Low-carbon economic dispatching system and method based on demand side response uncertainty

Abstract

The invention provides a low-carbon economic dispatching system and method based on demand side response uncertainty. The method comprises the following steps: constructing a low-carbon energy flow model about a regional integrated energy system; constructing an excitation type multi-load demand response uncertainty model, wherein the excitation type multi-load demand response uncertainty model comprises a transfer load and alternative load distribution calculation model, a load cost model in an under-response state and a load cost model in an over-response state; constructing a low-carbon scheduling model related to the regional integrated energy system, wherein the low-carbon scheduling model comprises an RIES carbon transaction model, an RIES operation constraint model and an RIES low-carbon economic scheduling model; and solving each model parameter by adopting a non-dominated sorting genetic algorithm to obtain a low-carbon scheduling optimal model. According to the method, the energy utilization rate can be effectively improved, the system operation cost can be reduced, risk loss caused by uncertainty can be quantitatively described, support is provided for risk management of system operators, and an optimal scheduling result is obtained.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering technology, and in particular to a low-carbon economic dispatching system and method based on demand-side response uncertainty. Background Art

[0002] With the rapid development of science and technology and society, traditional primary energy, mainly fossil energy, not only cannot meet the increasing demand for electricity, but also causes huge pollution to the natural environment. Traditional power generation systems are transforming into clean, low-carbon modern energy systems. Various studies have also proved that it is necessary to explore a low-carbon economic dispatch method.

[0003] The high carbon emissions of traditional energy generation have posed a specific challenge to the construction of new power systems. Faced with such severe energy and environmental challenges, the regional integrated energy system (RIES) with electricity, heat and gas coupling as the core provides an effective and feasible solution to improve energy conversion efficiency and unlock an effective way to reasonably allocate various load demands. With the increase in the penetration rate of distributed generation, flexible resources such as energy storage and various loads have the potential to reduce carbon emissions while participating in regulation. However, how to strike a balance between effectively meeting various load demands and reducing the system's carbon emission costs remains an open challenge. Therefore, in-depth research should be conducted on load uncertainty in RIES. An economical and low-carbon scheduling method is developed for various RIES scenarios, equipped with complementary energy resources. This strategic utilization is of great significance for the low-carbon transformation of both supply and demand sides. Summary of the invention

[0004] The purpose of the present invention is to provide a low-carbon economic dispatching system and method based on demand-side response uncertainty, so as to effectively improve energy utilization, reduce system operating costs, quantitatively describe the risk losses caused by uncertainty, and provide support for risk management of system operators to obtain the optimal dispatching results.

[0005] In a first aspect, the present invention provides a low-carbon economic dispatch method based on demand-side response uncertainty, the method comprising:

[0006] Construct a low-carbon energy flow model for a regional integrated energy system, which includes photovoltaic power generation equipment, wind turbines, power grids, power-to-gas equipment, cogeneration, and electric boilers;

[0007] Constructing an incentive-based multi-load demand response uncertainty model, wherein the incentive-based multi-load demand response uncertainty model includes a distribution calculation model of transfer loads and alternative loads, a load cost model in an under-response state, and a load cost model in an over-response state;

[0008] Construct a low-carbon dispatch model for a regional integrated energy system, which includes a RIES carbon trading model, a RIES operation constraint model, and a RIES low-carbon economic dispatch model;

[0009] The non-dominated sorting genetic algorithm is used to solve the various model parameters and obtain the optimal low-carbon scheduling model.

[0010] Furthermore, a low-carbon energy flow model is constructed according to the following formula:

[0011]

[0012] Among them, L e_load , L h_load , L g_load are electricity, heat and gas load demands respectively, α e , β e are the utilization rates of wind turbines and photovoltaic power generation equipment, P i_WT , P j_PV are the output power of wind power and photovoltaic power generation, P e_grid , P g_grid Electricity and natural gas purchased for RIES, and are the stored energy values ​​of the electricity storage system, heat storage system and gas storage system, respectively, e\_P2G , η e\_CHP , η e\_EB are the electricity conversion efficiencies of power-to-gas equipment, cogeneration and electric boilers, respectively, and η h\_CHP , η h\_EB are the heat conversion efficiency of cogeneration and electric boiler, respectively, g\_P2G and η g\_CHP are the gas conversion efficiency of cogeneration and electric boiler, P P2G , P CHP and P EB are the power outputs of the power-to-gas equipment, cogeneration and electric boilers respectively;

[0013] The carbon emission flow balance constraint associated with the low-carbon energy flow model is constructed according to the following formula:

[0014]

[0015] Among them, C y is the actual carbon emissions per unit, is the unit carbon emission quota, C x_load is the carbon emission trading amount of each load, ΔC y→y+1 is the remaining carbon emission trading quota.

[0016] Furthermore, the distribution calculation model of the transfer load and the replacement load is constructed according to the following formula:

[0017]

[0018] Among them, ε tr_e,h,gl is the average value of the demand response uncertainty deviation, λ tr_e,h,gl is the variance of the demand response uncertainty deviation, is the deviation value of the interruption load, g(·) represents the load distribution function;

[0019] According to the deviation value of the interrupted load, the demand response deviation probability density distribution function ξ is constructed tr_e,h,gl :

[0020]

[0021] The load cost model under the under-response state is constructed according to the following formula:

[0022]

[0023] The load cost model under over-response state is constructed according to the following formula:

[0024]

[0025] Among them, C′ 1e.1. , C′ 1h.l. and C′ 1g.l. They represent the load adjustment costs of power supply, heat supply and gas supply under the under-response state, C′ 2e.l. , C′ 2h.l. , C′ 2g.l. They represent the load adjustment costs of power supply, heat supply and gas supply under over-response state, c tr\_el 、c tr\_hl and c tr\_gl are the compensation costs of electricity, heat and gas transferable loads, respectively, c re\_el 、c re\_hl and c re\_gl are the compensation costs for interrupted loads of electricity, heat and gas, respectively. and are the deviations of the interruption loads of electricity, gas and heat, respectively. and are the deviations of electricity, gas and heat substitution loads, respectively. and are the deviations of the transferable loads of electricity, natural gas and thermal energy, respectively, tr\_el , L tr\_hl , L tr\_gl are the average values ​​of the transferable loads of electricity, heat and gas, respectively, br\_el and L br\_hlare the average values ​​of power and gas interruption loads, L te\_el , L re\_hl and L re\_gl are the average values ​​of electricity, gas and heat replacement loads, respectively, x is the proportion of transferable load.

[0026] Furthermore, the RIES carbon trading model is constructed according to the following formula:

[0027] ∑C y ′(t)-∑C y +∑C x-reload +∑C xl_uncreload =∑C y→y+1

[0028] Among them, C y The actual carbon emissions per unit, C y ′ is the unit carbon emission quota, C x-reload is the carbon emission value affected by DR, C xl_uncreload is the carbon emission value affected by the reward and punishment ladder carbon price, C y→y+1 It is a tradable unit of carbon emissions;

[0029] The RIES low-carbon economic dispatch model is constructed according to the following formula:

[0030] f=min{b1(c1f1+c2f3+c3f4)+b2f2}

[0031] Among them, c1, c2, c3, b1 and b2 are all weight coefficients, and c1+c2+c3=1, b1+b2=1, f1 is the operating cost of the regional integrated energy system, f2 is the carbon trading cost of the regional integrated energy system, f3 is the supply and demand mismatch cost, and f4 is the demand response uncertainty cost.

[0032] Furthermore, the RIES operation constraint model includes a gas network operation constraint model, a power grid operation constraint model, and a heat network operation constraint model;

[0033] The gas network operation constraint model is constructed according to the following formula:

[0034]

[0035] Among them, G ab，t is the node flow value, V a,t 、V b,t are the gas pressure values ​​at nodes a and b, respectively, K ab is the opening constant of the pipe, δ ab is the gas compression ratio, sgn(·) represents the sgn function;

[0036] The power grid constraint model is constructed according to the following formula:

[0037]

[0038] Among them, P o_wT,i,t , P j_PV,i,t , P z_ov,i,t are the output power limits of photovoltaic power generation equipment, wind turbines, and power grid, respectively. e,t P P2G,i,t and P EB,i,t are the power of the power grid, power-to-gas equipment and electric boiler respectively, P L,i,t and P Tr,i,t are the transferable electricity load and the replaceable electricity load, P kl,t and P kl,max is the active power and maximum active power between nodes l and k, Q kl,t and Q kl,max is the reactive power and maximum reactive power between nodes l and k, R kl is the resistance between nodes l and k; Z kl is the reactance between nodes l and k, U l,t , U k,t is the grid voltage at nodes l and k, I kl,t is the current between nodes k and l, U l,min , U l,max are the lower and upper limits of the voltage at node l, I kl,min ,I kl,max are the lower and upper limits of the current between nodes l and k respectively;

[0039] The heat network operation constraint model is constructed according to the following formula:

[0040]

[0041]

[0042] in, and are the thermal power and load power of node c, ΔH cd,t is the thermal power and heat loss between nodes c and d, is the difference between the supply water temperature and the ambient temperature, R cd is the thermal resistance of the pipe, is the transferable and replaceable heat load, H cd,t and ΔH cd,t are the heat power and heat loss between nodes c and d, c1 is the heat capacity of the heating medium, ν cd,max is the maximum flow rate of the medium, D cd is the cross-sectional area of ​​the pipe, L cdIndicates the length between cd nodes, Indicates the difference between the water supply temperature and the ambient temperature. represents the supply and return water temperature difference, and ρ represents the heat medium density.

[0043] Furthermore, the step of using a non-dominated sorting genetic algorithm to solve each model parameter to obtain the optimal low-carbon scheduling model includes:

[0044] Step 1, initialize the population and set the maximum number of iterations;

[0045] Step 2: Initialize the population according to the RIES constraints and calculate the fitness;

[0046] Step 3: Fast non-dominated sorting and congestion calculation;

[0047] Step 4: NSGA selects high-quality parent individuals;

[0048] Step 5: The normalized individuals undergo crossover and mutation to form offspring;

[0049] Step 6: Fast non-dominated sorting and congestion calculation;

[0050] Step 7: Form a new population;

[0051] Step 8: Update the optimal and worst fitness positions of the population, and determine whether the solution accuracy and number of iterations are met. If so, output a suitable solution, otherwise repeat steps 4 to 7.

[0052] In a second aspect, the present invention provides a low-carbon economic dispatching system based on demand-side response uncertainty, the system comprising:

[0053] A comprehensive energy model building module is used to build a low-carbon energy flow model for a regional comprehensive energy system, wherein the regional comprehensive energy system includes photovoltaic power generation equipment, wind turbines, power grids, power-to-gas equipment, cogeneration, and electric boilers;

[0054] An uncertainty model building module is used to build an incentive-type multi-load demand response uncertainty model, wherein the incentive-type multi-load demand response uncertainty model includes a distribution calculation model of transfer load and alternative load, a load cost model in an under-response state, and a load cost model in an over-response state;

[0055] A low-carbon dispatch model construction module is used to construct a low-carbon dispatch model for a regional integrated energy system, wherein the low-carbon dispatch model includes a RIES carbon trading model, a RIES operation constraint model, and a RIES low-carbon economic dispatch model;

[0056] The solution module is used to solve various model parameters using a non-dominated sorting genetic algorithm to obtain the optimal low-carbon scheduling model.

[0057] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned low-carbon economic dispatch method based on demand-side response uncertainty.

[0058] In a fourth aspect, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0059] The memory is used to store computer programs;

[0060] When the processor is used to execute the computer program stored in the memory, the above-mentioned low-carbon economic dispatch method based on demand-side response uncertainty is implemented.

[0061] Compared with the prior art, the present invention has the following advantages:

[0062] 1. The present invention can effectively reduce carbon emissions by incorporating the uncertainty of DR (demand response) of multiple types of energy users on the load side and the rewards and penalties of the ladder carbon trading mechanism into low-carbon scheduling to optimize and recalculate the operating economy of RIES.

[0063] 2. This paper combines the reward and punishment ladder carbon price model with DR uncertainty, and combines the constraints of electricity, heat and gas networks to establish a two-level RIES scheduling optimization model that includes system costs and carbon emission costs, and performs a comprehensive solution.

[0064] 3. The present invention uses an improved non-dominated sorting genetic algorithm to solve the problem, and the obtained optimal low-carbon scheduling model has obvious improvements in RIES energy utilization and carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of a low-carbon economic dispatch method based on demand-side response uncertainty proposed in one embodiment of the present invention;

[0066] Figure 2 A schematic diagram of the structure of a regional integrated energy system proposed in one embodiment of the present invention;

[0067] Figure 3 This is an overall architecture diagram of a low scheduling model proposed in one embodiment of the present invention;

[0068] Figure 4 A schematic diagram of the connection between demand response and carbon emissions proposed in one embodiment of the present invention;

[0069] Figure 5 A flowchart of solving a problem using a non-dominated sorting genetic algorithm according to an embodiment of the present invention;

[0070] Figure 6 A comparison chart of operating costs when the improved non-dominated genetic algorithm and the non-dominated genetic algorithm are used in the present invention;

[0071] Figure 7 This is a schematic diagram of the structure of a low-carbon economic dispatching system based on demand-side response uncertainty proposed in one embodiment of the present invention.

[0072] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0074] like Figure 1 As shown, an embodiment of the present invention provides a low-carbon economic dispatch method for demand-side response uncertainty, the method comprising steps S101 to S104, wherein:

[0075] Step S101: construct a low-carbon energy flow model for a regional integrated energy system, wherein the regional integrated energy system includes photovoltaic power generation equipment, wind turbines, power grids, power-to-gas equipment, cogeneration, and electric boilers.

[0076] like Figure 2 The figure shows the structural diagram of the regional integrated energy system. The low-carbon energy flow model of the regional integrated energy system constructed based on this is as follows:

[0077]

[0078] Among them, L e_load , L h_load , L g_load are electricity, heat and gas load demands respectively, α e , β e are the utilization rates of wind turbines and photovoltaic power generation equipment, P i_WT , P j_PV are the output power of wind power and photovoltaic power generation, P e_grid , Pg_grid Electricity and natural gas purchased for RIES, and are the stored energy values ​​of the electricity storage system, heat storage system and gas storage system, respectively, e\_P2G , η e\_CHP , η e\_EB are the electricity conversion efficiencies of power-to-gas equipment, cogeneration and electric boilers, respectively, and η h\_CHP , η h\_EB are the heat conversion efficiency of cogeneration and electric boiler, respectively, g\_P2G and η g\_CHP are the gas conversion efficiency of cogeneration and electric boiler, P P2G , P CHP and P EB These are the power outputs of the power-to-gas plant, the cogeneration plant and the electric boiler.

[0079] In addition, the carbon emission flow balance constraint associated with the low-carbon energy flow model is constructed according to the following formula:

[0080]

[0081] Among them, C y is the actual carbon emissions per unit, is the unit carbon emission quota, C x_1oad is the carbon emission trading amount of each load, ΔC y→y+1 is the remaining carbon emission trading quota.

[0082] Step S102: construct an incentive-based multi-load demand response uncertainty model, which includes a distribution calculation model of transfer load and alternative load, a load cost model in an under-response state, and a load cost model in an over-response state.

[0083] It should be noted that the distribution calculation model of the transfer load and the replacement load is first constructed according to the following formula:

[0084]

[0085] Among them, ε tr_e,h,gl is the average value of the demand response uncertainty deviation, λ tr_e,h,gl is the variance of the demand response uncertainty deviation, is the deviation value of the interruption load, g(·) represents the load distribution function;

[0086] In order to better describe the degree of DR over-response and under-response, the DR deviation probability density distribution function is used. tr_e,h,gl , expressed as:

[0087]

[0088] Taking into account the uncertainty of load response, the cost of regulating electricity, heat and gas loads, the load cost model under the under-response state can be expressed as follows:

[0089]

[0090] The load cost model in the over-response state is as follows:

[0091]

[0092] Among them, C′ 1e.l. , C′ 1h.1. and C′ 1g.1. They represent the load adjustment costs of power supply, heat supply and gas supply under the under-response state, C′ 2e.l. , C′ 2h.1. , C′ 2g.l. They represent the load adjustment costs of power supply, heat supply and gas supply under over-response state, c tr\_el 、c tr\_hl and c tr\_gl are the compensation costs of electricity, heat and gas transferable loads, respectively, c re\_el 、c re\_hl and c re\_gl are the compensation costs for interrupted loads of electricity, heat and gas, respectively. and are the deviations of the interruption loads of electricity, gas and heat, respectively. and are the deviations of electricity, gas and heat substitution loads, respectively. and are the deviations of the transferable loads of electricity, natural gas and thermal energy, respectively, tr\_el , L tr\_hl , L tr\_gl are the average values ​​of the transferable loads of electricity, heat and gas, respectively, br\_el and L br\_hl are the average values ​​of power and gas interruption loads, L re\_el , L re\_hl and L re\_gl are the average values ​​of electricity, gas and heat replacement loads, respectively, x is the proportion of transferable load.

[0093] Step S103: constructing a low-carbon dispatch model for a regional integrated energy system, wherein the low-carbon dispatch model includes a RIES carbon trading model, a RIES operation constraint model, and a RIES low-carbon economic dispatch model.

[0094] It should be noted that in this step, Figure 3 As shown in the figure, it is the overall architecture diagram of the low-carbon scheduling model. First, the RIES carbon trading model is constructed according to the following formula:

[0095] ∑C y ′(t)-∑C y +∑C x-reload +∑C xl_uncreload =∑C y→y+1

[0096] Among them, C y The actual carbon emissions per unit, C y ′ is the unit carbon emission quota, C x-reload is the carbon emission value affected by DR, C xl_uncreload is the carbon emission value affected by the reward and punishment ladder carbon price, C y→y+1 It is a tradable unit of carbon emissions.

[0097] like Figure 4 As shown in the figure, in order to effectively reduce the carbon emissions of RIES, the DR policy is adopted, the reward and punishment factors of the ladder carbon price are integrated, and the constraints of the electricity, heat and gas grids are combined to reduce the carbon emissions of RIES. Specifically, the RIES operation constraint model includes the gas grid operation constraint model, the power grid operation constraint model, and the heat grid operation constraint model, among which:

[0098] The gas network operation constraint model is constructed according to the following formula:

[0099]

[0100] Among them, G ab，t is the node flow value, V a,t 、V b,t are the gas pressure values ​​at nodes a and b, respectively, K ab is the opening constant of the pipe, δ ab is the gas compression ratio, sgn(·) represents the sgn function;

[0101] The power grid constraint model is constructed according to the following formula:

[0102]

[0103] Among them, P o_wT,i,t , P j_PV,i,t , P z_OV,i,t are the output power limits of photovoltaic power generation equipment, wind turbines, and power grid, respectively. e,t P P2G,i,t and P EB,i,t are the power of the power grid, power-to-gas equipment and electric boiler respectively, P L,i,t and P Tr,i,t are the transferable electricity load and the replaceable electricity load, Pkl,t and P kl,max is the active power and maximum active power between nodes l and k, Q kl,t and Q kl,max is the reactive power and maximum reactive power between nodes l and k, R kl is the resistance between nodes l and k; Z kl is the reactance between nodes l and k, U l,t , U k,t is the grid voltage at nodes l and k, I kl,t is the current between nodes k and l, U l,min , U l,max are the lower and upper limits of the voltage at node l, I kl,min ,I kl,max are the lower and upper limits of the current between nodes l and k respectively;

[0104] The heat network operation constraint model is constructed according to the following formula:

[0105]

[0106]

[0107] in, and are the thermal power and load power of node c, ΔH cd,t is the thermal power and heat loss between nodes c and d, is the difference between the supply water temperature and the ambient temperature, R cd is the thermal resistance of the pipe, is the transferable and replaceable heat load, H cd,t and ΔH cd,t are the heat power and heat loss between nodes c and d, c1 is the heat capacity of the heating medium, ν cd,max is the maximum flow rate of the medium, D cd is the cross-sectional area of ​​the pipe, L cd Indicates the length between cd nodes, Indicates the difference between the water supply temperature and the ambient temperature. represents the supply and return water temperature difference, and ρ represents the heat medium density.

[0108] In addition, the constructed RIES low-carbon economic dispatch model takes the lowest operating cost and the lowest carbon emission cost as dual optimization goals, as follows:

[0109] f=min{b1(c1f1+c2f3+c3f4)+b2f2}

[0110] Among them, c1, c2, c3, b1 and b2 are all weight coefficients, and c1+c2+c3=1, b1+b2=1, f1 is the operating cost of the regional integrated energy system, f2 is the carbon trading cost of the regional integrated energy system, f3 is the supply and demand mismatch cost, and f4 is the demand response uncertainty cost.

[0111] In addition, in the RIES scheduling process, the goal of the upper-level model is to minimize the operating cost of the entire system, while the goal of the lower-level scheduling model is to minimize the operating cost of the entire system.

[0112] Step S104: using a non-dominated sorting genetic algorithm to solve various model parameters to obtain an optimal low-carbon scheduling model.

[0113] It should be noted that in this step, Figure 5 As shown in the figure, the improved non-dominated sorting genetic algorithm is used to optimize the scheduling model. The specific process is as follows:

[0114] Step 1: Initialize the population and the maximum number of iterations, define RIES, DG, and load parameters;

[0115] Step 2: Initialize the population according to the RIES constraints and calculate the fitness;

[0116] Step 3: Fast non-dominated sorting and congestion calculation;

[0117] Step 4: NSGA selects high-quality parent individuals;

[0118] Step 5: The normalized individuals undergo crossover and mutation to form offspring;

[0119] Step 6: Fast non-dominated sorting and congestion calculation;

[0120] Step 7: Form a new population;

[0121] Step 8: Update the optimal and worst fitness positions of the population, and determine whether the solution accuracy and number of iterations are met. If so, output a suitable solution, otherwise repeat steps 4 to 7.

[0122] Take the IEEE 33-node power grid, 5-node heat grid and 7-node gas grid as examples. In the power grid, node 1 is the root node, and nodes 14 and 32 are connected to 120 MW wind turbines and 50 MW photovoltaic networks. Wind turbines and 50 MW photovoltaic power generation. In the gas network, nodes 4 and 5 are connected to the natural gas sales load, while nodes 6 and 7 are connected to the natural gas storage device. Gas storage equipment. In the heat network, nodes 2 and 5 are associated with commercial heating loads. The power-heat-gas network promotes energy conversion through energy coupling devices such as P2G (power-to-gas equipment), CHP (combined heat and power), and EB (electric boiler). The scheduling period is 24 hours.

[0123] The relevant parameters of distributed generation, coupling equipment, and the electric, thermal, and gas grid in the regional integrated energy system (RIES) are summarized in Table 1. The peak values ​​of the electric, thermal, and gas loads are 98 MW, 18 MW, and 22.3 MW, respectively, and the power capacity of the energy storage system (ESS) is 20 MW. The efficiencies of the P2G and CHP equipment are 0.6 and 0.65, respectively, while the thermal efficiency of CHP is 1.5. The unit power carbon quotas of CHP and P2G are 0.2 and 0.5, respectively, and the carbon emission interval is 2000 tons. The transfer and substitution load ratios are set to 15% and 5%, respectively. The expected demand response deviation of the electric, thermal, and gas loads is set to 0, while the standard deviations are based on the incentive demand response data of the RIES demonstration project in Northeast China, which are 0.5%, 0.3%, and 0.7% of the transferable load power, respectively.

[0124] Table 1 DG and coupling data parameters

[0125]

[0126]

[0127] The scheduling cycle of the model is 24 hours, and the scheduling step is set to 1 hour. The analysis considers the impact of DR and carbon trading modes on RIES low-carbon scheduling, and designs 6 scenarios, as shown in Table 2.

[0128] Table 2 RIES scenarios designed for different working conditions

[0129]

[0130] In a scheduling cycle, the various items of RIES optimized scheduling cost in the above six cases are shown in Table 3.

[0131] The comparative analysis of Scheme 1 and Scheme 2 in Table 3 shows that the system operation cost is reduced by 1.6% and the renewable energy consumption rate is increased by 2.5%. The carbon emissions of RIES are reduced accordingly.

[0132] The main focus of the analysis is the impact of DR uncertainty on RIES scheduling. In Table 3, when DR uncertainty is taken into account, the DR cost of RIES scheduling increases. In Table 5, when DR uncertainty is taken into account, the DR cost of RIES is reduced by 12.7%, resulting in an overall reduction in total cost. In Table 5, when DR uncertainty is taken into account, the DR cost of RIES is reduced by 12.7%, resulting in an overall reduction in total cost of RIES of $363.

[0133] Table 3 RIES scheduling results in different scenarios

[0134]

[0135]

[0136] The iterative comparison between the improved non-dominated genetic algorithm and the non-dominated genetic algorithm is as follows: Figure 6 As shown, there are Figure 6 ,It can be seen that the use of the improved non-dominated genetic algorithm can ,result in lower operating cost and better optimization effect.

[0137] In summary, the above-mentioned low-carbon economic dispatch method based on demand-side response uncertainty has the following advantages:

[0138] 1. The present invention can effectively reduce carbon emissions by incorporating the uncertainty of DR (demand response) of multiple types of energy users on the load side and the rewards and penalties of the ladder carbon trading mechanism into low-carbon scheduling to optimize and recalculate the operating economy of RIES.

[0139] 2. This paper combines the reward and punishment ladder carbon price model with DR uncertainty, and combines the constraints of electricity, heat and gas networks to establish a two-level RIES scheduling optimization model that includes system costs and carbon emission costs, and performs a comprehensive solution.

[0140] 3. The present invention uses an improved non-dominated sorting genetic algorithm to solve the problem, and the obtained optimal low-carbon scheduling model has obvious improvements in RIES energy utilization and carbon emissions.

[0141] like Figure 7 As shown, an embodiment of the present invention further provides a low-carbon economic dispatching system based on demand-side response uncertainty, the system comprising:

[0142] The integrated energy model building module 10 is used to build a low-carbon energy flow model for a regional integrated energy system, wherein the regional integrated energy system includes photovoltaic power generation equipment, wind turbines, power grids, power-to-gas equipment, cogeneration, and electric boilers;

[0143] The uncertainty model building module 20 is used to build an incentive-type multi-load demand response uncertainty model, wherein the incentive-type multi-load demand response uncertainty model includes a distribution calculation model of a transfer load and a substitute load, a load cost model in an under-response state, and a load cost model in an over-response state;

[0144] A low-carbon dispatch model construction module 30 is used to construct a low-carbon dispatch model for a regional integrated energy system, wherein the low-carbon dispatch model includes a RIES carbon trading model, a RIES operation constraint model, and a RIES low-carbon economic dispatch model;

[0145] The solution module 40 is used to solve various model parameters using a non-dominated sorting genetic algorithm to obtain an optimal low-carbon scheduling model.

[0146] On the other hand, the present invention further proposes a storage medium on which one or more programs are stored, and when the program is executed by a processor, the above-mentioned low-carbon economic dispatch method based on demand-side response uncertainty is implemented.

[0147] On the other hand, the present invention further proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned low-carbon economic dispatch method based on demand-side response uncertainty.

[0148] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0149] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0150] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0151] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A low-carbon economic dispatch method based on demand-side response uncertainty, characterized in that: The method comprises: Construct a low-carbon energy flow model for a regional integrated energy system, which includes photovoltaic power generation equipment, wind turbines, power grids, power-to-gas equipment, cogeneration, and electric boilers; Constructing an incentive-based multi-load demand response uncertainty model, wherein the incentive-based multi-load demand response uncertainty model includes a distribution calculation model of transfer loads and alternative loads, a load cost model in an under-response state, and a load cost model in an over-response state; Construct a low-carbon dispatch model for a regional integrated energy system, which includes a RIES carbon trading model, a RIES operation constraint model, and a RIES low-carbon economic dispatch model; The non-dominated sorting genetic algorithm is used to solve the various model parameters and obtain the optimal low-carbon scheduling model.

2. The low-carbon economic dispatching method based on demand-side response uncertainty according to claim 1 is characterized in that: The low-carbon energy flow model is constructed according to the following formula: Among them, L e_load , L h_load , L g_load are electricity, heat and gas load demands respectively, α e , β e are the utilization rates of wind turbines and photovoltaic power generation equipment, P i_WT , P j_PV are the output power of wind power and photovoltaic power generation, P e_grid , P g_grid Electricity and natural gas purchased for RIES, and are the stored energy values ​​of the power storage system, heat storage system and gas storage system respectively, η e\_P2G , η e\_CHP , η e\_EB are the electricity conversion efficiencies of power-to-gas equipment, cogeneration and electric boilers, respectively, and η h\_CHP , η h\_EB are the heat conversion efficiency of cogeneration and electric boiler, respectively, g\_P2G and η g\_CHP are the gas conversion efficiency of cogeneration and electric boiler, P P2G , P CHP and P EB are the power outputs of the power-to-gas equipment, cogeneration and electric boilers respectively; The carbon emission flow balance constraint associated with the low-carbon energy flow model is constructed according to the following formula: Among them, C y is the actual carbon emissions per unit, is the unit carbon emission quota, C x_load is the carbon emission trading amount of each load, ΔC y→y+1 is the remaining carbon emission trading quota.

3. The low-carbon economic dispatching method based on demand-side response uncertainty according to claim 2 is characterized in that: The distribution calculation model of transfer load and alternative load is constructed according to the following formula: Among them, ε tr_e，h，gl is the average value of the demand response uncertainty deviation, λ tr_e，h，gl is the variance of the demand response uncertainty deviation, is the deviation value of the interruption load, g(·) represents the load distribution function; According to the deviation value of the interrupted load, the demand response deviation probability density distribution function ξ is constructed tr_e，h，gl : The load cost model under the under-response state is constructed according to the following formula: The load cost model under over-response state is constructed according to the following formula: Among them, C′ 1e.l .、C′ 1h.l . and C′ 1g.l. They represent the load adjustment costs of power supply, heat supply and gas supply under the under-response state, C′ 2e.l. , C′ 2h.l. , C′ 2g.l. They represent the load adjustment costs of power supply, heat supply and gas supply under over-response state, c tr\ _ e1 、c tr\_h1 and c tr\_g1 are the compensation costs of electricity, heat and gas transferable loads, respectively, c re\_e1 、c re\_h1 and c re\_g1 are the compensation costs for interrupted loads of electricity, heat and gas, respectively. and are the deviations of the interruption loads of electricity, gas and heat, respectively. and are the deviations of electricity, gas and heat substitution loads, respectively. and are the deviations of the transferable loads of electricity, natural gas and thermal energy, respectively, tr\_e1 , L tr\_h1 , L tr\_g1 are the average values ​​of the transferable loads of electricity, heat and gas, respectively, br\_e1 and L br\_h1 are the average values ​​of power and gas interruption loads, L re\_e1 , L re\_h1 and L re\_g1 are the average values ​​of electricity, gas and heat replacement loads, respectively, x is the proportion of transferable load.

4. The low-carbon economic dispatching method based on demand-side response uncertainty according to claim 3 is characterized in that: The RIES carbon trading model is constructed according to the following formula: ∑C y ′(t)-∑C y +∑C x-reload +∑C xl_uncreload =∑C y→y+1 Among them, C y The actual carbon emissions per unit, C y ′ is the unit carbon emission quota, C x-reload is the carbon emission value affected by DR, C xl_uncreload is the carbon emission value affected by the reward and punishment ladder carbon price, C y→y+1 It is a tradable unit of carbon emissions; The RIES low-carbon economic dispatch model is constructed according to the following formula: f=min{b1(c1f1+c2f3+c3f4)+b2f2} Among them, c1, c2, c3, b1 and b2 are all weight coefficients, and c1+c2+c3=1, b1+b2=1, f1 is the operating cost of the regional integrated energy system, f2 is the carbon trading cost of the regional integrated energy system, f3 is the supply and demand mismatch cost, and f4 is the demand response uncertainty cost.

5. The low-carbon economic dispatching method based on demand-side response uncertainty according to claim 4 is characterized in that: The RIES operation constraint model includes a gas network operation constraint model, a power grid operation constraint model, and a heat network operation constraint model; The gas network operation constraint model is constructed according to the following formula: Among them, G ab，t is the node flow value, V a，t 、V b，t are the gas pressure values ​​at nodes a and b, respectively, K ab is the opening constant of the pipe, δ ab is the gas compression ratio, sgn(·) represents the sgn function; The power grid constraint model is constructed according to the following formula: Among them, P o_WT,i，t , P j_PV,i，t , P z_OV，i，t are the output power limits of photovoltaic power generation equipment, wind turbines, and power grid, respectively. e，t P P2G,i，t and P EB，i，t are the power of the power grid, power-to-gas equipment and electric boiler respectively, P L，i，t and P Tr,i，t are the transferable electricity load and the replaceable electricity load, P kl，t and P kl，max is the active power and maximum active power between nodes 1 and k, Q kl，t and Q kl，max is the reactive power and maximum reactive power between nodes l and k, R kl is the resistance between nodes l and k; Z kl is the reactance between nodes l and k, U l，t , U k，t is the grid voltage at node 1 and node k, I kl，t is the current between nodes k and l, U l，min , U l，max are the lower and upper limits of the voltage at node 1, I kl，min ,I kl，max are the lower and upper limits of the current between nodes 1 and k respectively; The heat network operation constraint model is constructed according to the following formula: in, and are the thermal power and load power of node c, ΔH cd，t is the thermal power and heat loss between nodes c and d, is the difference between the supply water temperature and the ambient temperature, R cd is the thermal resistance of the pipe, is the transferable and replaceable heat load, H cd，t and ΔH cd，t are the heat power and heat loss between nodes c and d, c1 is the heat capacity of the heating medium, v cd,max is the maximum flow rate of the medium, D cd is the cross-sectional area of ​​the pipe, L cd Indicates the length between cd nodes, Indicates the difference between the water supply temperature and the ambient temperature. represents the supply and return water temperature difference, and ρ represents the heat medium density.

6. The low-carbon economic dispatching method based on demand-side response uncertainty according to claim 5 is characterized in that: The step of using a non-dominated sorting genetic algorithm to solve each model parameter to obtain the optimal low-carbon scheduling model includes: Step 1, initialize the population and set the maximum number of iterations; Step 2: Initialize the population according to the RIES constraints and calculate the fitness; Step 3: Fast non-dominated sorting and congestion calculation; Step 4: NSGA selects high-quality parent individuals; Step 5: The normalized individuals undergo crossover and mutation to form offspring; Step 6: Fast non-dominated sorting and congestion calculation; Step 7: Form a new population; Step 8: Update the optimal and worst fitness positions of the population, and determine whether the solution accuracy and number of iterations are met. If so, output a suitable solution, otherwise repeat steps 4 to 7.

7. A low-carbon economic dispatch system based on demand-side response uncertainty, characterized in that: The system comprises: A comprehensive energy model building module is used to build a low-carbon energy flow model for a regional comprehensive energy system, wherein the regional comprehensive energy system includes photovoltaic power generation equipment, wind turbines, power grids, power-to-gas equipment, cogeneration, and electric boilers; An uncertainty model building module is used to build an incentive-type multi-load demand response uncertainty model, wherein the incentive-type multi-load demand response uncertainty model includes a distribution calculation model of transfer load and alternative load, a load cost model in an under-response state, and a load cost model in an over-response state; A low-carbon dispatch model construction module is used to construct a low-carbon dispatch model for a regional integrated energy system, wherein the low-carbon dispatch model includes a RIES carbon trading model, a RIES operation constraint model, and a RIES low-carbon economic dispatch model; The solution module is used to solve various model parameters using a non-dominated sorting genetic algorithm to obtain the optimal low-carbon scheduling model.

8. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by a processor, implement the low-carbon economic dispatch method based on demand-side response uncertainty as described in any one of claims 1 to 6.

9. An electronic device, comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the low-carbon economic dispatch method based on demand-side response uncertainty as described in any one of claims 1-6.

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