CN115908047A - Multi-time scale operation method of comprehensive energy system and application thereof - Google Patents
Multi-time scale operation method of comprehensive energy system and application thereof Download PDFInfo
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Abstract
A multi-time scale operation method of an integrated energy system, in particular to a multi-time scale optimization operation method considering electricity-gas-heat-hydrogen demand response and a stepped carbon emission cost mechanism, comprises the following steps: establishing a comprehensive energy system low-carbon economic optimization framework considering electricity-gas-heat-hydrogen requirements; a multi-time scale optimization plan that takes into account operating characteristics of the IES element; establishing a day-ahead optimization model; establishing an intra-day rolling optimization model; and (5) establishing a real-time optimization model. The invention comprehensively considers a stepped carbon emission cost mechanism, introduces hydrogen load requirements, optimizes the working characteristics of coupling equipment, and establishes a multi-time scale optimization model of three stages of rolling in day-real time. The method takes the minimum integral operation cost of IES, the minimum carbon emission cost and the minimum wind and light abandoning cost as economic targets, converts the original problem into a mixed integer linear problem, calls a commercial solver to solve, and verifies the feasibility of the proposed strategy by comparing and distributing various situation optimization results.
Description
Technical Field
The invention relates to the field of comprehensive energy system planning, and provides a multi-time scale operation method of a comprehensive energy system, in particular to a multi-time scale optimization operation method considering electricity-gas-heat-hydrogen demand response and a stepped carbon emission cost mechanism.
Background
With the development of social economy, the traditional energy utilization requirements are rapidly expanded, and meanwhile, some emerging energy utilization requirements emerge, so that for realizing the aims of human sustainable development and environmental protection, various countries accelerate the process of energy structure transformation and make an energy low-carbon development strategy. The method provides conditions for the coordination development and transformation of the transverse multi-energy complementation and the longitudinal source network charge storage of the energy power system. The comprehensive energy system breaks through barriers of various traditional and independent operation energy systems such as electricity, gas and heat, the multi-energy system is coupled to carry out unified planning and coordinated operation according to the principle of energy complementary characteristics and energy cascade utilization, and the energy utilization efficiency and the system reliability of the comprehensive network are improved. The energy supply structure is continuously optimized, and the research and the application of IES are deeper and wider.
With the development of society, people's demand for clean energy is increasing, and it is under this background that this concept of comprehensive energy system is coming. The comprehensive energy system is taken as a main component of an energy Internet and is currently considered as a main operation form of future human social energy, and under the condition of collaborative optimization of different energy forms, the energy utilization rate can be better improved, and the effect of 1+1 >.
Existing research rarely considers multi-time scale adjustment, low-carbon targets and demand response simultaneously, or does not relate to hydrogen energy utilization scenes, or is too single on the established low-carbon targets or is not suitable for non-scale competitive subjects. Therefore, how to establish a comprehensive energy system multi-time scale low-carbon operation optimization strategy considering electricity-gas-heat-hydrogen demand response is a problem to be solved urgently by the technical personnel in the field.
Disclosure of Invention
On the premise of establishing a comprehensive energy system low-carbon economic optimization framework considering the electricity-gas-heat-hydrogen requirements, a multi-scale coordination optimization model is established by considering a multi-time scale optimization plan of IES element characteristics, an original problem is converted into a mixed integer linear problem, a commercial solver is called to solve, and the feasibility of each strategy is verified, wherein the technical scheme is as follows:
a multi-time scale operation method of an integrated energy system, in particular to a multi-time scale optimization operation method considering electricity-gas-heat-hydrogen demand response and a stepped carbon emission cost mechanism, which is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a comprehensive energy system low-carbon economic optimization framework considering electricity-gas-heat-hydrogen requirements;
and 2, step: a multi-time scale optimization plan that takes into account operating characteristics of the IES elements;
and step 3: establishing a day-ahead optimization model;
and 4, step 4: establishing an intra-day rolling optimization model;
and 5: and (5) establishing a real-time optimization model.
Advantageous effects
On the basis of the established IES multi-time scale low-carbon operation optimization model considering the electricity-gas-heat-hydrogen demand response, the simulation is carried out by setting a plurality of scenes, and the result is analyzed from a plurality of angles such as a multi-time scale, a clean energy installed capacity ratio, an adjustable thermoelectric ratio and the like, so that the following beneficial effects are obtained:
1) Compared with the traditional single carbon emission cost mechanism, the stepped carbon emission cost mechanism has more excellent carbon emission reduction performance and economy.
2) The adjustable GT thermoelectric ratio and the mixed use of GT and GB natural gas-hydrogen can optimize the energy flow conversion process according to the actual natural gas price and load requirements and improve the operation economy.
3) The multi-time scale optimization operation mode can well deal with load and new energy output prediction under different time dimensions, and can carry out grading adjustment according to flexibility and demand response level of internal equipment of the IES, so that both the prediction and actual measurement conditions in an operation period can be effectively processed.
Drawings
FIG. 1 is a schematic diagram of GT operating state
FIG. 2 is a schematic diagram of the EB run state;
FIG. 3 is a schematic view of the GB operating state;
FIG. 4 is a schematic view of an EL operating state;
FIG. 5 is a schematic diagram of the MR operation state;
fig. 6 is a schematic diagram of a decoupling operation flow of an electric-to-gas link.
Detailed Description
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-time scale operation method of an integrated energy system, in particular to a multi-time scale optimization operation method considering electricity-gas-heat-hydrogen demand response and a stepped carbon emission cost mechanism, comprising the following steps:
step 1: establishing a comprehensive energy system low-carbon economic optimization framework considering electricity-gas-heat-hydrogen requirements;
and 2, step: a multi-time scale optimization plan that takes into account the characteristics of the IES elements;
and step 3: establishing a day-ahead optimization model;
and 4, step 4: establishing an intra-day rolling optimization model;
and 5: and (5) establishing a real-time optimization model.
The specific combined prediction process is as follows:
(1) The method comprises the following steps of establishing a comprehensive energy system low-carbon economic optimization framework considering electricity-gas-heat-hydrogen requirements:
1) Adjustable thermoelectric ratio and natural gas-hydrogen mixed combustion characteristic
The energy conversion process of the gas turbine and the gas boiler is improved. The method is different from the characteristic that GT and GB in the traditional modeling can only burn natural gas, and the model allows the GT and GB to use natural gas-hydrogen mixed gas in a certain proportion as input; and aiming at the characteristic that the ratio of the generated heat and the electricity of the GT in the traditional model is fixed and unchanged, the model allows the proportion of the heating power and the generating power in the working process to be adjusted according to the electricity price and the gas price in different time periods.
2) Electric-to-gas link decoupling operation process and coupling equipment modeling
The hydrogen energy has the characteristics of high calorific value, zero emission, large hydrogen element amount, easiness in obtaining and the like, and has wide application and development space under the large background of clean energy in the future. The main process of electric gas conversion can be summarized as shown in figure 1, and relates to equipment such as an electrolytic cell, a methane reactor and the like, and a hydrogen energy utilization scene is enlarged by decoupling the traditional P2G process.
3) Stepped carbon cost and carbon sequestration revenue model
In order to embody the IES low-carbon operation concept and promote energy conservation and emission reduction, an economic model combining step-type carbon emission tax with fixed carbon income is adopted. The sources of carbon emissions in IES are mainly reflected in two aspects: the first is the upper-level electricity and gas purchase, and the second is the carbon emission in the coupling unit. The use of electric energy does not generate direct carbon emission, but the carbon emission in the consumption behavior of electric energy consumption can be measured by introducing the concept of 'virtual carbon emission', and the reasonable taxation is carried out on the carbon emission. Meanwhile, for the carbon sequestration benefit of the MR, the MR can be subsidized and included in an IES economic cost model as economic benefit.
4) Step type carbon cost metering model
Compared with the traditional single carbon emission rate, in order to further limit the carbon emission, the invention adopts a step-type carbon emission tax system. A plurality of carbon emission intervals are divided by a stepped carbon cost mechanism, and for the carbon emission of consumption behaviors in a certain time, the intervals are distinguished and priced, and the emission is higher, and the tax is heavier.
(2) Multiple time scale optimization plans considering IES element characteristics
1) IES element operating characteristics
The components contained in the IES are mainly classified into three categories, namely, coupling devices, distributed power sources and energy storage components. Its characteristics are summarized as follows:
(1) the flexibility of the coupling equipment is not as good as that of the energy storage equipment, and the coupling equipment is not used as a quick adjusting mode in the model, but the standby adjusting capacity of the coupling equipment has a certain adjusting function;
(2) the distributed power supply can quickly abandon wind and light within the upper limit range of output, the output has good adjustability, but uncertainty and volatility of the output;
(3) the energy storage element has the advantages of flexible arrangement and quick adjustment, but the energy storage capacity has certain limitation, and the energy storage element is suitable for quickly adjusting a supply and demand curve which fluctuates randomly in the IES.
2) Classification of demand response resources
The four types of energy loads, namely electricity, gas, heat and hydrogen, in the IES can utilize the demand response characteristics to carry out demand response management on the IES. Four types of demand response loads are divided into price type (PDR) and incentive type (IDR) according to the management mode of regional power grids on electric load demand response resources (DR). The price of electric energy and gas energy in the model built by the method adopts a day-ahead pricing mode, so that the PDR is not considered in optimization, and only the IDR load response is considered. The IDR is divided into the following categories according to the length of the optimized instruction time of the response IES:
(1) class a IDR, plan made 1 day ahead;
(2) b type IDR, the response time is 15 min-1 h;
(3) c type IDR, the response time is 5-15 min;
(4) class D IDR, real-time response.
3) Multi-time scale optimization plan
The IES multi-time scale low carbon optimization framework designed herein is as follows:
(1) day-ahead optimization: the time step is 1h, and the execution period is 24h. In the stage, a working plan and an A-type IDR load calling plan of the coupling equipment need to be determined;
(2) rolling optimization in days: the time step is 15min, and the execution period is 4h. In the stage, an output plan of distributed power generation, a coupling equipment standby output plan and a calling plan of B-type IDR need to be formulated. To correct for deviations from the optimization plan before the day;
(3) and (3) real-time optimization: the execution period is 5min. In the stage, working states of various energy storage devices and C-type and D-type IDR calling quantities need to be formulated, and finally, the electricity purchasing quantity and the gas purchasing quantity of the upper-level distribution power grid and the natural gas grid are determined.
And taking the control quantity obtained by optimization of the previous stage as a determined quantity to be substituted into an optimization model of the later stage for calculation.
(3) Day-ahead optimization model
The multi-scenario random planning method suitable for large uncertainty is adopted in day-ahead optimization, and IES operation safety is met for errors under different various load and distributed power supply output prediction scenarios.
1) Objective function
And on the basis of the minimum total system operation cost, the objective function of the day-ahead optimization model converts the abandoned wind light quantity and the load loss into penalty cost, and the penalty cost is added into the system operation cost, and the energy purchasing carbon cost and the carbon fixing benefit are considered. The model is represented as follows:
in the formula: f. of 1 An objective function for a day-ahead optimization model, representing the operating cost of the IES; f. of buy (t)、f sto (t)、f cp1 (t)、 f DG (t)、f load (t) cost functions of upper-level energy purchasing, energy storage devices, coupling equipment, distributed power generation and user load management at the moment t are respectively; n is a radical of s,ahead Considering the number of scenes for the pre-optimization model; p is a radical of s,ahead The probability coefficient of occurrence of the s scene in the optimization process before the day; p e/g,buy,s (t) power for purchasing power and gas from the upper level at the time of the s-th scene t; c. C e/g The unit electricity and gas purchase cost; p is e/g/h/H2,sto,s (t) is the charge-discharge energy power of electricity/gas/heat/hydrogen stored at s scene t moment; c (P) e/g/h/H2,sto,s (t)) is an energy storage device cost function;
W(P e/g/h/H2,sto,s (t)) is a maintenance cost function of the energy storage device; p PV/WT,s (t) the output of photovoltaic and fan in distributed power generation at s scene t moment; k is a radical of c,PV/WT (t) punishing a cost coefficient for abandoning wind and abandoning light;
the predicted output of photovoltaic and fan in distributed power generation at s scene t moment; c (P) PV/WT,s (t)) is a cost function of distributed generation at time t of scenario s; n belongs to { e, g, H, H2} and is a variable for indicating four types of loads of electricity/gas/heat/hydrogen by n, and is used for simplifying the space of formula description; k is a radical of n,IDR,A 、k n，IDR,B Respectively the cost coefficients of A and B IDRs of various loads; | VP n,IDR,A,s (t)|、|VP n,IDR,B,s (t) | is the calling amount of A and B IDRs of various loads at t moment of s scene respectively; k is a radical of c,load,n Penalty coefficient for each type of load missing; p loss,n,s And (t) is the loss amount of various loads at the time of s scene t.
2) Constraint conditions
(1) Power balance constraint
Electric power balance constraint:
natural gas balance constraint:
and thermal power balance constraint:
hydrogen energy balance constraint:
in the formula:
the rated power of the external charging and discharging energy of the electricity/gas/heat/hydrogen storage at the s scene t moment is respectively; />
The expected electrical/gas/heat/hydrogen load in the model is optimized for time t.
(2) Distributed generation output constraints
The distributed generation output value is smaller than the predicted value.
(3) Energy storage device operational constraints
The energy storage device is mainly constrained by the charging and discharging energy power and the energy storage state during operation. Because the electric/gas/heat/hydrogen storage and transportation constraints are consistent, the electric storage is taken as an example for explanation, and the energy storage constraints in other forms are not repeated:
in the formula:
respectively charging and discharging the electricity storage; />
Capacity for electricity storage; s. the e,s (t)、/>
Respectively representing the energy storage-capacity ratio state of the electricity storage at the time of s scene t and the upper limit and the lower limit of the energy storage-capacity ratio state. The energy-to-capacity ratio of the energy storage device should be equal at the beginning and end of an optimization period T.
(5) Adjusting constraints for each scene
|P machine,s (t)-P machine,bs (t)|≤ψ machine (9)
In the formula: p machine,s (t) outputting force values for reference scenes of various types of coupling equipment; p machine,bs (t) is a force output value of each type of coupling equipment in the s scene; psi machine For the adjustment margin of various types of coupling devices, it is herein assumed to be 10% of the maximum output of the device.
(6) Constraints on various types of DR resources
In the formula:
increased load capacity for each type of load, class A and class B IDRs, respectively;
3) Optimizing result processing
Solving the day-ahead optimization model, and selecting:
(1) a coupling device operating state;
(2) class a IDR call volume;
and substituting the parameters as determination parameters into the subsequent day and real-time optimization model.
(4) Intraday rolling optimization model
In the optimization stage, system data obtained by actual measurement is fed back to an intra-day rolling optimization model, and an optimal control sequence is solved by combining ultra-short-term prediction data of wind and light loads with the time step length of 15min in 4h in the future.
1) Objective function
Basically the same as the optimization before the day, the objective function of the rolling optimization in the day is the minimum total operating cost of the IES, the adjustable spare capacity output of the coupled equipment and the call volume cost of the IDR type load, the IDR type A parameter is determined, and the total load cost is the sum of the IDR type B and the IDR type C. The objective function is of the same form as above:
in the formula: n is a radical of s,dayin Considering the number of scenes for the optimization model in the day; p is a radical of s,dayin The occurrence probability coefficient of the s scene in the optimization process in the day is obtained; k is a radical of n,IDR,C Cost factor for a class C IDR for a class of loads; | VP n,IDR,C,s (t) | is the call quantity of a certain load C type IDR at the t moment of s scene.
2) Constraint conditions
The intra-day rolling optimization model adopts a multi-scenario stochastic programming method to cope with the influence brought by uncertainty like the previous optimization model, so that constraint conditions are basically consistent with those in the previous optimization model and are not repeated. Adding the constraint of class C IDR as formula (10) and the coupling device spare capacity output constraint:
in the formula: VP machin,s And (t) is a standby output value of various coupling devices. This formula indicates that the reserve capacity output of each type of coupling device satisfies the upper and lower limits, and the sum of the reserve capacity output and its day-ahead output plan satisfies the capacity limit.
3) Optimizing results
The intra-day rolling optimization will determine, based on the parameters determined by the prior-day optimization:
(1) an output plan of distributed power generation;
(2) a coupling device reserve capacity contribution plan;
(3) b-class IDR load calling amount;
and substituted as a determination parameter into the subsequent real-time optimization model.
(5) Real-time optimization model
The time step of real-time optimization is 5min, the real-time load fluctuation amplitude under the time scale is very small, and the multi-scene random optimization method is not suitable. An opportunistic constraint method is used herein to set certain constraints so that the probability that the constraints will be satisfied is not less than a certain confidence level.
1) Objective function
Basically the same as the intraday rolling optimization, the objective function of the real-time optimization is the minimum of the total operating cost of the IES, and the change is only the call volume cost of the load in the IDR class, specifically the sum of the IDR class C and the IDR class D. The objective function is of the same form as above:
2) Constraint conditions
And adopting an opportunity constraint method for the real-time optimization model. At the moment, the deviation values of the real-time loads of electricity, gas, heat and hydrogen obtained by short-term prediction are considered to respectively meet a certain truncated normal distribution, the regulation and control requirements on the energy storage device are that the supply rates of various loads under respective set confidence levels are only required to be met, and the balance of the rest weak deviation can be met through real-time upper-level electricity, gas and load purchasing regulation. Therefore, the constraint conditions at this time are basically the same as those of rolling optimization before and during the day without distinguishing various scenes, and are not described again, but are changed in the partial constraints of power balance and distributed generation output:
electric power balance constraint:
natural gas balance constraint:
thermal power balance constraint:
hydrogen energy balance constraint:
in the formula: pr { g } is a confidence expression;
confidence levels are electrical/gas/thermal/hydrogen power balance.
And (3) distributed generation output constraint:
in the formula:
respectively optimizing the actual upper limits of the photovoltaic output and the actual upper limits of the fans in real time; />
Respectively are the output plans of the photovoltaic and the fan obtained in the rolling optimization in the day. The constraint means that the output of distributed generation in real-time optimization cannot exceed the real-time maximum output upper limit under the restriction of practical conditions on the basis of rolling optimization values in the extended days.
3) Optimizing results
The following optimization results can be determined by calculating the real-time optimization model:
(1) working states and output of various energy storage devices;
(2) class C IDR and class D IDR call volume;
(3) purchasing electricity and gas to the upper level.
(6) Model linearization processing
At this time, the obtained model is a mixed integer nonlinear model, and the absolute value term and the energy storage state constraint in the objective function causing the model nonlinearity need to be linearized. The processed model is a mixed integer linear model, and a commercial solver Gurobi can be called by Matlab + Yalmip to solve.
Examples
To verify the validity of the model, an example was set up for analysis. The model optimization period is 24 hours a day, and the load and the prediction errors of the distributed power generation in the day-ahead, day-in and real-time are respectively set to be 3%, 1%, 0.5% and 5%, 3% and 1%. The output day-ahead prediction reference values of various loads and photovoltaic wind power are in the form of per unit values, wherein the peak values of the loads of electricity, gas, heat and hydrogen are set to be 1000kW, 600kW, 400kW and 200kW, and the rated capacities of the wind power and the photovoltaic are both 400kW. In the real-time optimization model, the error rates of various loads meet the truncation normal distribution of N (0, 1/1200), and the confidence coefficient is 0.9.
(1) Effectiveness analysis of multi-time scale low-carbon optimization method
To verify the effectiveness of the low-carbon optimization method provided herein, the IES optimization results under three scenarios are selected for comparative analysis:
scene 1: economic targets for carbon emissions are not considered;
scene 2: considering the single carbon emission price and the carbon fixation subsidy;
scene 3: considering the step-type carbon emission price and carbon fixation subsidy.
And carrying out multi-time scale calculation on the three scenes and comparing and analyzing the IES optimization results in the day-ahead, in-day and real-time stages. Partial results of the three types of scene day-ahead optimization are shown in table 1:
TABLE 1 optimization of partial results before day for each scene
As can be seen from table 1, in the optimization at the previous stage, the total carbon emissions of scene 2 and scene 3, which consider the economic objective of carbon emission, are reduced by 1.38% and 1.57% respectively, compared to scene 1, indicating that considering the economic objective of low carbon contributes to reducing the consumed carbon emissions of the IES. And the carbon emission cost of the scene 2 and the scene 3 is compared, the latter carbon emission cost is reduced by 28% and a larger carbon emission reduction benefit is obtained, which shows that considering the stepped carbon cost mechanism, the carbon emission reduction benefit and the economic benefit are better.
In summary, in the scenario 3, compared to the first two scenarios, the GT output is called more, and the EB and EL outputs are called less, because the electricity demand is transferred to the gas purchase instead of reducing the carbon emission cost by correcting the excessive carbon emission cost caused by the excessive use of the electric energy under the action of the stepped carbon cost model. The fact that the MR does not work in the period indicates that the conversion of hydrogen into natural gas has no economic advantage in the optimization process in the future, which is mainly caused by the fact that the natural gas price is relatively low and the cheap electric energy supply ratio of distributed power generation is low.
Taking the scenario 3 as an example, analyzing the spare capacity operating condition of the coupling device under rolling optimization in the day, comparing the operating conditions of the coupling devices in the day before and after, it can be found that, in the case of predicting load variation in the day, the GB operating condition is not changed basically, the EB has a down regulation of the output in the period 1-00; in the optimization within a day, the hydrogen load is mainly reduced in the whole day, so that the MR converts the reduced hydrogen load demand into natural gas and collects the carbon fixation benefit before 20:00, and the MR does not exert the force after 20:00 and the EL increases the force in order to meet the constraint that the energy storage states of the hydrogen storage tanks at the beginning and the end of the optimization period in the last 4h rolling period are equal. Fig. 1-5 illustrate the operational status of the coupling devices of scenario 3 during the day ahead-day phase.
Some parameters of the three types of scenes after real-time optimization are shown in table 2:
TABLE 2 real-time optimization of partial results for each scenario
| Index (es) | Scene 1 | |
Scene 3 |
| Class A IDR cost/ |
0 | 490.74 | 490.74 |
| Class B IDR cost/ |
0 | 0 | 7.03 |
| Class C IDR cost/cost | 289.19 | 296.10 | 296.83 |
| Class D IDR cost/cost | 148.42 | 154.31 | 157.93 |
| Cost/dollar in |
0 | 23.93 | 15.35 |
| Total cost/element | 20273 | 21993 | 21544 |
| Total carbon emission/kg | 28286 | 27894 | 27843 |
Looking at table 2, from the IDR and dump load costs, the class B load is almost 0 in all three scenarios, while the class a IDR cost for scenario 2 and scenario 3 is much greater than the 0 cost for scenario 1, which is the result of the interaction of the carbon emission mechanism with the load demand response, i.e. by reducing the carbon emission cost appropriately, while the class B IDR still functions in scenario 3 because the higher-order carbon emission cost from the stepped carbon emission mechanism is still acting on the load demand response to reduce the total economic cost. The no obvious difference in IDR cost of class C and class D under the three scenes is due to the fact that the IDR cost is constrained by the real-time load fluctuation balance and the energy storage initial and final states with the same prediction error. (3) Model benefit analysis under different clean energy power generation installed capacities
In order to analyze the working state of the model under the condition of distributed clean power generation in different proportions, a PV/WT installed capacity interval of 200-1000kW and a step size of 200kW in the model are selected for simulation, and carbon emission parameters and the electricity abandonment condition of the model are shown in Table 3.
TABLE 3 partial parameters of different clean energy power generation installed capacities
| Index (I) | 200kW | 400kW | 600kW | 800kW | 1000kW |
| Cost/dollar for electricity purchasing carbon emission | 930.6 | 463.9 | 0 | 0 | 0 |
| Cost/dollar in carbon emission from gas purchase | 894.6 | 739.3 | 670.1 | 589.2 | 503.8 |
| Total carbon emission cost/dollar | 1825.2 | 1203.2 | 670.1 | 589.2 | 503.8 |
| Wind curtailment charge/dollar | 1.6 | 3.0 | 4.6 | 6.8 | 9.3 |
| Light abandon punishment charge/element | 0.8 | 2.0 | 2.4 | 3.5 | 5.8 |
| Total cost/element | 25363 | 21469 | 17798 | 14565 | 11827 |
As can be seen from table 3, as the installed capacity of clean distributed power generation increases, the total operating cost of the system tends to decrease greatly, which is mainly due to the decrease of the total energy purchase amount and the reduction of carbon emission cost. The cost of the electric carbon emission can be ensured to be 0 when the clean energy installed machine exceeds 600kW, but the cost of the gas carbon emission is slowly reduced after that, which is caused by the limited capacity of the related device for converting electricity into gas. In order to better absorb the higher proportion of clean energy access, the installed capacity of the coupling device also needs to be improved to a certain extent. Meanwhile, the wind and light abandoning power and punishment cost in the model are far less than the reduction of the total cost, which shows that the model has good clean energy consumption capability.
(4) Analysis of adjustable thermoelectric ratio and natural gas-hydrogen mixed utilization benefit
In order to analyze the benefit of GT adjustable electric heating ratio and GT and GB gas-hydrogen mixing, three variables of 'natural gas price is high and low', 'whether the electric heating ratio can be adjusted' and 'whether the natural gas-hydrogen mixing' are selected to carry out 16 groups of control variable contrast simulation, wherein the gas price is set to be four grades of low, medium, high and ultrahigh according to unit prices of 0.35, 0.6, 0.8 and 1.4 yuan/kWh, and four operation conditions of adjustable mixability, adjustable unmixability, non-adjustable mixability and non-adjustable mixability are divided according to whether the electric heating ratio is adjustable and whether the natural gas-hydrogen mixing is available. The simulation results are shown in tables 4 and 5.
TABLE 4 economic cost results of the 16 group comparison simulations
| Gas price | Case 1/ | Case | 2/yuan | Case 3/Yuan | Case 4/Yuan |
| Low gas price | 20774 | 20928 | 21248 | 21380 | |
| Price of middle gas | 27195 | 27215 | 27325 | 27369 | |
| High gas price | 31553 | 31562 | 31563 | 31594 | |
| Extra high gas price | 41286 | 41690 | 41323 | 41970 |
Table 5 carbon emissions results of 16-group comparison simulations
| Gas price | Case 1/ | Case | 2/kg | Case 3/kg | Case 4/kg |
| Low gas price | 27786 | 27787 | 28034 | 28034 | |
| Price of middle gas | 28714 | 28714 | 28737 | 28737 | |
| High gas price | 29633 | 29283 | 29684 | 29334 | |
| Extra high gas price | 30829 | 30207 | 30829 | 30207 |
As can be seen from table 4, the system operating cost of the operating condition 1 is the least at the same gas price, which indicates that the scheme considering the adjustable thermoelectric ratio and considering the natural gas-hydrogen mixture has the most general economical efficiency. And when the natural gas price is lower, the situation 2 is more economical than the situation 3, and the lower the gas price is, the more obvious the GT thermoelectric ratio variation degree is, the larger the time interval range is, which shows that the adjustable thermoelectric ratio of the GT can more play the economic role under the lower gas price. On the contrary, under the condition of high gas price, the electricity has price advantage, so that hydrogen generated by EL is more involved in the electricity generation and heat generation process of the substitute natural gas, the consumption of the high natural gas is reduced, and the economy of a natural gas-hydrogen mixing strategy is exerted.
As can be seen from Table 5, the GT thermoelectric ratio adjustable performance is good to reduce carbon emission under medium and low gas prices, while the natural gas-hydrogen mixing strategy has little carbon emission reduction effect due to low gas prices and insufficient economy. The electric energy at high gas price replaces natural gas more to supply heat energy, so that the total carbon emission of the system is increased, the adjusting effect of the GT thermoelectric ratio is not obvious, and the natural gas-hydrogen mixing strategy is used, so that the electric energy is converted into hydrogen energy to participate in GT and GB work, so that the total economic cost is reduced.
The invention provides a multi-time scale optimization operation strategy considering electricity-gas-heat-hydrogen demand response and a stepped carbon emission cost mechanism, comprehensively considers the stepped carbon emission cost mechanism, introduces hydrogen load demand, optimizes the working characteristics of coupling equipment, and establishes a multi-time scale optimization model of three stages of rolling-real time in day-ahead. The method takes the minimum integral operation cost of IES, the minimum carbon emission cost and the minimum wind and light abandoning cost as economic targets, converts the original problem into a mixed integer linear problem, calls a commercial solver to solve, and verifies the feasibility of the proposed strategy by comparing and distributing various situation optimization results.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A multi-time scale operation method of an integrated energy system, in particular to a multi-time scale optimization operation method considering electricity-gas-heat-hydrogen demand response and a stepped carbon emission cost mechanism, which is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a comprehensive energy system low-carbon economic optimization framework considering electricity-gas-heat-hydrogen requirements;
and 2, step: a multi-time scale optimization plan that takes into account operating characteristics of the IES elements;
and 3, step 3: establishing a day-ahead optimization model;
and 4, step 4: establishing an intra-day rolling optimization model;
and 5: and (5) establishing a real-time optimization model.
2. The integrated energy system multi-timescale operation method of claim 1, further comprising: the step 1 further comprises the following steps:
1) The adjustable thermoelectric ratio and the natural gas-hydrogen mixed combustion characteristic are as follows: according to the electricity price and the gas price at different time periods, the proportion of the heating power and the generating power in the working process is adjusted;
2) An electric-to-gas link decoupling operation process and coupling equipment modeling: by decoupling the traditional P2G process, the hydrogen energy utilization scene is enlarged;
3) A stepped carbon cost and carbon sequestration gain model: for the carbon sequestration benefit of the MR, subsidies can be made on the MR and the subsidies are included in an IES economic cost model as economic benefits;
4) Step-type carbon cost metering model: and adopting a step-type carbon emission tax system, dividing a plurality of carbon emission intervals, and differentiating and pricing according to the intervals aiming at consumption behaviors in a certain time.
3. The integrated energy system multi-time scale operation method of claim 1, characterized by: the step 2 further comprises the following steps:
(1) day-ahead optimization: the time step is 1h, and the execution period is 24h. In the stage, a working plan and an A-type IDR load calling plan of the coupling equipment need to be determined;
(2) rolling optimization in a day: the time step is 15min, and the execution period is 4h. In the stage, an output plan of distributed power generation, a standby output plan of coupling equipment and a calling plan of B-type IDR need to be formulated so as to correct the deviation of a day-ahead optimization plan;
(3) and (3) real-time optimization: the execution period is 5min; in the stage, working states of various energy storage devices and C-type and D-type IDR calling quantities need to be formulated, and finally, the electricity purchasing quantity and the gas purchasing quantity of the upper-level distribution power grid and the natural gas grid are determined.
4. The integrated energy system multi-time scale operation method of claim 3, characterized by:
the operating characteristics of the IES element include the following:
(1) the flexibility of the coupling equipment is not as good as that of the energy storage equipment, but the standby regulation capacity of the coupling equipment has a certain regulation function;
(2) the distributed power supply can quickly abandon wind and light within the upper limit range of output, the output of the distributed power supply has adjustability, but uncertainty and volatility of the output;
(3) the energy storage element has the advantages of flexible arrangement and quick adjustment, but the energy storage capacity has certain limitation, and the energy storage element is suitable for quickly adjusting a supply and demand curve which fluctuates randomly in the IES.
5. The integrated energy system multi-timescale operation method of claim 1, further comprising: the step 3 further comprises the following steps:
1) Objective function
On the basis of the minimum total system operation cost, the objective function of the day-ahead optimization model converts the wind curtailment quantity and the load loss quantity into penalty cost to be added into the system operation cost, and considers the energy purchasing carbon cost and the carbon fixing income, and the model is expressed as follows:
in the formula: f. of 1 An objective function for a day-ahead optimization model, representing the operating cost of the IES; f. of buy (t)、f sto (t)、f cp1 (t)、f DG (t)、f load (t) cost functions of upper-level energy purchasing, energy storage devices, coupling equipment, distributed power generation and user load management at the moment t are respectively; n is a radical of hydrogen s,ahead Considering the number of scenes for the pre-optimization model; p is a radical of s,ahead The probability coefficient of occurrence of the s scene in the optimization process before the day; p e/g,buy,s (t) power for purchasing power and gas from the upper level at the time of the s-th scene t; c. C e/g The unit electricity and gas purchasing cost; p is e/g/h/H2,sto,s (t) is the charge-discharge energy power of electricity/gas/heat/hydrogen stored at s scene t moment; c (P) e/g/h/H2,sto,s (t)) is an energy storage device cost function;
W(P e/g/h/H2,sto,s (t)) is a maintenance cost function of the energy storage device; p PV/WT,s (t) photovoltaic and fan in s field in distributed power generationOutput at scene t moment; k is a radical of formula c,PV/WT (t) punishing a cost coefficient for abandoning wind and abandoning light;
the predicted output of photovoltaic and fan in distributed power generation at s scene t moment; c (P) PV/WT,s (t)) is a cost function of distributed generation at time t of scenario s; n is an element { e, g, H, H2} which is a variable that n refers to four types of loads of electricity/gas/heat/hydrogen, and is used for simplifying the space of the formula description; k is a radical of n,IDR,A 、k n,IDR,B Respectively the cost coefficients of A and B IDRs of various loads; | VP n,IDR,A,s (t)|、|VP n,IDR,B,s (t) | is the calling amount of A and B IDRs of various loads at t moment of s scene respectively; k is a radical of c,load,n Penalty coefficient for each type of load missing; p loss,n,s And (t) is the loss of various loads at the time of s scene t.
6. The integrated energy system multi-time scale operation method of claim 1, characterized by: the step 3 further comprises the following constraint conditions:
(1) power balance constraint
Electric power balance constraint:
natural gas balance constraint:
thermal power balance constraint:
hydrogen energy balance constraint:
in the formula:
respectively the rated power of external charging and discharging energy of electricity/gas/heat/hydrogen stored at the moment of s scene t; />
For the expected electrical/gas/heat/hydrogen load in the optimization model at time t;
(2) distributed generation output constraints
The distributed generation output value is smaller than the predicted value;
(3) energy storage device operational constraints
The energy storage device is mainly constrained by the charging and discharging energy power and the energy storage state during operation. Because of the consistency of the electric/gas/heat/hydrogen storage and transportation constraints, the electric storage is taken as an example for explanation:
in the formula:
respectively charging and discharging the electricity storage; />
Capacity for electricity storage; s e,s (t)、/>
Respectively representing the energy storage-capacity ratio state of the electricity storage at the time of s scene t and the upper limit and the lower limit of the energy storage-capacity ratio state. The energy storage-capacity ratio states of the energy storage device are equal at the beginning and the end in an optimization period T;
(1) adjusting constraints for each scene
|P machine,s (t)-P machine,bs (t)|≤ψ machine (9)
In the formula: p machine,s (t) giving out force values for the reference scenes of various types of coupling equipment; p machine,bs (t) is a force output value of each type of coupling equipment in the s scene; psi machine Setting the adjustment margin of various coupling devices as 10% of the maximum output of the devices;
(2) constraints on various types of DR resources
In the formula:
the load quantity is respectively increased for IDRs of A type and B type of loads;
7. The integrated energy system multi-time scale operation method of claim 6, wherein: the step 4 further comprises the following steps:
1) Objective function
The objective function of rolling optimization in the day is the minimum total operating cost of the IES, the adjustable standby capacity output of the coupled equipment and the call volume cost of IDR type load, the IDR parameter in the A type is determined, and the total load cost is the sum of IDR in the B type and the IDR in the C type; the objective function is of the form:
in the formula: n is a radical of s,dayin Considering the number of scenes for the optimization model in the day; p is a radical of formula s,dayin The probability coefficient of occurrence of the s scene in the optimization process in the day; k is a radical of n,IDR,C Cost factor for a class C IDR for a class of loads; | VP n,IDR,C,s (t) | is the calling quantity of a certain load C type IDR at the t moment of s scene;
2) Constraint conditions
Coupling device spare capacity output constraint:
in the formula: VP machin,s And (t) is a standby output value of various coupling devices. The formula indicates that the reserve capacity output value of each type of coupling equipment meets the upper and lower limits, and the sum of the reserve capacity output and the day-ahead output plan meets the capacity limit.
8. The integrated energy system multi-time scale operation method of claim 1, characterized by: the step 5 further comprises the following steps:
1) Objective function
Basically the same as the rolling optimization in the day, the objective function of the real-time optimization is the minimum of the total cost of the running of the IES, the variable is the call volume cost of the load only in the IDR class, specifically the sum of the IDR class C and the IDR class D, and the objective function is in the form as follows:
2) Constraint conditions
Electric power balance constraint:
natural gas balance constraint:
thermal power balance constraint:
hydrogen energy balance constraint:
in the formula: pr { g } is a confidence expression;
confidence levels are electrical/gas/thermal/hydrogen power balance.
And (3) distributed generation output constraint:
in the formula:
respectively optimizing the actual upper limit of the photovoltaic output and the actual upper limit of the fan in real time; />
Respectively are the output plans of the photovoltaic and the fan obtained in the rolling optimization in the day. The constraint means that the output of distributed generation in real-time optimization cannot exceed the real-time maximum output upper limit under the restriction of practical conditions on the basis of rolling optimization values in the extended days.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the method of any of claims 1 to 8.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of any one of claims 1 to 8.
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