CN111969602A - Day-ahead random optimization scheduling method and device for comprehensive energy system - Google Patents

Day-ahead random optimization scheduling method and device for comprehensive energy system Download PDF

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CN111969602A
CN111969602A CN202010819359.7A CN202010819359A CN111969602A CN 111969602 A CN111969602 A CN 111969602A CN 202010819359 A CN202010819359 A CN 202010819359A CN 111969602 A CN111969602 A CN 111969602A
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CN111969602B (en
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李帆
张承慧
孙波
陈阿莲
张立志
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Shandong University
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Abstract

The invention belongs to the field of environmental protection and energy conservation, and provides a day-ahead random optimization scheduling method and device of a comprehensive energy system. The method comprises the steps of obtaining operation parameters of the comprehensive energy system, solving a day-ahead random optimization scheduling model based on a dynamic programming parallel optimization method, calculating an optimal energy storage scheme of an energy storage device by utilizing a plurality of CPU cores to execute a dynamic programming program in parallel, calculating an optimal output scheme of other equipment except the energy storage device by utilizing a GPU, and finally obtaining the day-ahead random optimization scheduling scheme and applying the day-ahead random optimization scheduling scheme to the comprehensive energy system; the day-ahead random optimization scheduling model comprises the following steps: the expected cost of operation of the integrated energy system is minimized under time series samples of sets of renewable energy and loads, taking into account prediction errors caused by random fluctuations in renewable energy and user loads.

Description

Day-ahead random optimization scheduling method and device for comprehensive energy system
Technical Field
The invention belongs to the field of environmental protection and energy conservation, and particularly relates to a day-ahead random optimization scheduling method and device of a comprehensive energy system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Under the dual pressure of energy and environmental crisis, the energy structure of China is turning to a green, low-carbon, safe and efficient sustainable energy supply and demand system. The multi-energy complementary comprehensive energy system has great advantages in improving the renewable energy ratio and the comprehensive energy utilization rate, becomes an important development direction of a new generation of energy technology, and is widely concerned by the industry. The advanced energy management technology is an important measure for ensuring the efficient and stable operation of the comprehensive energy system. However, renewable energy sources and loads have random fluctuation characteristics, so that great errors exist in day-ahead prediction data, and the formulation of an optimal scheduling scheme is seriously influenced. On the other hand, in the comprehensive energy system, the energy storage device has important functions of adjusting the system electric-heat ratio, stabilizing the renewable energy fluctuation and the like, but the problem of optimizing and scheduling is very complex, and the conventional method is difficult to obtain the optimal scheduling scheme and cannot exert the optimal performance of the system. The optimization scheduling problem of the comprehensive energy system becomes a key technical problem restricting the development of the comprehensive energy system.
The inventor finds that the optimal scheduling problem of renewable energy randomness and multi-dimensional energy storage is not comprehensively considered in the current optimal scheduling method of the comprehensive energy system, a targeted solution is not given, and an optimal scheduling scheme is difficult to obtain, so that the capacity efficiency of the comprehensive energy system is influenced.
Disclosure of Invention
In order to solve the problems, the invention provides a day-ahead random optimization scheduling method and a day-ahead random optimization scheduling device for an integrated energy system, which fully consider prediction errors caused by random fluctuation of renewable energy sources and user loads, establish a day-ahead random optimization scheduling model of the system, provide a parallel optimization method based on dynamic planning, and respectively design parallel computing tasks suitable for a CPU (central processing unit) and a GPU (graphics processing unit), thereby obtaining an optimal scheduling scheme of the system and improving the economy of the system.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a day-ahead random optimization scheduling method of an integrated energy system.
A day-ahead random optimization scheduling method of an integrated energy system comprises the following steps:
acquiring operation parameters of the comprehensive energy system, solving a day-ahead random optimization scheduling model based on a parallel optimization method of dynamic programming, calculating an optimal energy storage scheme of the energy storage device by utilizing a plurality of CPU cores to execute a dynamic programming program in parallel, calculating an optimal output scheme of other equipment except the energy storage device by utilizing a GPU, and finally obtaining the day-ahead random optimization scheduling scheme and applying the day-ahead random optimization scheduling scheme to the day-ahead random scheduling of the comprehensive energy system;
the day-ahead random optimization scheduling model comprises the following steps: the expected cost of operation of the integrated energy system is minimized under time series samples of sets of renewable energy and loads, taking into account prediction errors caused by random fluctuations in renewable energy and user loads.
The invention provides a day-ahead random optimization scheduling device of an integrated energy system.
A day-ahead random optimization scheduling device of an integrated energy system comprises:
the parameter acquisition module is used for acquiring the operation parameters of the comprehensive energy system;
the scheduling scheme solving module is used for solving a day-ahead random optimization scheduling model based on a dynamic programming parallel optimization method, calculating an optimal energy storage scheme of the energy storage device by utilizing a plurality of CPU cores to execute a dynamic programming program in parallel, calculating an optimal output scheme of other equipment except the energy storage device by utilizing a GPU, and finally obtaining the day-ahead random optimization scheduling scheme and applying the day-ahead random optimization scheduling scheme to the day-ahead random scheduling of the comprehensive energy system;
the day-ahead random optimization scheduling model comprises the following steps: the expected cost of operation of the integrated energy system is minimized under time series samples of sets of renewable energy and loads, taking into account prediction errors caused by random fluctuations in renewable energy and user loads.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for the stochastic optimization scheduling of energy systems of day ahead as described above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for the stochastic optimization scheduling of energy systems as described above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
in the comprehensive energy system, renewable energy and load have random fluctuation characteristics, so that large errors exist in day-ahead predicted data, the optimization scheduling problem of the multi-dimensional energy storage device is very complex, the conventional method is difficult to obtain the optimal scheduling scheme of the system, and the economic benefit of the system is seriously influenced.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic diagram of an integrated energy system according to an embodiment of the present invention;
fig. 2 is a flowchart of solving a day-ahead random optimization scheduling model based on a dynamic programming parallel optimization algorithm according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
The embodiment provides a day-ahead random optimization scheduling method of an integrated energy system, which comprises the following steps:
acquiring operation parameters of the comprehensive energy system, solving a day-ahead random optimization scheduling model based on a parallel optimization method of dynamic programming, calculating an optimal energy storage scheme of the energy storage device by utilizing a plurality of CPU cores to execute a dynamic programming program in parallel, calculating an optimal output scheme of other equipment except the energy storage device by utilizing a GPU, and finally obtaining the day-ahead random optimization scheduling scheme and applying the day-ahead random optimization scheduling scheme to the day-ahead random scheduling of the comprehensive energy system;
the day-ahead random optimization scheduling model comprises the following steps: the expected cost of operation of the integrated energy system is minimized under time series samples of sets of renewable energy and loads, taking into account prediction errors caused by random fluctuations in renewable energy and user loads.
The integrated energy system operating parameters of the embodiment include: the energy storage system comprises a gas purchase price, an electricity sale price, renewable energy power generation power, input/output power of an electricity storage device, an electric load, input electric power of an electric refrigerator, input/output power of a cold storage device, a cold load, power generation efficiency of a generator set, waste heat recovery efficiency, efficiency of an absorption refrigerator, efficiency of an electric refrigerator, energy retention coefficient and charge/discharge efficiency of the electricity storage device, rated charge/discharge power of the electricity storage device, rated stored energy of the electricity storage device, energy retention coefficient and storage/discharge efficiency of the cold storage device, rated stored energy of the cold storage device, and rated input/output power of the cold storage device.
The structure of the integrated energy system is shown in fig. 1. The system can supply power to users by utilizing photovoltaic power, wind power, a generator set and electric energy of a power grid; in summer, the waste heat of the generator set is used for driving the absorption refrigerator to refrigerate or the absorption refrigerator is used for refrigerating; the waste heat of the generator set and the gas boiler are directly utilized for heating in winter; the electricity storage, cold storage and heat storage devices can be used for stabilizing the fluctuation of renewable energy sources and adjusting the thermoelectric ratio of the system, and the operation efficiency of the system can be improved.
In the optimized scheduling of the integrated energy system, an operator wants to minimize the operation cost of the system on the premise of meeting the user load, and particularly, in the case of allowing electricity to be sold to a power grid, the economic efficiency is usually used as an evaluation index. On the other hand, renewable energy sources and user demands have random fluctuation, and the day-ahead prediction data has certain error compared with the actual data. In the optimization in the day ahead, the influence of the prediction error should be fully considered, and an optimized scheduling scheme of the system is formulated, so that the scheme has certain adaptability to different renewable energy sources and load data, namely, under the condition of source and load fluctuation, the system can still obtain a better optimized scheduling effect, and the phenomenon of sudden drop of the system performance is avoided. Thus, the objective function of the day-ahead random optimization schedule is defined as: with multiple sets of time series samples of renewable energy and load (multiple sets of source, load time series samples can be generated using the monte carlo method), the expected cost of system operation is minimized. The objective function of the day-ahead random optimization is:
Figure BDA0002633925200000051
wherein t ∈ [1,24 ]]Represents the tth period of 24 hours; p is a radical ofgFor purchase of gas price, peFor the purchase/sale of electricity, F (t) total amount of gas purchased by the system, EgridAnd (t) the electricity purchased/sold by the system.
The day-ahead random optimization needs to satisfy the balance constraints of cold, heat and electricity supply and demand, for example, the energy balance constraint in the summer operation mode is as follows:
Egrid+Eres+Fηpgu,e+αs,e＝Eload+Eec
F(1-ηpgu,e)ηrhCOPac+EecCOPec+αs,c＝Cload
wherein E isresGenerating power for a renewable energy source; alpha is alphas,eInput/output power for the electric storage device; eloadIs an electrical load; eecInputting electric power to the electric refrigerator; alpha is alphas,cIs the input/output power of the cold storage device; cloadIs a cold load; etapgu,e、ηrh、COPac、COPecThe power generation efficiency, the waste heat recovery efficiency, the absorption refrigerator efficiency and the electric refrigerator efficiency of the generator set are respectively.
The energy balance constraints of the electrical storage device are:
αsoc,e(t+1)＝ηsoc,eαsoc,e(t)-ηs,eαs,e(t)
wherein alpha issoc,e(t) is the stored energy of the electricity storage device at time t; etasoc,e、ηs,eRespectively the energy retention factor and the charge/discharge efficiency of the electric storage device. Considering that the initial storage capacity and the residual capacity of the power storage device in an optimization period are equal, and the input, the output and the storage capacity are all providedWith an upper bound, then:
αsoc,e(1)＝αsoc,e(25)
|αs,e(t)|≤αs,e,rc
0≤αsoc,e(t)≤αsoc,e,rc
wherein alpha iss,e,rcRated charge/discharge power for the electric storage device; alpha is alphasoc,e,rcIs the rated stored energy of the electric storage device.
The energy balance constraint of the cold storage device is as follows:
αsoc,c(t+1)＝ηsoc,cαsoc,c(t)-ηs,cαs,c(t)
wherein alpha issoc,c(t) is the energy storage capacity of the cold storage device at time t; etasoc,c、ηs,cRespectively the energy conservation factor and the cold storage/discharge efficiency of the cold storage device. Considering that the initial storage and the residual capacity of the cold storage device should be equal in an optimized period, and the input, the output and the storage have upper bounds, then:
αsoc,c(1)＝αsoc,c(25)
0≤αsoc,c(t)≤αsoc,c,rc
|αs,c(t)|≤αs,c,rc
wherein alpha issoc,c,rcRated energy storage capacity of the cold storage device; alpha is alphas,c,rcIs the rated input/output power of the cold storage device. The input power of other energy conversion devices, such as generator sets, absorption chillers, gas boilers, should also be limited within the rated range.
The optimization problem comprises a plurality of groups of sources, charge data samples and multi-dimensional energy storage devices, combination optimization needs to be carried out on other equipment, the problem can be regarded as a nonlinear optimization problem with complex constraints, and constraint conditions of the energy storage devices are difficult to process by genetic algorithms, particle swarm optimization and the like, so that the problem is easy to fall into local optimization. Although the dynamic planning can obtain the multi-dimensional energy storage optimal scheduling scheme, the equipment combination optimization scheme cannot be directly calculated, the calculation time is long when the dynamic planning is combined with other optimization methods, and the difficulty in obtaining the optimal solution is high. The embodiment provides a parallel optimization method based on dynamic programming, which is characterized in that parallel computing tasks suitable for a CPU and a GPU are respectively designed, a plurality of CPU cores are used for executing a dynamic programming program in parallel to compute an optimal energy storage scheme, and the GPU is used for computing an optimal output scheme of other equipment except an energy storage device, so that the problem of random optimization in the future is rapidly solved.
The calculation flow of the solving algorithm is shown in fig. 2, and the left half part adopts dynamic programming as a main frame for processing the complex constraints of the energy storage device and calculating the minimum operation cost under different electricity, cold/hot stored energy time by time. Taking the summer operation mode as an example, the dynamic planning needs to store the electricity and the cold quantity alphasoc,e(t+1)、αsoc,cEach discrete point of (t +1) (discretization is typically performed at 1% of rated capacity) is enumerated and the dynamically planned objective function is calculated time by time:
Figure BDA0002633925200000071
wherein the content of the first and second substances,
Figure BDA0002633925200000072
representing the minimum total operation expected cost from the t decision step (including the t decision step), wherein the lower subscripts i and h respectively represent the values of electricity storage and cold capacity i and h after the current decision step is finished;
Figure BDA0002633925200000073
and the expected value of the minimum cost increment of the system in the tth decision step is shown, the upper corner marks i and h respectively show that the electricity storage capacity and the cold capacity at the beginning of the current decision step are i and h, and the values of the i and the h are mutually independent. But do not
Figure BDA0002633925200000074
The upper corner marks i and h are respectively
Figure BDA0002633925200000081
The lower subscripts i and h take the same value, i.e. the initial energy storage of the decision step t is the same as the ending energy storage of the decision step t-1.
Figure BDA0002633925200000082
Is defined as:
Figure BDA0002633925200000083
in this embodiment, in the process of solving a day-ahead random optimization scheduling model by using a dynamic programming-based parallel optimization method, a relational table f (t) between discretization combinations of input/output power of the power storage device and input/output power of the cold storage device at the current time t and a minimum operation cost increment expected value is established by using GPU calculation;
starting a parallel computing task of the GPU and the CPU, and computing a relation table f (t +1) of discretization combination of input/output power of the power storage device and input/output power of the cold storage device at the t +1 moment and a minimum operation cost increment expected value by the GPU;
the CPU inquires the relation table f (t) to obtain the minimum operation cost increment expected value and calculates the minimum total operation expected cost; and waiting for the parallel task to be finished, and retrieving the minimum total operation expected cost of the calculation result and the corresponding energy storage value of the electricity storage device and the energy storage value of the cold storage device from the GPU and the CPU task as optimal values.
Wherein, the process of obtaining the minimum operation cost increment expected value by the CPU inquiring the relation table f (t) is as follows:
and enumerating the value of the energy storage combination of the electricity storage device and the cold storage device at each moment by using the CPU, and calculating the corresponding minimum operation cost increment expected value by using an interpolation method according to the relation table f (t).
For example:
Figure BDA0002633925200000084
as shown in the right half of fig. 2, the idea of parallel computation is: establishing alpha using GPU computationss,e(t) and alphas,c(t) discretized combination with minimum operating cost delta expectation
Figure BDA0002633925200000085
Can be expressed as:
Figure BDA0002633925200000086
enumerating each alpha with the CPUsoc,e(t)、αsoc,c(t) and alphasoc,e(t+1)、αsoc,cThe value of the (t +1) combination is interpolated from f to calculate the corresponding
Figure BDA0002633925200000087
Recalculated and recorded
Figure BDA0002633925200000088
And corresponding alphasoc,e *(t)、αsoc,c *The value of (t). The specific parallel computing process is as follows: first discretizing alphas,e(t)、αs,c(t)、αs,e(t+1)、αs,c(t+1)、αsoc,e(t+1)、αsoc,c(t +1), if t is 1, calculating a table f (t) of t by using the GPU; then, starting parallel computing tasks of the GPU and the CPU, wherein the table f (t +1) when the GPU computes t +1, and the CPU inquires the table f (t) to obtain
Figure BDA0002633925200000091
And calculate
Figure BDA0002633925200000092
Retrieving the computation results from the GPU and CPU tasks while waiting for the parallel tasks to end
Figure BDA0002633925200000093
And corresponding optimum alphasoc,e(t)、αsoc,cThe value of (t).
Example two
The embodiment provides a day-ahead random optimization scheduling device of an integrated energy system, which comprises:
(1) and the parameter acquisition module is used for acquiring the operation parameters of the comprehensive energy system.
The integrated energy system operating parameters of the embodiment include: the energy storage system comprises a gas purchase price, an electricity sale price, renewable energy power generation power, input/output power of an electricity storage device, an electric load, input electric power of an electric refrigerator, input/output power of a cold storage device, a cold load, power generation efficiency of a generator set, waste heat recovery efficiency, absorption refrigerator efficiency, electric refrigerator efficiency, energy retention coefficient and charge/discharge efficiency of the electricity storage device, rated charge/discharge power of the electricity storage device, rated stored energy of the electricity storage device, energy retention coefficient and storage/discharge efficiency of the cold storage device, rated stored energy of the cold storage device, and rated input/output power of the cold storage device.
(2) The scheduling scheme solving module is used for solving a day-ahead random optimization scheduling model based on a dynamic programming parallel optimization method, calculating an optimal energy storage scheme of the energy storage device by utilizing a plurality of CPU cores to execute a dynamic programming program in parallel, calculating an optimal output scheme of other equipment except the energy storage device by utilizing a GPU, and finally obtaining the day-ahead random optimization scheduling scheme and applying the day-ahead random optimization scheduling scheme to the day-ahead random scheduling of the comprehensive energy system;
the day-ahead random optimization scheduling model comprises the following steps: the expected cost of operation of the integrated energy system is minimized under time series samples of sets of renewable energy and loads, taking into account prediction errors caused by random fluctuations in renewable energy and user loads.
The structure of the integrated energy system is shown in fig. 1. The system can supply power to users by utilizing photovoltaic power, wind power, a generator set and electric energy of a power grid; in summer, the waste heat of the generator set is used for driving the absorption refrigerator to refrigerate or the absorption refrigerator is used for refrigerating; the waste heat of the generator set and the gas boiler are directly utilized for heating in winter; the electricity storage, cold storage and heat storage devices can be used for stabilizing the fluctuation of renewable energy sources and adjusting the thermoelectric ratio of the system, and the operation efficiency of the system can be improved.
In the optimized scheduling of the integrated energy system, an operator wants to minimize the operation cost of the system on the premise of meeting the user load, and particularly, in the case of allowing electricity to be sold to a power grid, the economic efficiency is usually used as an evaluation index. On the other hand, renewable energy sources and user demands have random fluctuation, and the day-ahead prediction data has certain error compared with the actual data. In the optimization in the day ahead, the influence of the prediction error should be fully considered, and an optimized scheduling scheme of the system is formulated, so that the scheme has certain adaptability to different renewable energy sources and load data, namely, under the condition of source and load fluctuation, the system can still obtain a better optimized scheduling effect, and the phenomenon of sudden drop of the system performance is avoided. Thus, the objective function of the day-ahead random optimization schedule is defined as: with multiple sets of time series samples of renewable energy and load (multiple sets of source, load time series samples can be generated using the monte carlo method), the expected cost of system operation is minimized. The objective function of the day-ahead random optimization is:
Figure BDA0002633925200000101
wherein t ∈ [1,24 ]]Represents the tth period of 24 hours; p is a radical ofgFor purchase of gas price, peFor the purchase/sale of electricity, F (t) total amount of gas purchased by the system, EgridAnd (t) the electricity purchased/sold by the system.
The day-ahead random optimization needs to satisfy the balance constraints of cold, heat and electricity supply and demand, for example, the energy balance constraint in the summer operation mode is as follows:
Egrid+Eres+Fηpgu,e+αs,e＝Eload+Eec
F(1-ηpgu,e)ηrhCOPac+EecCOPec+αs,c＝Cload
wherein E isresGenerating power for a renewable energy source; alpha is alphas,eInput/output power for the electric storage device; eloadIs an electrical load; eecInputting electric power to the electric refrigerator; alpha is alphas,cIs the input/output power of the cold storage device; cloadIs a cold load; etapgu,e、ηrh、COPac、COPecThe power generation efficiency, the waste heat recovery efficiency, the absorption refrigerator efficiency and the electric refrigerator efficiency of the generator set are respectively.
The energy balance constraints of the electrical storage device are:
αsoc,e(t+1)＝ηsoc,eαsoc,e(t)-ηs,eαs,e(t)
wherein alpha issoc,e(t) is the stored energy of the electricity storage device at time t; etasoc,e、ηs,eRespectively the energy retention factor and the charge/discharge efficiency of the electric storage device. Considering that the initial storage and the residual of the power storage device should be equal in an optimization period, and the input, the output and the storage have upper bounds, then there are:
αsoc,e(1)＝αsoc,e(25)
|αs,e(t)|≤αs,e,rc
0≤αsoc,e(t)≤αsoc,e,rc
wherein alpha iss,e,rcRated charge/discharge power for the electric storage device; alpha is alphasoc,e,rcIs the rated stored energy of the electric storage device.
The energy balance constraint of the cold storage device is as follows:
αsoc,c(t+1)＝ηsoc,cαsoc,c(t)-ηs,cαs,c(t)
wherein alpha issoc,c(t) is the energy storage capacity of the cold storage device at time t; etasoc,c、ηs,cRespectively the energy conservation factor and the cold storage/discharge efficiency of the cold storage device. Considering that the initial storage and the residual capacity of the cold storage device should be equal in an optimized period, and the input, the output and the storage have upper bounds, then:
αsoc,c(1)＝αsoc,c(25)
0≤αsoc,c(t)≤αsoc,c,rc
|αs,c(t)|≤αs,c,rc
wherein alpha issoc,c,rcRated energy storage capacity of the cold storage device; alpha is alphas,c,rcIs the rated input/output power of the cold storage device. The input power of other energy conversion devices, such as generator sets, absorption chillers, gas boilers, should also be limited within the rated range.
The optimization problem comprises a plurality of groups of sources, charge data samples and multi-dimensional energy storage devices, combination optimization needs to be carried out on other equipment, the problem can be regarded as a nonlinear optimization problem with complex constraints, and constraint conditions of the energy storage devices are difficult to process by genetic algorithms, particle swarm optimization and the like, so that the problem is easy to fall into local optimization. Although the dynamic planning can obtain the multi-dimensional energy storage optimal scheduling scheme, the equipment combination optimization scheme cannot be directly calculated, the calculation time is long when the dynamic planning is combined with other optimization methods, and the difficulty in obtaining the optimal solution is high. The embodiment provides a parallel optimization method based on dynamic programming, which is characterized in that parallel computing tasks suitable for a CPU and a GPU are respectively designed, a plurality of CPU cores are used for executing a dynamic programming program in parallel to compute an optimal energy storage scheme, and the GPU is used for computing an optimal output scheme of other equipment except an energy storage device, so that the problem of random optimization in the future is rapidly solved.
The calculation flow of the solving algorithm is shown in fig. 2, and the left half part adopts dynamic programming as a main frame for processing the complex constraints of the energy storage device and calculating the minimum operation cost under different electricity, cold/hot stored energy time by time. Taking the summer operation mode as an example, the dynamic planning needs to store the electricity and the cold quantity alphasoc,e(t+1)、αsoc,cEach discrete point of (t +1) (discretization is typically performed at 1% of rated capacity) is enumerated and the dynamically planned objective function is calculated time by time:
Figure BDA0002633925200000121
wherein the content of the first and second substances,
Figure BDA0002633925200000122
representing the minimum total operation expected cost from the t decision step (including the t decision step), wherein the lower subscripts i and h respectively represent the values of electricity storage and cold capacity i and h after the current decision step is finished;
Figure BDA0002633925200000123
and the expected value of the minimum cost increment of the system in the tth decision step is shown, the upper corner marks i and h respectively show that the electricity storage capacity and the cold capacity at the beginning of the current decision step are i and h, and the values of the i and the h are mutually independent. But do not
Figure BDA0002633925200000124
The upper corner marks i and h are respectively
Figure BDA0002633925200000125
The lower subscripts i and h take the same value, i.e. the initial energy storage of the decision step t is the same as the ending energy storage of the decision step t-1.
Figure BDA0002633925200000126
Is defined as:
Figure BDA0002633925200000127
in this embodiment, in the process of solving a day-ahead random optimization scheduling model by using a dynamic programming-based parallel optimization method, a relational table f (t) between discretization combinations of input/output power of the power storage device and input/output power of the cold storage device at the current time t and a minimum operation cost increment expected value is established by using GPU calculation;
starting a parallel computing task of the GPU and the CPU, and computing a relation table f (t +1) of discretization combination of input/output power of the power storage device and input/output power of the cold storage device at the t +1 moment and a minimum operation cost increment expected value by the GPU;
the CPU inquires the relation table f (t) to obtain the minimum operation cost increment expected value and calculates the minimum total operation expected cost; and waiting for the parallel task to be finished, and retrieving the minimum total operation expected cost of the calculation result and the corresponding energy storage value of the electricity storage device and the energy storage value of the cold storage device from the GPU and the CPU task as optimal values.
Wherein, the process of obtaining the minimum operation cost increment expected value by the CPU inquiring the relation table f (t) is as follows:
and enumerating the value of the energy storage combination of the electricity storage device and the cold storage device at each moment by using the CPU, and calculating the corresponding minimum operation cost increment expected value by using an interpolation method according to the relation table f (t).
For example:
Figure BDA0002633925200000131
as shown in the right half of fig. 2, the idea of parallel computation is: establishing alpha using GPU computationss,e(t) and alphas,c(t) discretized combination with minimum operating cost delta expectation
Figure BDA0002633925200000132
Can be expressed as:
Figure BDA0002633925200000133
enumerating each alpha with the CPUsoc,e(t)、αsoc,c(t) and alphasoc,e(t+1)、αsoc,cThe value of the (t +1) combination is interpolated from f to calculate the corresponding
Figure BDA0002633925200000134
Recalculated and recorded
Figure BDA0002633925200000135
And corresponding alphasoc,e *(t)、αsoc,c *The value of (t). The specific parallel computing process is as follows: first discretizing alphas,e(t)、αs,c(t)、αs,e(t+1)、αs,c(t+1)、αsoc,e(t+1)、αsoc,c(t +1), if t is 1, calculating a table f (t) of t by using the GPU; then, starting parallel computing tasks of the GPU and the CPU, wherein the table f (t +1) when the GPU computes t +1, and the CPU inquires the table f (t) to obtain
Figure BDA0002633925200000136
And calculate
Figure BDA0002633925200000137
Retrieving the computation results from the GPU and CPU tasks while waiting for the parallel tasks to end
Figure BDA0002633925200000138
And corresponding optimum alphasoc,e(t)、αsoc,c(t) ofThe value is obtained.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the method for the day-ahead stochastic optimal scheduling of an integrated energy system as described above.
Example four
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the steps in the method for randomly optimizing and scheduling the integrated energy system in the day-ahead.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A day-ahead random optimization scheduling method of an integrated energy system is characterized by comprising the following steps:
acquiring operation parameters of the comprehensive energy system, solving a day-ahead random optimization scheduling model based on a parallel optimization method of dynamic programming, calculating an optimal energy storage scheme of the energy storage device by utilizing a plurality of CPU cores to execute a dynamic programming program in parallel, calculating an optimal output scheme of other equipment except the energy storage device by utilizing a GPU, and finally obtaining the day-ahead random optimization scheduling scheme and applying the day-ahead random optimization scheduling scheme to the day-ahead random scheduling of the comprehensive energy system;
the day-ahead random optimization scheduling model comprises the following steps: the expected cost of operation of the integrated energy system is minimized under time series samples of sets of renewable energy and loads, taking into account prediction errors caused by random fluctuations in renewable energy and user loads.
2. The method according to claim 1, wherein the stochastic day-ahead optimization scheduling model satisfies cold, heat, and power supply and demand balance constraints.
3. The method for day-ahead stochastic optimal scheduling of an integrated energy system of claim 1, wherein the plurality of sets of time series samples of renewable energy and loads are generated using a monte carlo method.
4. The method for the day-ahead stochastic optimal scheduling of the integrated energy system according to claim 1, wherein the objective function of the day-ahead stochastic optimal scheduling model is: the sum of the gas cost and the electric energy cost is a minimum value in a 24 hour period.
5. The method of the day-ahead stochastic optimal scheduling of the integrated energy system of claim 1, wherein in solving the day-ahead stochastic optimal scheduling model based on the dynamic programming parallel optimization method, the dynamic programming is used as a main frame for processing the complex constraints of the energy storage device and calculating the minimum operating cost under different electricity, cold/hot energy storage time by time.
6. The day-ahead random optimization scheduling method of the integrated energy system according to claim 1, wherein in the process of solving the day-ahead random optimization scheduling model by using a dynamic programming-based parallel optimization method, a relational table f (t) between discretization combinations of input/output power of the electricity storage device and input/output power of the cold storage device at the current time t and the minimum operation cost increment expected value is established by using a GPU;
starting a parallel computing task of the GPU and the CPU, and computing a relation table f (t +1) of discretization combination of input/output power of the power storage device and input/output power of the cold storage device at the t +1 moment and a minimum operation cost increment expected value by the GPU;
the CPU inquires the relation table f (t) to obtain the minimum operation cost increment expected value and calculates the minimum total operation expected cost; and waiting for the parallel task to be finished, and retrieving the minimum total operation expected cost of the calculation result and the corresponding energy storage value of the electricity storage device and the energy storage value of the cold storage device from the GPU and the CPU task as optimal values.
7. The method for randomly and optimally scheduling the integrated energy system in the day ahead according to claim 6, wherein the process of inquiring the relation table f (t) by the CPU to obtain the minimum operation cost increment expected value comprises the following steps:
and enumerating the value of the energy storage combination of the electricity storage device and the cold storage device at each moment by using the CPU, and calculating the corresponding minimum operation cost increment expected value by using an interpolation method according to the relation table f (t).
8. A day-ahead random optimization scheduling device of an integrated energy system is characterized by comprising:
the parameter acquisition module is used for acquiring the operation parameters of the comprehensive energy system;
the scheduling scheme solving module is used for solving a day-ahead random optimization scheduling model based on a dynamic programming parallel optimization method, calculating an optimal energy storage scheme of the energy storage device by utilizing a plurality of CPU cores to execute a dynamic programming program in parallel, calculating an optimal output scheme of other equipment except the energy storage device by utilizing a GPU, and finally obtaining the day-ahead random optimization scheduling scheme and applying the day-ahead random optimization scheduling scheme to the day-ahead random scheduling of the comprehensive energy system;
the day-ahead random optimization scheduling model comprises the following steps: the expected cost of operation of the integrated energy system is minimized under time series samples of sets of renewable energy and loads, taking into account prediction errors caused by random fluctuations in renewable energy and user loads.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for the day-ahead stochastic optimal scheduling of an integrated energy system according to any of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps in the method for day-ahead stochastic optimal scheduling of an integrated energy system according to any of claims 1-7.
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