A method for economic dispatch of regional integrated energy system containing carbon capture equipment and solar thermal power plants considering comprehensive demand response

Abstract

A regional integrated energy system economic dispatch method for carbon capture equipment and solar thermal power stations considering comprehensive demand response, establishing an electric and thermal integrated demand response model, solving the electric and thermal load data after the response; establishing a combined heat and power solar thermal power station equipment operation model, analyzing the light-electricity-heat multi-energy conversion characteristics of the model; establishing a power-to-gas-carbon capture gas power plant coordinated operation model, analyzing the model's wind abandonment, carbon recycling and complementary utilization of raw materials process; establishing a fuzzy membership model of wind and solar uncertainty, analyzing the deterministic conversion process of fuzzy opportunity constraints; with the goal of minimizing the total operating cost considering wind abandonment cost and carbon benefit, establishing an economic dispatch model for a regional integrated energy system containing fuzzy opportunity constraints, and optimizing the solution to obtain the most economic dispatch method. The method of the present invention realizes the coordinated operation of P2G-CCGPP and CHPCSP, and can effectively improve the operating economy of RIES while achieving a high level of wind power consumption and system energy conservation and emission reduction.

Description

Technical Field

[0001] The present invention relates to the field of regional integrated energy optimization scheduling, and in particular to an economic scheduling method for a regional integrated energy system of carbon capture equipment and a solar thermal power station taking into account comprehensive demand response. Background Art

[0002] The low-carbon transformation of the energy system and the vigorous promotion of the development of new energy are key technical means for carbon neutrality. As a specific manifestation of the geographical distribution and functional status of an integrated energy system with multi-energy mutual assistance and energy cascade utilization, the regional integrated energy system (RIES) provides a new idea for realizing new energy consumption and energy conservation and emission reduction.

[0003] The core energy supply equipment in RIES is usually a "gas-electricity-heat" or "gas-electricity-cold" multi-energy coupling device, and its energy products all contain a high concentration of CO2. Carbon capture and storage technology, as a technology with large-scale emission reduction potential, can achieve a rapid and efficient reduction in the carbon emission level of the unit, and can better serve the "carbon neutrality" development goal. Most existing studies only analyze the scheduling characteristics of carbon capture, without considering the subsequent utilization of the captured CO2. At present, the first industrial-scale demonstration device of carbon capture gas power plant (CCGPP) in China has been successfully put into operation at Datang International Beijing Gaojing Thermal Power Plant, providing a new way for clean CO2 capture, and at the same time, it can also provide economical gas production raw materials for P2G equipment; on the other hand, P2G can transport clean gas raw materials for CCGPP. The coordinated operation of P2G and CCGPP can effectively tap the fuel complementarity potential between the two and better improve the operation economy of the system. However, in-depth research on this aspect is still lacking at home and abroad.

[0004] As a new type of power generation carrier that realizes the multi-energy conversion of "light-heat-electricity", the coordinated operation of concentrated solar power (CSP) and gas-fired units can effectively alleviate the operating limitations of the "heat-to-electricity" mode of the multi-energy coupling system. At the same time, it can also increase the consumption space of new energy power generation by controlling the heat energy flow rate of the heat storage system. However, the energy dissipation of CSP in the process of light-heat conversion and steam power generation means that the production efficiency of CSP still needs to be improved, which greatly affects the power generation cost of CSP. Considering the power generation characteristics of CSP, the cogeneration model can be introduced into its energy generation process. In traditional CSP, the combined heat and power unit replaces the traditional steam turbine to construct the combined heat and power concentrating solar power (CHPCSP) operation model, which can significantly improve the energy output of CSP power stations and effectively shorten the investment recovery period of power stations.

[0005] In addition, integrated demand response (IDR), as a derivative of traditional demand response in RIES, can not only alleviate the imbalance between supply and demand in the system by guiding users' electricity consumption behavior to cooperate with the output of the source side, but also provide a new solution to the problem of wind power abandonment caused by the anti-peak regulation characteristics of wind power.

[0006] Most of the existing literature only studies the optimization of time-of-use electricity prices and the stabilization of load fluctuations. However, related studies point out that the heat-to-electricity ratio on the load side has a certain impact on the operating efficiency of thermal power units. Improving the heat-to-electricity matching on the supply and demand side will improve the operating economy of the system. Therefore, it is possible to consider using comprehensive demand response to strengthen the heat-to-electricity matching on the supply and demand side and improve the energy-saving and emission reduction effects of the system on the basis of traditional demand response to stabilize the fluctuation of electricity load. Summary of the invention

[0007] In view of the problems of wind power abandonment and energy conservation and emission reduction, the present invention provides a regional integrated energy system economic dispatch method for carbon capture equipment and solar thermal power stations that considers comprehensive demand response. First, the P2G-CCGPP coupling operation model and the heat and power combined operation principle of CHPCSP are analyzed on the energy side; the electric and thermal comprehensive demand response considering volatility and heat and power matching is introduced on the load side to improve the energy saving rate of the system economic dispatch; on this basis, a two-stage optimization dispatch model of the regional integrated energy system is established: in the first stage, an electric and thermal comprehensive demand response model is established, which not only smoothes the load volatility, but also improves the heat and power matching degree on the supply and demand sides, providing a load-side foundation for achieving system energy conservation and emission reduction, and helping to reduce the total operation and dispatch cost in the next stage. In the second stage, with the goal of minimizing the total operation cost considering wind abandonment cost and carbon benefit, an economic dispatch model of the regional integrated energy system with fuzzy opportunity constraints is established to solve the optimal output and comprehensive operation cost of the system units in each time period.

[0008] The technical solution adopted by the present invention is:

[0009] A method for economic dispatching a regional integrated energy system of carbon capture equipment and a solar thermal power station considering comprehensive demand response comprises the following steps:

[0010] Step 1: In the first stage, a comprehensive electricity and heat demand response model is established to solve the electricity and heat load data after the response;

[0011] Step 2: Establish a combined heat and power solar thermal power station equipment operation model and analyze the light-electricity-heat multi-energy conversion characteristics of the model;

[0012] Step 3: Establish a coordinated operation model of a power-to-gas-carbon capture gas-fired power plant, and analyze the model's wind curtailment, carbon recycling, and complementary utilization of raw materials;

[0013] Step 4: Establish a fuzzy membership model for wind and solar uncertainty and analyze the deterministic conversion process of fuzzy opportunity constraints;

[0014] Step 5. In the second stage, the goal is to minimize the total operating cost taking into account the cost of wind curtailment and carbon benefits. An economic dispatch model for the regional integrated energy system with fuzzy opportunity constraints is established, and the optimal dispatch method is obtained through optimization.

[0015] In step 1, the comprehensive electricity and heat demand response model is as follows:

[0016] By introducing electric load demand response on the load side, the user's electricity consumption behavior is changed to a certain extent by optimizing the electricity price with a time scale of h, and the load volatility is smoothed, thereby alleviating the wind abandonment phenomenon caused by the anti-peak regulation characteristics of wind power. At the same time, the thermal load demand response targets the impact of the heat-to-electricity ratio on the energy utilization efficiency on the supply and demand side, guides the thermal load to approach the load curve under the optimal heat-to-electricity ratio, so that the thermal power unit is close to the optimal operating efficiency and achieves the purpose of energy conservation and emission reduction. The optimization goal of the electric load demand response is to minimize the load volatility, and the optimization goal of the thermal load demand response is to minimize the deviation between the thermal load after the response and the target thermal load under the optimal heat-to-electricity ratio, which can be specifically expressed as:

[0017]

[0018] Where: P DR,t is the electric load after demand response in period t; P E,t P is the electric load before demand response in period t; avg is the expected value of the electric load; Q DR,t is the heat load after demand response in period t; Q best,t is the heat load under the optimal heat-to-electricity ratio in time period t; T is the total number of time periods in the full scheduling cycle; μ is the optimal heat-to-electricity ratio, which is 1.29 in the present invention. F1 and F2 represent the optimization targets of the electric load demand response and the heat load demand response, respectively.

[0019] In the step 1, the constraints of the electric and thermal integrated demand response model are established, including: electric load demand response elasticity coefficient equation constraint, thermal load heat supply relationship constraint, user temperature perception constraint, electricity price change constraint, load transfer constraint, and user expenditure constraint, specifically:

[0020] 1) Electric load demand response elasticity coefficient equation constraint:

[0021] Price-based demand response can formulate appropriate time-of-use electricity prices based on consumer psychology to change users' electricity consumption behavior. Through the electricity price elasticity coefficient matrix, the relationship between the electricity price change rate and the load change rate can be established. The relationship between the electricity price elasticity coefficient and the load change before and after demand response is specifically expressed as:

[0022]

[0023]

[0024] Where: ΔP DR,i is the demand response change of the electric load in period i; μ E is the proportion of elastic electric load; τ E,ij is the elastic coefficient of the electric load demand response. When i and j are equal, τ E,ij is the elastic coefficient. When i and j are not equal, τE,ij is the mutual elastic coefficient; ΔC DR,i is the change in electricity price after demand response in period i. E,j represents the electricity price before the response in period j, P DR,i represents the electric load after the response in period i, P E,i It represents the electric load before the response in time period i, and T represents the dispatching period, which is 24h.

[0025] 2) Constraints on the relationship between heat load and heat supply:

[0026] When heating residential users, in order to ensure that the indoor temperature is within the appropriate range, the heating equipment needs to continuously provide heat to the indoor air. The relationship between the load-side heat supply and the indoor temperature of the residential user is specifically expressed as follows:

[0027]

[0028] Where: T out,t and T in,t are the user's outdoor temperature and indoor temperature during period t respectively; H is the proportion of heating users; ΔQ DR,t is the demand response change of the heat load during period t; C air is the specific heat capacity of air; N is the number of heating users in the area; q t is the indoor temperature heat dissipation considering the building thermal resistance in period t; R is the thermal resistance of the building material; Δt represents the unit scheduling period, which is 1h.

[0029] 3) User temperature perception constraints:

[0030] Users have a certain degree of ambiguity in their perception of heating, and it is necessary to ensure that the indoor temperature of the building changes within the user's insensitive range, which can be specifically expressed as:

[0031] T in,down ≤T in,t ≤T in,up (5);

[0032] Where: T in,up and T in,down They are the upper and lower limits of the user's insensitive area respectively.

[0033] 4) Constraints on electricity price changes:

[0034] In principle, the optimization of electricity prices requires that the peak electricity price is greater than the fixed electricity price, and the valley electricity price is less than the fixed electricity price; the peak electricity price is greater than the normal electricity price, and the valley electricity price is less than the normal electricity price; the peak-valley electricity price ratio is greater than a certain multiple limit, which is specifically expressed as:

[0035]

[0036] Where: C g,t is the electricity price during off-peak hours; C p,t is the electricity price during normal times; C f,t is the electricity price during the peak period; λ is the multiple limit between the electricity price during the peak period and the electricity price during the valley period. E,t Represents the electricity price before the response in period t.

[0037] 5) Load transfer constraints:

[0038] The total amount of electric load and heat load before and after demand response cannot change during the entire dispatching cycle, that is, the sum of the transfer amount is 0; to ensure the stability of load supply, the load transfer amount cannot exceed the limit value in a single period. Specifically expressed as:

[0039]

[0040] Where: χ1 is the electric load transfer limit in a single period; χ2 is the thermal load transfer limit in a single period; ΔP DR,t , ΔQ DR,t They respectively represent the electric load and thermal load participating in demand response in period t.

[0041] 6) User spending constraints:

[0042] The basic premise for users to participate in demand response is to benefit from it, so the user's cost expenditure after the electric load response should be lower than the cost expenditure before the response. Specifically expressed as:

[0043]

[0044] In the step one, the optimization target of the electric load demand response is to minimize the load volatility, and the load data with the minimum deviation from the electric load mean is obtained by iterative solution, which is used as the electric load data after response. The electric load data after response is transmitted to the thermal load demand response model, and the optimization target of the thermal load demand response is to maximize the heat-electric matching degree of the load. The load data with the minimum deviation from the thermal load under the optimal heat-electric ratio is obtained by solution, which is used as the thermal load data after response.

[0045] In step 2, the operation model of the combined heat and power solar thermal power station equipment is as follows:

[0046] The light-heat power conversion relationship of the concentrated solar collector system is as follows:

[0047] P solar,t =η S-H S SF D t (9);

[0048] Where: P solar,t is the heat energy collected by the concentrating solar collector system during period t; η S-H is the photothermal conversion efficiency; SSF is the solar mirror field area; D t is the solar radiation index (DNI) in period t.

[0049] The heat energy collected by the solar collector system is mainly used to supply the power generation system, and the other part is stored in the heat storage system through high-temperature molten salt. In order to ensure the stable operation of CHPCSP, some heat energy needs to be discarded:

[0050]

[0051] Where: P ge-h,t The heat energy supplied to the power generation system during time t; Q HS,ch,t and Q HS,dis,t are the heat storage and heat release of the heat storage system in period t respectively; η HS,ch and η HS,dis are the heat storage efficiency and heat release efficiency of the heat storage system respectively; P solar,t Represents the solar thermal energy input to the solar thermal power station during period t.

[0052] The heat energy supplied to the power generation system is used for BT power generation and heating, among which the heating capacity of BT comes from the waste heat of steam after the turbine generates electricity.

[0053]

[0054] Where: η BT is the power generation efficiency of BT; P CHPCSP,t and Q CHPCSP,t are the electrical output and thermal output of BT in CHPCSP during period t respectively; K2 is the thermal-electric ratio of BT.

[0055] In step 3, the coordinated operation model of the power-to-gas-carbon capture gas power plant is as follows:

[0056] The energy consumption of a carbon capture gas power plant (CCGPP) is mainly composed of fixed energy consumption and carbon capture operation energy consumption. The carbon capture operation energy consumption mainly comes from the power generation process of the gas unit, and the emission products of its combustion of natural gas contain a certain concentration of CO2. The gas unit and the waste heat recovery device together form a cogeneration unit. In order to simplify the model analysis, this invention regards the heat-to-electricity ratio of the thermal power unit as a constant value.

[0057]

[0058] Where: P CCGPP,t is the total energy consumption of CCGPP system in period t; P A,t is the fixed energy consumption of CCGPP system during period t; P CC,tis the carbon capture energy consumption during period t; CC is the energy consumption for capturing unit mass of CO2; is the carbon capture capacity of the system during period t; η CC,t is the carbon capture rate of the system during period t; e GT is the unit carbon emission intensity of the gas unit; E GT,t is the total output of the gas generator set during period t; P GT,t and Q GT,t are the electrical output and thermal output of the gas-fired unit during period t respectively; K1 is the thermal-electricity ratio of the gas-fired unit.

[0059] Power to gas (P2G) technology can use abandoned wind power to provide part of the natural gas fuel source for the system, and can also realize the reuse of CO2 captured by the system. The gas production process of P2G equipment mainly consists of two steps: water electrolysis and methanation. In the methanation process, the consumption of CO2 is equal to the production of CH4. Therefore, the carbon-gas conversion relationship of P2G equipment can be expressed as:

[0060]

[0061] Where: is the carbon utilization of P2G in period t; is the density of CO2 at one standard atmospheric pressure; is the gas production of the P2G equipment during period t; P P2G,t is the energy consumption of the P2G device during period t; H L It is the low calorific value of natural gas.

[0062] In addition, the carbon capture energy consumption and P2G energy consumption in the P2G-CCGPP system are used together to absorb the wind curtailment. When the sum of the carbon capture energy consumption and the P2G energy consumption is greater than the amount of wind curtailment, the system can fully absorb the wind curtailment. The amount of wind curtailment can be specifically expressed as:

[0063] ΔP WT,curt,t =max{P WT,pre,t -P WT,t -P CC,t -P P2G,t ,0} (14);

[0064] Where: ΔP WT,curt,t is the abandoned wind volume in period t; P WT,pre,t is the predicted power of the wind turbine during period t; P WT,t is the actual output of the fan during period t.

[0065] The P2G gas production process will release a large amount of reaction heat, and the recovered heat energy can be supplied to the heat load. Therefore, the exploration of the P2G heating potential will improve the energy efficiency of the equipment. According to the calculation of the literature (Li Dongsen, Gao Ciwei, Zhao Ming. Capacity planning and operation strategy of heat storage tank in integrated energy system considering power-to-gas heat recovery [J]. Electric Power Automation Equipment, 2019, 39(08): 161-168.), for every 1kW·h of electricity consumed by the P2G equipment, the reaction heat power that can be injected into the heat network is 0.1188kW·h.

[0066] In step 4, the fuzzy membership model of wind and solar uncertainty and its deterministic transformation method are as follows:

[0067] The optimization problem with fuzzy chance constraints can be described as:

[0068]

[0069] Where: f(x) is the objective function; g(x,ξ) is the constraint function containing fuzzy parameters; x is the decision variable; ξ is the fuzzy parameter; β is the confidence level; Pr{} is the event feasibility.

[0070] The fuzzy parameters of wind power and solar thermal can be expressed by trapezoidal membership function:

[0071]

[0072] Where: μ(P F ) is the membership function; is a parameter that determines the shape of the membership function and is related to the predicted value P pre The following relationship exists:

[0073]

[0074] Where: v k is the proportionality coefficient, which is generally obtained based on the historical data of the fan and load.

[0075] When the constraint function has the following form:

[0076] g(x,ξ)=h1(x)ξ1+h2(x)ξ2+…+h n (x)ξ n +h0(x) (18);

[0077] Where: k is the trapezoidal fuzzy parameter h1(x), h2(x), …h n (x) respectively represent the first function, the second function, ..., the nth function containing the decision variable x. ξ1, ξ2...ξ nThey represent the first fuzzy parameter, the second fuzzy parameter,…, the nth fuzzy parameter respectively.

[0078] The following functions can be defined:

[0079]

[0080]

[0081] In particular, if h(x) = 1, then h + (x) = 1, h - (x) = 0; if h(x) = -1, then h + (x) = 0, h - (x)=1.

[0082] When the confidence β is greater than 0.5, the fuzzy chance constraint can be transformed into a clear equivalent form:

[0083]

[0084] Represent four trapezoidal fuzzy parameters respectively; They represent the two special functions defined in equations (19) and (20) respectively; h0(x), t, and k represent the function with decision variable x, the total scheduling time period, and the kth scheduling time period respectively.

[0085] In the step five, the total system operating cost is minimized as the optimization goal, and the operation and maintenance cost, fuel cost, wind curtailment cost, carbon cost, electricity interaction cost and thermal energy interaction cost are comprehensively considered. The equipment operation constraints, power interaction constraints, fuzzy opportunity constraints considering the uncertainty of wind and solar power are taken into account, and other constraints are taken into account. An economic dispatch model of a regional integrated energy system with fuzzy opportunity constraints is established; the fuzzy opportunity constraints are deterministically transformed, and the CPLEX solver is used to optimize and solve the model to obtain the most economically optimal dispatch plan.

[0086] In step 5, the objective function and related constraints of the regional integrated energy system economic dispatch model are:

[0087] a. Objective function:

[0088] The RIES low-carbon economic dispatch model takes the lowest total operating cost as its optimization goal, and comprehensively considers operation and maintenance costs, fuel costs, wind curtailment costs, carbon costs, electricity interaction costs and thermal energy interaction costs.

[0089] min F=F O +F F +F WT,curt +F C +F E +F H(twenty two);

[0090] Where: F is the comprehensive cost of each device in RIES participating in economic dispatch; F O F is the operation and maintenance cost; F F is the fuel cost; WT,curt Penalty cost for wind curtailment; F C is the carbon cost; F E is the electricity interaction cost; F H is the thermal interaction cost.

[0091] ①. Operation and maintenance costs:

[0092] Each device within RIES will incur certain operation and maintenance costs during the operation of the scheduling cycle.

[0093]

[0094] Where: k O,WT k is the wind turbine operation and maintenance cost coefficient; O,CHPCSP Operation and maintenance cost coefficient of CSP power station with combined heat and power generation; k O,GT is the gas turbine operation and maintenance cost coefficient; k O,ES k is the operation and maintenance cost coefficient of the power storage device; O,P2G is the operation and maintenance cost coefficient of P2G; k O,CC is the operation and maintenance cost coefficient of the carbon capture device; k O,HS is the operation and maintenance cost coefficient of the heat storage device; P ES,ch,t With P ES,dis,t are the charging and discharging power of ES in period t respectively. O represents the total operation and maintenance cost, and T represents the total scheduling period.

[0095] ②Fuel cost:

[0096] The system fuel cost mainly comes from the gas-fired units of the CCGPP system. The gas-fired units give priority to using the natural gas produced by P2G, thus reducing some fuel costs.

[0097]

[0098] Where: is the unit price of natural gas; η GT is the power generation efficiency of the gas unit. GT,t represents the power output of the gas turbine during period t, H L Indicates the lower calorific value of natural gas, It represents the gas production volume of P2G during period t.

[0099] ③. Wind power abandonment penalty cost:

[0100] In order to ensure the stable operation of the system, it is necessary to abandon wind power at appropriate times according to the supply and demand relationship. Since the operating energy consumption of carbon capture equipment and P2G equipment absorbs part of the abandoned wind power, the penalty cost of abandoned wind can be expressed as:

[0101]

[0102] Where: k curt is the penalty cost per unit of wind power abandoned. ΔP WT,curt,t represents the wind power abandoned during period t.

[0103] ④ Carbon cost:

[0104] Carbon costs mainly include carbon storage costs and carbon trading costs. For the CO2 captured by the carbon capture system, the part that is not used by the P2G equipment needs to be stored, which generates carbon storage costs. In addition, when the carbon emissions of the gas unit are lower than its carbon emission benchmark quota, the remaining carbon emission quota can be sold to make a profit.

[0105]

[0106] Where: for the cost of carbon sequestration; For carbon trading costs; is the storage price per unit mass of CO2; is the transaction price per unit mass of CO2; is the mass of CO2 emitted to the atmosphere by the gas unit during period t; is the carbon emission quota of the gas-fired unit during period t; E is the carbon emission benchmark per unit power. GT,t Represents the total output of the gas turbine during period t.

[0107] ⑤. Electricity and heat interaction costs:

[0108] RIES will generate electricity and heat interaction costs in the power interaction with the main power grid and the main thermal network.

[0109]

[0110] Where: k E,t and k H,t are the electricity and heat interactive unit prices in period t respectively; P ex,t and Q ex,t They are the electric and thermal interaction powers between RIES and the main power grid and the main heating network during period t respectively.

[0111] b. Constraints:

[0112] (1) P2G-CCGPP system operation constraints:

[0113]

[0114] Where: P GT,max and P GT,min They are the upper and lower limits of the gas unit's power output; R GT,up and R GT,down are the ramp-up rate and ramp-down rate of the gas turbine unit respectively; P P2G,max and P P2G,min are the upper and lower limits of P2G energy consumption respectively; CC,max and P CC,min are the upper and lower limits of energy consumption of the carbon capture system respectively; η CC,max and η CC,min are the upper and lower limits of the carbon capture rate of the carbon capture system respectively.

[0115] (2) CHPCSP operation constraints:

[0116]

[0117] Where: P CHPCSP,max and P CHPCSP,min are the upper and lower limits of the power output of the CHPCSP system; R CHPCSP,up and R CHPCSP,down is the ramp-up rate and ramp-down rate of the CHPCSP system; E HS,max and E HS,min are the upper and lower limits of HS capacity respectively; u HS,ch,t and u HS,dis,t is a 0-1 variable, indicating the heat storage and release state of HS in period t. Considering that HS cannot store and release heat at the same time in the same scheduling period, u HS,ch,t with u HS,dis,t The sum cannot be greater than 1. CHPCSP,t represents the power output of the CSP power station during period t, P CHPCSP,t-1 represents the thermal output of the CSP power station at time t-1, Q HS,ch,t represents the heat storage device's charge during period t, Q HS,dis,t represents the heat release of the heat storage device during period t, Q HS,ch,max Indicates the maximum heat capacity of the heat storage device, Q HS,dis,max Indicates the maximum heat release of the heat storage device, E HS,t Indicates the capacity of the heat storage device.

[0118] (3) Fuzzy opportunity constraints for power balance considering wind and solar uncertainty:

[0119] Considering that the uncertainty of wind and solar power will affect the electric power balance of the system and the thermal power transfer balance of the solar thermal power station, the fuzzy opportunity constraint representation of the two can be obtained based on fuzzy mathematics theory:

[0120]

[0121] Where: Pr{} is the credibility of the event; β is the confidence level, which is 0.95; P DR,t Indicates the amount of electrical load after the response in period t; P wt,t represents the fan output during period t; P CHPCSP,t represents the power output of the CSP power station during period t; P GT,t represents the electrical output of the gas turbine during period t; P ex,t represents the electric interaction power with the main power grid during period t; P ES,dis,t represents the discharge power of the storage device during period t; η ES,dis Indicates the discharge efficiency of the power storage device; P ES,ch,t represents the charging power of the power storage device during period t; η ES,ch Indicates the charging efficiency of the power storage device; P P2G,t represents the power consumption of P2G during period t; P CC,t Represents the power consumption of the carbon capture device during period t.

[0122]

[0123] Where: Pr{} is the credibility of the event; β is the confidence level, which is 0.95; P ge-h,t The heat energy supplied to the power generation system during time t; Q HS,ch,t and Q HS,dis,t are the heat storage and heat release of the heat storage system in period t respectively; η HS,ch and η HS,dis are the heat storage efficiency and heat release efficiency of the heat storage system respectively; P solar,t Represents the solar thermal energy input to the solar thermal power station during period t.

[0124] 4) Thermal power balance constraints

[0125]

[0126] Where: Q DR,t Indicates the heat load after the response in period t; Q CHPCSP,t represents the heat supply of the CSP power station during period t; Q GT,t represents the heat supply of the gas turbine during period t; Q HS,dis,t represents the heat release of the heat storage device during period t; η HS,dis Indicates the heat release efficiency of the heat storage device; Q HS,ch,t represents the heat capacity of the heat storage device during period t; η HS,ch Indicates the charging efficiency of the heat storage device; Q ex,t represents the heat interaction power with the main heating network during period t; Q P2G,t is the heat generation power of P2G during period t.

[0127] 5) Other constraints

[0128] The wind turbine operation constraints, battery operation constraints and power interaction constraints can be expressed as:

[0129] P wt,min ≤P wt,t ≤P wt,max (33);

[0130] Where: P wt,min and P wt,max Respectively represent the minimum and maximum values ​​of the fan output; P wt,t Indicates the fan output during period t.

[0131]

[0132] Where: P ES,min and P ES,max They represent the minimum charge and discharge capacity and the maximum charge and discharge capacity of the power storage device respectively; E ES,min and E ES,max Respectively represent the lower and upper limits of the capacity state of the power storage device; P ES,t and E ES,t They respectively represent the charge and discharge amount and capacity state of the energy storage device during period t.

[0133]

[0134] Where: P ex,min and P ex,max They represent the minimum and maximum values ​​of the power interaction with the main power grid respectively; Q ex,min , Q ex,max Respectively represent the minimum and maximum values ​​of the thermal interaction power with the main heating network, P ex,t and Q ex,t They represent the electrical interaction power with the main grid and the thermal interaction power with the main heating network respectively.

[0135] The present invention provides a regional integrated energy system economic dispatch method for carbon capture equipment and solar thermal power plants taking into account comprehensive demand response, and the technical effects are as follows:

[0136] 1) The comprehensive electricity and heat demand response can improve the system's operating economy and wind curtailment absorption level by smoothing the volatility of the load side and improving the system's heat and electricity matching.

[0137] 2) The coordinated operation of P2G-CCGPP reduces the system's fuel cost and carbon emission level. In addition, P2G and carbon capture equipment play a significant role in the absorption of wind power curtailment.

[0138] 3) CHPCSP can effectively achieve the thermal-electric decoupling of the system, and bear the main power supply and heating needs of the system when there is sufficient sunlight. Compared with conventional CSP power stations and photovoltaic power stations, CHPCSP significantly improves the energy saving rate of the system and promotes the system's consumption of new energy.

[0139] 4) The coordinated operation of P2G-CCGPP and CHPCSP can effectively improve the operating economy of RIES while achieving a high level of wind power consumption and system energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0140] Figure 1 It is a structural diagram of the combined heat and power solar thermal power station of the present invention.

[0141] Figure 2 It is a structural diagram of the P2G-CCGPP system of the present invention.

[0142] Figure 3 It is a solution flow chart of the two-stage optimization model of the present invention.

[0143] Figure 4 This is the overall structural diagram of the regional integrated energy system of the present invention.

[0144] Figure 5 This is a comparison chart of the solution results of the comprehensive demand response of the present invention.

[0145] Figure 6 This is a diagram of the regional comprehensive energy and electricity dispatching results of the present invention.

[0146] Figure 7 This is a diagram of the regional comprehensive energy thermal scheduling results of the present invention.

[0147] Figure 8 It is a comparison chart of wind power abandonment and consumption in various comparison scenarios described in the present invention.

[0148] Fig. 9 This is a scheduling comparison diagram of the combined heat and power solar thermal power station described in the present invention and a traditional power station. DETAILED DESCRIPTION

[0149] A method for economic dispatching a regional integrated energy system of carbon capture equipment and a solar thermal power station considering comprehensive demand response comprises the following steps:

[0150] Step 1: Input electric and heating loads and other comprehensive demand response model parameters;

[0151] The electric heat load is the electric heat load data for 24 hours. Other comprehensive demand response model parameters are the optimal heat-to-electricity ratio, the electric load elasticity coefficient, the proportion of heating users, the thermal resistance of building materials, the number of heating users in the area, and the specific heat capacity of air.

[0152] Step 2: The optimization goal of the electric load demand response is to minimize load volatility. The iterative solution is used to obtain the load data with the minimum deviation from the mean value of the electric load as the electric load value after the response; the electric load data after the response is passed to the thermal load demand response model. The optimization goal of the thermal load demand response is to maximize the heat-electricity matching of the load. The load data with the minimum deviation from the thermal load under the optimal heat-electricity ratio is obtained as the thermal load data after the response. The optimization goal of the electric load demand response is to minimize load volatility, and the optimization goal of the thermal load demand response is to minimize the deviation between the thermal load after the response and the target thermal load under the optimal heat-electricity ratio. It can be specifically expressed as:

[0153]

[0154] Where: P DR,t is the electric load after demand response in period t; P E,t P is the electric load before demand response in period t; avg is the expected value of the electric load; Q DR,t is the heat load after demand response in period t; Q best,t is the heat load under the optimal heat-to-electricity ratio in time period t; T is the total number of time periods in the full scheduling cycle; μ is the optimal heat-to-electricity ratio, which is taken as 1.29 in the present invention.

[0155] The constraints for establishing the comprehensive demand response model for electricity and heat include the equation constraint of the elasticity coefficient of electricity load demand response, the heat load heating relationship constraint, the user temperature perception constraint, the electricity price change constraint, the load transfer constraint, and the user expenditure constraint, specifically:

[0156] 1) Electric load demand response elasticity coefficient equation constraint:

[0157] Price-based demand response can formulate appropriate time-of-use electricity prices based on consumer psychology to change users' electricity consumption behavior. Through the electricity price elasticity coefficient matrix, the relationship between the electricity price change rate and the load change rate can be established. The relationship between the electricity price elasticity coefficient and the load change before and after demand response is specifically expressed as:

[0158]

[0159]

[0160] Where: ΔP DR,i is the demand response change of the electric load in period i; μ E is the proportion of elastic electric load; τ E,ij is the elastic coefficient of the electric load demand response. When i and j are equal, τ E,ij is the elastic coefficient. When i and j are not equal, τ E,ij is the mutual elastic coefficient; ΔC DR,iis the change in electricity price after demand response in period i.

[0161] 2) Constraints on the relationship between heat load and heat supply:

[0162] When heating residential users, in order to ensure that the indoor temperature is within the appropriate range, the heating equipment needs to continuously provide heat to the indoor air. The relationship between the load-side heat supply and the indoor temperature of the residential user is specifically expressed as follows:

[0163]

[0164] Where: T out,t and T in,t are the user's outdoor temperature and indoor temperature during period t respectively; H is the proportion of heating users; ΔQ DR,t is the demand response change of the heat load during period t; C air is the specific heat capacity of air; N is the number of heating users in the area; q t is the indoor temperature heat dissipation considering the building thermal resistance during period t; R is the thermal resistance of the building material;

[0165] 3) User temperature perception constraints:

[0166] Users have a certain degree of ambiguity in their perception of heating, and it is necessary to ensure that the indoor temperature of the building changes within the user's insensitive range, which can be specifically expressed as:

[0167] T in,down ≤T in,t ≤T in,up (5)

[0168] Where: T in,up and T in,down They are the upper and lower limits of the user's insensitive area respectively.

[0169] 4) Constraints on electricity price changes:

[0170] In principle, the optimization of electricity prices requires that the peak electricity price is greater than the fixed electricity price, and the valley electricity price is less than the fixed electricity price; the peak electricity price is greater than the normal electricity price, and the valley electricity price is less than the normal electricity price; the peak-valley electricity price ratio is greater than a certain multiple limit, which is specifically expressed as:

[0171]

[0172] Where: C g,t is the electricity price during off-peak hours; C p,t is the electricity price during normal times; C f,t is the electricity price during the peak period; λ is the multiple limit between the electricity price during the peak period and the electricity price during the valley period.

[0173] 5) Load transfer constraints:

[0174] The total amount of electric load and heat load before and after demand response cannot change during the entire dispatching cycle, that is, the sum of the transfer amount is 0; to ensure the stability of load supply, the load transfer amount cannot exceed the limit value in a single period. Specifically expressed as:

[0175]

[0176] Where: χ1 is the electric load transfer limit in a single period; χ2 is the thermal load transfer limit in a single period.

[0177] 6) User spending constraints:

[0178] The basic premise for users to participate in demand response is to benefit from it, so the user's cost expenditure after the electric load response should be lower than the cost expenditure before the response. Specifically expressed as:

[0179]

[0180] Step 3: Transfer the original load data and the responded electricity and heat load data to the second-stage regional integrated energy system economic dispatch model;

[0181] Step 4: Read the electric and thermal load data after the first phase response, read the predicted wind power and solar thermal data, and other model parameters;

[0182] Step 5: Establish the equipment operation model of the combined heat and power solar thermal power station and analyze its light-electricity-heat multi-energy conversion characteristics. Figure 1 The equipment operation model of the combined heat and power solar thermal power station is:

[0183] The light-heat power conversion relationship of the concentrated solar collector system is as follows:

[0184] P solar,t =η S-H S SF D t (9)

[0185] Where: P solar,t is the heat energy collected by the concentrating solar collector system during period t; η S-H is the photothermal conversion efficiency; S SF is the solar mirror field area; D t is the solar radiation index (DNI) in period t.

[0186] The heat energy collected by the solar collector system is mainly used to supply the power generation system, and the other part is stored in the heat storage system through high-temperature molten salt. In order to ensure the stable operation of CHPCSP, some heat energy needs to be discarded.

[0187]

[0188] Where: P ge-h,t The heat energy supplied to the power generation system during time t; Q HS,ch,t and Q HS,dis,t are the heat storage and heat release of the heat storage system in period t respectively; η HS,ch and η HS,dis are the heat storage efficiency and heat release efficiency of the heat storage system respectively; Q CSP,curt,t is the heat rejection of the CSP system during period t.

[0189] The heat energy supplied to the power generation system is used for BT power generation and heating, among which the heating capacity of BT comes from the waste heat of steam after the turbine generates electricity.

[0190]

[0191] Where: η BT is the power generation efficiency of BT; P CHPCSP,t and Q CHPCSP,t are the electrical output and thermal output of BT in CHPCSP during period t respectively; K2 is the thermal-electric ratio of BT.

[0192] Step 6: Establish a coordinated operation model of a P2G-carbon capture gas power plant and analyze its wind curtailment absorption, carbon recycling and complementary utilization of raw materials. The system structure of a P2G-carbon capture gas power plant is as follows: Figure 2 The P2G-carbon capture gas power plant coordinated operation model is as follows:

[0193] The energy consumption of CCGPP is mainly composed of fixed energy consumption and carbon capture operation energy consumption. The carbon capture operation energy consumption mainly comes from the power generation process of the gas unit, and the emission products of its combustion of natural gas contain a certain concentration of CO2. The gas unit and the waste heat recovery device together form a cogeneration unit. In order to simplify the model analysis, this invention regards the heat-to-electricity ratio of the thermal power unit as a constant value.

[0194]

[0195] Where: P CCGPP,t is the total energy consumption of CCGPP system in period t; P A,t is the fixed energy consumption of CCGPP system during period t; P CC,t is the carbon capture energy consumption during period t; CC is the energy consumption per unit mass of CO2 capture; M CC,CO2,t is the carbon capture capacity of the system during period t; η CC,t is the carbon capture rate of the system during period t; e GT is the unit carbon emission intensity of the gas unit; E GT,t is the total output of the gas generator set during period t; P GT,t and Q GT,tare the electrical output and thermal output of the gas-fired unit during period t respectively; K1 is the thermal-electricity ratio of the gas-fired unit.

[0196] P2G technology can use abandoned wind power to provide part of the natural gas fuel source for the system, and can also realize the reuse of CO2 captured by the system. The gas production process of P2G equipment mainly consists of two steps: water electrolysis and methanogenesis.

[29] , in the methanation process, the consumption of CO2 is equal to the production of CH4, so the carbon-gas conversion relationship of the P2G device can be expressed as:

[0197]

[0198] Where: is the carbon utilization of P2G in period t; is the density of CO2 at one standard atmospheric pressure; is the gas production of the P2G equipment during period t; P P2G,t is the energy consumption of the P2G device during period t; H L It is the low calorific value of natural gas.

[0199] In addition, the carbon capture energy consumption and P2G energy consumption in the P2G-CCGPP system are used together to absorb the wind curtailment. When the sum of the carbon capture energy consumption and the P2G energy consumption is greater than the amount of wind curtailment, the system can fully absorb the wind curtailment. The amount of wind curtailment can be specifically expressed as:

[0200] ΔP WT,curt,t =max{P WT,pre,t -P WT,t -P CC,t -P P2G,t ,0} (14)

[0201] Where: ΔP WT,curt,t is the abandoned wind volume in period t; P WT,pre,t is the predicted power of the wind turbine during period t; P WT,t is the actual output of the fan during period t.

[0202] The P2G gas production process will release a large amount of reaction heat, and the recovered heat energy can be supplied to the heat load. Therefore, the exploration of the P2G heating potential will improve the energy efficiency of the equipment. According to calculations in relevant literature, for every 1kW·h of electricity consumed by the P2G equipment, the reaction heat power that can be injected into the heat network is 0.1188kW·h.

[0203] Step 7: Establish a fuzzy membership model for wind and solar uncertainty and analyze the deterministic conversion principle of fuzzy opportunity constraints. The specific method is as follows:

[0204] The optimization problem with fuzzy chance constraints can be described as:

[0205]

[0206] Where: f(x) is the objective function; g(x,ξ) is the constraint function containing fuzzy parameters; x is the decision variable; ξ is the fuzzy parameter; β is the confidence level; Pr{} is the event feasibility.

[0207] The fuzzy parameters of wind power and solar thermal can be expressed by trapezoidal membership function:

[0208]

[0209] Where: μ(P F ) is the membership function; is a parameter that determines the shape of the membership function and is related to the predicted value P pre The following relationship exists:

[0210]

[0211] Where: v k is the proportionality coefficient, which is generally obtained based on the historical data of the fan and load.

[0212] When the constraint function has the following form:

[0213] g(x,ξ)=h1(x)ξ1+h2(x)ξ2+…+h n (x)ξ n +h0(x) (18)

[0214] Where: k is the trapezoidal fuzzy parameter

[0215] The following functions can be defined:

[0216]

[0217]

[0218] In particular, if h(x) = 1, then h + (x) = 1, h - (x) = 0; if h(x) = -1, then h + (x) = 0, h - (x)=1.

[0219] When the confidence β is greater than 0.5, the fuzzy chance constraint can be transformed into a clear equivalent form:

[0220]

[0221] Step 8: Taking the minimum total system operating cost as the optimization goal, comprehensively consider the operation and maintenance cost, fuel cost, wind curtailment cost, carbon cost, electricity interaction cost and thermal energy interaction cost, and take into account the equipment operation constraints, power interaction constraints, fuzzy opportunity constraints considering the uncertainty of wind and solar power, and other constraints to establish a low-carbon economic dispatch model for the regional integrated energy system; transform the fuzzy opportunity constraints into deterministic ones, and use the CPLEX solver to optimize the model and solve it to obtain the most economical dispatch plan.

[0222] The economic dispatch model of regional integrated energy system is as follows:

[0223] a. Objective function

[0224] The RIES low-carbon economic dispatch model takes the lowest total operating cost as its optimization goal, and comprehensively considers operation and maintenance costs, fuel costs, wind curtailment costs, carbon costs, electricity interaction costs and thermal energy interaction costs.

[0225] min F=F O +F F +F WT,curt +F C +F E +F H (twenty two)

[0226] Where: F is the comprehensive cost of each device in RIES participating in economic dispatch; F O F is the operation and maintenance cost; F F is the fuel cost; WT,curt Penalty cost for wind curtailment; F C is the carbon cost; F E is the electricity interaction cost; F H is the thermal interaction cost.

[0227] 1) Operation and maintenance costs:

[0228] Each device within RIES will incur certain operation and maintenance costs during the operation of the scheduling cycle.

[0229]

[0230] Where: k O,WT k is the wind turbine operation and maintenance cost coefficient; O,CHPCSP Operation and maintenance cost coefficient of CSP power station with combined heat and power generation; k O,GT is the gas turbine operation and maintenance cost coefficient; k O,ES k is the operation and maintenance cost coefficient of the power storage device; O,P2G is the operation and maintenance cost coefficient of P2G; k O,CC is the operation and maintenance cost coefficient of the carbon capture device; k O,HS is the operation and maintenance cost coefficient of the heat storage device; P ES,ch,t With PES,dis,t are the charging and discharging powers of ES during period t respectively.

[0231] 2) Fuel cost:

[0232] The system fuel cost mainly comes from the gas-fired units of the CCGPP system. The gas-fired units give priority to using the natural gas produced by P2G, thus reducing some fuel costs.

[0233]

[0234] Where: is the unit price of natural gas; η GT is the power generation efficiency of the gas unit.

[0235] 3) Wind power abandonment penalty cost:

[0236] In order to ensure the stable operation of the system, it is necessary to abandon wind power at appropriate times according to the supply and demand relationship. Since the operating energy consumption of carbon capture equipment and P2G equipment absorbs part of the abandoned wind power, the penalty cost of abandoned wind can be expressed as:

[0237]

[0238] Where: k curt is the penalty cost per unit of wind power abandoned.

[0239] 4) Carbon cost:

[0240] Carbon costs mainly include carbon storage costs and carbon trading costs. For the CO2 captured by the carbon capture system, the part that is not used by the P2G equipment needs to be stored, which generates carbon storage costs. In addition, when the carbon emissions of the gas unit are lower than its carbon emission benchmark quota, the remaining carbon emission quota can be sold to make a profit.

[0241]

[0242] Where: for the cost of carbon sequestration; For carbon trading costs; is the storage price per unit mass of CO2; is the transaction price per unit mass of CO2; is the mass of CO2 emitted to the atmosphere by the gas unit during period t; is the carbon emission quota of the gas-fired unit during period t; It is the carbon emission benchmark per unit power.

[0243] 5) Electricity and heat interaction costs:

[0244] RIES will generate electricity and heat interaction costs in the power interaction with the main power grid and the main thermal network.

[0245]

[0246] Where: k E,t and k H,t are the electricity and heat interactive unit prices in period t respectively; P ex,t and Q ex,t They are the electric and thermal interaction powers between RIES and the main power grid and the main heating network during period t respectively.

[0247] b. Constraints:

[0248] 1) P2G-CCGPP system operation constraints:

[0249]

[0250] Where: P GT,max and P GT,min They are the upper and lower limits of the gas unit's power output; R GT,up and R GT,down are the ramp-up rate and ramp-down rate of the gas turbine unit respectively; P P2G,max and P P2G,min are the upper and lower limits of P2G energy consumption respectively; CC,max and P CC,min are the upper and lower limits of energy consumption of the carbon capture system respectively; η CC,max and η CC,min are the upper and lower limits of the carbon capture rate of the carbon capture system respectively.

[0251] 2) CHPCSP operation constraints:

[0252]

[0253] Where: P CHPCSP,max and P CHPCSP,min are the upper and lower limits of the power output of the CHPCSP system; R CHPCSP,up and R CHPCSP,down is the ramp-up rate and ramp-down rate of the CHPCSP system; E HS,max and E HS,min are the upper and lower limits of HS capacity respectively; u HS,ch,t and u HS,dis,t is a 0-1 variable, indicating the heat storage and release state of HS in period t. Considering that HS cannot store and release heat at the same time in the same scheduling period, u HS,ch,t with u HS,dis,t The sum cannot be greater than 1.

[0254] 3) Fuzzy opportunity constraints on power balance considering wind and solar uncertainty:

[0255] Considering that the uncertainty of wind and solar power will affect the electric power balance of the system and the thermal power transfer balance of the solar thermal power station, the fuzzy opportunity constraint representation of the two can be obtained based on fuzzy mathematics theory:

[0256]

[0257]

[0258] Where: Pr{} is the credibility of the event; β is the confidence level, which is 0.95.

[0259] 4) Thermal power balance constraints:

[0260]

[0261] Where: Q P2G,t is the heat generation power of P2G during period t.

[0262] 5) Other constraints:

[0263] The wind turbine operation constraints, battery operation constraints and power interaction constraints can be expressed as:

[0264] P wt,min ≤P wt,t ≤P wt,max (33)

[0265]

[0266]

[0267] The specific solution flow chart of the model can be seen Figure 3 , the overall structure of the regional integrated energy system is as follows Figure 4 shown.

[0268] In order to verify the effectiveness of the proposed electric-heat integrated demand response model, P2G-CCGPP system and CHPCSP in improving the system's wind power absorption capacity, reducing the system's carbon emission level and system operating costs, the present invention constructs the following six operating scenarios for comparative analysis:

[0269] Scenario 1: The electricity and heat demand response is not considered, RIES does not include P2G and carbon capture devices, and the CSP plant only supplies electricity;

[0270] Scenario 2: Based on Scenario 1, the CSP plant is transformed into a CSP plant with combined heat and power generation.

[0271] Scenario 3: Based on Scenario 2, carbon capture equipment is added;

[0272] Scenario 4: Based on Scenario 3, add P2G equipment;

[0273] Scenario 5: Based on Scenario 4, the demand response that only considers load fluctuation is added;

[0274] Scenario 6: Based on Scenario 4, the comprehensive electric and thermal demand response considering load fluctuation and thermal-electric matching is added, which is the model proposed in the present invention.

[0275] Based on the comprehensive operating cost, this paper optimizes and solves six example scenarios. The specific results are shown in Table 1.

[0276] Table 1. Comparison of comprehensive costs of various scenarios

[0277]

[0278] From scenario 1 to scenario 6, the total operating cost of the system gradually decreases, indicating that after adding electric heat demand response and a variety of equipment, the comprehensive operating economy of the system is constantly improving.

[0279] The fuel cost of the system mainly comes from the natural gas consumed by the gas-fired power plant during the production process. For scenario 1, since the gas-fired power plant is the only heat-generating equipment in the system and is severely affected by "thermal-electric coupling", it must be close to full-load capacity. The part of the insufficient heat load supply needs to purchase heat from the heat network, which incurs a large amount of heat purchase costs. For scenarios 2 to 6, the introduction of combined heat and power solar thermal power stations has greatly improved the situation of insufficient heat load supply in the system, thereby reducing fuel costs. The coordinated operation of P2G equipment and CCGPP effectively reduces the system's demand for purchased natural gas fuel. The introduction of demand response reduces the output ratio of gas units and reduces fuel costs.

[0280] From scenario 1 to scenario 6, the carbon cost and wind curtailment cost are greatly reduced. The reason is that the coordinated operation of P2G and carbon capture equipment realizes the recycling of carbon, making the system's carbon emission level lower than the system's carbon emission limit, thereby converting carbon emission penalties into carbon emission benefits; the operating energy consumption of P2G and carbon capture realizes the consumption of wind curtailment power, thereby greatly reducing the cost of wind curtailment. Demand response reduces load volatility and alleviates the wind curtailment phenomenon caused by the anti-peak regulation characteristics of wind power.

[0281] Comparing scenarios 5 and 6, it can be seen that although conventional demand response has reduced system operating costs to a certain extent by smoothing load fluctuations, the effect of demand response on system energy conservation and emission reduction still needs to be strengthened. In scenario 6, the electric heat demand response can better smooth the fluctuation of loads while improving the heat-electric matching degree on the load side, making the output of the electric heat coupling unit more uniform and reasonable, which reduces the wind curtailment cost, carbon cost and fuel cost in scenario 6 compared with scenario 5, and improves the effect of system energy conservation and emission reduction.

[0282] Figure 5This is a load curve comparison diagram of the comprehensive demand response described in the present invention before and after the application. Figure 4 It can be seen that after comprehensive demand response, the peak-to-valley difference and volatility of electric and thermal loads are significantly reduced. According to calculations, the peak-to-valley difference of electric load is reduced by 50.19% compared with before optimization, and the peak-to-valley difference of thermal load is reduced by 14.9% compared with before optimization; the standard deviation of electric load is reduced by 52.49% compared with before optimization, and the standard deviation of thermal load is reduced by 17.74% compared with before optimization. In terms of thermal-electric matching, the Euclidean distance between the thermal load after demand response and the target thermal load under the optimal thermal-electric ratio is reduced by 5.82% compared with before response.

[0283] Figure 6 , Figure 7 This is the electricity and heat dispatch result diagram of the regional comprehensive energy system described in the present invention. Figure 6 It can be seen that when solar thermal energy is sufficient during the day, the gas-fired power plant reduces its output, and the CHPCSP takes on the main power load supply, so the energy consumption of P2G and carbon capture is also reduced accordingly. When the electricity price is low, the system purchases electricity from the main grid, and the power storage device stores part of the electricity; when the electricity price is high, the power storage device discharges, and the system sells the surplus electricity to the main grid, thereby realizing electricity interaction benefits. Figure 7 It can be seen that when solar thermal energy is sufficient, CHPCSP is the main heat source. The heat storage system releases heat energy when there is no light to supply the heat load; when there is sufficient light energy during the day, it stores heat energy. Due to the surplus of solar thermal energy during the day, the system sells heat to the heat network on the basis of meeting the heat load and obtains heat exchange benefits.

[0284] Figure 8 This is the wind power consumption situation of each comparison scenario described in the present invention. Figure 8 The wind curtailment and absorption situation of each scene, where the concentric circles represent the reference lines of wind curtailment absorption rate, which are 20%, 40%, 60%, 80% and 100% respectively, and the points on the curve inside the circle represent the actual wind curtailment absorption rate at each moment. After calculation, the wind curtailment absorption rates of scenes 1-6 are 80.93%, 86.67%, 95.80%, 98.90%, 99.18% and 99.95% respectively. Affected by the anti-peaking characteristics of wind power and the output of solar thermal power, scene 1 has serious wind curtailment in the period from 19:00 to 8:00. After the introduction of CHPCSP in scene 2, the system realizes "thermal and electric decoupling", which better avoids "determining electricity by heat", and the output of the unit is more flexible, which promotes the curtailment and absorption of wind. The wind power absorption rates of scenes 3-6 have all been significantly improved, verifying the positive role of P2G, carbon capture and comprehensive demand response in the absorption of wind curtailment. The ability of the comprehensive electric and thermal demand response in scenario 6 to improve the wind power consumption rate is significantly higher than that of the demand response that only considers the leveling in scenario 5. By comparison, it can be seen that the model proposed in the present invention can effectively improve the wind power consumption capacity of the regional comprehensive energy system.

[0285] Fig. 9This is a scheduling comparison diagram of the combined heat and power solar thermal power station described in the present invention and the traditional power station. Fig. 9 It can be seen that compared with the other two power stations, CHPCSP has an outstanding advantage in the new energy consumption rate. The reason is that CHCSP has a unique combined heat and power mode, which can give priority to meeting the thermal load and electrical load of the system during the period of sufficient sunlight, reducing the power output of the gas unit operating in the "heat-to-electricity" model, and increasing the photovoltaic consumption space. Since both CSP and CHPCSP are equipped with heat storage systems, the heat energy stored during the day can be released to meet the heat load during the period of no sunlight. Similarly, the output of the gas unit can also be reduced, thereby creating a consumption space for wind power. In addition, the comprehensive energy utilization efficiency of the CHPCSP power station is relatively high. During the period of sufficient sunlight, the system gives priority to using the CHPCSP unit to meet the thermal load, reducing the amount of gas purchased by the system, and to a certain extent improving the economy of the system and reducing the carbon emissions of the system.

[0286] Based on the analysis of the above example results, it can be explained that the electric-heat integrated demand response proposed in the present invention can improve the system's operating economy and wind curtailment absorption level by smoothing the volatility of the load side and improving the system's thermal-electric matching degree; the P2G-CCGPP operation model proposed in the present invention reduces the system's fuel cost and carbon emission level. In addition, P2G and carbon capture equipment have a significant effect on wind curtailment absorption; the CHPCSP proposed in the present invention can effectively realize the system's thermal-electric decoupling, and when the sunlight is sufficient, it undertakes the system's main power supply and heating needs. Compared with conventional CSP power stations and photovoltaic power stations, CHPCSP significantly improves the system's energy-saving rate and promotes the system's absorption of new energy; the coordinated operation model of P2G-CCGPP and CHPCSP proposed in the present invention can effectively improve the operating economy of RIES while achieving a high level of wind power absorption and system energy conservation and emission reduction.

Claims

1. A method for economic dispatching of a regional integrated energy system of carbon capture equipment and solar thermal power plants considering comprehensive demand response, characterized in that The following steps are involved: Step 1: In the first stage, a comprehensive electricity and heat demand response model is established to solve the electricity and heat load data after the response; Step 2: Establish a combined heat and power solar thermal power station equipment operation model and analyze the light-electricity-heat multi-energy conversion characteristics of the model; Step 3: Establish a coordinated operation model of a power-to-gas-carbon capture gas-fired power plant, and analyze the model's wind curtailment, carbon recycling, and complementary utilization of raw materials; Step 4: Establish a fuzzy membership model for wind and solar uncertainty and analyze the deterministic conversion process of fuzzy opportunity constraints; Step 5: In the second stage, the goal is to minimize the total operating cost by considering the wind curtailment cost and carbon benefit, establish an economic dispatch model of the regional integrated energy system with fuzzy opportunity constraints, and optimize the solution to obtain the dispatch method with the best economic efficiency; In step 1, the comprehensive electricity and heat demand response model is as follows: The optimization goal of the electric load demand response is to minimize the load fluctuation, and the optimization goal of the heat load demand response is to minimize the deviation between the heat load after the response and the target heat load under the optimal heat-to-electricity ratio, which can be specifically expressed as: Where: P DR,t is the electric load after demand response in period t; P E,t is the electric load before demand response in period t; P avg is the expected value of the electric load; Q DR,t is the heat load after demand response in period t; Q best,t is the heat load under the optimal heat-to-electricity ratio in period t; T is the total number of periods in the full scheduling cycle; μ is the optimal heat-to-electricity ratio, F1 and F2 represent the optimization targets of the electric load demand response and the heat load demand response, respectively; In step 5, the objective function of the regional integrated energy system economic dispatch model includes the following parts: The RIES low-carbon economic dispatch model takes the lowest total operating cost as the optimization goal, comprehensively considering the operation and maintenance cost, fuel cost, wind curtailment cost, carbon cost, electricity interaction cost and thermal energy interaction cost; min F=F O +F F +F WT,curt +F C +F E +F H (22); Where: F is the comprehensive cost of each device in RIES participating in economic dispatch; F O F is the operation and maintenance cost; F F is the fuel cost; WT,curt Penalty cost for wind curtailment; F C is the carbon cost; F E is the electricity interaction cost; F H is the thermal interaction cost; ①. Operation and maintenance costs: Each device within RIES will incur certain operation and maintenance costs during the operation of the scheduling cycle; Where: k O,WT k is the wind turbine operation and maintenance cost coefficient; O,CHPCSP Operation and maintenance cost coefficient of CSP power station with combined heat and power generation; k O,GT is the gas turbine operation and maintenance cost coefficient; k O,ES k is the operation and maintenance cost coefficient of the power storage device; O,P2G is the operation and maintenance cost coefficient of P2G; k O,CC is the operation and maintenance cost coefficient of the carbon capture device; k O,HS is the operation and maintenance cost coefficient of the heat storage device; P ES,ch,t With P ES,dis,t are the charging and discharging power of ES in period t respectively; F O represents the total operation and maintenance cost, T represents the total scheduling period; ②Fuel cost: The system fuel cost mainly comes from the gas-fired units of the CCGPP system. The gas-fired units give priority to using the natural gas produced by P2G, thus reducing some fuel costs. Where: is the unit price of natural gas; η GT is the power generation efficiency of the gas unit; P GT,t represents the power output of the gas turbine during period t, H L Indicates the lower calorific value of natural gas, represents the gas production volume of P2G during period t; ③. Wind power abandonment penalty cost: In order to ensure the stable operation of the system, it is necessary to abandon wind power at appropriate times according to the supply and demand relationship. Since the operating energy consumption of carbon capture equipment and P2G equipment absorbs part of the abandoned wind power, the penalty cost of abandoned wind can be expressed as: Where: k curt is the penalty cost per unit of wind power abandonment; △P WT,curt,t represents the wind power abandoned during period t; ④ Carbon cost: Carbon costs mainly include carbon storage costs and carbon trading costs. For CO2 captured by the carbon capture system, the part that is not used by the P2G equipment needs to be stored, which generates carbon storage costs. In addition, when the carbon emissions of the gas unit are lower than its carbon emission benchmark quota, the remaining carbon emission quota is sold to make a profit. Where: for the cost of carbon sequestration; For carbon trading costs; is the storage price per unit mass of CO2; is the transaction price per unit mass of CO2; is the mass of CO2 emitted to the atmosphere by the gas unit during period t; is the carbon emission quota of the gas-fired unit during period t; E is the carbon emission benchmark per unit power; GT,t represents the total output of the gas turbine during period t; ⑤. Electricity and heat interaction costs: RIES will generate electricity and heat interaction costs in the power interaction with the main power grid and the main heat grid; Where: k E,t and k H,t are the electricity and heat interactive unit prices in period t respectively; P ex,t and Q ex,t They are the electric and thermal interaction powers between RIES and the main power grid and the main heating network during period t respectively.

2. A method for economic dispatching a regional integrated energy system of carbon capture equipment and a solar thermal power station considering comprehensive demand response according to claim 1, characterized in that: In the step 1, the constraints of the electric and thermal integrated demand response model are established, including: electric load demand response elasticity coefficient equation constraint, thermal load heat supply relationship constraint, user temperature perception constraint, electricity price change constraint, load transfer constraint, and user expenditure constraint, specifically: 1) Electric load demand response elasticity coefficient equation constraint: Price-based demand response can formulate appropriate time-of-use electricity prices according to consumer psychology to change users' electricity consumption behavior; through the electricity price elasticity coefficient matrix, the relationship between the electricity price change rate and the load change rate can be established. The relationship between the electricity price elasticity coefficient and the load change before and after demand response is specifically expressed as follows: Where: △P DR,i is the demand response change of the electric load in period i; μ E is the proportion of elastic electric load; τ E,ij is the elastic coefficient of the electric load demand response. When i and j are equal, τ E,ij is the elastic coefficient. When i and j are not equal, τ E,ij is the mutual elastic coefficient; △C DR,i is the change in electricity price after demand response in period i; C E,j represents the electricity price before the response in period j, P DR,i represents the electric load after the response in period i, P E,i represents the electric load before the response in period i, T represents the dispatching period, which is 24h; 2) Constraints on the relationship between heat load and heat supply: When heating residential users, in order to ensure that the indoor temperature is within the appropriate range, the heating equipment needs to continuously provide heat to the indoor air. The relationship between the load-side heat supply and the indoor temperature of the residential user is specifically expressed as follows: Where: T out,t and T in,t are the user's outdoor temperature and indoor temperature during period t respectively; H is the proportion of heating users; △Q DR,t is the demand response change of the heat load during period t; C air is the specific heat capacity of air; N is the number of heating users in the area; q t is the indoor temperature heat dissipation considering the building thermal resistance during period t; R is the thermal resistance of the building material; △t represents the unit scheduling period; 3) User temperature perception constraints: Users have a certain degree of ambiguity in their perception of heating, and it is necessary to ensure that the indoor temperature of the building changes within the user's insensitive range, which can be specifically expressed as: T in,down ≤T in,t ≤T in,up (5); Where: T in,up and T in,down They are the upper and lower limits of the user's insensitive area respectively; 4) Constraints on electricity price changes: In principle, the optimization of electricity prices requires that the peak electricity price is greater than the fixed electricity price, and the valley electricity price is less than the fixed electricity price; the peak electricity price is greater than the normal electricity price, and the valley electricity price is less than the normal electricity price; the peak-valley electricity price ratio is greater than a certain multiple limit, which is specifically expressed as: Where: C g,t is the electricity price during off-peak hours; C p,t is the electricity price during normal times; C f,t is the electricity price during the peak period; λ is the multiple limit between the electricity price during the peak period and the electricity price during the valley period; C E,t represents the electricity price before the response in period t; 5) Load transfer constraints: The total amount of electric load and heat load before and after demand response cannot change during the entire dispatching cycle, that is, the sum of the transfer amount is 0; to ensure the stability of load supply, the load transfer amount cannot exceed the limit value in a single period; specifically expressed as: Where: χ1 is the electric load transfer limit in a single period; χ2 is the thermal load transfer limit in a single period; △P DR,t , △Q DR,t They represent the electric load and heat load participating in demand response in period t respectively; 6) User spending constraints: The basic premise for users to participate in demand response is to benefit from it. Therefore, the user's cost expenditure after the load response should be lower than the cost expenditure before the response. Specifically, it can be expressed as:

3. According to claim 1, a method for economic dispatching of a regional integrated energy system of carbon capture equipment and a solar thermal power station taking into account comprehensive demand response, characterized in that: In the step one, the optimization target of the electric load demand response is to minimize the load volatility, and the load data with the minimum deviation from the electric load mean is obtained by iterative solution, which is used as the electric load data after response. The electric load data after response is transmitted to the thermal load demand response model, and the optimization target of the thermal load demand response is to maximize the heat-electric matching degree of the load. The load data with the minimum deviation from the thermal load under the optimal heat-electric ratio is obtained by solution, which is used as the thermal load data after response.

4. According to claim 1, a method for economic dispatching of a regional integrated energy system of carbon capture equipment and a solar thermal power station taking into account comprehensive demand response, characterized in that: In step 2, the operation model of the combined heat and power solar thermal power station equipment is as follows: The light-heat power conversion relationship of the concentrated solar collector system is as follows: P solar,t =the S-H S SF D t (9); Where: P solar,t The thermal energy collected by the concentrating solar collector system during period t; η S-H is the photothermal conversion efficiency; S SF is the solar mirror field area; D t is the solar radiation index during period t; Part of the heat energy collected by the solar collector system is used to supply the power generation system, and the other part is stored in the heat storage system through high-temperature molten salt. In order to ensure the stable operation of CHPCSP, part of the heat energy needs to be discarded: Where: P ge-h,t The heat energy supplied to the power generation system during time t; Q HS,ch,t and Q HS,dis,t are the heat storage and heat release of the heat storage system in period t respectively; η HS,ch and η HS,dis are the heat storage efficiency and heat release efficiency of the heat storage system respectively; P solar,t represents the solar thermal energy input to the solar thermal power station during period t; The heat energy supplied to the power generation system is used for BT power generation and heating, where the BT heating capacity comes from the waste heat of steam after the turbine generates electricity; Where: η BT is the power generation efficiency of BT; P CHPCSP,t and Q CHPCSP,t are the electrical output and thermal output of BT in CHPCSP during period t respectively; K2 is the thermal-electric ratio of BT.

5. According to claim 1, a method for economic dispatching of a regional integrated energy system of carbon capture equipment and a solar thermal power station taking into account comprehensive demand response, characterized in that: In step 3, the coordinated operation model of the power-to-gas-carbon capture gas power plant is as follows: The energy consumption of a carbon capture gas power plant includes fixed energy consumption and carbon capture operation energy consumption. The carbon capture operation energy consumption comes from the power generation process of the gas unit, and the emission products of its combustion of natural gas contain a certain concentration of CO2; the gas unit and the waste heat recovery device together form a cogeneration unit. In order to simplify the model analysis, the heat-to-electricity ratio of the thermal power unit is regarded as a constant value; Where: P CCGPP,t is the total energy consumption of CCGPP system in period t; P A,t is the fixed energy consumption of CCGPP system during period t; P CC,t is the carbon capture energy consumption during period t; CC is the energy consumption for capturing unit mass of CO2; is the carbon capture amount of the system during period t; η CC,t is the carbon capture rate of the system during period t; e GT is the unit carbon emission intensity of the gas unit; E GT,t is the total output of the gas generator set during period t; P GT,t and Q GT,t are the electrical output and thermal output of the gas-fired unit during period t respectively; K1 is the thermal-electricity ratio of the gas-fired unit; The gas production process of the P2G device mainly consists of two steps: water electrolysis and methanation. In the methanation process, the consumption of CO2 is equal to the production of CH4. Therefore, the carbon-gas conversion relationship of the P2G device is expressed as: Where: is the carbon utilization of P2G in period t; is the density of CO2 at one standard atmospheric pressure; is the gas production of the P2G equipment during period t; P P2G,t is the energy consumption of the P2G device during period t; H L It is the low calorific value of natural gas; In addition, the carbon capture energy consumption and P2G energy consumption in the P2G-CCGPP system are used together to absorb the wind curtailment. When the sum of the carbon capture energy consumption and the P2G energy consumption is greater than the amount of wind curtailment, the system can fully absorb the wind curtailment. The amount of wind curtailment can be specifically expressed as: △P WT,curt,t =max{P WT,pre,t -P WT,t -P CC,t -P P2G,t ,0} (14); Where: △P WT,curt,t is the abandoned wind volume in period t; P WT,pre,t is the predicted power of the wind turbine during period t; P WT,t is the actual output of the fan during period t.

6. According to claim 1, a method for economic dispatching of a regional integrated energy system of carbon capture equipment and a solar thermal power station taking into account comprehensive demand response, characterized in that: In step 4, the fuzzy membership model of wind and solar uncertainty and its deterministic transformation method are as follows: The optimization problem with fuzzy chance constraints can be described as: Where: f(x) is the objective function; g(x,ξ) is the constraint function with fuzzy parameters; x is the decision variable; ξ is the fuzzy parameter; β is the confidence level; Pr{} is the event feasibility; The fuzzy parameters of wind power and solar thermal can be expressed by trapezoidal membership function: Where: μ(P F ) is the membership function; is a parameter that determines the shape of the membership function and is related to the predicted value P pre The following relationship exists: Where: v k is the proportionality coefficient, which is generally obtained based on the historical data of the fan and load; When the constraint function has the following form: g(x,ξ)=h1(x)ξ1+h2(x)ξ2+…+h n (x)ξ n +h0(x) (18); Where: k is the trapezoidal fuzzy parameter h1(x), h2(x), …h n (x) represent the first function, the second function, ..., the nth function containing the decision variable x; ξ1, ξ2...ξ n Respectively represent the first fuzzy parameter, the second fuzzy parameter, …, the nth fuzzy parameter; Define the following function: In particular, if h(x) = 1, then h + (x) = 1, h - (x) = 0; if h(x) = -1, then h + (x) = 0, h - (x) = 1; When the confidence β is greater than 0.5, the fuzzy chance constraint can be transformed into a clear equivalent form: Represent four trapezoidal fuzzy parameters respectively; They represent the two special functions defined in equations (19) and (20) respectively; h0(x), t, and k represent the function with decision variable x, the total scheduling time period, and the kth scheduling time period respectively.

7. According to claim 1, a method for economic dispatching of a regional integrated energy system of carbon capture equipment and a solar thermal power station taking into account comprehensive demand response, characterized in that: In the step five, the total system operating cost is minimized as the optimization goal, and the operation and maintenance cost, fuel cost, wind curtailment cost, carbon cost, electricity interaction cost and thermal energy interaction cost are comprehensively considered. The equipment operation constraints, power interaction constraints, fuzzy opportunity constraints considering the uncertainty of wind and solar power are taken into account, and other constraints are taken into account. An economic dispatch model of a regional integrated energy system with fuzzy opportunity constraints is established; the fuzzy opportunity constraints are deterministically transformed, and the CPLEX solver is used to optimize and solve the model to obtain the most economically optimal dispatch plan.

8. According to claim 1, a method for economic dispatching of a regional integrated energy system of carbon capture equipment and a solar thermal power station taking into account comprehensive demand response, characterized in that: In step 5, the relevant constraints of the objective function of the regional integrated energy system economic dispatch model are: (1) P2G-CCGPP system operation constraints: Where: P GT,max and P GT,min are the upper and lower limits of the gas unit's electrical output; R GT,up and R GT,down are the ramp-up rate and ramp-down rate of the gas turbine unit respectively; P P2G,max and P P2G,min are the upper and lower limits of P2G energy consumption respectively; P CC,max and P CC,min They are the upper and lower limits of energy consumption of the carbon capture system, respectively; η CC,max and η CC,min are the upper and lower limits of carbon capture rate of the carbon capture system, respectively; (2) CHPCSP operation constraints: Where: P CHPCSP,max and P CHPCSP,min are the upper and lower limits of the power output of the CHPCSP system; R CHPCSP,up and R CHPCSP,down is the ramp-up rate and ramp-down rate of the CHPCSP system; E HS,max and E HS,min are the upper and lower limits of HS capacity respectively; u HS,ch,t and u HS,dis,t is a 0-1 variable, indicating the heat storage and release state of HS in period t. Considering that HS cannot store and release heat at the same time in the same scheduling period, u HS,ch,t with u HS,dis,t The sum cannot be greater than 1; P CHPCSP,t represents the power output of the CSP power station during period t, P CHPCSP,t-1 represents the thermal output of the CSP power station at time t-1, Q HS,ch,t represents the heat storage device's charge during period t, Q HS,dis,t represents the heat release of the heat storage device during period t, Q HS,ch,max Indicates the maximum heat capacity of the heat storage device, Q HS,dis,max Indicates the maximum heat release of the heat storage device, E HS,t Indicates the capacity of the heat storage device; (3) Fuzzy opportunity constraints for power balance considering wind and solar uncertainty: Considering that the uncertainty of wind and solar power will affect the electric power balance of the system and the thermal power transfer balance of the solar thermal power station, the fuzzy opportunity constraint representation of the two can be obtained based on fuzzy mathematics theory: Where: Pr{} is the credibility of the event; β is the confidence level; P DR,t Indicates the amount of electrical load after the response in period t; P wt,t represents the fan output during period t; P CHPCSP,t represents the power output of the CSP power station during period t; P GT,t represents the electrical output of the gas turbine during period t; P ex,t P represents the electric interaction power with the main power grid during period t; ES,dis,t represents the discharge power of the power storage device during period t; η ES,dis Indicates the discharge efficiency of the power storage device; P ES,ch,t represents the charging power of the power storage device during period t; η ES,ch Indicates the charging efficiency of the power storage device; P P2G,t represents the power consumption of P2G during period t; P CC,t represents the power consumption of the carbon capture device during period t; Where: Pr{} is the credibility of the event; β is the confidence level; P ge-h,t The heat energy supplied to the power generation system during time t; Q HS,ch,t and Q HS,dis,t are the heat storage and heat release of the heat storage system in period t respectively; η HS,ch and η HS,dis are the heat storage efficiency and heat release efficiency of the heat storage system respectively; P solar,t represents the solar thermal energy input to the solar thermal power station during period t; 4) Thermal power balance constraints: Where: Q DR,t Indicates the heat load after the response in period t; Q CHPCSP,t represents the heat supply of the CSP power station during period t; Q GT,t represents the heat supply of the gas turbine during period t; Q HS,dis,t It represents the heat release of the heat storage device during period t; η HS,dis Indicates the heat release efficiency of the heat storage device; Q HS,ch,t represents the heat capacity of the heat storage device during period t; η HS,ch Indicates the charging efficiency of the heat storage device; Q ex,t represents the thermal interaction power with the main heating network during period t; Q P2G,t is the heat generation power of P2G during period t; 5) Other constraints: The wind turbine operation constraints, battery operation constraints and power interaction constraints can be expressed as: P wt,min ≤P wt,t ≤P wt,max (33); Where: P wt,min and P wt,max Respectively represent the minimum and maximum values ​​of the fan output; P wt,t represents the fan output during period t; Where: P ES,min and P ES,max They represent the minimum charge and discharge capacity and the maximum charge and discharge capacity of the power storage device respectively; E ES,min and E ES,max Respectively represent the lower limit and upper limit of the capacity state of the power storage device; P ES,t and E ES,t They respectively represent the charge and discharge amount and capacity state of the power storage device during period t; Where: P ex,min and P ex,max They represent the minimum and maximum values ​​of the power interaction with the main power grid respectively; Q ex,min , Q ex,max Respectively represent the minimum and maximum values ​​of the thermal interaction power with the main heating network, P ex,t and Q ex,t They represent the electrical interaction power with the main grid and the thermal interaction power with the main heating network respectively.

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