respectively. This electricity is then transmitted to electric heating devices, RSOC, and electric loads. Gas energy is converted into heat energy through gas boilers and CHP devices. This energy, along with the heat generated by the electric heating devices and RSOC, is then transferred to a thermal storage device for storage. A multi-layered load demand response is established for the electric, thermal, and hydrogen loads, with different response modes being adopted for users with different energy usage characteristics.

[0103] Based on the day-ahead 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 operation cost of the system's day-ahead scheduling is the lowest, and the scheduling is optimized with an accuracy of 1 hour.

[0105] Preferably, the total scheduling 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 Specifically:

[0113]

[0114] Among them, q∈(RSOC, PV, WG, CHP, GB, EH) represents the set of system energy conversion devices, 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 device at time t of the system; represents the output power of device q at time t, c q,ma represents the equipment 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 respectively, represents the environmental cost of cogeneration of the system 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 transferable load power of energy j 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 as follows:

[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 integrated energy system and the external system have energy interaction constraints, and the energy interaction constraints are specifically:

[0129]

[0130] Among them, P buy,j,max and P buy,j,min Respectively represent the upper and lower limits of the system's purchased energy 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. This is usually used to limit the rate at which the equipment starts, stops, or adjusts power to protect the equipment and ensure system stability. Based on this, the equipment ramp constraints are specifically:

[0135]

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

[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 to minimize the total operating cost of the system's scheduling within the day, with 1 hour as the control time domain and 15 minutes as the sampling accuracy for optimized scheduling.

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

[0140]

[0141] Among them, G downis the total daily operation cost of the system.

[0142] Preferably, the intraday optimal scheduling complies with the day-ahead optimal operating equipment status and 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 operating status constraints are scaled and used for intraday scheduling. The equipment operating status constraints are specifically:

[0144]

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

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

[0147]

[0148] in, represents the load transfer amount during the optimal scheduling time t within the day, represents the load transferred during 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-up speed in the intraday optimization scheduling are all taken as one-fourth of the day-ahead scheduling.

[0150] Preferably, Figure 4 As shown, the day-ahead optimization scheduling makes short-term forecasts of 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 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 day-ahead dispatch equipment and demand response dispatch volume, with a one-hour control horizon and a 15-minute sampling accuracy. Intraday scheduling begins with the first control horizon, predicting the clean energy output and load demand for that horizon based on historical data. Then, intraday optimization scheduling is performed. Based on the predicted clean energy output and load demand, the output and load of each device in the system are adjusted through two-layer optimization scheduling. The control horizon is then adjusted, and these steps are repeated. When the cost of the two-stage day-ahead and intraday optimization scheduling model 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] Using a control variable approach, based on a two-stage day-ahead and intraday optimization dispatch model, the authors considered whether multiple load demands were considered both day-ahead and intraday. Using clean energy output and load demand data from the Sichuan and Chongqing regions, they derived optimized system costs for different typical days. The results showed that, considering only curtailable loads, the intraday optimal dispatch costs for three typical days (summer, transitional season, and winter) were 3.8%, 4.6%, and 1.9% lower than the day-ahead optimal dispatch costs, respectively. When considering complete demand responses, the intraday optimal dispatch costs were 3.1%, 4.7%, and 2.0% lower than the day-ahead optimal dispatch costs, respectively.

[0161] A typical day during the transition season was selected to simulate the day-ahead and intraday operation of the RSOC integrated energy system. The daily supply of electricity, heat, and hydrogen loads was determined. The results show that during the early stages of the transition season, the electricity load was primarily supplied by clean energy output, while clean energy output was insufficient in the later stages. The shortfall in electricity load was partially met by SOFC hydrogen-consuming power generation, cogeneration, and purchased electricity. The heat load was primarily supplied by electric heating, with the shortfall in heat load being supplemented by SOFC thermal power, gas boilers, and cogeneration. The hydrogen load was primarily supplied by SOEC electrolysis. In the early stages, the system had sufficient hydrogen supply capacity, and in addition to supplying hydrogen, the system also stored some hydrogen in hydrogen storage devices. However, in the later stages, hydrogen supply capacity was insufficient, and to avoid reducing some of the hydrogen load, the system purchased a small amount of hydrogen from external sources.

[0162] Different hydrogen prices were set and their impact on the optimized operation of the integrated energy system during a typical day during the transition season was analyzed. The results showed that as the hydrogen price increased, the total operating cost of the integrated energy system gradually increased, as did the gas and electricity purchase costs, 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 system cost stabilized.

[0163] By controlling the variable operating characteristics of the RSOC and CHP equipment, and observing the optimized operation of the integrated energy system on a typical day during the transition season under different conditions, the results show that under variable operating conditions, the system's electricity purchase costs, environmental costs, and operation and maintenance costs increase compared to rated operating conditions. While accounting for the equipment's variable operating characteristics may increase costs to a certain extent, it can also make the configuration results more reasonable and accurate, and more in line with project reality.

[0164] Example 4

[0165] like Figure 5 As shown, a two-stage optimization scheduling system for the RSOC integrated energy system, day-ahead and intraday, 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 establishing 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 two-stage day-ahead and intraday 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 scheduling target and day-ahead constraints to obtain the optimal operating status data of the system equipment;

[0170] The scheduling plan output module is used to perform rolling adjustments on the day-ahead and 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 method of each functional module in the system of this embodiment corresponds to the above method one by one, and will not be repeated here.

[0172] Example 5

[0173] Based on the same inventive concept, the present invention further provides a computer device, comprising 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 for storing 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, namely, the day-ahead and the day-intraday, as described in Example 1 or 2.

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

[0177] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0178] Through the above technical solutions, it can be seen that the present invention discloses a two-stage optimization scheduling method and system for the 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 supply and demand balance, 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 based on 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 operating cost of the system.

[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 actual systems, the present invention needs to be adapted 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 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] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0186] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily 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 is not limited to the embodiments shown herein but is intended to conform 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 by: 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 established respectively; 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 SOFC variable operating parameters, 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, e1, e2, e3, E1, E2 and E3 represent the SOEC operating parameters under variable conditions; The multi-element 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, 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 transferable load power of energy source j at time t; η j,IDR,A represents the transferable load ratio 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 represents the load reduction ratio of energy source j; 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; Optimizing the day-ahead and intraday two-stage optimization scheduling model based on the day-ahead scheduling target and the day-ahead constraints 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 adjusted to obtain the optimal scheduling operation plan.

2. The RSOC integrated energy system two-stage optimization scheduling method according to claim 1, characterized in that: The cogeneration model is specifically as follows: in, represents the electric power generated by the cogeneration at time t, η GT,pe represents the gas turbine electrical efficiency, represents the combined heat and power input gas power 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, and c1, c2, c3 and e4 represent the variable operating parameters of the cogeneration unit.

3. The RSOC integrated energy system two-stage optimization scheduling method according to claim 1, characterized in that: The day-ahead scheduling target is: the total scheduling operation cost of the system on 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.

4. The RSOC integrated energy system two-stage optimization scheduling method according to claim 3, 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.

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

6. The RSOC integrated energy system two-stage optimization scheduling method according to claim 5, 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 ramping constraints.

7. The RSOC integrated energy system two-stage optimization scheduling method according to claim 1, characterized in that: 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.

8. A two-stage day-ahead and intraday optimization scheduling system for an RSOC integrated energy system, applied to a two-stage day-ahead and intraday optimization scheduling method for an RSOC integrated energy system according to any one of claims 1 to 7, 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 solution 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 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-element load demand response model; 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 constraint conditions to obtain the optimal operating status data of the system equipment; The scheduling plan 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 plan.

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