CN115860413A - Grid-connected combined cooling heating and power micro-grid system economic scheduling method based on load demand response and double-layer adjustable robust optimization - Google Patents
Grid-connected combined cooling heating and power micro-grid system economic scheduling method based on load demand response and double-layer adjustable robust optimization Download PDFInfo
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Abstract
The invention discloses a combined cooling heating and power type microgrid optimization scheduling method based on load demand response and double-layer adjustable robust optimization. Firstly, modeling is carried out on the micro-grid system by using a load demand response strategy. Then, with the lowest system operation cost under the worst operation condition of renewable energy sources and loads as an objective function, decomposing the double-layer adjustable robust optimization model into two linear models according to a strong dual theory, and solving and formulating the optimal economic dispatching strategy of the system by using a column constraint generation algorithm. In addition, by selecting a proper robust adjusting coefficient, the robustness of the dispatching plan can be properly adjusted, and the economy and the safety of system operation are balanced. The method provided by the invention can effectively improve the energy utilization rate of the system and reduce the economic cost of system operation while reducing the influence of renewable energy and load uncertainty.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to an economic scheduling method of a grid-connected combined cooling heating and power microgrid system based on load demand response and double-layer adjustable robust optimization.
Background
With the gradual depletion of resources such as petroleum and coal, development and utilization of renewable energy sources are very concerned in all countries at present. The grid-connected combined cooling heating and power micro-grid system containing renewable energy has the advantages of high energy utilization rate, high power supply reliability and the like, so that the grid-connected combined cooling, heating and power micro-grid system has received wide attention. The microgrid system integrates distributed power supplies, energy storage and loads, not only provides various energy supplies for load demands, but also is one of effective means for improving the permeability of renewable energy. For economic dispatch of such systems, the systems are often modeled as traditional deterministic optimization models. However, the prediction error of renewable energy and load presents a challenge to the economic and safe operation of the system. Therefore, to ensure the economic operation of such systems, the effects of the uncertainty in renewable energy and load must be minimized.
Disclosure of Invention
Aiming at the problems, the invention provides the economic dispatching method of the grid-connected cooling heating power cogeneration microgrid system based on load demand response and double-layer adjustable robust optimization, and the method can reduce the uncertain influence of renewable energy sources and loads, improve the energy utilization efficiency of the system, effectively reduce the operation cost of the system and improve the operation stability of the system.
The invention adopts the following technical scheme:
a grid-connected combined cooling heating and power microgrid system economic dispatching method based on load demand response and double-layer adjustable robust optimization comprises the following specific design scheme:
step (1) modeling a grid-connected combined cooling heating and power microgrid system;
step (2) optimizing the electric load by adopting load demand response and obtaining time-of-use electricity price data;
step (3) establishing a double-layer adjustable robust optimization model and decomposing the model into two linear models;
and (4) carrying out iterative solution by using a column constraint generation algorithm.
Further, the combined cooling heating and power microgrid system connected to the power grid in the step (1) comprises a renewable energy system, energy storage equipment, heat storage equipment, a micro gas turbine, a waste heat recovery device, an electric boiler, a gas boiler, an absorption refrigerator and an electric refrigerator, wherein the waste heat recovery device, the electric boiler, the absorption refrigerator and the electric refrigerator are modeled as follows:
a waste heat recovery device: the waste heat recovery device can recover waste heat gas generated by the micro gas turbine during power generation so as to supply heat load requirements.
Wherein eta is mt 、η loss 、COP mt And
respectively representing the generating efficiency, the loss rate, the performance coefficient and the generating power of the micro gas turbine; />
And η hr Respectively the heating power and the heating efficiency of the waste heat recovery device;
an electric boiler: an electric boiler can convert electrical energy into thermal energy to supply thermal load demand.
Wherein eta is ed 、COP eb 、
And &>
Respectively the efficiency, the performance coefficient, the heating power and the power consumption of the electric boiler;
an electric refrigerator: the electric chiller converts electrical energy into cooling energy to supply the cooling load demand.
Wherein the COP ec 、
And &>
The performance coefficient, the refrigeration power and the power consumption of the electric refrigerator are respectively;
absorption refrigerator: absorption chillers can absorb thermal energy and convert it to cooling energy to supply the cooling load demand.
Wherein the COP ac 、
And &>
Are respectively provided withThe performance coefficient, the refrigeration power and the heat consumption power of the absorption refrigerator;
further, in the step (2), the electric load is optimized by adopting load demand response. The method comprises the following steps:
firstly, introducing a membership function to express the characteristics of the electrical load, and dividing the peak-valley level time period of the electrical load according to a fuzzy clustering analysis method:
wherein the content of the first and second substances,
for electrical loading before optimization, P old,min And P old,max In order to optimize the minimum and maximum values of the pre-electrical load,
and &>
Is the membership function of the electric load in the peak and valley time periods.
Then, obtaining a transfer closure matrix R by an absolute value subtraction method λ And taking the classification tree lambda =3 to obtain peak-valley bisection classification time period data of the power load.
And then, establishing a multi-objective optimization model by using an objective function with the minimum peak-to-valley difference value of the electric load and the maximum comprehensive satisfaction degree of electricity utilization, and solving by using a multi-objective genetic algorithm to obtain the time-of-use electricity price. The multi-objective optimization model is as follows:
wherein q is i Represents three of peak (f), valley (g) and plateau (p)Load at seed time, p i,min And p i,max The minimum value and the maximum value of the electricity price in three time periods, P new,min And P new,max Is the minimum and maximum values of the optimized electrical load, S l And S p Is the user habit change satisfaction and the user expense satisfaction, S l,min ,S l,max ,S p,min And S p,max Is the minimum and maximum values of the user habit change satisfaction and the minimum and maximum values of the user expense satisfaction, lambda l And λ p Is the weight coefficient, Δ q i ,Δp i ,p i The load variation, the electricity price variation and the electricity price data in the peak-valley normal period.
And finally, establishing an electric load response electricity price model according to the time-of-use electricity price data by introducing the demand elasticity coefficient to obtain the optimized electric load. The model specifically comprises:
wherein p is 0 Is the initial electricity price.
Further, the two-layer robust optimization model in the step (3) specifically includes:
wherein c is a coefficient matrix, y is a matrix representing the output power of the equipment at the source side of the system, T represents a matrix transpose, u is a matrix describing the interaction state of the energy storage system and the interaction state of the system and the power grid,
for uncertainty variables describing renewable energy and load, based on the evaluation of the load and the evaluation of the load>
Is an uncertainty set.
Y i ＝{P mt ,P dis ,P chr ,P grid ,P excess ,P ec, P eb ,H b
H dis ,H chr ,H ac ,P pv ,P wt ,P load ,H h ,Q c }
U i ＝{U bat,dis ,U grid ,U tst,dis }
Wherein, P mt 、P dis 、P chr 、P grid 、P excess 、P ec 、P eb 、H b 、H dis 、H chr 、H ac 、P pv 、P wt 、P load 、H h And Q c Respectively representing the power generation power of the micro gas turbine, the charge and discharge power of the electric energy storage system, the power purchased and sold to the power grid, the electric refrigeration consumed power, the electric boiler consumed power, the boiler heating power, the heat storage system charge and discharge power, the absorption refrigerator consumed heat power, the photovoltaic power generation power, the wind power generation power and three loads of electricity, heat and cold, U bat,dis 、U grid And U tst,dis Representing discharge state of electric energy storage system, state of purchasing electricity from power grid and heat storageThe system can release heat;
the double-layer robust optimization model is divided into an outer-layer min structure model and an inner-layer max-min structure model, wherein the inner-layer model is based on a strong dual theory, the min structure is converted into a max structure, then two max structures are combined, and the model is linearized, and the inner-layer and outer-layer models are specifically structured as follows:
an outer layer model:
where c, D, K, F, G, I, D, and h are coefficient matrices. The outer model takes the minimized system operation cost as an objective function and takes u as an optimization variable.
Inner layer model:
wherein, gamma, lambda, v, pi are dual variables, O 120 Is a 120X 1 zero matrix, E 120 Is an identity matrix of order 120 and,
is the upper bound of pi when>
When the model is large enough, the inner layer model is a linear model, and the operation complexity of the algorithm is reduced. And x and deltax are prediction data of renewable energy sources and loads in a deterministic optimization model and the maximum deviation value under the condition of severe operation of the system. Gamma-shaped i Is the robust adjustment coefficient; b is a diagonal matrix with 0-1 state variables as elements, each element represents whether the renewable energy and the load can be taken to the maximum value of the interval with the uncertainty concentrated in the corresponding time period, and the form of B is as follows:
B i ＝{B pv ,B wt ,,B load ,B h ,B c ,}
wherein, B pv 、B wt 、B load 、B h And B c And the matrix is used for judging the severe condition of the uncertainty factor and is respectively used for judging whether the photovoltaic output, the wind power output and the three loads of electricity, heat and cold reach an interval boundary value with concentrated uncertainty.
The severe operation condition of the system is as follows:
X i ＝{P pv ,P wt ,P load ,H h ,,Q c ,}
further, the step (3) of adjusting the adjustable robust optimization model means that the double-layer robust optimization model introduces a robust adjustment coefficient Γ for adjusting the robustness of the system model to balance the economy and stability of system operation, and specifically includes:
Γ i ＝{Γ pv ,Γ wt ,Γ load ,Γ h ,Γ c }
wherein, gamma is a robust adjusting coefficient and takes the value of [1,24]. When the value of Γ is larger, the robustness of the system is stronger, and when the value of Γ is 0, the robust model is equivalent to a traditional deterministic optimization model.
Further, the column constraint generation algorithm and the solving step thereof in the step (4) are specifically as follows:
the main idea of the column constraint generation algorithm is as follows: the column constraint generation algorithm decomposes the original problem into two independent and coupled sub-problems, transmits the coupling parameters of the two sub-problems in the solving process, and continuously iterates and alternately solves to finally obtain the optimal solution of the original problem.
Solving a column constraint generation algorithm:
step (4.1): setting the upper and lower bounds of the day-ahead operation cost as UB = + ∞ and LB =0 respectively, setting the iteration times as k =1 and epsilon =5, and giving the initial severe operation condition of renewable energy and load demand
Step (4.2): bad operation condition of input renewable energy and load demand
Solving the outer layer optimization model to obtain the economic optimal planned output y of each unit of the system k Energy storage equipment state, system and power grid interaction state u k And minimum operating cost LB of the system k The lower bound of the updated running cost is LB = LB k 。
Step (4.3): u obtained in step (4.2) is input k Solving an inner layer optimization model, checking whether the energy storage equipment, the system and the power grid interaction equipment can cope with the fluctuation of the uncertainty variable, and obtaining the uncertainty variable under the new system severe operation condition
And minimum operating cost UB of the system k And updating the upper bound UB = min { UB, UB } of the running cost k }。
Step (4.4): judging whether the condition UB-LB is equal to or less than epsilon or not, if so, stopping iteration and returningScheduling plan y back to optimal day ahead k (ii) a Otherwise, the optimization model of the inner layer obtained in the step (4.3) is used for obtaining
Inputting the data into an outer optimization model, enabling k = k +1, and jumping to the step (4.2) to continue iterative optimization until the algorithm converges.
Compared with the prior art, the invention has the advantages and positive effects that:
(1) The invention establishes a double-layer robust optimization model with load demand response to solve the problem of uncertainty of renewable energy and load in a grid-connected combined cooling heating and power microgrid system.
(2) The invention introduces a robust adjustment coefficient, adjusts the robustness of the scheduling plan and improves the economic benefit of system operation.
(3) The invention optimizes the system electric load through load demand response, and forms reasonable electricity utilization guide for users, thereby reducing the peak-valley difference of the electric load and improving the reliability of system operation.
Drawings
FIG. 1 is a structural diagram of a grid-connected cooling, heating and power cogeneration microgrid system in an embodiment of the invention;
FIG. 2 is a flow chart of a system scheduling policy in an embodiment of the present invention;
FIG. 3 is a diagram illustrating the classification results of load demand responses in accordance with an embodiment of the present invention;
FIG. 4 is a graph of load demand response pareto results in accordance with an embodiment of the present invention;
fig. 5 is a graph comparing electrical loads before and after optimization in an example of the present invention.
Detailed Description
The invention is further clarified by combining a specific implementation case and the attached drawings, the invention provides a grid-connected cooling heating power cogeneration microgrid system economic dispatching method based on load demand response and double-layer adjustable robust optimization, the grid-connected cooling heating power cogeneration microgrid system structure is shown in figure 1, a system dispatching strategy is established for improving the economic operation benefit of the system, the system dispatching strategy flow is shown in figure 2, and the specific implementation steps are as follows:
(1) Modeling for grid-connected combined cooling heating and power microgrid system
The structure diagram of the grid-connected cooling, heating and power cogeneration microgrid system is shown in fig. 1, and system components comprise a renewable energy system, energy storage equipment, heat storage equipment, a micro gas turbine, a waste heat recovery device, an electric boiler, a gas boiler, an absorption refrigerator and an electric refrigerator, and a plurality of key equipment are introduced below.
A waste heat recovery device: the waste heat recovery device can recover waste heat gas generated by the micro gas turbine during power generation so as to supply heat load requirements.
Wherein eta is mt 、η loss 、COP mt And
respectively representing the generating efficiency, the loss rate, the performance coefficient and the generating power of the micro gas turbine; />
And η hr Respectively the heating power and the heating efficiency of the waste heat recovery device;
an electric boiler: an electric boiler can convert electrical energy into thermal energy to supply thermal load demand.
Wherein eta is eb 、COP eb 、
And &>
Respectively the efficiency, the performance coefficient, the heating power and the power consumption of the electric boiler;
an electric refrigerator: the electric chiller converts electrical energy into cooling energy to supply the cooling load demand.
Wherein the COP ec 、
And &>
The performance coefficient, the refrigeration power and the power consumption of the electric refrigerator are respectively;
absorption refrigerator: absorption chillers can absorb thermal energy and convert it to cooling energy to supply the cooling load demand.
Wherein the COP ac 、
And &>
The coefficient of performance, the refrigeration power and the heat consumption power of the absorption refrigerator are respectively.
(2) Optimizing electrical loads and obtaining time-of-use electricity price data by adopting load demand response
The purpose of load demand response is to form reasonable power utilization guidance of a user, so that the peak-valley difference of the power load is reduced, and the running reliability of the system is improved:
firstly, introducing a membership function to express the characteristics of the electrical load, and dividing the peak-valley level time period of the electrical load according to a fuzzy clustering analysis method:
wherein the content of the first and second substances,
for electrical loading before optimization, P old,min And P old,max In order to optimize the minimum and maximum values of the pre-electrical load,
and/or>
Is the membership function of the electric load in the peak and valley time periods.
Then, obtaining a transfer closure matrix R by an absolute value subtraction method λ And taking the classification tree lambda =3 to obtain peak-valley bisection classification time period data of the power load.
And then, establishing a multi-objective optimization model by using an objective function with the minimum peak-to-valley difference value of the electric load and the maximum comprehensive satisfaction degree of electricity utilization, and solving by using a multi-objective genetic algorithm to obtain the time-of-use electricity price. The multi-objective optimization model is as follows:
wherein q is i Represents the load at three periods of peak (f), trough (g) and plateau (p), p i,min And p i,max The minimum value and the maximum value of the electricity price in three time periods, P new,min And P new,max Is the minimum and maximum values of the optimized electrical load, S l And S p Is the user habit change satisfaction and the user expense satisfaction, S l,min ,S l,max ,S p,min And S p,max Is the minimum and maximum values of the user habit change satisfaction and the minimum and maximum values of the user expense satisfaction, lambda l And λ p Is the weight coefficient, Δ q i ,Δp i ,p i Is the load variation, electricity price variation and electricity price number in the peak-valley normal periodAccordingly.
And finally, establishing an electric load response electricity price model according to the time-of-use electricity price data by introducing a demand elasticity coefficient, wherein the model specifically comprises the following steps:
wherein p is 0 Is the initial electricity price.
(3) Establishment of double-layer robust optimization model based on strong dual principle
Wherein c is a coefficient matrix, y is a matrix representing the output power of the equipment at the source side of the system, T represents a matrix transpose, u is a matrix describing the interaction state of the energy storage system and the interaction state of the system and the power grid,
for uncertainty variables describing renewable energy and load, based on the evaluation of the load and the evaluation of the load>
Is an uncertainty set.
Y i ＝{P mt ,P dis ,P chr ,P grid ,P excess ,P ec ,P eb ,H b
H dis ,H chr ,H ac ,P pv ,P wt ,P load ,H h ,Q c }
U i ＝{U bat,dis ,U grid ,U tst,dis }
Wherein, P mt 、P dis 、P chr 、P grid 、P excess 、P ec 、P eb 、H b 、H dis 、H chr 、H ac 、P pv 、P wt 、P load 、H h And Q c Respectively representing the power generation power of the micro gas turbine, the charge and discharge power of the electric energy storage system, the power purchased and sold to the power grid, the electric refrigeration consumed power, the electric boiler consumed power, the boiler heating power, the heat storage system charge and discharge power, the absorption refrigerator consumed heat power, the photovoltaic power generation power, the wind power generation power and three loads of electricity, heat and cold, U bat,dis 、U grid And U tst,dis The method comprises the following steps of representing the discharge state of an electric energy storage system, the state of purchasing electricity to a power grid and the heat release state of a heat energy storage system;
the double-layer robust optimization model is decomposed into an outer-layer min structure model and an inner-layer max-min structure model, wherein the inner-layer model is based on a strong dual theory, the min structure is converted into a max structure, then two max structures are combined, and the model is linearized, and the inner-layer and outer-layer models are specifically structured as follows:
an outer layer model:
where c, D, K, F, G, I, D, and h are coefficient matrices. The outer model takes the minimized system operation cost as an objective function and takes u as an optimization variable.
Inner layer model:
wherein, gamma, lambda, v, pi are dual variables, O 120 Is a 120X 1 zero matrix, E 120 Is an identity matrix of order 120 and,
is an upper bound of π when>
When the model is large enough, the inner layer model is a linear model, and the operation complexity of the algorithm is reduced. And x and deltax are prediction data of renewable energy sources and loads in a deterministic optimization model and the maximum deviation value under the condition of severe operation of the system. Gamma-shaped i Is the robust adjustment coefficient; b is a diagonal matrix with 0-1 state variables as elements, each element represents whether the renewable energy and the load can be taken to the maximum value of the interval with the uncertainty concentrated in the corresponding time period, and the form of B is as follows:
B i ＝{B pv ,B wt ,B load ,B h ,B c ,}
wherein, B pv 、B wt 、B load 、B h And B c The matrix is used for judging the severe condition of the uncertainty factor and is respectively used for judging whether the photovoltaic output, the wind power output and the three loads of electricity, heat and cold reach an interval boundary value with concentrated uncertainty;
the severe operation condition of the system is as follows:
X i ＝{P pv ,P wt ,P load ,H h ,,Q c ,}
(4) And establishing an adjustable robust optimization model, wherein the adjustable means that the double-layer robust optimization model introduces a robust adjusting coefficient gamma for adjusting the robustness of the system model so as to balance the economy and the stability of the system operation.
Γ i ＝{Γ pv ,Γ wt ,Γ load ,Γ h ,Γ c }
Wherein, gamma is a robust adjusting coefficient and takes the value of [1,24]. When the value of gamma is larger, the robustness of the system is stronger, and when the value of gamma is 0, the robust model is equivalent to a traditional deterministic optimization model.
(5) Solving the double-layer adjustable robust optimization model by using a running row constraint generation algorithm, which specifically comprises the following steps:
the main idea of the column constraint generation algorithm is as follows: the column constraint generation algorithm decomposes the original problem into two independent and coupled sub-problems, transmits the coupling parameters of the two sub-problems in the solving process, and continuously iterates and alternately solves to finally obtain the optimal solution of the original problem.
Solving the column constraint generation algorithm:
step 1: setting the upper and lower bounds of the day-ahead operation cost as UB = + ∞ and LB =0 respectively, setting the iteration times as k =1 and epsilon =5, and giving the initial severe operation condition of renewable energy and load demand
Step 2: bad operation condition of input renewable energy and load demand
Solving the outer layer optimization model to obtain the economic optimal planned output y of each unit of the system k Energy storage equipment state, system and power grid interaction state u k And minimum operating cost LB of the system k The lower bound of the updated running cost is LB = LB k 。
And step 3: inputting u obtained in step 2 k Solving an inner layer optimization model, checking whether the energy storage equipment, the system and the power grid interaction equipment can cope with the fluctuation of the uncertainty variable, and obtaining the uncertainty variable under the new system severe operation condition
And minimum operating cost UB of the system k And updating the upper bound UB = min { UB, UB } of the running cost k }。
And 4, step 4: judging whether the condition UB-LB is equal to or less than epsilon, if so, stopping iteration and returning to the optimal day-ahead scheduling plan y k (ii) a Otherwise, the optimization model of the inner layer obtained in the step 3 is used
Inputting the data into an outer optimization model, enabling k = k +1, and jumping to step 2 to continue iterative optimization until the algorithm converges.
In order to verify the effectiveness of the provided economic dispatching method for the combined cooling, heating and power microgrid system, the economic cost of the following five different operation strategies is contrastively analyzed.
a. Operation strategy 1: under a normal day-ahead prediction error, and with load-bearing demand response of gamma =6, and a double-layer adjustable robust optimization grid-connected cooling, heating and power cogeneration microgrid system.
b. Operation strategy 2: the grid-connected cooling, heating and power cogeneration microgrid system is under a small day-ahead prediction error and has load-bearing demand response of gamma =6 and double-layer adjustable robust optimization.
c. Operation strategy 3: under a large day-ahead prediction error, and with load-bearing demand response of gamma =6, and a double-layer adjustable robust optimization grid-connected cooling, heating and power cogeneration microgrid system.
d. Operation strategy 4: the grid-connected cooling, heating and power cogeneration microgrid system is under a small day-ahead prediction error and has load-bearing demand response of gamma =3 and double-layer adjustable robust optimization.
e. Operation strategy 5: under the normal day-ahead prediction error, the grid-connected cooling, heating and power cogeneration microgrid system containing load demand response and traditional deterministic optimization.
First, a classification process for classifying the electric loads by the fuzzy clustering analysis is shown in fig. 3. As can be seen from fig. 3, the classification number is 3, and the classification information of the electrical load of the system can be obtained, and is shown in table 1.
And then, time-of-use electricity price data is obtained by solving the multi-target load demand response model. The pareto chart obtained after the solution is shown in fig. 4. The time-of-use electricity rate results obtained from the results shown in table 2 are shown as point a in fig. 4.
Finally, a curve after optimization is obtained by introducing the required elastic coefficient to solve, and a comparison schematic diagram of the electrical loads before and after optimization is shown in fig. 5. It can be seen from fig. 5 that load demand response is adopted to implement peak clipping and valley filling on the initial electrical load of the user, thereby improving the operation stability of the system.
Table 3 shows the system operating costs under five operating strategies, where the real-time operating cost is an important index for measuring the economic operating efficiency of the system, and the more the planning cost and the adjustment cost in the past are, the more the robustness level of the system is illustrated.
As can be seen from table 3, comparing operation strategy 1 and operation strategy 5, under the same prediction error, the system with two-layer adjustable robust optimization has a 6.16% reduction in real-time operation cost compared to the conventional deterministic optimization system. Under the same scheduling strategy, the optimization effect of the double-layer adjustable robust optimization strategy is gradually improved along with the improvement of the prediction precision. However, when the prediction error is small, the adjustment cost of the system is negative, which indicates that the robustness of the system is too high at this time, and the robust adjustment coefficient needs to be adjusted to reduce the robustness of the system. Therefore, the robust adjustment coefficient of the system with smaller prediction error is reduced to 3, and an operation strategy 4 is obtained. As can be seen from table 3, the situation that the day-ahead planning cost of the operation strategy 4 is greatly reduced compared to the situation that the day-ahead planning cost of the operation strategy 2 is reduced indicates that the robustness of the system is reduced at this time, and the real-time operation cost at this time is also lower than the real-time operation cost of the operation strategy 2, which indicates that the economic benefit of the system is improved after the robust adjustment coefficient is corrected.
In conclusion, the grid-connected combined cooling heating and power micro-grid system based on load demand response and double-layer adjustable robust optimization can effectively solve the problem of uncertainty of renewable energy and load, reduce the system operation cost and improve the economical efficiency and stability of system operation.
TABLE 1 electric load Peak-to-Valley time period Classification results
TABLE 2 time of use electricity price results
TABLE 3 comparison of operating costs of the system under five operating strategies
The above examples are intended to illustrate the present invention, but not to limit the present invention, and any modifications and changes made within the spirit of the present invention and the scope of the claims fall within the scope of the present invention.
Claims (6)
1. The combined cooling heating and power type microgrid optimization scheduling method based on load demand response and double-layer adjustable robust optimization is characterized in that: the method comprises the following steps:
step 1: modeling a combined cooling heating and power micro-grid system;
step 2: optimizing the electric load by adopting load demand response and obtaining time-of-use electricity price data;
and step 3: establishing a double-layer adjustable robust optimization model and decomposing the model into two linear models;
and 4, step 4: and (5) performing iterative solution by using a column constraint generation algorithm.
2. The cooling, heating and power cogeneration type microgrid optimization scheduling method based on load demand response and double-layer adjustable robust optimization is characterized in that: the intercooling heat and power cogeneration type microgrid device in the step 1 comprises a renewable energy source system, an energy storage device, a heat storage device, a micro gas turbine, a waste heat recovery device, an electric boiler, a gas boiler, an absorption refrigerator and an electric refrigerator, wherein the waste heat recovery device, the electric boiler, the absorption refrigerator and the electric refrigerator are modeled as follows:
a waste heat recovery device: the waste heat recovery device recovers waste heat gas generated by the micro gas turbine during power generation so as to supply heat load requirements;
wherein eta is mt 、η loss 、COP mt And
respectively representing the generating efficiency, the loss rate, the performance coefficient and the generating power of the micro gas turbine; />
And η hr Respectively the heating power and the heating efficiency of the waste heat recovery device;
an electric boiler: the electric boiler converts electric energy into heat energy to supply heat load demand;
wherein eta is eb 、COP eb 、
And &>
Respectively the efficiency, the performance coefficient, the heating power and the power consumption of the electric boiler;
an electric refrigerator: the electric refrigerator converts the electric energy into cooling energy to supply the cold load demand;
wherein the COP ec 、
And &>
The performance coefficient, the refrigeration power and the power consumption of the electric refrigerator are respectively;
absorption refrigerator: the absorption refrigerator absorbs heat energy and converts the heat energy into cooling energy to supply a cooling load demand;
wherein the COP ac 、
And &>
The coefficient of performance, the refrigeration power and the heat consumption power of the absorption refrigerator are respectively.
3. The cooling, heating and power cogeneration type microgrid optimization scheduling method based on load demand response and double-layer adjustable robust optimization is characterized in that: the step 2 of optimizing the electrical load by adopting load demand response specifically comprises the following steps:
step 2.1: introducing a membership function to represent the characteristics of the electric load, and dividing the peak-valley level time of the electric load according to a fuzzy clustering analysis method:
wherein the content of the first and second substances,
for electrical loading before optimization, P old,min And P old,max For optimizing the minimum and maximum values of the electrical load before it is exceeded, <' >>
And
is a membership function of the electric load in the peak and valley time periods; />
Step 2.2: obtaining a transfer closure matrix R by an absolute value subtraction method λ And taking the classification tree lambda =3 to obtain the power consumptionThe peak-valley of the load equally divides the time section data;
step 2.3: establishing a multi-objective optimization model by using an objective function with the minimum peak-to-valley difference value of the electric load and the maximum comprehensive satisfaction degree of electricity utilization, and solving by using a multi-objective genetic algorithm to obtain a time-of-use electricity price; the multi-objective optimization model is as follows:
min(P new，max -P new，min ) 2 -(λ 1 S 1 +λ p S p )
wherein q is i Represents the load at three periods of peak (f), valley (g) and plateau (p), p i,min And p i,max The minimum value and the maximum value of the electricity price in three time periods, P new,min And P new,max Is the minimum and maximum values of the optimized electrical load, S l And S p Is the user habit change satisfaction and the user expense satisfaction, S l,min ,S l,max ,S p,min And S p,max Is the minimum and maximum values of the user habit change satisfaction and the minimum and maximum values of the user expense satisfaction, lambda l And λ p Is the weight coefficient, Δ q i ,Δp i ,p i The load variation, the electricity price variation and the electricity price data in the peak-valley normal time period;
step 2.4: establishing an electric load response electricity price model according to the time-of-use electricity price data by introducing a demand elasticity coefficient to obtain an optimized electric load; the electric load response electricity price model specifically comprises the following steps:
wherein p is 0 Is the initial electricity price.
4. The cooling, heating and power cogeneration type microgrid optimization scheduling method based on load demand response and double-layer adjustable robust optimization is characterized in that: the double-layer robust optimization model in the step 3 is specifically as follows:
wherein c is a coefficient matrix, y is a matrix representing the output power of the equipment at the source side of the system, T represents a matrix transpose, u is a matrix describing the interaction state of the energy storage system and the interaction state of the system and the power grid,
for uncertainty variables describing renewable energy and load, based on the evaluation of the load and the evaluation of the load>
Is an uncertainty set;
Y i ＝{P mt ，P dis ，P chr ，P grid ，P excess ，P ec ，P eb ，H b H dis ，H chr ，H ac ，P pv ，V wt ，P load ，H h ,Q c }
U i ＝{U bat，dis ，U grid ，U tst，dis }
wherein, P mt 、P dis 、P chr 、P grid 、P excess 、P ec 、P eb 、H b 、H dis 、H chr 、H ac 、P pv 、P wt 、P load 、H h And Q c Respectively representing the power generation power of the micro gas turbine, the charge and discharge power of the electric energy storage system, the power purchased and sold to the power grid, the electric refrigeration consumed power, the electric boiler consumed power, the boiler heating power, the heat storage system charge and discharge power, the absorption refrigerator consumed heat power, the photovoltaic power generation power, the wind power generation power and three loads of electricity, heat and cold, U bat,dis 、U grid And U tst,dis Showing the discharge state of the electric energy storage system, the state of purchasing electricity to the power grid and the heat release state of the heat energy storage system,
and
representing the fluctuation range of three loads of renewable energy output and cold, heat and electricity of the system;
the double-layer robust optimization model is divided into an outer-layer min structure model and an inner-layer max-min structure model, wherein the inner-layer model is based on a strong dual theory, the min structure is converted into a max structure, then two max structures are combined, and the model is linearized, and the inner-layer and outer-layer models are specifically structured as follows:
an outer layer model:
wherein D, K, F, G, I, D and h are coefficient matrices; the outer layer model takes the minimized system operation cost as an objective function and takes u as an optimized variable;
inner layer model:
wherein, gamma, lambda, v, pi are dual variables, O 120 Is a 120X 1 zero matrix, E 120 Is an identity matrix of order 120 and,
is the upper bound of pi when>
When the model is large enough, the inner layer model is a linear model, and x and delta x are prediction data of renewable energy sources and loads in a deterministic optimization model and the maximum deviation value of the system under severe operation conditions; gamma-shaped i Is the robust adjustment coefficient; b is a diagonal matrix with 0-1 state variables as elements, each element represents whether the renewable energy and the load can be taken to the maximum value of the interval with the uncertainty concentrated in the corresponding time period, and the form of B is as follows:
B i ＝{B pv ,B wt ,B load ，B h ,B c ,}
wherein, B pv 、B wt 、B load 、B h And B c The matrix is used for judging the severe condition of the uncertainty factor and is respectively used for judging whether the photovoltaic output, the wind power output and the three loads of electricity, heat and cold reach an interval boundary value with concentrated uncertainty;
the severe operation condition of the system is as follows:
X i ＝{P pv ,P wt ,P load ,H h ,Q c ,}。
5. the grid-connected cooling, heating and power cogeneration microgrid system economic dispatching method based on load demand response and double-layer adjustable robust optimization is characterized in that: the step 3, in which the adjustable robust optimization model is adjustable means that the double-layer robust optimization model introduces a robust adjustment coefficient Γ for adjusting the robustness of the system model, which specifically includes:
Γ i ＝{Γ pv ，Γ wt ，Γ load ，Γ h ，Γ c }
when the value of gamma is larger, the robustness of the system is stronger, and when the value of gamma is 0, the robust model is equivalent to a traditional deterministic optimization model.
6. The grid-connected cooling, heating and power cogeneration microgrid system economic dispatching method based on load demand response and double-layer adjustable robust optimization of claim 1 is characterized in that: the column constraint generation algorithm and the solving step in the step 4 are specifically as follows:
step 4.1: setting the upper and lower bounds of the day-ahead operation cost as UB = + ∞ and LB =0 respectively, setting the iteration times as k =1 and epsilon =5, and giving the initial severe operation condition of renewable energy and load demand
Step 4.2: bad operation condition of input renewable energy and load demand
Solving the outer layer optimization model to obtain the economic optimal planned output y of each unit of the system k Energy storage equipment state, system and power grid interaction state u k And minimum operating cost LB of the system k The lower bound of the updated running cost is LB = LB k ；
Step 4.3: u obtained in step 4.2 is input k Solving an inner layer optimization model, checking whether the energy storage equipment, the system and the power grid interaction equipment can cope with the fluctuation of the uncertainty variable, and obtaining the uncertainty variable under the new system severe operation condition
And minimum operating cost UB of the system k And updating the upper bound UB = min { UB, UB } of the running cost k }；
Step 4.4:judging whether the condition UB-UB is less than or equal to epsilon or not, if so, stopping iteration and returning to the optimal day-ahead scheduling plan y k (ii) a Otherwise, the value obtained by the inner layer optimization model in the step 4.3 is used
Inputting the data into an outer optimization model, enabling k = k +1, and jumping to a step 4.2 to continue iterative optimization until the algorithm converges. />
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117060491A (en) * | 2023-10-11 | 2023-11-14 | 中国电建集团西北勘测设计研究院有限公司 | Operation optimization method, system, medium and equipment of wind-solar hybrid energy storage system |
| CN117455422A (en) * | 2023-12-26 | 2024-01-26 | 山东赛马力发电设备有限公司 | Thermal energy management system based on micro-grid |
| CN117674302A (en) * | 2024-02-01 | 2024-03-08 | 浙江省白马湖实验室有限公司 | Combined heat and power load scheduling method based on two-stage integrated learning |
| CN117674302B (en) * | 2024-02-01 | 2024-05-03 | 浙江省白马湖实验室有限公司 | Combined heat and power load scheduling method based on two-stage integrated learning |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117060491A (en) * | 2023-10-11 | 2023-11-14 | 中国电建集团西北勘测设计研究院有限公司 | Operation optimization method, system, medium and equipment of wind-solar hybrid energy storage system |
| CN117060491B (en) * | 2023-10-11 | 2024-01-30 | 中国电建集团西北勘测设计研究院有限公司 | Operation optimization method, system, medium and equipment of wind-solar hybrid energy storage system |
| CN117455422A (en) * | 2023-12-26 | 2024-01-26 | 山东赛马力发电设备有限公司 | Thermal energy management system based on micro-grid |
| CN117455422B (en) * | 2023-12-26 | 2024-03-08 | 山东赛马力发电设备有限公司 | Thermal energy management system based on micro-grid |
| CN117674302A (en) * | 2024-02-01 | 2024-03-08 | 浙江省白马湖实验室有限公司 | Combined heat and power load scheduling method based on two-stage integrated learning |
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