CN112836882A - Regional comprehensive energy system operation optimization method considering equipment load rate change - Google Patents

Regional comprehensive energy system operation optimization method considering equipment load rate change Download PDF

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CN112836882A
CN112836882A CN202110172976.7A CN202110172976A CN112836882A CN 112836882 A CN112836882 A CN 112836882A CN 202110172976 A CN202110172976 A CN 202110172976A CN 112836882 A CN112836882 A CN 112836882A
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林达
冯怿彬
赵波
张雪松
穆云飞
张宝
倪筹帷
李志浩
黄军浩
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Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
Hangzhou Yineng Energy Retrenchment Technology Co
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Abstract

The invention discloses a regional comprehensive energy system operation optimization method considering equipment load rate change. The technical scheme adopted by the invention is as follows: constructing a fitting equation model of the efficiency of the energy conversion equipment changing along with the load rate; constructing an RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate; constructing a two-stage regional comprehensive energy system operation optimization mathematical model considering the change of the equipment load rate, solving a day-ahead optimization problem by taking the minimized operation cost as an objective function, and obtaining a regional comprehensive energy system operation optimization scheme considering the change of the equipment load rate; considering load fluctuation, a rolling optimization method is combined in a daily stage, the RIES operation and the penalty cost are minimum as optimization targets, a daily schedule is adjusted, and an optimized scheduling scheme in a small time scale is solved. The invention reduces the cost prediction error of the operation scheme and provides a more reasonable and economic operation scheme for the RIES.

Description

Regional comprehensive energy system operation optimization method considering equipment load rate change
Technical Field
The invention belongs to the technical field of operation optimization of regional comprehensive energy systems, and particularly relates to a two-stage regional comprehensive energy system operation optimization method considering equipment load rate change.
Background
The advent of Regional Integrated Energy Systems (RIES) has provided opportunities to resolve the conflict between the greatly increasing Energy demand and increasingly stringent environmental protection. The system breaks through the traditional modes of independent planning, independent design and independent operation among various energy supply and utilization systems in the energy industry, coordinates and optimizes different energy supply and utilization systems on the whole in the planning, design, construction and operation stages, and exploits the complementary characteristics among different energy sources such as electricity, gas and heat, thereby realizing the comprehensive utilization of various energy forms. The construction of the RIES and the development of related technologies are helpful for realizing advantage complementation between different energy sources, improving the comprehensive utilization efficiency of the energy sources, promoting the large-scale development and utilization of renewable energy sources, and finally realizing the safe, economic and sustainable supply of the energy sources. Therefore, the relevant research is greatly promoted.
The types of the RIES equipment are various, the operation mechanism is complex, and in order to give full play to the economy and flexibility of the RIES, a reasonable and effective operation scheme needs to be made to coordinate the operation of various kinds of equipment, so that multi-energy complementary optimization is realized. In order to simplify the calculation and complexity, the current research on modeling the rees device often ignores the influence of the load rate change, and considers the device efficiency as a constant. However, in general, the efficiency of the energy conversion device is in a variable working condition state due to fluctuation of the load factor and the environment in the operation process, and the model analysis result has a large error due to the fact that the efficiency is simplified into a constant. Deviations between the rees plant model and the actual system will cause the ice operating economics to deviate from expectations, ultimately affecting the rationality of the operating scheme. Therefore, consideration of the equipment load rate changes is of great significance to the rees operation optimization.
The coupling relation between the energy structure and the equipment in the RIES is complex, and the modeling modes are various. An Energy Hub (EH) model proposed by Geidl M and Andersson of Federal institute of technology, Zurich provides a general model for the RIES, describes the Energy transmission, conversion and storage relation of the RIES through a coupling matrix, can realize the cooperative optimization of various forms of Energy, and is widely applied to ICES optimization configuration and scheduling research. The document [1] proposes an RIES operation optimization model containing energy storage equipment; documents [ 2-4 ] further consider the influence of demand side response and renewable energy uncertainty in operation optimization on the basis of the model; the literature establishes a dynamic economic dispatching model with minimum operating cost as an optimization target and solves the dynamic economic dispatching model.
In the above studies, the impact of load rate changes on the optimization of the rees operation has not been considered. In practice, the efficiency of energy conversion equipment usually fluctuates significantly with the load factor, i.e. the efficiency of the equipment at different load factors varies significantly. Neglect of load rate changes of each device influences the accuracy of the device model to a certain extent, and further influences the rationality and economy of the RIES scheduling scheme.
In the current research on the characteristics of the efficiency of the RIES equipment changing along with the load rate, the literature [5] analyzes the efficiency change of a Combined Heat and Power (CHP) unit under different loads, and proves that the CHP has greater difference in economy under different loads and the necessity of considering the load rate change; document [6] summarizes the fitting equation form of the efficiency of part of common energy conversion equipment changing along with the load rate, but does not give specific application of the fitting equation form in the operation optimization of the LINES; the literature [7-8] researches the variable working condition characteristics of the heat pump, performs experimental research by building a test platform, and analyzes the influence of the parameter changes such as load rate and air temperature on the efficiency of the heat pump; the literature [ 9-11 ] researches the variable working condition characteristics of the boiler, establishes corresponding calculation models aiming at different types of boilers and is used for solving the problem of modeling the variable working condition characteristics of the boiler; documents [12-13] summarize the form of fitting equations for variable operating conditions of some conventional energy conversion devices.
At present, the study on the variable working condition characteristics of energy conversion equipment is mostly limited to single energy conversion equipment, a unified modeling framework suitable for the variable working condition characteristics of various energy conversion equipment is lacked, and an application method of an equipment variable working condition characteristic model under an RIES modeling framework is not provided.
The aforementioned documents are as follows:
[1]Geidl M，Koeppel G，Favreperrod P，et al.Energy hubs for the future[J].IEEE Power&Energy Magazine，2007，5(1)：24-30。
[2]Heidari A，Mortazavi S S，Bansal R C.Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies[J].Applied Energy，2020，261(1)：114393。
[3]Pazouki S，Haghifam M R R，Moser A.Uncertainty modeling in optimal operation of energy hub in presence of wind，storage and demand response[J].International Journal of Electrical Power&Energy Systems，2014，61(8)：335-345。
[4]Zhang X，Shahidehpour M，Alabdulwahab A，et al.Hourly electricity demand response in the stochastic day-ahead scheduling of coordinated electricity and natural gas network[J].IEEE Transactions on Power Systems，2016，31(1)：592-601。
[5] von aspiration force, golden red light, combined cooling heating and power generation system and energy storage variable working condition characteristic [ J ]. China Motor engineering newspaper, 2006, 26 (4): 25-30.
[6] Chenghao, owl, Queenli, etc. regional integrated energy system planning research overview [ J ] electric power system automation, 2019,43(07): 8-19.
[7] Wu Ji Ying, Mar Yi Min, Cao Wen Sheng, air-cooled heat pump system variable working condition performance of methane machine driven [ J ] chemical science declaration, 2020,71(08): 3789-.
[8] Dingcaifeng, Caoyu, water source heat pump variable working condition experimental research [ J ] refrigeration technology, 2017,40(02): 42-45.
[9] The variable working condition thermodynamic calculation and analysis system of the boiler is applied to [ J ]. inner Mongolia power technology, 2011,29(5): 72-75.
[10] ChuanRuxian, Huqin, exhaust-heat boiler variable working condition calculation [ J ] engineering thermal physics report, 1990(01) 17-20.
[11]Liu F,Sui J,Liu T,et al.Energy and exergy analysis in typical days of a steam generation system with gas boiler hybrid solar-assisted absorption heat transformer[J].Applied Thermal Engineering,2017,115:715-725。
[12] Chenghao, owl, Queenli, and the like, regional comprehensive energy system planning research overview [ J ] electric power system automation, 2019,43(07): 8-19.
[13] Plum blossom, Zhangzhongping, Liulili. micro energy grid systems device operational characteristics and control research overview [ J ] energy savings, 2020, 39(05): 173-.
Disclosure of Invention
The invention aims to provide a two-stage regional comprehensive energy system operation optimization method considering equipment load rate change, so as to improve equipment modeling accuracy and system operation economy and enable a scheduling scheme to be more reasonable.
Therefore, the invention adopts the following technical scheme: the method for optimizing the operation of the regional integrated energy system by considering the load rate change of the equipment comprises the following steps:
constructing a fitting equation model of the efficiency of the energy conversion equipment changing along with the load rate;
constructing an RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate;
constructing a two-stage regional comprehensive energy system operation optimization mathematical model considering the change of the equipment load rate, wherein the two-stage regional comprehensive energy system operation optimization mathematical model consists of an RIES day-ahead operation optimization model and an RIES day-in operation optimization model, and solving a day-ahead optimization problem by taking the minimized operation cost as an objective function to obtain a regional comprehensive energy system operation optimization scheme considering the change of the equipment load rate; considering load fluctuation, a rolling optimization method is combined in a daily stage, the RIES operation and the penalty cost are minimum as optimization targets, a daily schedule is adjusted, and an optimized scheduling scheme in a small time scale is solved.
Further, the process of constructing the fitting equation model of the efficiency of the energy conversion equipment changing with the load factor is as follows:
in order to uniformly express the relation between the efficiency and the load rate of each device, the following polynomial expression is constructed:
Figure BDA0002939375490000051
wherein η is the plant efficiency; n is the fitting order; k is a radical ofnIs a fitting coefficient; n is the equipment load rate;
the equipment load factor is obtained by the following formula:
Figure BDA0002939375490000052
in the formula, PoutOutputting energy for equipment in kW;
Figure BDA0002939375490000053
representing the capacity of the equipment in kW.
Further, the construction process of the RIES model considering the change of the efficiency of the energy conversion device with the load factor is as follows:
the relationship between input energy and output energy of the energy conversion device in the rees is expressed by the following formula:
Pout＝ηPin (3)
in the formula, PinInputting energy for equipment, wherein the unit is kW; poutOutputting energy for equipment in kW; eta is the equipment efficiency;
considering the relation between the efficiency and the load rate of each device, the relation is expressed as follows:
Figure BDA0002939375490000054
in the formula, n is the fitting order; k is a radical ofnIs a fitting coefficient; n is the equipment load rate;
the constraint of the output power of the energy conversion equipment is expressed as follows:
Figure BDA0002939375490000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002939375490000062
maximum value of output energy for the device;
the energy storage device model is established as follows:
assuming that the charging and discharging power of the energy storage device is constant in the delta t time period, the relationship between the energy stored by the device before and after charging and discharging is as follows:
WES,t+1＝WES,t(1-σES)+EES,tΔt (6)
in the formula, WES,t、WES,t+1Respectively storing energy before and after charging and discharging of the equipment, wherein the unit is kWh; sigmaESSelf-discharging rate of the energy storage device; eES,tConsidering the actual charging and discharging energy power of the energy storage equipment after the charging and discharging energy efficiency loss at the time t, and taking the charging of the equipment as positive and the unit as kW; delta t is the operation optimization step length, and the unit is h;
the limitation on the charge and discharge power of the energy storage device is described as follows:
Figure BDA0002939375490000063
Figure BDA0002939375490000064
in the formula etaES,C、ηES,DRespectively charging and discharging energy efficiency for the energy storage equipment;
Figure BDA0002939375490000065
respectively is the upper limit of the charging and discharging energy power of the equipment, and the unit is kW;
the stored energy constraints of the energy storage device are as follows:
Figure BDA0002939375490000066
in the formula (I), the compound is shown in the specification,
Figure BDA0002939375490000067
the maximum energy storage of the equipment is expressed in kWh;
the RIES model is as follows:
Figure BDA0002939375490000071
wherein α is an energy form; l isαA load level in the form of energy a; pout,α,iOutputting the alpha energy form of the device i; sαThe energy charging and discharging efficiency is the energy form alpha; eαIs an energy form alpha actual charging and discharging energy vector, takes the charging of the charging equipment as positive,
Figure BDA0002939375490000072
in the formula etaα,C、ηα,DRespectively charge and discharge energy efficiency.
Furthermore, in the mathematical model of the RIES day-ahead operation optimization model,
the objective of the rees operation optimization is to minimize the operation cost on the premise of meeting the user load demand, and the objective function is as shown in formula (12):
minfc＝Celec+Cgas (12)
in the formula, CelecThe unit is yuan for the electricity purchasing cost; cgasThe unit is Yuan for the cost of gas purchase;
Figure BDA0002939375490000073
Figure BDA0002939375490000074
in the formula, celec,tThe unit of the t time period is the unit of yuan/(kWh); pelec,tPurchasing power in kW for t time period; c. CgasIs unit gas purchase price, unit is yuan/m3；Pgas,tThe unit is m for the gas purchase amount in t time period3。
Further, in the mathematical model of the RIES day-ahead operation optimization model, the constraint conditions include equipment model constraints:
1) energy conversion equipment restraint
Energy conversion device constraints include equations (4) - (5);
2) energy storage device constraints
The energy storage device constraints include equations (6) - (9).
Furthermore, considering the periodicity and period coupling characteristics of the energy storage device, it is assumed that the energy storage is consistent at the beginning and end of the scheduling cycle, that is:
WES,start＝WES,end (15)
in the formula: wES,start、WES,endRespectively, the energy storage capacity at the beginning and the end of the device scheduling period is expressed in kWh.
Furthermore, in the mathematical model of the RIES day-ahead operation optimization model, the constraint conditions further include system operation constraints:
1) system energy balance constraints
The system energy balance constraint is shown as equation (10);
2) tie line power constraint
The tie line power constraint is expressed as:
Figure BDA0002939375490000081
in the formula:
Figure BDA0002939375490000082
represents the upper limit of the tie line power in kW; pelec,tRepresenting the tie line power in kW.
Furthermore, in the mathematical model of the running optimization model in the RIES day,
an objective function: in order to realize day-ahead and day-in two-stage cooperative scheduling and ensure the globality of a scheduling scheme in a short time scale day, the output of equipment is corrected on the basis of the day-ahead scheduling scheme, penalty cost is brought into an objective function, the objective function of an optimization scheduling model in day-in operation is the RIES operation cost in each control time domain, the penalty cost is minimum, and the objective function is shown as a formula (17):
fc＝Celec+Cgas+Cp (17)
in the formula, CpFor penalty cost, a penalty term is introduced into the objective function by considering the deviation degree of each device output power and the day-ahead scheduling plan, and the unit is an element.
Further, in the present invention,
Figure BDA0002939375490000091
in the formula, cp,iIs a penalty factor, with units of elements; delta Pout,i,tThe degree of deviation of the output power of each device from the day-ahead schedule is given in kWh.
Furthermore, in the mathematical model of the optimization model operated in the RIES day, the constraint conditions are consistent with those of the optimization model operated before the RIES day.
For the above model, the present invention solves the problem using a genetic algorithm.
The invention has the following beneficial effects: the traditional optimization operation method cannot describe the process of the equipment efficiency changing along with the load rate. The invention models the relation between the equipment efficiency and the load rate, so that the variable efficiency of the conversion equipment is reflected along with the change relation of the load rate in the operation optimization process; based on the two-stage RIES operation optimization method considering the load rate change of the equipment, the efficiency change characteristic of the energy conversion equipment is considered, and the accuracy of an equipment model is improved; the method provided by the invention better conforms to the actual operation condition of equipment, reduces the cost prediction error of the operation scheme, and provides a more reasonable and economic operation scheme for the RIES.
Drawings
FIG. 1 is a graph of the thermoelectric load of a RIES computing system in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of the time-of-use electricity price of the RIES example computing system in an application example of the present invention;
FIG. 3 is a schematic diagram of a RIES example system day-ahead electric power operation optimization scheme in an application example of the present invention (FIG. 3a is a schematic diagram of an electric power scheduling scheme under consideration of device load rate changes, and FIG. 3b is a schematic diagram of an electric power scheduling scheme under consideration of device load rate changes);
fig. 4 is a schematic diagram of a thermal power operation optimization scheme before the day of an RIES computing system in an application example of the present invention (fig. 4a is a schematic diagram of a thermal power scheduling scheme under consideration of device load rate changes, and fig. 4b is a schematic diagram of a thermal power scheduling scheme under consideration of device load rate changes).
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
The embodiment provides a regional comprehensive energy system optimization operation method considering the change of the equipment load rate.
Firstly, constructing a fitting equation model of the efficiency of the energy conversion equipment changing along with the load rate; subsequently, a mathematical model of the rees is constructed; and finally, constructing a two-stage RIES operation optimization model considering the variable working condition characteristics of the equipment, solving the optimization problem by adopting a genetic algorithm, and providing an operation optimization scheme considering the load rate change of the equipment for the ICES. The method provided by the invention can provide an operation optimization scheme for the RIES, the operation optimization result reasonably reflects the efficiency change caused by the change of the equipment load rate, the accuracy of the equipment model is improved, and a more reasonable and economic operation scheme is provided for the RIES.
Step one, establishing a fitting equation model of the efficiency of the energy conversion equipment changing along with the load rate:
in order to uniformly express the relation between the efficiency and the load rate of each device, the following polynomial expression is constructed:
Figure BDA0002939375490000101
in the formula: eta is the equipment efficiency; n is the fitting order; k is a radical ofnIs a fitting coefficient; and N is the load rate of the equipment.
The plant load factor can be found by the following formula:
Figure BDA0002939375490000111
in the formula: poutOutputting energy for equipment, kW;
Figure BDA0002939375490000112
representing the equipment capacity, kW.
Step two, establishing an RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate:
in general, the input-to-output relationship of energy conversion devices in a RIES can be expressed by the following equation:
Pout＝ηPin (3)
in the formula: pinEnergy is input to the equipment, kW.
Considering the relationship between the efficiency and the load rate of each device, it can be expressed as:
Figure BDA0002939375490000113
the energy conversion device output power constraint may be expressed as:
Figure BDA0002939375490000114
the energy storage device model is established as follows:
the charging and discharging mechanisms and energy conversion relations of different energy storage devices are similar, and the invention is described by adopting a general energy storage device model. Assuming that the charging and discharging power of the energy storage device is constant in the delta t time period, the relationship between the energy stored by the device before and after charging and discharging is as follows:
WES,t+1＝WES,t(1-σES)+EES,tΔt (6)
in the formula: wES,t、WES,t+1Respectively storing energy, kWh, before and after charging and discharging of the equipment; sigmaESSelf-discharging rate of the energy storage device; eES,tConsidering the actual charging and discharging energy power of the energy storage equipment after the charging and discharging energy efficiency loss at the time t, and taking the charging of the equipment as positive kW; and delta t is the running optimization step length h.
The limitation on the charge and discharge power of the energy storage device can be described as follows:
Figure BDA0002939375490000121
Figure BDA0002939375490000122
in the formula: etaES,C、ηES,DRespectively charging and discharging energy efficiency for the energy storage equipment;
Figure BDA0002939375490000123
and the upper limit of charging and discharging energy power, kW, of the equipment is respectively set.
The stored energy constraints of the energy storage device are as follows:
Figure BDA0002939375490000124
in the formula:
Figure BDA0002939375490000125
the maximum energy storage of the plant, kWh.
The available RIES models are as follows:
Figure BDA0002939375490000126
in the formula: alpha is in the form of energy, such as electricity, heat, etc.; l isαA load level in the form of energy a; pout,α,iOutputting the alpha energy form of the device i; sαThe energy charging and discharging efficiency is the energy form alpha; eαIs an energy form alpha actual charging and discharging energy vector, and the energy charging to the energy charging equipment is positive.
Figure BDA0002939375490000127
In the formula: etaα,C、ηα,DThe charge-discharge efficiency is respectively.
Step three, establishing an RIES day-ahead operation optimization model
1. Objective function
The objective of the rees operation optimization is to minimize the operation cost while meeting the user load requirements.
The objective function is shown in equation (15):
minfc＝Celec+Cgas (12)
in the formula: celecFor purchasing electricity cost, Yuan; cgasIs a good choice for purchasing gas.
Figure BDA0002939375490000131
Figure BDA0002939375490000132
In the formula: c. Celec,tThe unit of the time period t is the unit of the electricity purchase price, yuan/(kWh); pelec,tPurchasing electric power, kW, for a period of t; c. CgasIs unit gas purchase price, yuan/m3；Pgas,tFor a period of t, the gas quantity m3。
2. Constraint conditions
The constraints of the rias operation optimization model considering the variation of the load rate of the equipment are as follows.
2.1 plant model constraints
1) Energy conversion equipment restraint
The energy conversion device constraints include equations (4) - (5).
2) Energy storage device constraints
The energy storage device constraints include equations (6) - (9).
Furthermore, considering the periodicity and period coupling characteristics of the energy storage device, it is assumed that the energy storage is consistent at the beginning and end of the scheduling cycle, that is:
WES,start＝WES,end (15)
in the formula: wES,start、WES,endRespectively, the energy storage capacity, kWh, at the beginning and end of the device scheduling period.
2.2 System operational constraints
1) System energy balance constraints
The system energy balance constraint is shown as equation (10).
2) Tie line power constraint
The tie-line power constraint may be expressed as:
Figure BDA0002939375490000133
in the formula:
Figure BDA0002939375490000141
represents the upper limit of the tie line power, kW.
The present invention solves the above-described optimization problem using a genetic algorithm.
Step four, establishing an operation optimization model in the RIES day
1. Objective function
In order to realize day-ahead and day-in two-stage cooperative scheduling, ensure the globality of a scheduling scheme within a short time scale day to a certain extent, and correct the output of equipment on the basis of the day-ahead scheduling scheme, the invention brings penalty cost into an objective function. And the intra-day optimized scheduling model objective function is the minimum of RCES operation cost and penalty cost in each control time domain. The objective function is shown in equation (17):
fc＝Celec+Cgas+Cp (17)
in the formula, CpFor penalty cost, a penalty term is introduced into the objective function by considering the deviation degree of each device output power and the day-ahead scheduling plan, and the unit is an element.
Figure BDA0002939375490000142
In the formula, cp,iIs a penalty factor, a dollar; delta Pout,i,tThe degree of departure of each device output power from the day-ahead schedule, kWh.
2. Constraint conditions
And the constraint conditions of the running optimization model in the RIES day considering the load rate change characteristics of the equipment are consistent with those of the running optimization model in the day before.
Application example
The application example adopts a certain garden in the south of China, and the structure of the garden is shown in figure 2. The system comprises a CHP unit 1600kW, a gas boiler 2050kW and a storage battery 200 kWh. The relevant parameters of the device are shown in table 1.
TABLE 1 Main plant parameters
Figure BDA0002939375490000143
Figure BDA0002939375490000151
The load curve is shown in fig. 1, taking a typical day as an example. Referring to FIG. 2, the upper limit of the tie line power is 3MW, and the gas value is 4.16 yuan/m3. And optimizing the operation period 24h, wherein the unit time delta t before the day is 1h, the unit time delta t in the day is 0.5h, and the predicted time domain t is 4 h.
In order to verify the effectiveness of the method, the following two scenes are constructed for comparison:
scene I: the RIES is run according to a scheduling scheme resulting from a two-stage run optimization method that takes into account changes in the load rate of the device.
Scene II: the rees operates in a scheduling scheme that is derived from an operation optimization method that does not take into account changes in the load rate of the equipment.
The scheduling scheme obtained under the scene II constructed by the method is only a prediction scheme under an ideal state because the actual efficiency change of equipment is not considered, and the corresponding obtained cost is prediction cost. However, in actual operation, because the operation efficiency of the equipment is difficult to reach the rated efficiency, the scheduling scheme has large errors and is difficult to execute, and certain correction needs to be performed on the output of the equipment. The strategy adopted by the invention is as follows: the electric load shortage is bought by the power grid, and the heat load shortage is complemented by other equipment. And comparing the revised scheduling scheme cost as an actual cost.
The results of comparing the operating costs of the rees within one day under two scenarios, i.e., without considering the change in the load rate of the equipment, are shown in table 2.
TABLE 2 running cost under two scenarios
Figure BDA0002939375490000161
The RIES predicted running total cost in one day of the scene I in the day scheduling stage is 52009.56 yuan, and the RIES predicted running total cost in one day of the scene II is 50587.84 yuan.
Compared with the predicted operation cost of two scenes, the gas purchase cost of the scene I is reduced by 37.7% relative to the scene II, the electricity purchase cost is increased by 58.8%, and the dependence on the power grid under the scene I is high. In the scheduling stage in the day, the scene I finely adjusts the scheduling scheme in the day by rolling optimization, the load requirement is met under the condition that the operation cost is increased by 2.5% relative to the predicted value in the day, but the operation cost is increased by 5.9% relative to the predicted value in the day in the scene II. And the actual running cost is 1255.86 yuan higher than that of scenario I.
In order to analyze the operation optimization of the RIES in more detail, the invention carries out comparative discussion on the electric and thermal operation optimization schemes under two scenes.
The electric power operation optimization scheme for both scenarios is shown in fig. 3. It can be seen from the figure that the optimization schemes of the electrical load operation in the two scenarios are greatly different, in the scenario I, electricity is mainly purchased from the power grid to meet most of the electrical load, but in the scenario II, the CHP serves as a main output device to meet most of the electrical load.
In scenario I, when considering the change of the equipment load rate, in the time period (0-6 hours, 23 hours) when the electricity price is low, since the electricity price is low, the economic benefit of supplying the electricity load by using the CHP is not as high as that of purchasing electricity from the power grid, most of the electricity load is supplied by the power grid; and the battery is charged at that time for coping with the electric load at the peak time of electricity price (10 hours, 19-20 hours). At the time of electricity price peak, electricity price is increased, so that electricity purchasing cost from the power grid is increased, economy is reduced, and therefore the CHP unit is started to supply partial electric loads, but the CHP unit is limited by heat loads, cannot supply all electric loads, and still needs to purchase electricity from the power grid.
In the scenario II, when the change of the load rate of the device is not considered, the charging and discharging states of the storage battery at different periods are mainly influenced by the electricity price, so the charging and discharging rule is basically similar to the scenario I. Because the change of the load rate of the equipment is not considered in the scene II, the efficiency of the equipment is constant, and the CHP is always used as a main output equipment in each period due to high utilization rate of the total energy of the cogeneration and better economy, operates in a mode of ordering electricity by heat, supplies partial electric loads, and purchases electricity from a power grid to meet the residual electric power requirement.
The thermal power operation optimization scheme for both scenarios is shown in fig. 4. It can be seen from the figure that the optimization scheme of the heat load operation in the two scenarios is also greatly different, in the scenario I, the gas boiler is used as the main energy supply equipment most of the time, but in the scenario II, the CHP bears most of the heat load, and the gas boiler only participates in energy supply at the peak of energy consumption.
Considering the load rate change of the equipment in the scenario I, when the electricity price is constant, the scheduling scheme is not only related to the efficiency characteristic of the equipment, but also related to the load level at the current moment. When the heat load is smaller, the CHP and the HP are operated at a low load rate, and although the efficiency of the CHP and the HP is lower than that of the CHP and the HP in full-load operation, the economy of the RIES operation can be improved to a certain extent due to the fact that the CHP is better in the combined heat and power economy, and therefore the CHP is used as the heat supply equipment (4); when the load is gradually increased, the load level is gradually close to the rated capacity of the gas-fired boiler, and at the moment, the load rate is ensured to be at a higher level by only using the gas-fired boiler for energy supply, and the efficiency is also ensured to be at a higher level; however, when the heat load is greater than the capacity of the gas boiler, the gas boiler is not enough to satisfy the full heat load, and the CHP can be operated at full load to ensure that the CHP is at a high load rate and the rest heat load is satisfied by the gas boiler (12 h). However, when the electricity price is at the peak (10 hours, 19-20 hours), the CHP is more economical to supply heat and electricity loads at the same time due to the higher cost of purchasing electricity from the power grid.
In scenario II, the efficiency of the device is considered constant regardless of the change in the load rate of the device. When the equipment is fixed, due to the fact that CHP combined heat and power is good in economy, CHP is used as main heating equipment, and the gas-fired boiler is used for coping with heat load peaks. In practice, however, at some point in time, due to the low CHP load rate, which is lower than the rated efficiency, the use of CHP energy will increase the rees operating cost, and therefore the operating scheme may be somewhat unreasonable when the plant load rate variation is not considered, and the expected operating cost of the operating scheme is also far from the actual operating cost.
It can be seen that when the change of the equipment load rate is not considered, the equipment is often operated in a low load rate state because the influence of the equipment load rate on the equipment efficiency is not considered, so that the actual operation efficiency of the equipment is low, and the running of the equipment is not beneficial to the economic operation of the RIES; the actual operation efficiency of the equipment is greatly changed along with the load rate, so that the system operation scheme is not in accordance with the actual operation condition, and the cost is estimated to have large deviation, so that the economic dispatching and analysis of the RIES are inaccurate, and the running economy of the RIES is influenced; when the load rate change of the equipment is considered, under a certain energy price, the load level and the equipment capacity influence the operation efficiency of the equipment, and further influence the operation scheme of the RIES, so that the equipment with similar capacity is selected as the output equipment according to the load level, and the output equipment is more economical.
Due to the existence of load prediction errors, the load prediction method is executed according to a day-ahead optimization scheduling scheme, on one hand, the output of equipment in a part of time intervals is difficult to completely meet the requirement of operation in a day, on the other hand, the energy supply of the equipment in the part of time intervals is larger than the load requirement, energy waste is caused, and the running economy of the RIES is influenced. In the scenario I, an optimized scheduling scheme of a small time scale is generated and issued for execution through rolling optimization on the basis of a day-ahead optimized scheduling scheme, the output of the RIES equipment is adjusted, and the running economy of the RIES can be improved through fine adjustment of the day-ahead optimized scheduling scheme under the condition that the RIES load requirement is met.
In summary, the invention provides a two-stage park integrated energy system operation optimization method considering equipment load rate change, and the conclusion is as follows:
1) when the traditional RIES operation optimization method does not consider the change of the load rate of the equipment, the process of the change of the efficiency of the equipment along with the load rate cannot be described. The efficiency of the equipment does not accord with the actual operation condition, and the operation optimization scheme is unreasonable.
2) The RIES operation optimization method considering the change of the equipment load rate considers the change of the efficiency of the energy conversion equipment along with the load rate, and improves the accuracy of an equipment model.
3) Compared with the operation scheme obtained by the traditional RIES, the method provided by the invention is more in line with the actual operation condition of the equipment, reduces the cost prediction error of the scheduling scheme, and provides a more reasonable and economic operation scheme for the RIES.
4) Due to the existence of load prediction errors, the day-ahead optimal scheduling scheme is difficult to completely meet the day-to-day operation requirement. The primary full-time optimization method has extensive scheduling, cannot respond to load fluctuation in time, often easily causes load supply to deviate from actual load, and has poor applicability of a scheduling scheme; the two-stage optimization method comprising rolling optimization is fine in scheduling, and on the basis of a day-ahead optimization scheduling plan, the output of the equipment is optimized and adjusted according to load changes, so that the system load requirements can be met, and the influence of load prediction errors on the running economy of the RIES is reduced.
The above-mentioned embodiments only express the embodiments of the present invention, and therefore, should not be interpreted as limiting the scope of the present invention, and should not be interpreted as limiting the structure of the present invention in any way. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.

Claims (10)

1. The method for optimizing the operation of the regional integrated energy system by considering the change of the load rate of equipment is characterized by comprising the following steps of:
constructing a fitting equation model of the efficiency of the energy conversion equipment changing along with the load rate;
constructing an RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate;
constructing a two-stage regional comprehensive energy system operation optimization mathematical model considering the change of the equipment load rate, wherein the two-stage regional comprehensive energy system operation optimization mathematical model consists of an RIES day-ahead operation optimization model and an RIES day-in operation optimization model, and solving a day-ahead optimization problem by taking the minimized operation cost as an objective function to obtain a regional comprehensive energy system operation optimization scheme considering the change of the equipment load rate; considering load fluctuation, a rolling optimization method is combined in a daily stage, the RIES operation and the penalty cost are minimum as optimization targets, a daily schedule is adjusted, and an optimized scheduling scheme in a small time scale is solved.
2. The method for optimizing regional integrated energy system operation considering equipment load rate change according to claim 1, wherein the fitting equation model of the efficiency of the energy conversion equipment changing along with the load rate is constructed by the following process:
in order to uniformly express the relation between the efficiency and the load rate of each device, the following polynomial expression is constructed:
Figure FDA0002939375480000011
wherein η is the plant efficiency; n is the fitting order; k is a radical ofnIs a fitting coefficient; n is the equipment load rate;
the equipment load factor is obtained by the following formula:
Figure FDA0002939375480000012
in the formula, PoutOutputting energy for equipment in kW;
Figure FDA0002939375480000013
representing the capacity of the equipment in kW.
3. The method of claim 1, wherein the RIES model considering the variation of the efficiency of the energy conversion device with the load factor is constructed as follows:
the relationship between input energy and output energy of the energy conversion device in the rees is expressed by the following formula:
Pout＝ηPin (3)
in the formula, PinInputting energy for equipment, wherein the unit is kW; poutOutputting energy for equipment in kW; eta is the equipment efficiency;
considering the relation between the efficiency and the load rate of each device, the relation is expressed as follows:
Figure FDA0002939375480000021
in the formula, n is the fitting order; k is a radical ofnIs a fitting coefficient; n is the equipment load rate;
the constraint of the output power of the energy conversion equipment is expressed as follows:
Figure FDA0002939375480000022
in the formula (I), the compound is shown in the specification,
Figure FDA0002939375480000023
maximum value of output energy for the device;
the energy storage device model is established as follows:
assuming that the charging and discharging power of the energy storage device is constant in the delta t time period, the relationship between the energy stored by the device before and after charging and discharging is as follows:
WES,t+1＝WES,t(1-σES)+EES,tΔt (6)
in the formula, WES,t、WES,t+1Respectively storing energy before and after charging and discharging of the equipment, wherein the unit is kWh; sigmaESSelf-discharging rate of the energy storage device; eES,tConsidering the actual charging and discharging energy power of the energy storage equipment after the charging and discharging energy efficiency loss at the time t, and taking the charging of the equipment as positive and the unit as kW; delta t is the operation optimization step length, and the unit is h;
the limitation on the charge and discharge power of the energy storage device is described as follows:
Figure FDA0002939375480000024
Figure FDA0002939375480000025
in the formula etaES,C、ηES,DRespectively charging and discharging energy efficiency for the energy storage equipment;
Figure FDA0002939375480000026
respectively is the upper limit of the charging and discharging energy power of the equipment, and the unit is kW;
the stored energy constraints of the energy storage device are as follows:
Figure FDA0002939375480000027
in the formula (I), the compound is shown in the specification,
Figure FDA0002939375480000028
the maximum energy storage of the equipment is expressed in kWh;
the RIES model is as follows:
Figure FDA0002939375480000031
wherein α is an energy form; l isαA load level in the form of energy a; pout,α,iOutputting the alpha energy form of the device i; sαThe energy charging and discharging efficiency is the energy form alpha; eαIs an energy form alpha actual charging and discharging energy vector, takes the charging of the charging equipment as positive,
Figure FDA0002939375480000032
in the formula etaα,C、ηα,DRespectively charge and discharge energy efficiency.
4. The method of claim 1, wherein the mathematical model of the RIES day-ahead operation optimization model is a mathematical model of the RIES day-ahead operation optimization model,
the objective of the rees operation optimization is to minimize the operation cost on the premise of meeting the user load demand, and the objective function is as shown in formula (12):
minfc＝Celec+Cgas (12)
in the formula, CelecThe unit is yuan for the electricity purchasing cost; cgasThe unit is Yuan for the cost of gas purchase;
Figure FDA0002939375480000033
Figure FDA0002939375480000034
in the formula, celec,tThe unit of the t time period is the unit of yuan/(kWh); pelec,tPurchasing power in kW for t time period; c. CgasIs unit gas purchase price, unit is yuan/m3；Pgas,tThe unit is m for the gas purchase amount in t time period3。
5. The method of claim 4, wherein the constraint conditions in the mathematical model of the rees day-ahead operation optimization model include plant model constraints:
1) energy conversion equipment restraint
Energy conversion device constraints include equations (4) - (5);
2) energy storage device constraints
The energy storage device constraints include equations (6) - (9).
6. The method for optimizing the operation of the regional integrated energy system according to claim 5, wherein the periodicity of the energy storage device and the time-interval coupling characteristic are considered, and the energy storage is assumed to be consistent at the beginning and the end of the scheduling period, namely:
WES,start＝WES,end (15)
in the formula: wES,start、WES,endRespectively, the energy storage capacity at the beginning and the end of the device scheduling period is expressed in kWh.
7. The method of claim 5, wherein the constraints of the mathematical model of the RIES day-ahead operation optimization model further include system operation constraints:
1) system energy balance constraints
The system energy balance constraint is shown as equation (10);
2) tie line power constraint
The tie line power constraint is expressed as:
Figure FDA0002939375480000041
in the formula:
Figure FDA0002939375480000042
represents the upper limit of the tie line power in kW; pelec,tRepresenting the tie line power in kW.
8. The method of claim 1, wherein the mathematical model of the optimization model for operation within the RIES day is a mathematical model of the optimization model for operation within the RIES day,
an objective function: in order to realize day-ahead and day-in two-stage cooperative scheduling and ensure the globality of a scheduling scheme in a short time scale day, the output of equipment is corrected on the basis of the day-ahead scheduling scheme, penalty cost is brought into an objective function, the objective function of an optimization scheduling model in day-in operation is the RIES operation cost in each control time domain, the penalty cost is minimum, and the objective function is shown as a formula (17):
fc＝Celec+Cgas+Cp (17)
in the formula, CpFor penalty cost, a penalty term is introduced into the objective function by considering the deviation degree of each device output power and the day-ahead scheduling plan, and the unit is an element.
9. The method of optimizing regional integrated energy system operation in consideration of changes in plant load factor according to claim 8,
Figure FDA0002939375480000051
in the formula, cp,iIs a penalty factor, with units of elements; delta Pout,i,tThe degree of deviation of the output power of each device from the day-ahead schedule is given in kWh.
10. The method according to claim 1 or 8, wherein the constraints of the mathematical model of the RIES intraday operation optimization model are consistent with the constraints of the RIES intraday operation optimization model.
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