CN115511658A - Building energy optimization method considering breakage of energy storage device - Google Patents
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Abstract
The invention relates to a building energy optimization method considering breakage of an energy storage device, which comprises the following steps of: step 1, according to building energy utilization characteristics and equipment classification, comprehensively considering the problem of peak-valley difference on the power grid side, optimally distributing building energy, and establishing a power utilization cost multi-objective optimization model; step 2, solving the building electricity cost multi-objective optimization model established in the step 1 by adopting a quantum genetic algorithm of the improved revolving door to obtain the minimum building electricity cost; and 3, performing load electricity purchasing and energy storage scheduling according to the minimum building electricity utilization cost obtained in the step 2, and further performing optimized distribution on building energy. The invention enables an optimal distribution of the energy distribution on the supply side.
Description
Technical Field
The invention belongs to the technical field of energy management, relates to a building energy optimization method, and particularly relates to a building energy optimization method considering breakage of an energy storage device.
Background
In recent years, global economy has rapidly developed. The rapid development of people's life increases the energy demand of all countries, and the high energy consumption is inevitable behind the economic development. Under the background of using a large amount of traditional fossil energy, a great deal of pollution problems caused by products or emissions of the traditional fossil energy are very serious, the sustainable development of the human society is threatened, and low-carbon transformation is imminent.
The total energy consumption of the building at the present stage accounts for 36% of the total energy consumption of all terminals of the society, and the energy consumption of commercial buildings with centralized units and large population density accounts for about 21% -24%. Meanwhile, the human ideological concept, behavior habit and energy consumption unit management problem can not improve the energy waste phenomenon, the building energy consumption ratio can be increased continuously, and great pressure is brought to the power supply of the power grid.
The energy optimization method can optimize energy distribution according to various parameters such as electricity price and load. The energy storage equipment is arranged to play a role in peak clipping and valley filling and improving the reliability of the system. However, the energy storage device has a short service life, and the reliability is lowered after long-term use. For this reason, the rate of depletion of the energy storage device becomes an important part of the concern. Therefore, the problem of peak-valley difference on the power grid side needs to be comprehensively considered according to building energy utilization characteristics and equipment classification, the breaking cost of the energy storage device is reduced, and building energy is optimally distributed.
In summary, how to develop a building energy optimization method considering energy storage device breakage plays an important role in the application of intelligent power utilization and a user microgrid, the building energy optimization method not only needs to provide an energy optimization decision scheme, but also needs to stabilize the charge state of energy storage equipment and reduce the discharge depth, so that the building energy optimization method is beneficial to the continuous operation of a building energy supply side, reduces the breakage cost of the energy storage equipment, and has important significance in realizing energy conservation, emission reduction and power utilization safety of a building.
Through searching, the patent documents of the prior art which are the same as or similar to the invention are not found.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a building energy optimization method considering the breakage of an energy storage device, which can optimally distribute energy distribution on a supply side.
The invention solves the practical problem by adopting the following technical scheme:
a building energy optimization method considering breakage of an energy storage device comprises the following steps:
step 1, according to building energy utilization characteristics and equipment classification, comprehensively considering the problem of peak-valley difference at the power grid side, optimally distributing building energy, and establishing a power utilization cost multi-objective optimization model;
step 2, solving the building power consumption cost multi-objective optimization model established in the step 1 by adopting a quantum genetic algorithm of the improved revolving door to obtain the minimum building power consumption cost;
and 3, performing load electricity purchasing and energy storage scheduling according to the minimum building electricity utilization cost obtained in the step 2, and further performing optimized distribution on building energy.
Moreover, the optimization multi-objective function of the electricity consumption cost multi-objective optimization model in the step 1 is as follows:
min F total ＝(F buy +F bat -F sell ) (1)
in the formula: f total Net electricity cost for the user; f buy The cost for purchasing electricity; f bat The cost is reduced for the storage battery; f sell And selling the electricity for the user.
Wherein:
in the formula:
Moreover, the energy conservation constraint of the optimization multi-objective function of the electricity consumption cost multi-objective optimization model in the step 1 is as follows:
in the formula: p l t The total power of the regulated equipment.
Further, the specific steps of step 2 include:
(1) Setting initialization algorithm parameters, generating a population of a certain scale, and inputting time step data;
(2) Secondly, calculating an initial population value according to the power consumption cost multi-objective optimization model, and entering a strategy operation cycle; in strategy patrol, continuously and circularly calculating output power and start-stop time of a strategy according to time, searching an optimal energy distribution balance point, and carrying out load electricity purchasing and energy storage scheduling;
(3) Thirdly, dynamically adjusting the quantum rotation angle on the basis of QGA, and calculating an individual fitness value:
the improved formula is:
in the formula: f. of l The fitness of the current individual; f. of min The searched optimal fitness value is obtained; theta min 、θ max Minimum and maximum quantum rotation angles, respectively; Δ θ is a quantum rotation angle variation value.
(4) And finally, judging whether the constraint conditions of the multi-objective optimization model are met, and finally outputting the minimum building electricity consumption cost.
Moreover, the specific method of the step 3 is as follows:
preferentially ensure photovoltaic and exert oneself, call the minimum power consumption cost multi-objective optimization model of building power consumption cost, guarantee that energy storage depreciation expense is minimum, look for best energy distribution balance point, carry out energy memory's action control, and then carry out optimal distribution to building energy:
(1) If the photovoltaic electric quantity is greater than the building load, the photovoltaic output bears the load completely, the stored energy and the residual photovoltaic electric quantity participate in electricity selling until the stored energy reaches the minimum value, at the moment, the photovoltaic is charged with the stored energy, and the residual photovoltaic output is sold to the power grid;
(2) When the photovoltaic electric quantity does not meet the load requirement, the photovoltaic electric quantity is completely used for the load, and the storage battery supplies power simultaneously until the stored energy reaches the minimum value;
(3) And if the sum of the electric quantity of the photovoltaic and the stored energy is not enough for load use, electricity needs to be purchased from a power grid. When the electricity is in the valley, the storage battery is charged at the same time, and the storage battery is not charged in other time periods.
The invention has the advantages and beneficial effects that:
1. the invention provides a building energy optimization method considering energy storage device breakage, which comprehensively considers the problem of peak-valley difference at the power grid side according to building energy utilization characteristics and equipment classification, optimizes and distributes building energy, solves the problem by adopting an Improved quantum genetic algorithm (Improved dynamic adaptation of revolute doors for QGA, IDA-QGA) of a revolving door, obtains the lowest net electricity utilization cost of a building, seeks supply and demand balance points, controls the action of an energy storage device, and reduces the breakage of the energy storage device.
2. The optimization method based on the price type establishes the optimized objective function, and the example results show that the optimization method can better manage the over-discharge of the energy storage device and the peak-valley difference of the cut-down power grid side, optimally distribute the energy distribution of the supply side, and after the optimization method is used, the charge state of the energy storage equipment is more stable, the discharge depth is reduced, the breakage of the energy storage equipment is reduced, the energy storage and equipment electricity purchasing cost is reduced, and the balance of supply and demand is realized.
Drawings
FIG. 1 is a block diagram of a building energy optimization system of the present invention;
FIG. 2 is a coulometric analysis diagram of the present invention;
fig. 3 is a power consumption rate analysis chart according to the present invention.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
a building energy optimization method considering breakage of an energy storage device comprises the following steps:
step 1, according to building energy utilization characteristics and equipment classification, comprehensively considering the problem of peak-valley difference on the power grid side, optimally distributing building energy, and establishing a power utilization cost multi-objective optimization model;
according to building with can characteristics and equipment classification, the poor problem of peak valley of comprehensive consideration electric wire netting side uses battery storage electric quantity and photovoltaic power generation electric quantity when the peak hour price, charges for the battery during the price of electricity during the valley, when guaranteeing building load demand daytime, reduces the cost of power consumption.
In the objective function of the electricity consumption cost, the depreciation cost of the storage battery is considered, and the electricity purchasing cost and the electricity selling cost in different periods are considered to be changed according to the peak-valley electricity price, so that an electricity consumption cost multi-objective optimization model is established;
the electricity consumption cost multi-objective optimization model in the step 1 has an optimization multi-objective function as follows:
minF total ＝(F buy +F bat -F sell ) (1)
in the formula: f total Net electricity cost for the user; f buy The electricity purchasing cost after the photovoltaic energy storage participates in regulation and control; f bat The cost is reduced for the storage battery; f sell And selling the electricity for the user.
Wherein:
in the formula:
The energy conservation constraint of the optimization multi-objective function of the electricity consumption cost multi-objective optimization model in the step 1 is as follows:
in the formula: p l t The total power of the regulated equipment.
Step 2, solving the building electricity cost multi-objective optimization model established in the step 1 by adopting a quantum genetic algorithm of the improved revolving door to obtain the minimum building electricity cost;
in the embodiment, a final result can be obtained through faster calculation by a quantum genetic algorithm for dynamically adjusting the quantum rotation angle, and a global optimal solution can be better searched.
The specific method of the step 2 comprises the following steps: and judging the weather type and operating a scheduling strategy by reading the weather conditions. Preferentially processing photovoltaic output, calling a target function with the minimum net cost of building electricity, ensuring the minimum energy storage breaking cost, and performing action control on an energy storage device and flow between other energy and building loads;
in this example, a modified quantum genetic algorithm was used for the calculation. Firstly, setting initialization algorithm parameters, generating a population with a certain scale, and inputting time step data; and secondly, calculating an initial population value according to the power consumption cost multi-objective optimization model, and entering a strategy operation cycle. In the strategy patrol, the output power and the start-stop time are calculated according to the continuous cycle of the strategy, the optimal energy distribution balance point is searched, and the load electricity purchasing and the energy storage scheduling are carried out. And thirdly, dynamically adjusting the quantum rotation angle on the basis of QGA, and calculating an individual fitness value.
The improved formula is:
in the formula: f. of l The fitness of the current individual; f. of min The searched optimal fitness value is obtained; theta.theta. min 、θ max Minimum and maximum quantum rotation angles, respectively; and delta theta is a quantum rotation angle change value.
And finally, judging whether the constraint conditions of the multi-objective optimization model are met or not, and finally outputting the minimum building electricity consumption cost.
And 3, performing load electricity purchasing and energy storage scheduling according to the minimum building electricity utilization cost obtained in the step 2, and further performing optimized distribution on building energy.
The specific method of the step 3 comprises the following steps:
preferentially ensure photovoltaic output, call the minimum power consumption cost multi-objective optimization model of building power consumption cost, guarantee that the energy storage depreciation expense is minimum, look for the best energy distribution balance point, carry out energy memory's action control, and then carry out the optimal distribution to the building energy:
(1) If the photovoltaic electric quantity is larger than the building load, the photovoltaic output bears the load completely, the stored energy and the residual photovoltaic electric quantity participate in electricity selling until the stored energy reaches the lowest value, at the moment, the photovoltaic carries out energy storage charging, and the residual photovoltaic output is sold to a power grid;
(2) When the photovoltaic electric quantity does not meet the load requirement, the photovoltaic electric quantity is completely used for the load, and the storage battery supplies power simultaneously until the stored energy reaches the minimum value;
(3) And if the sum of the photovoltaic power and the stored energy is not enough for load use, electricity needs to be purchased from a power grid. When the electricity is in the valley, the storage battery is charged at the same time, and the storage battery is not charged in other time periods.
The invention is further illustrated by the following examples:
the building load regulation and control system comprises a photovoltaic power generation system, an energy storage system, a building power load and the like. The building load dispatching system sends the electric energy generated by the photovoltaic to a building, an energy storage system and a power grid according to a priority sequence; the energy storage system can buffer electric energy. Fig. 1 is a structural diagram of a building load scheduling system.
The energy optimization allocation strategy is as follows:
(1) And if the photovoltaic electric quantity is greater than the building load, the photovoltaic output bears the load completely, and the stored energy and the photovoltaic residual electric quantity participate in electricity selling until the stored energy reaches the lowest value. At this moment, the photovoltaic carries out the energy storage and charges, and remaining photovoltaic is exerted oneself and is sold for the electric wire netting.
(2) When the photovoltaic electric quantity does not meet the load requirement, the photovoltaic electric quantity is completely used for the load, and the storage battery supplies power simultaneously until the stored energy reaches the lowest value.
(3) And if the sum of the photovoltaic power and the stored energy is not enough for load use, electricity needs to be purchased from a power grid. When the valley electricity is used, the storage battery is charged at the same time, and the storage battery is not charged in other time periods.
Taking a certain building as an example, the rated capacity of distributed photovoltaic in the building system is 50kW, and the rated capacity of the energy storage system is 30kWh. The charge-discharge efficiency is 0.95, the maximum charge-discharge power is 15kW, the initial energy storage electric quantity is 80%, and the upper limit value and the lower limit value of the calibrated energy storage electric quantity are respectively 80% and 20%. The division into 96 periods is made at Δ t =0.25 h. The rest of the basic parameters in the examples are shown in tables 1-2.
TABLE 1 load parameters and building parameters
| Performance parameter | Value taking |
| Range of illumination power | 050kW*180 |
| Total indoor illuminance | 720lux |
| Fitting coefficients a, b | 0.93；0.09 |
| Power of air conditioner | 1.53kW*20 |
| Equivalent heat capacity | 0.18kWh/℃ |
| Equivalent thermal resistance | 5.56℃/kW |
TABLE 2 day ahead real-time electricity prices
In fig. 2, it is shown that in the IDA-QGA algorithm, the load and the energy-storage power purchase amount are respectively reduced by 2.18kWh and 18.69kWh compared with the QGA algorithm, and it can be seen that the energy-storage power purchase amount is significantly reduced; the photovoltaic grid-connected electric quantity is improved by 9.67kWh compared with the photovoltaic grid-connected electric quantity, so that the photovoltaic electric quantity is effectively utilized; the stored energy and the electricity selling amount are basically equal.
The final electricity cost used by the building can be obtained by referring to the time-of-use electricity price and the electricity consumption, the result of which is shown in fig. 3, through the optimization of the IDA-QGA algorithm, the depreciation cost of the energy storage device is reduced by 2.97 yuan, and the net electricity cost of the building is reduced by 21.37 yuan.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the present invention includes, but is not limited to, those examples described in this detailed description, as well as other embodiments that can be derived from the teachings of the present invention by those skilled in the art and that are within the scope of the present invention.
Claims (5)
1. A building energy optimization method considering breakage of an energy storage device is characterized by comprising the following steps: the method comprises the following steps:
step 1, according to building energy utilization characteristics and equipment classification, comprehensively considering the problem of peak-valley difference on the power grid side, optimally distributing building energy, and establishing a power utilization cost multi-objective optimization model;
step 2, solving the building electricity cost multi-objective optimization model established in the step 1 by adopting a quantum genetic algorithm of the improved revolving door to obtain the minimum building electricity cost;
and 3, performing load electricity purchasing and energy storage scheduling according to the minimum building electricity utilization cost obtained in the step 2, and further performing optimized distribution on building energy.
2. The method of claim 1, wherein the method comprises: the optimization multi-objective function of the electricity consumption cost multi-objective optimization model in the step 1 is as follows:
minF total ＝(F buy +F bat -F sell ) (1)
in the formula: f total Net electricity cost for the user; f buy For the cost of electricity purchase; f bat The cost is reduced for the storage battery; f sell And selling the electricity for the user.
Wherein:
in the formula:
3. The method of claim 1, wherein the method comprises: the energy conservation constraint of the optimization multi-objective function of the electricity consumption cost multi-objective optimization model in the step 1 is as follows:
in the formula: p l t The total power of the regulated equipment.
4. The method of claim 1, wherein the method comprises: the specific steps of the step 2 comprise:
(1) Setting initialization algorithm parameters, generating a population of a certain scale, and inputting time step data;
(2) Secondly, calculating an initial population value according to the power consumption cost multi-objective optimization model, and entering a strategy operation cycle; in strategy patrol, continuously and circularly calculating output power and start-stop time of a strategy according to time, searching an optimal energy distribution balance point, and carrying out load electricity purchasing and energy storage scheduling;
(3) Thirdly, dynamically adjusting the quantum rotation angle on the basis of QGA, and calculating an individual fitness value:
the improved formula is:
in the formula: f. of l The fitness of the current individual; f. of min The searched optimal fitness value is obtained; theta min 、θ max Minimum and maximum quantum rotation angles, respectively; Δ θ is a quantum rotation angle variation value.
(4) And finally, judging whether the constraint conditions of the multi-objective optimization model are met, and finally outputting the minimum building electricity consumption cost.
5. The method of claim 1, wherein the method comprises: the specific method of the step 3 comprises the following steps:
preferentially ensure photovoltaic output, call the minimum power consumption cost multi-objective optimization model of building power consumption cost, guarantee that the energy storage depreciation expense is minimum, look for the best energy distribution balance point, carry out energy memory's action control, and then carry out the optimal distribution to the building energy:
(1) If the photovoltaic electric quantity is larger than the building load, the photovoltaic output bears the load completely, the stored energy and the residual photovoltaic electric quantity participate in electricity selling until the stored energy reaches the lowest value, at the moment, the photovoltaic carries out energy storage charging, and the residual photovoltaic output is sold to a power grid;
(2) When the photovoltaic electric quantity does not meet the load requirement, the photovoltaic electric quantity is completely used for the load, and the storage battery supplies power simultaneously until the stored energy reaches the minimum value;
(3) And if the sum of the photovoltaic power and the stored energy is not enough for load use, electricity needs to be purchased from a power grid. When the electricity is in the valley, the storage battery is charged at the same time, and the storage battery is not charged in other time periods.
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| CN117154849A (en) * | 2023-09-05 | 2023-12-01 | 杭州梵迪智能科技有限公司 | Intelligent building operation and maintenance management system and method based on Internet |
| CN117200279A (en) * | 2023-11-07 | 2023-12-08 | 深圳海辰储能科技有限公司 | Intelligent building energy storage distribution method and related device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117154849A (en) * | 2023-09-05 | 2023-12-01 | 杭州梵迪智能科技有限公司 | Intelligent building operation and maintenance management system and method based on Internet |
| CN117200279A (en) * | 2023-11-07 | 2023-12-08 | 深圳海辰储能科技有限公司 | Intelligent building energy storage distribution method and related device |
| CN117200279B (en) * | 2023-11-07 | 2024-02-27 | 深圳海辰储能科技有限公司 | Intelligent building energy storage distribution method and related device |
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