Cogeneration system scheduling method and system based on compressed carbon dioxide energy storage

Abstract

The invention discloses a cogeneration system scheduling method and system based on compressed carbon dioxide energy storage, a cogeneration system comprises a compressed carbon dioxide energy storage device, a power distribution network and a regional heat supply network, the power distribution network comprises a plurality of new energy units and electric loads, and the regional heat supply network comprises regional heat sources and heat loads. The scheduling method comprises the following steps: acquiring power generation power predicted values of a plurality of new energy units in a current scheduling period, chargeable and dischargeable energy power of an energy storage module in a compressed carbon dioxide energy storage device and available heat energy of a regional heat source; obtaining an electrical load demand prediction value of the power distribution network and a thermal load demand prediction value of the regional heat supply network; and obtaining a scheduling strategy of the compressed carbon dioxide energy storage device based on the scheduling model of the combined heat and power generation system in combination with the thermoelectric data and the load data. The scheduling strategy of the compressed carbon dioxide energy storage device is adjusted to adapt to the power grid demand, the load change of the power grid and the heat supply network is responded in real time, the operation mode is adjusted, and the cost effectiveness is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of cogeneration control, and in particular to a cogeneration system scheduling method and system based on compressed carbon dioxide energy storage. Background Art

[0002] With the acceleration of global energy transformation, combined heat and power system (CHP) is receiving more and more attention as a key technology to achieve efficient energy utilization and optimal configuration. CHP system integrates multiple energy forms such as electricity and heat to improve energy utilization efficiency, reduce carbon emissions and achieve sustainable development of energy.

[0003] Compressed Carbon Dioxide Energy Storage (CCES) technology, as a new type of energy storage, has shown great potential in the field of energy storage due to its advantages such as high energy density, environmental friendliness and a wide operating temperature range. At the same time, CCES technology also has the significant advantage of combined heat and power, which can provide heat energy while storing electrical energy, giving it a unique advantage in the combined heat and power system.

[0004] At present, although there are schemes to apply compressed carbon dioxide energy storage systems to cogeneration systems, there are still some problems in the specific implementation of cogeneration systems. First, the power generation and heat generation in cogeneration systems are interdependent, which increases the complexity of traditional scheduling problems. Second, the traditional centralized scheduling method lacks robustness and flexibility in the face of increasingly complex power system topology and a large number of distributed power sources. In addition, the scheduling problem of cogeneration systems is modeled as a high-dimensional and non-smooth objective function with a large number of nonlinear constraints, which requires powerful optimization algorithms and a considerable amount of time to solve. In the distributed optimization scheduling method, although the calculation amount is smaller and the reliability is higher, it is necessary to set a "leader" to bear the power deviation, which does not completely belong to the distributed solution method. In addition, the scheduling strategy of the cogeneration system needs to consider the changes in the thermal power allocation ratio scheduling and wide operating conditions to evaluate the flexibility of the system's operating thermal power capacity and response tracking. Summary of the invention

[0005] The purpose of the embodiment of the present invention is to provide a scheduling method for a cogeneration system based on compressed carbon dioxide energy storage, which can flexibly adjust the charging and discharging strategy of the energy storage module to adapt to the grid demand and maximize cost-effectiveness, respond to load changes in the grid and the heat network in real time, and optimize resource allocation by automatically adjusting the operating mode to maximize cost-effectiveness.

[0006] To solve the above technical problems, a first aspect of an embodiment of the present invention provides a scheduling method for a cogeneration system based on compressed carbon dioxide energy storage, wherein the cogeneration system comprises: a compressed carbon dioxide energy storage device, a distribution network and a regional heating network, wherein the distribution network comprises: a plurality of new energy units and electric loads, and the regional heating network comprises: a regional heat source and a heat load, and the scheduling method comprises the following steps:

[0007] Obtaining the predicted power generation values ​​of the multiple new energy units in the current scheduling cycle, the chargeable and dischargeable power of the energy storage module in the compressed carbon dioxide energy storage device, and the obtainable heat energy of the regional heat source;

[0008] Obtaining a predicted value of the electric load demand of the distribution network and a predicted value of the heat load demand of the regional heating network;

[0009] Based on the cogeneration system scheduling model, the scheduling strategy of the compressed carbon dioxide energy storage device is obtained in combination with the active power value of power generation, the chargeable and dischargeable power, the obtainable thermal energy, the predicted value of the electric load demand and the predicted value of the thermal load demand.

[0010] Furthermore, the obtaining of the scheduling strategy of the compressed carbon dioxide energy storage device includes:

[0011] Obtaining the power generation prediction values ​​of the plurality of new energy generating units in the current scheduling cycle, and calculating the difference between the power load demand prediction value of the distribution network and the power generation prediction value;

[0012] When the difference is less than 0, if the difference is less than or equal to the electric energy power that can be absorbed by the energy storage module of the compressed carbon dioxide energy storage device, the energy storage module is controlled to discharge to meet the difference between the predicted value of the electric load demand and the predicted value of the power generation power of the several new energy units; if the difference is greater than the electric energy power that can be absorbed by the energy storage module of the energy storage device, a wind and solar abandonment strategy is adopted to lose excess new energy power generation;

[0013] When the difference is equal to 0, it is determined that the predicted value of generated power meets the predicted value of electric load demand;

[0014] When the difference is greater than 0, if the difference is less than or equal to the releasable electric energy power of the energy storage module, the releasable electric energy power of the energy storage module is controlled to meet the difference between the electric load demand forecast value and the power generation forecast value; if the difference is greater than the releasable electric energy power of the energy storage module, after obtaining the available thermal energy of the regional heat source in the current scheduling cycle and converting it into electric energy, the releasable electric energy power of the energy storage module and the electric energy obtained after the conversion of the available thermal energy of the regional heat source are controlled to jointly meet the difference between the electric load demand forecast value and the power generation forecast value.

[0015] Further, after the control of the releasable electric energy power of the energy storage module and the electric energy obtained by converting the obtainable heat energy of the regional heat source to jointly meet the difference between the electric load demand prediction value and the power generation power prediction value, it also includes:

[0016] If the sum of the releasable electric energy of the energy storage module and the electric energy converted from the obtainable thermal energy of the regional heat source is still less than the difference between the electric load demand forecast value and the power generation power forecast value, the exchange active power of the main transmission network or the exchange heat of the main heating network is obtained to meet the remaining electric load demand forecast value.

[0017] Furthermore, the obtaining of the exchange active power of the main transmission network or the exchange heat of the main heating network to meet the remaining predicted value of the electric load demand includes:

[0018] Obtaining the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network;

[0019] Based on the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network, the smaller one of the two is selected as a supplementary energy source to meet the remaining predicted value of the electric load demand.

[0020] Further, the obtaining of the scheduling strategy of the compressed carbon dioxide energy storage device includes:

[0021] Obtaining the maximum output thermal power and the minimum output thermal power of the regional heat source;

[0022] When the predicted value of the heat load demand of the regional heating network is less than the minimum output thermal power of the regional heat source, controlling part of the power of the regional heat source to meet the predicted value of the heat load demand, and storing the remaining power of the regional heat source in the heat storage module of the compressed carbon dioxide energy storage device;

[0023] When the heat load demand forecast value is greater than or equal to the minimum output thermal power of the regional heat source and less than the maximum output thermal power, dispatching the thermal energy of the regional heat source according to the heat load demand forecast value;

[0024] When the predicted heat load demand value is greater than or equal to the maximum output thermal power of the regional heat source, the regional heat source is made to meet the predicted heat load demand value with the maximum output thermal power, and at the same time, the energy storage module is controlled to generate heat to meet the predicted heat load demand value.

[0025] Furthermore, after making the regional heat source meet the predicted value of the heat load demand with the maximum output thermal power and controlling the energy storage module to generate heat, the method further includes:

[0026] If the sum of the thermal power corresponding to the releasable electric energy power of the energy storage module and the maximum output thermal power of the regional heat source is still less than the predicted value of the heat load demand, the exchange active power of the main transmission network or the exchange heat of the main heating network is obtained to meet the remaining predicted value of the heat load demand.

[0027] Further, the obtaining of the exchange active power of the main transmission network or the exchange heat of the main heating network includes:

[0028] Obtaining the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network;

[0029] Based on the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network, the smaller one of the two is selected as a supplementary energy source to meet the remaining predicted value of the thermal load demand.

[0030] Furthermore, the distribution network power flow constraint formula of the cogeneration system scheduling model is:

[0031]

[0032] Among them, I t,ij Represents the current value at time t on line ij; V t,i represents the voltage amplitude of node i at time t; P t,ij and Q t,ij Represents the active and reactive power on line ij at time t; Ω i Represents the set of all nodes connected to i; and Respectively represent the active and reactive power of the load at node i at time t; r ij and x ij Represent the resistance and reactance of line ij respectively; Represents the maximum current on line ij; and Represent the maximum and minimum values ​​of the voltage amplitude at node i respectively;

[0033] The heat balance constraint formula of the cogeneration system scheduling model is:

[0034] P hsou,t +P mh,t +P chl,t =P hload,t +P cwh,t ;

[0035] Among them, P hsou,t is the heating power of the regional heat source at time t, P mh,t is the heating power of the main heating network at time t, P chl,t is the heating power provided by CCES to the regional heat load at time t, Phload,t is the predicted value of heat load demand of the district heating network, P cwh,t represents the heat absorbed by CCES from the heat source at time t;

[0036] The new energy grid connection constraint formula of the cogeneration system scheduling model is:

[0037]

[0038] in, is the actual grid-connected power of new energy, Power generation for renewable energy;

[0039] The power constraint formula of the main transmission grid is:

[0040]

[0041] in, is the minimum value of active power exchange between the balancing nodes of the distribution network and the transmission network, To balance the actual exchange active power value of the node, It is the maximum value of active power exchange of balanced nodes.

[0042] Accordingly, a second aspect of an embodiment of the present invention provides a cogeneration system scheduling system based on compressed carbon dioxide energy storage, wherein the cogeneration system comprises: a compressed carbon dioxide energy storage device, a distribution network and a regional heating network, wherein the distribution network comprises: a plurality of new energy units and electric loads, and the regional heating network comprises: a regional heat source and an electric load, including:

[0043] A thermoelectric data acquisition module, which is used to obtain the predicted power generation values ​​of the multiple new energy units in the current scheduling cycle, the chargeable and dischargeable power of the energy storage module in the compressed carbon dioxide energy storage device, and the obtainable thermal energy of the regional heat source;

[0044] A load data acquisition module, which is used to obtain the predicted value of the electric load demand of the distribution network and the predicted value of the heat load demand of the regional heating network;

[0045] A scheduling strategy formulation module is used to obtain a scheduling strategy for the compressed carbon dioxide energy storage device based on a cogeneration system scheduling model, in combination with the predicted value of generated power, the rechargeable and dischargeable power, the obtainable thermal energy, the predicted value of the electric load demand and the predicted value of the thermal load demand.

[0046] Accordingly, a third aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the above-mentioned cogeneration system scheduling method based on compressed carbon dioxide energy storage.

[0047] Accordingly, a fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned cogeneration system scheduling method based on compressed carbon dioxide energy storage.

[0048] The above technical solution of the embodiment of the present invention has the following beneficial technical effects:

[0049] 1. Able to respond to load changes of power grid and heat network in real time, optimize resource allocation by automatically adjusting operation mode, and maximize cost-effectiveness;

[0050] 2. By obtaining the predicted values ​​of electric load demand and heat load demand within the preset scheduling period, as well as the power grid electricity price and the unit price of external heat source heat purchase, the operation of the cogeneration system can be accurately predicted and planned, reducing energy waste and improving the economy of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a method for scheduling a cogeneration system based on compressed carbon dioxide energy storage provided by an embodiment of the present invention;

[0052] Figure 2 It is a block diagram of a scheduling module of a cogeneration system based on compressed carbon dioxide energy storage provided by an embodiment of the present invention. Description of the drawings:

[0054] 1. Thermal power data acquisition module, 2. Load data acquisition module, 3. Scheduling strategy formulation module. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0056] The CCES system in the embodiment of the present invention is composed of two main stages: energy storage and energy release. The operating mechanism is as follows: in the energy storage stage, gaseous carbon dioxide is compressed by a compressor to reach a high temperature and high pressure state, and then heat exchange is carried out through an intercooler to transfer the compression heat to the heat storage tank for storage; after multi-stage compression and inter-stage heat exchange, the high-pressure carbon dioxide is stored in a high-pressure storage tank. That is, during the compression process, the electric energy is finally stored in the form of the internal energy of CO2 gas and the thermal energy of the heat carrier medium. In the energy release stage, the carbon dioxide is then further heated by the reheater, absorbing heat from the heat storage system, and finally enters the compressor to expand and work, generating electric energy to be output to the electric load of the distribution network. The carbon dioxide after work is stored in the low-pressure gas storage chamber, and the heat exchange medium is stored in the cold storage tank after releasing heat for the next round of circulation. That is, during the expansion process, the internal energy of CO2 gas and the thermal energy of the heat carrier medium are released in the form of electric energy.

[0057] At the same time, the heat storage tank can not only store compression heat, but also store heat from external heat sources; in addition to being used to heat carbon dioxide, the stored heat can also supply the heat load of the regional heating network, reducing the purchase of heat from external heat sources.

[0058] The compression power of the compressor in the above compression process can be expressed as:

[0059]

[0060] Where: P c,k,t is the total compression power of node k in period t; m c,k,t is the mass flow rate of carbon dioxide entering the compressor at node k during period t; c p is the specific heat capacity of carbon dioxide at constant pressure. N c is the total number of compressor stages; T cin (i) is the inlet temperature of the i-th stage compressor; β c (i) is the compression ratio of the i-th compressor; γ is the adiabatic index of carbon dioxide; η c (i) is the isentropic efficiency of the i-th stage compressor.

[0061] The power generation of the expander during the power generation process can be expressed as:

[0062]

[0063] Where: P ex,k,t is the total power of the compressor at node k during period t; m ex,k,t is the mass flow rate of carbon dioxide flowing through the compressor at node k during period t; N ex is the total number of compressor stages; T exin (j) is the inlet temperature of the j-th compressor; β ex (j) is the expansion ratio of the j-th compressor; η ex(j) is the isentropic efficiency of the j-th compressor.

[0064] The air pressure in the low-pressure air storage chamber is expressed as:

[0065]

[0066] Where: p s,k,t and are the gas pressure and pressure change rate in the gas storage device of CCES at node k at time t; V s is the volume of the gas storage device; R g is the gas constant. s is the gas temperature in the gas storage device; p s,k,min and p s,k,max They respectively represent the lower and upper limits of the air pressure in the low-pressure air storage chamber.

[0067] The heat absorbed by the heat storage device during the compression process is:

[0068]

[0069] Where: ε c is the heat transfer coefficient between the heat carrier medium and carbon dioxide gas, T C is the temperature of the heat-carrying medium in the cold storage tank.

[0070] The heat released by the heat storage device during the expansion process is:

[0071]

[0072] The heat in the heat storage tank is expressed as:

[0073]

[0074] Where: Q h,k,t is the energy stored in the thermal storage tank of CCES at node k at time t, P cwh,k,t represents the heat absorbed by CCES from the heat source at time t, P chl,k,t represents the energy supplied by CCES to the regional heat load at time t; Q h,k,min and Q h,k,max They represent the lower and upper limits of the energy stored in the thermal storage tank respectively.

[0075] Please refer to Figure 1 Based on the above-mentioned compressed carbon dioxide energy storage device (CCES), a first aspect of an embodiment of the present invention provides a method for scheduling a cogeneration system based on compressed carbon dioxide energy storage, wherein the cogeneration system comprises: a compressed carbon dioxide energy storage device, a distribution network and a regional heating network, wherein the distribution network comprises: a plurality of new energy units and electric loads, and the regional heating network comprises: a regional heat source and a heat load, and comprises the following steps:

[0076] Step S100, obtaining the predicted power generation values ​​of several new energy units in the current scheduling cycle, the chargeable and dischargeable power of the energy storage module in the compressed carbon dioxide energy storage device, and the obtainable thermal energy of the regional heat source.

[0077] Step S200, obtaining the predicted value of the electric load demand of the distribution network and the predicted value of the thermal load demand of the regional heating network.

[0078] Step S300, based on the cogeneration system scheduling model, combined with the active power value of power generation, the chargeable and dischargeable power, the obtainable thermal energy, the predicted value of the electric load demand and the predicted value of the thermal load demand, the scheduling strategy of the compressed carbon dioxide energy storage device is obtained.

[0079] The scheduling method of the cogeneration system based on compressed carbon dioxide energy storage obtains the power generation and input power of the new energy units in the distribution network in real time, as well as the energy exchange value between the distribution network and the main transmission network, and between the regional heating network and the main heating network. The scheduling model of the cogeneration system integrates these data to optimize the operation strategy of the compressed carbon dioxide energy storage device, aiming to achieve efficient energy utilization and economic system operation, while ensuring the stability and reliability of thermal power supply.

[0080] The dispatching strategies of compressed carbon dioxide energy storage devices include electric energy dispatching strategies, thermal energy dispatching strategies and combined dispatching strategies.

[0081] Furthermore, obtaining the scheduling strategy of the compressed carbon dioxide energy storage device in step S300 includes:

[0082] Step S311, obtaining the predicted power generation values ​​of several new energy units in the current scheduling cycle, and calculating the difference between the predicted power generation value and the predicted power generation value of the distribution network load demand.

[0083] Step S312: When the difference is less than 0, if the difference is less than or equal to the power that the energy storage module of the compressed carbon dioxide energy storage device can absorb, the energy storage module is controlled to discharge to meet the difference between the predicted value of the electric load demand and the predicted value of the power generation of several new energy units; if the difference is greater than the power that the energy storage module of the energy storage device can absorb, the wind and solar abandonment strategy is adopted to lose the excess new energy power generation. Step S313: When the difference is equal to 0, it is determined that the predicted value of power generation meets the predicted value of the electric load demand.

[0084] Step S314, when the difference is greater than 0, if the difference is less than or equal to the releasable electric energy power of the energy storage module, the releasable electric energy power of the energy storage module is controlled to meet the difference between the predicted value of the electric load demand and the predicted value of the power generation; if the difference is greater than the releasable electric energy power of the energy storage module, the available thermal energy of the regional heat source in the current scheduling cycle is obtained and converted into electric energy, and then the releasable electric energy power of the energy storage module and the electric energy obtained after the conversion of the available thermal energy of the regional heat source are controlled to jointly meet the difference between the predicted value of the electric load demand and the predicted value of the power generation.

[0085] New energy units include wind turbines and photovoltaic generators. By accurately predicting wind and photovoltaic power generation and combining them with the predicted value of electricity load demand, the system can make more accurate energy management decisions and optimize the allocation of power resources. When the predicted value of renewable energy power generation is insufficient to meet demand, the energy storage module is allowed to intervene in power generation to ensure that the predicted value of electricity load demand is met, thereby improving the reliability of power supply. In addition, unnecessary energy conversion is avoided when the power supply and demand are balanced, reducing the redundancy of system operation.

[0086] When faced with excess renewable energy generation, a flexible solution is provided. When the difference is less than or equal to the power that can be released by the energy storage module, the energy storage module can be used efficiently to reduce energy waste. When the difference exceeds the power that can be released by the energy storage module, the system can intelligently use regional heat sources and convert them into electricity to supplement the power supply. This thermoelectric conversion capability not only improves the comprehensive utilization rate of energy, but also enhances scheduling flexibility.

[0087] Furthermore, after the control in step S314 of the releasable electric energy power of the energy storage module and the electric energy obtained by converting the obtainable heat energy of the regional heat source to jointly satisfy the difference between the predicted value of the electric load demand and the predicted value of the generated power, the following is further included:

[0088] Step S315, if the sum of the releasable electric energy power of the energy storage module and the electric energy converted by the obtainable thermal energy of the regional heat source is still less than the difference between the predicted value of the electric load demand and the predicted value of the power generation power, the exchange active power of the main transmission network or the exchange heat of the main heating network is obtained to meet the remaining predicted value of the electric load demand.

[0089] Furthermore, in step S315, obtaining the exchange active power of the main transmission network or the exchange heat of the main heating network to meet the remaining predicted value of the electric load demand includes:

[0090] Step S315a, obtaining the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network.

[0091] Step S315b, based on the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network, select the smaller one of the two as the supplementary energy source to meet the remaining predicted value of the electric load demand.

[0092] In addition, the above process also takes economic factors into consideration. By balancing the electricity price and the heat source price, it can operate at the lowest cost and maximize economic benefits. The intelligence and automation of the entire dispatching process reduce human intervention, reduce operating costs, and improve response speed. Overall, these dispatching steps achieve efficient, economical and stable operation of the cogeneration system under different operating conditions through refined management and multi-energy collaborative optimization. At the same time, the above process also ensures that when the predicted value of renewable energy power generation is insufficient to meet the predicted value of electric load demand, the system can supplement electricity through the collaborative work of energy storage modules and regional heat sources, thereby enhancing the stability and reliability of power supply. When internal resources still cannot meet demand, it is allowed to fill the power gap by purchasing external network heat sources and converting them into electricity, which not only provides additional energy security, but also demonstrates the adaptability of the system to changes in external market conditions.

[0093] Furthermore, obtaining the scheduling strategy of the compressed carbon dioxide energy storage device in step S300 includes:

[0094] Step S321, obtaining the maximum output thermal power and the minimum output thermal power of the regional heat source.

[0095] By obtaining the maximum and minimum thermal power output of the regional heat source, important parameter basis is provided for subsequent heat load scheduling, ensuring that the operation of the heat source is within a safe and economical range.

[0096] Step S322, when the predicted value of the heat load demand of the regional heating network is less than the minimum output thermal power of the regional heat source, part of the power of the regional heat source is controlled to meet the predicted value of the heat load demand, and the remaining power of the regional heat source is stored in the energy storage module of the compressed carbon dioxide energy storage device.

[0097] When the heat load demand forecast value is low, by partially using the regional heat source and storing the remaining heat energy in the energy storage module, the energy utilization efficiency is improved, energy waste is reduced, and additional heat energy reserves are provided for the peak heat load demand forecast value.

[0098] Step S323, when the heat load demand prediction value is greater than or equal to the minimum output thermal power of the regional heat source and less than the maximum output thermal power, the heat energy of the regional heat source is dispatched according to the heat load demand prediction value.

[0099] When the predicted value of heat load demand is moderate, the regional heat source is dispatched according to actual demand to ensure the efficient operation of the heat source and the satisfaction of the heat load.

[0100] Step S324, when the predicted heat load demand value is greater than or equal to the maximum output thermal power of the regional heat source, the regional heat source is made to meet the predicted heat load demand value with the maximum output thermal power, and at the same time, the energy storage module is controlled to generate heat to meet the predicted heat load demand value.

[0101] When the predicted heat load demand reaches its peak, the predicted heat load demand is maximized by operating the regional heat source at maximum output and combining it with the energy storage module. At the same time, the use of the energy storage module also improves the flexibility and reliability of the system.

[0102] The above steps achieve the optimal operation of the cogeneration system under different heat load demand forecast values ​​through intelligent scheduling and precise control. They not only improve the energy utilization efficiency and reduce operating costs, but also enhance the adaptability and stability of the system. In addition, by rationally allocating heat sources and energy storage modules, the system can better cope with fluctuations in heat load and ensure the continuity and reliability of heat supply. This scheduling strategy that comprehensively considers heat source output, heat load demand forecast values ​​and energy storage capacity helps to improve the overall performance of the cogeneration system and achieve a more efficient, economical and reliable heat supply.

[0103] Furthermore, after making the regional heat source meet the predicted value of the heat load demand with the maximum output heat power in step S324 and controlling the energy storage module to generate heat, the method further includes:

[0104] Step S325, if the sum of the thermal power corresponding to the releasable electric energy power of the energy storage module and the maximum output thermal power of the regional heat source is still less than the predicted value of the heat load demand, the exchange active power of the main transmission network or the exchange heat of the main heating network is obtained to meet the remaining predicted value of the heat load demand.

[0105] Furthermore, the step S325 of obtaining the exchange active power of the main transmission network or the exchange heat of the main heating network includes:

[0106] Step S325a, obtaining the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network.

[0107] Step S325b, based on the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network, select the smaller one of the two as the supplementary energy source to meet the remaining thermal load demand forecast value.

[0108] By controlling the conversion of electrical energy into thermal energy in the energy storage module, it is possible to respond more flexibly to fluctuations in the forecast value of thermal load demand, especially during peak hours of the forecast value of thermal load demand. This conversion capability can serve as an important supplementary means to ensure that the forecast value of thermal load demand is met in a timely manner; it enhances self-sufficiency, reduces dependence on external energy, and improves the level of autonomous control of energy. It can also improve the economy of the entire cogeneration system because it allows electrical energy to be stored when electricity prices are low and converted into thermal energy when needed, thereby achieving optimal scheduling between electricity prices and heat prices and reducing overall operating costs. At the same time, this electric-to-heat conversion capability also provides additional means of regulation, which helps to balance the load on the power grid, especially during peak hours of power grid demand, and can reduce pressure on the power grid.

[0109] In addition, the distribution network flow constraint formula of the cogeneration system scheduling model is:

[0110]

[0111] Among them, I t,ij Represents the current value at time t on line ij; V t,i represents the voltage amplitude of node i at time t; P t,ij and Q t,ij Represents the active and reactive power on line ij at time t; Ω i Represents the set of all nodes connected to i; and Respectively represent the active and reactive power of the load at node i at time t; r ij and x ij Represent the resistance and reactance of line ij respectively; Represents the maximum current on line ij; and Represent the maximum and minimum values ​​of the voltage amplitude at node i respectively.

[0112] The above distribution network power flow constraints involve voltage square terms and current square terms, which are nonlinear constraints. The Distflow power flow model is introduced to replace the node voltage and branch current variables. The constraint is linearized by the formula and converted into the second-order cone form by second-order cone relaxation:

[0113]

[0114] After an equivalent transformation, it can be written in the form of the following standard second-order cone

[0115]

[0116] Finally, the problem is solved by the gurobi solver, and the joint scheduling strategy of CCES thermal energy and electric energy in each time period on the day before is finally obtained.

[0117] In the above scheduling process, the compression process and the energy release process cannot occur at the same time, that is:

[0118] s c,k,t +s ex,k,t ≤1s c,k,t ,s ex,k,t ∈{0,1};

[0119] Where: s c,k,t and ex,k,t They are 0-1 variables representing the charging and discharging status of the carbon dioxide energy storage at node k in the distribution network at time t.

[0120] At the same time, in order to ensure the continuous operation of the CCES system, the following conditions need to be met:

[0121]

[0122] Where: p s,k,T and p s,k,0 Respectively represent the pressure of the air storage chamber at the end and the initial moment of a cycle; Q h,k,T and Q h,k,T Respectively represent the heat storage capacity of the heat storage tank at the end and initial moment of a cycle.

[0123] The CCES system can act as a heat storage system as well as a heat source or heat load to achieve electric-to-heat conversion. It can effectively improve the regional thermal energy utilization rate, meet the predicted value of heat load demand, and reduce the cost of purchasing heat from outside.

[0124] The heat balance constraint formula of the cogeneration system scheduling model is:

[0125] P hsou,t +P mh,t +P chl,t =P hload,t +P cwh,t ;

[0126] Among them, P hsou,t is the heating power of the regional heat source at time t, P mh,t is the heating power of the main heating network at time t, P chl,t is the heating power provided by CCES to the regional heat load at time t, P hload,t is the predicted value of heat load demand of the district heating network, P cwh,t represents the heat absorbed by CCES from the heat source at time t;

[0127] The new energy grid connection constraint formula of the cogeneration system scheduling model is:

[0128]

[0129] in, is the actual grid-connected power of new energy, Power generation for renewable energy;

[0130] The power constraint formula of the main transmission grid is:

[0131]

[0132] in, is the minimum value of active power exchange between the balancing nodes of the distribution network and the transmission network, To balance the actual exchange active power value of the node, It is the maximum value of active power exchange of balanced nodes.

[0133] The combined dispatch strategy of heat and electricity based on compressed carbon dioxide energy storage (CCES) is to minimize the operating cost of the entire cogeneration system, while taking into account the penalty for wind and solar power abandonment, as well as the cost of purchasing electricity and heat from outside. The dispatch target can be expressed as follows:

[0134]

[0135] Where: Indicates penalties for abandoning new energy sources. Indicates the cost of purchasing electricity from the external network, It represents the cost of purchasing heat from external heat sources. Among them, the price of electricity purchased from the external network adopts the peak and valley electricity price.

[0136] Specifically,

[0137]

[0138] Where: Ω Re Represents the set of new energy nodes connected to the distribution network i; π re Represents the penalty coefficient for abandoning electricity from new energy sources; and They refer to the active power generated by the new energy node i in the distribution network at time t and the active power actually input to the distribution network; is the active power exchanged between the transmission network and the distribution network at time t; s,t It is the price of electricity purchased from the main power grid at time t. The power grid adopts time-of-use electricity price, so the electricity price is a function of time. is the heat exchanged between the regional heating network and the main heating network at time t; h For hot prices from main hot online shopping hot.

[0139] If there is a conflict in the dispatching strategy of CCES, the priority of electricity and heat dispatching is determined by cost. If in the tth time period, the electricity price is greater than or equal to the unit price of heat purchase; if the energy storage module meets the predicted value of the electric load demand, the charging and discharging demand of the electric load is met first; if the energy storage module does not meet the predicted value of the electric load demand, it will first discharge to meet the predicted value of the electric load demand, and the remaining demand will be supplied by the heat source; if in the tth time period, the electricity price is less than the unit price of heat purchase; if the energy storage module meets the predicted value of the heat load demand, the heat absorption and release demand of the electric load will be met first; if the energy storage module does not meet the predicted value of the heat load demand, it will first absorb and release heat to meet the predicted value of the heat load demand, and the remaining demand will be supplied by electricity.

[0140] The above technical solution flexibly adjusts the charging and discharging strategy of the energy storage module to adapt to the grid demand and maximize cost-effectiveness. First, it can respond to the load changes of the power grid and the heat network in real time, and optimize resource allocation by automatically adjusting the operation mode to maximize cost-effectiveness. Secondly, by obtaining the predicted values ​​of the electric load demand and the thermal load demand within the preset scheduling cycle, as well as the grid electricity price and the external heat source purchase price, the operation of the cogeneration system can be accurately predicted and planned, reducing energy waste and improving the economy of the system operation.

[0141] The above-mentioned day-ahead dispatching method also takes into account the long-term stability and reliability of the system operation. Through reasonable charging and discharging plans, it ensures that the system can operate stably throughout the entire dispatching cycle and avoids the risks caused by energy price fluctuations. Finally, this method provides operators with sufficient time to make adjustments and preparations through day-ahead optimized dispatching, improves the dispatching flexibility and response speed of the system, and also provides the power grid with more stable and predictable power and heat support, which helps to improve the stability and reliability of the entire energy system.

[0142] Accordingly, please refer to Figure 2 The second aspect of the embodiment of the present invention provides a cogeneration system scheduling system based on compressed carbon dioxide energy storage, the cogeneration system includes: a compressed carbon dioxide energy storage device, a distribution network and a regional heating network, the distribution network includes: a plurality of new energy units and electric loads, the regional heating network includes: a regional heat source and a heat load, and the scheduling system includes:

[0143] Thermoelectric data acquisition module 1, which is used to obtain the predicted power generation value of several new energy units in the current scheduling cycle, the chargeable and dischargeable power of the energy storage module in the compressed carbon dioxide energy storage device, and the obtainable thermal energy of the regional heat source;

[0144] A load data acquisition module 2 is used to obtain the predicted value of the electric load demand of the distribution network and the predicted value of the heat load demand of the regional heating network;

[0145] The scheduling strategy formulation module 3 is used to obtain the scheduling strategy of the compressed carbon dioxide energy storage device based on the scheduling model of the cogeneration system, combined with the predicted value of generated power, the rechargeable and dischargeable power, the available thermal energy, the predicted value of the electric load demand and the predicted value of the thermal load demand.

[0146] Each functional module in the above-mentioned cogeneration system scheduling system can also be expanded into a corresponding functional unit according to the detailed steps of the corresponding scheduling method to implement the detailed steps of each scheduling method.

[0147] Accordingly, a second aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the above-mentioned cogeneration system scheduling method based on compressed carbon dioxide energy storage.

[0148] Accordingly, a third aspect of an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned cogeneration system scheduling method based on compressed carbon dioxide energy storage.

[0149] The embodiment of the present invention aims to protect a method and system for scheduling a cogeneration system based on compressed carbon dioxide energy storage. The cogeneration system includes: a compressed carbon dioxide energy storage device, a distribution network and a regional heating network. The distribution network includes: a number of new energy units and electric loads. The regional heating network includes: a regional heat source and a thermal load. The scheduling method includes the following steps: obtaining the predicted values ​​of the generated power of a number of new energy units in the current scheduling cycle, the rechargeable and dischargeable power of the energy storage module of the compressed carbon dioxide energy storage device, and the obtainable thermal energy of the regional heat source; obtaining the predicted value of the electric load demand of the distribution network and the predicted value of the thermal load demand of the regional heating network; based on the scheduling model of the cogeneration system, combined with the active power value of the generated power, the rechargeable and dischargeable power, the obtainable thermal energy, the predicted value of the electric load demand and the predicted value of the thermal load demand, obtain the scheduling strategy of the compressed carbon dioxide energy storage device. The above technical scheme has the following effects:

[0150] 1. Able to respond to load changes of power grid and heat network in real time, optimize resource allocation by automatically adjusting operation mode, and maximize cost-effectiveness;

[0151] 2. By obtaining the predicted values ​​of electric load demand and heat load demand within the preset scheduling period, as well as the power grid electricity price and the unit price of external heat source heat purchase, the operation of the cogeneration system can be accurately predicted and planned, reducing energy waste and improving the economy of system operation.

[0152] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0153] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for dispatching a cogeneration system based on compressed carbon dioxide energy storage, characterized in that: The cogeneration system includes: a compressed carbon dioxide energy storage device, a distribution network and a regional heating network. The distribution network includes: a plurality of new energy units and electric loads. The regional heating network includes: a regional heat source and a heat load. The scheduling method includes the following steps: Obtaining the predicted power generation values ​​of the multiple new energy units in the current scheduling cycle, the chargeable and dischargeable power of the energy storage module in the compressed carbon dioxide energy storage device, and the obtainable heat energy of the regional heat source; Obtaining a predicted value of the electric load demand of the distribution network and a predicted value of the heat load demand of the regional heating network; Based on the cogeneration system scheduling model, the scheduling strategy of the compressed carbon dioxide energy storage device is obtained in combination with the predicted value of generated power, the rechargeable and dischargeable power, the obtainable thermal energy, the predicted value of the electric load demand and the predicted value of the thermal load demand.

2. The method for dispatching a cogeneration system based on compressed carbon dioxide energy storage according to claim 1, characterized in that: The obtaining of the scheduling strategy of the compressed carbon dioxide energy storage device includes: Obtaining the power generation prediction values ​​of the plurality of new energy generating units in the current scheduling cycle, and calculating the difference between the power load demand prediction value of the distribution network and the power generation prediction value; When the difference is less than 0, if the difference is less than or equal to the electric energy power that can be absorbed by the energy storage module of the compressed carbon dioxide energy storage device, the energy storage module is controlled to discharge to meet the difference between the predicted value of the electric load demand and the predicted value of the power generation power of the several new energy units; if the difference is greater than the electric energy power that can be absorbed by the energy storage module of the energy storage device, a wind and solar abandonment strategy is adopted to lose excess new energy power generation; When the difference is equal to 0, it is determined that the predicted value of generated power meets the predicted value of electric load demand; When the difference is greater than 0, if the difference is less than or equal to the releasable electric energy power of the energy storage module, the releasable electric energy power of the energy storage module is controlled to meet the difference between the electric load demand forecast value and the power generation forecast value; if the difference is greater than the releasable electric energy power of the energy storage module, after obtaining the available thermal energy of the regional heat source in the current scheduling cycle and converting it into electric energy, the releasable electric energy power of the energy storage module and the electric energy obtained after the conversion of the available thermal energy of the regional heat source are controlled to jointly meet the difference between the electric load demand forecast value and the power generation forecast value.

3. The method for dispatching a cogeneration system based on compressed carbon dioxide energy storage according to claim 2, characterized in that: After the control of the releasable electric energy power of the energy storage module and the electric energy obtained by converting the obtainable heat energy of the regional heat source to jointly satisfy the difference between the predicted value of the electric load demand and the predicted value of the generated power, it also includes: If the sum of the releasable electric energy of the energy storage module and the electric energy converted from the obtainable thermal energy of the regional heat source is still less than the difference between the electric load demand forecast value and the power generation power forecast value, the exchange active power of the main transmission network or the exchange heat of the main heating network is obtained to meet the remaining electric load demand forecast value.

4. The method for dispatching a cogeneration system based on compressed carbon dioxide energy storage according to claim 3, characterized in that: The obtaining of the exchange active power of the main transmission network or the exchange heat of the main heating network to meet the remaining predicted value of the electric load demand includes: Obtaining the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network; Based on the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network, the smaller one of the two is selected as a supplementary energy source to meet the remaining predicted value of the electric load demand.

5. The method for dispatching a cogeneration system based on compressed carbon dioxide energy storage according to claim 1, characterized in that: The obtaining of the scheduling strategy of the compressed carbon dioxide energy storage device includes: Obtaining the maximum output thermal power and the minimum output thermal power of the regional heat source; When the predicted value of the heat load demand of the regional heating network is less than the minimum output thermal power of the regional heat source, controlling part of the power of the regional heat source to meet the predicted value of the heat load demand, and storing the remaining power of the regional heat source in the heat storage module of the compressed carbon dioxide energy storage device; When the heat load demand forecast value is greater than or equal to the minimum output thermal power of the regional heat source and less than the maximum output thermal power, dispatching the thermal energy of the regional heat source according to the heat load demand forecast value; When the predicted heat load demand value is greater than or equal to the maximum output thermal power of the regional heat source, the regional heat source is made to meet the predicted heat load demand value with the maximum output thermal power, and at the same time, the energy storage module is controlled to generate heat to meet the predicted heat load demand value.

6. The method for dispatching a cogeneration system based on compressed carbon dioxide energy storage according to claim 5, characterized in that: After making the regional heat source meet the predicted value of the heat load demand with the maximum output thermal power and controlling the energy storage module to generate heat with electric energy, the method further includes: If the sum of the thermal power corresponding to the releasable electric energy power of the energy storage module and the maximum output thermal power of the regional heat source is still less than the predicted value of the heat load demand, the exchange active power of the main transmission network or the exchange heat of the main heating network is obtained to meet the remaining predicted value of the heat load demand.

7. The method for dispatching a cogeneration system based on compressed carbon dioxide energy storage according to claim 6, characterized in that: The obtaining of the exchange active power of the main transmission network or the exchange heat of the main heating network comprises: Obtaining the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network; Based on the unit active power cost of the main transmission network and the unit exchange heat cost of the main heating network, the smaller one of the two is selected as a supplementary energy source to meet the remaining predicted value of the thermal load demand.

8. The method for dispatching a cogeneration system based on compressed carbon dioxide energy storage according to any one of claims 1 to 7, characterized in that: The distribution network flow constraint formula of the cogeneration system scheduling model is: Among them, I t,ij Represents the current value at time t on line ij; V t,i represents the voltage amplitude of node i at time t; P t,ij and Q t,ij Represents the active and reactive power on line ij at time t; Ω i Represents the set of all nodes connected to i; and Respectively represent the active and reactive power of the load at node i at time t; r ij and x ij Represent the resistance and reactance of line ij respectively; Represents the maximum current on line ij; V i max and V i min Represent the maximum and minimum values ​​of the voltage amplitude at node i respectively; The heat balance constraint formula of the cogeneration system scheduling model is: P hsou,t +P mh,t +P chl,t =P hload,t +P cwh,t ; Among them, P hsou,t is the heating power of the regional heat source at time t, P mh,t is the heating power of the main heating network at time t, P chl,t is the heating power provided by CCES to the regional heat load at time t, P hload,t is the predicted value of heat load demand of the district heating network, P cwh,t represents the heat absorbed by CCES from the heat source at time t; The new energy grid connection constraint formula of the cogeneration system scheduling model is: in, is the actual grid-connected power of new energy, Power generation for renewable energy; The power constraint formula of the main transmission grid is: in, is the minimum value of active power exchange between the balancing nodes of the distribution network and the transmission network, To balance the actual exchange active power value of the node, It is the maximum value of active power exchange of balanced nodes.

9. A cogeneration system scheduling system based on compressed carbon dioxide energy storage, characterized in that: The cogeneration system includes: a compressed carbon dioxide energy storage device, a distribution network and a regional heating network. The distribution network includes: a number of new energy units and electric loads. The regional heating network includes: a regional heat source and a heat load. The dispatching system includes: A thermoelectric data acquisition module, which is used to obtain the predicted power generation values ​​of the multiple new energy units in the current scheduling cycle, the chargeable and dischargeable power of the energy storage module in the compressed carbon dioxide energy storage device, and the obtainable thermal energy of the regional heat source; A load data acquisition module, which is used to obtain the predicted value of the electric load demand of the distribution network and the predicted value of the heat load demand of the regional heating network; A scheduling strategy formulation module is used to obtain a scheduling strategy for the compressed carbon dioxide energy storage device based on a cogeneration system scheduling model, in combination with the predicted value of generated power, the rechargeable and dischargeable power, the obtainable thermal energy, the predicted value of the electric load demand and the predicted value of the thermal load demand.

10. An electronic device, characterized in that: include: at least one processor; And a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the cogeneration system scheduling method based on compressed carbon dioxide energy storage as described in any one of claims 1-8.

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