CN107358345B - Distributed combined cooling heating and power system optimization operation method considering demand side management - Google Patents
Distributed combined cooling heating and power system optimization operation method considering demand side management Download PDFInfo
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Abstract
The invention relates to a distributed combined cooling heating and power system optimization operation method considering demand side management, which comprises the following steps: 1) establishing a translatable load model from the angle of matching the thermoelectric ratios of the energy supply side and the demand side at each moment by combining the electricity utilization characteristics of various translatable loads in the combined cooling heating and power system, and respectively carrying out load translation on the cooling heating and power loads; 2) and on the basis of the combined cooling heating and power system after load translation, an optimized scheduling model is established to carry out optimized solution on the output of each device in the combined cooling, heating and power system, and an optimized operation result is obtained. Compared with the prior art, the method solves the optimized scheduling model of the CCHP system after load translation, and effectively reduces various costs.
Description
Technical Field
The invention relates to the field of planning of distributed energy sources in a power distribution network, in particular to an optimized operation method of a distributed combined cooling heating and power system considering demand side management.
Background
With the increase of global economy and population, people have increasingly increased demand for fossil energy, thereby causing problems such as global energy crisis and environmental pollution. The current energy development is developing towards the global energy Internet direction of 'interconnection, coordination and compatibility'. A combined cooling, heating and power (CCHP) system, which is used as a main carrier for converting electric energy and heat energy, plays an important role in future energy development and becomes an important component of the energy internet.
The CCHP system energy optimization management is an important aspect in the research of a combined cooling heating and power system, and the document 'energy optimization management of a combined cooling and power system' (Guo Li, Dong, Wang Chengshan, and the like; power system automation, 2009, 33 (19): 96-100) takes a combined supply system with cooling load and power load as main research objects, establishes an optimal economic objective function and performs energy optimization management on the combined cooling and power system; the literature, "micro-grid operation optimization including an electric-thermal combined system" (Li Zheng Mao, Zhang Feng, Lijun, etc. Motor engineering reports 2015, 35 (14): 3569 + 3576) combines a cold-heat-electricity combined supply system mainly comprising a heat load and an electric load with a micro-grid, and aims to achieve optimal economy so as to realize coordinated optimization operation between the combined supply system and the micro-grid; the document 'optimization scheduling of a combined cooling and heating system responding to peak-valley electricity prices' (Leyimei, Chenrui-first, Zhouyun, etc.. electric power system and its automatic chemical report, 2016, 28 (4): 25-30) establishes an optimization model with optimal economic cost by adopting peak-valley time-of-use electricity prices and fixed electricity prices, and contrasts and analyzes an energy optimization management scheme of the combined cooling and heating system under different electricity price systems. However, only with a single-target optimization method with optimal economy, the combined cooling, heating and power system cannot meet ideal requirements in the aspects of energy utilization rate, environmental protection and the like. The method includes the steps that a multi-objective optimization model considering primary energy consumption, carbon dioxide emission, system operation cost and the like is established in a literature 'control strategy and operation optimization of a combined cooling and power system for comprehensive solar energy utilization' (Liu Xingyue, Wu hong bin, power system automation, 2015, 39 (12): 1-6), the operation strategy of a CCHP system and the output of each device are optimized, and the effectiveness of the model is proved by an optimization result; the document 'energy-saving coordination optimization scheduling of a distributed combined cooling heating and power system' (Zhou army, low-grade flood, hair dragon, and the like. power grid technology, 2012, 36 (6): 8-14) establishes a multi-objective optimization model considering economic cost, environmental cost and cooling heating and power coordination cost, and solves a multi-objective function problem by using a membership function fuzzy algorithm. The literature, "small combined cooling heating and power system optimization operation research considering variable load characteristics" (Wei Da Jun, Su Xiang Li, Su Ge power grid technology, 2015, 39 (11): 3240-.
The above documents are all research on the optimized operation of the CCHP system from the perspective of the energy supply side, the matching between the output thermoelectric ratio of the energy supply side of the combined cooling heating and power system and the thermoelectric ratio of the load side of the system is poor, the fluctuation of the system equipment output is large, and meanwhile, the comprehensive benefit in the optimized operation of the combined cooling and power system is also greatly influenced. The document 'analysis of the loads of cold, heat and electricity of a gas distributed energy supply system' (Li Wei, Zhao Shi Yuan, Shenyang institute of Engineers, 2015, 11 (4): 313-; in the literature, "optimization analysis of performance of operation modes of a combined cooling, heating and power system" (Jiang Hua, Yang Xiao xi, Yang Minglin, etc. engineering thermal physics, 2003, 34 (10): 1818-. The result shows that the energy-saving benefit has the optimal value when the cold-electricity ratio of the cold-electricity combined supply system reaches a certain value; the literature, "analysis of energy-saving benefits of a natural gas distributed cogeneration system based on a viewing angle of a demand side" (Renhong Bo, Wu Qiong, Renjian, China Motor engineering reports 2015, 35 (17): 4430-.
However, the prior art also has the problems that the most economical output thermoelectric ratio of the energy supply side (namely, a combined cooling heating and power system) is poor in matching with the thermoelectric ratio required by the actual load side, so that the output fluctuation of energy supply equipment is large, the operation efficiency is low, the economical efficiency is poor, and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a distributed combined cooling heating and power system optimized operation method considering demand side management, so that the problem of comprehensive optimized operation of the combined cooling heating and power system is researched from the control angles of an energy supply side and a load demand side, and the comprehensive operation cost of a distributed combined cooling heating and power system is reduced.
The purpose of the invention can be realized by the following technical scheme:
a distributed combined cooling heating and power system optimization operation method considering demand side management comprises the following steps:
1) establishing a translatable load model from the angle of matching the thermoelectric ratios of the energy supply side and the demand side at each moment by combining the electricity utilization characteristics of various translatable loads in the combined cooling heating and power system, and respectively carrying out load translation on the cooling heating and power loads;
2) and on the basis of the combined cooling heating and power system after load translation, an optimized scheduling model is established to carry out optimized solution on the output of each device in the combined cooling, heating and power system, and an optimized operation result is obtained.
In the step 1), on the premise of ensuring that the total amount of the cold and hot loads in the whole scheduling cycle is not changed, the electric load and the cold and hot loads of the combined supply system are respectively translated, so that the thermoelectric ratio of the demand side at each time interval is matched with the thermoelectric ratio of the supply side, and the optimization variables solved according to the translatable load model are the shifting-out amount and the shifting-in amount of various translatable loads at each time interval in a typical day.
The load translation expression of the combined cooling heating and power system is as follows:
Sload,t＝Sfcload,t+Sshiftin,t-Sshiftout,t
in the formula, Sload,tIs the load value after t time shift, Sfcload,tFor a predicted value of the load for the period t, Sshiftin,t、Sshiftout,tThe translatable load values that move in and out, respectively, for time t, specifically,
where T is the scheduling period, KtotalOf the kind of translatable load, xk,m,tFor the number of shifts of class k translatable loads from period m into period t, S1,kThe load value of the kth class translation load in the 1 st working period, L is the maximum continuous working time of the translation load, S(l+1),kThe load value of the k-th class translation load in the l + 1-th time period.
In the combined cooling heating and power system, the moving quantity of the kth type translatable load meets the following requirements:
in the formula, xk.tThe number of loads that can be shifted originally in the kth class of loads during the t period, dkShift time margin for class k loads, xk.t.t'The number of shifts from time t into time t' for class k translatable loads.
In the model of the translatable load, the load model,
the electrical load translation objective function is:
in the formula, Pload,tIs the electric load value after t time shift, Pmload,tFor a target value of the electrical load for a period of t, Pfcload,iThe predicted value of the electric load in the period i is obtained, and K (i) is the electricity selling price of the electric network in the period i;
the heat load translation objective function is:
in the formula, Hload,tFor translated electricity of time period tLoad value, Hmload,tIs a target value of the thermal load for a period of t, FHEThe rated thermoelectric ratio of the gas turbine set;
the cold load translation objective function is:
in the formula, Lload,tIs the value of the cooling load after the translation of the period t, Lmload,tIs a target value of the cooling load for a period of t, FCEThe rated cold-electricity ratio of the gas turbine set.
The translatable load model is solved by adopting an interior point method.
In the step 2), the optimal scheduling model takes the minimum comprehensive cost of one day as an objective function, and the objective function expression is as follows:
in the formula, CtotalFor a total cost of one day, CM(t)、CG(t)、CE(t) the operation and maintenance cost, the purchase energy cost and the pollution gas treatment cost of the combined supply system at the moment t respectively, N is the number of system equipment, kiFor the operating maintenance cost factor, P, of the ith device of the systemi(t) is the output of the ith device during the period t, KgFor natural gas prices, K for electricity prices from the grid, CP(t) interaction cost of combined supply system and power grid, KBPFor the power price of the distributed energy pole on the internet, 0.4593 yuan/(kWh), P is selectedEXC(t) the interactive power between the system and the power grid at the moment t, wherein the positive time represents the purchase of the power from the power grid, the negative time represents the sale of the power to the power grid, and Eg(t) Natural gas consumption during t, ujDischarge coefficient of j-th gas generated for coal burning, vjEmission coefficient of J-th greenhouse gas produced for burning natural gas, J being the kind of greenhouse gas, alphajThe treatment cost of the jth polluted gas, etaeAnd ηdFor the power generation efficiency and the line transmission efficiency of the power plant,Pgridand (t) the power purchasing amount of the system to the power grid in the period t.
The constraint conditions of the optimized scheduling model comprise energy balance constraint, tie line power constraint and controllable unit constraint, wherein,
the energy balance constraint is:
in the formula, Pload(t)、Qload(t) and Lload(t) pure electric load value, thermal load value and cold load value, P, respectively, at time period tGT(t) is the power of the gas internal combustion engine, w (t) is the distribution coefficient of the waste heat in the period t for refrigeration, PEB(t)、QEB(t) electric power and heating power of the electric boiler at time t, PEC(t) and LEC(t) electric refrigerator power and refrigeration power at time t, QTES.ch(t) and QTES.dis(t) input and output powers of the energy storage means for a period of t, Qhrec(t) and LAR(t) the power of the waste heat recovery device and the power of the absorption type refrigerating machine in the period of t;
the tie line power constraint is:
in the formula (I), the compound is shown in the specification,
and
lower and upper values for tie line power;
the controllable unit constraints are:
0≤Pi≤Ni
in the formula, PiThe output power of the ith unit; n is a radical ofiThe capacity of the ith unit.
And solving the optimized scheduling model by adopting a genetic algorithm.
And when the optimized scheduling model is solved by adopting a genetic algorithm, combining the load curve of each season of typical days after load translation with the predicted load curve before load translation as initial data.
Compared with the prior art, the invention has the following advantages:
(1) the invention makes full use of the translation characteristic of the cold, heat and electricity load to translate the cold, heat and electricity load, so that the cold, heat and electricity load is more consistent with the economic thermoelectric ratio of a cold, heat and electricity system, and the running stability and the economical efficiency of the CCHP unit are improved;
(2) and the optimized scheduling model of the CCHP system is solved after the cooling, heating and power loads are translated, so that the comprehensive energy supply cost is effectively reduced.
(3) In the optimized scheduling model established by the invention, the influence of various factors such as economy, energy, environment and the like on the optimized operation of the combined supply system is considered, the energy index and the environment index are converted into economic indexes, the cost in all aspects is integrated to be used as a system optimization objective function, and the solution result more comprehensively takes social benefits into account.
Drawings
FIG. 1 is a CCHP system energy flow diagram designed by the present invention;
FIG. 2 is a flow chart of the method of optimizing operation of the present invention;
FIG. 3 is typical daily load data for each season in the embodiment;
FIG. 4 is a graph illustrating the load shifting results in summer;
FIG. 5 is a graph illustrating the results of winter load shifting;
FIG. 6 is a schematic diagram of a scheduling result before load shifting in summer;
FIG. 7 is a schematic diagram of a scheduling result after load shifting in summer;
FIG. 8 is a diagram illustrating a scheduling result before load shifting in winter;
fig. 9 is a schematic diagram of a scheduling result after load shifting in winter.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The invention provides a distributed combined cooling heating and power system optimization operation method considering demand side management, which combines the power utilization characteristics of various translatable loads in a combined cooling and heating power system (CCHP system), establishes a translatable load model from an angle meeting the matching of thermoelectric ratios of an energy supply side and a demand side at various moments, and respectively carries out load translation on cooling, heating and power loads; and a multi-objective optimization operation mathematical model comprehensively considering factors such as economy, energy, environment and the like is constructed, the output of each device of the system is optimized and solved, and the influence of load translation on the operation of the CCHP system is compared through example analysis.
1. CCHP system
The structure of the combined cooling heating and power system designed by the invention is shown in figure 1. The gas internal combustion engine in the CCHP system generates power to supply power to users, high-temperature smoke generated at the same time is recovered by the waste heat recovery device, and the recovered heat is provided for a heat load or is conveyed to an absorption refrigerator for refrigeration. When the electric energy produced by the gas internal combustion engine set is larger than the electric load, the redundant electric energy is sold to an external power grid; and conversely, when the electric energy is less than the electric load, purchasing the electricity from the power grid. When the heat energy recovered by the waste heat recovery device is larger than the cold and hot load requirements, the excess waste heat energy is stored in the energy storage device; when the recovered heat energy is less than the cold and hot load, the heat energy of the energy storage device is preferentially utilized, and the insufficient part is supplemented by the heating of the electric boiler or the refrigeration of the electric refrigerator.
2. Load shift objective function
The load translation expression of the CCHP system is as follows:
Sload,t＝Sfcload,t+Sshiftin,t-Sshiftout,t (1)
in the formula, Sload,tIs the load value after t time shift, Sfcload,tFor a predicted value of the load for the period t, Sshiftin,t、Sshiftout,tThe translatable load values that move in and out, respectively, for time t, specifically,
where T is the scheduling period, KtotalOf the kind of translatable load, xk,m,tFor the number of shifts of class k translatable loads from period m into period t, S1,kThe load value of the kth class translation load in the 1 st working period, L is the maximum continuous working time of the translation load, S(l+1),kThe load value of the k-th class translation load in the l + 1-th time period.
In the CCHP system, the moving quantity of the kth type translatable load satisfies the following conditions:
in the formula, xk.tThe number of loads that can be shifted originally in the kth class of loads during the t period, dkShift time margin for class k loads, xk.t.t'The number of shifts from time t into time t' for class k translatable loads.
3. Various translatable load translation objective functions of CCHP system
In the process of translating the loads, on the premise of ensuring that the total amount of the cold and hot loads in the whole scheduling period is not changed, the electric loads and the cold and hot loads of the combined supply system are translated respectively, and the thermoelectric ratio of the demand side at each time interval is matched with the thermoelectric ratio of the supply side through translation between the loads. Firstly, the electric load is translated, and the target load translated by the electric load is in inverse proportion to the electricity price. And then shifting the cold and hot loads, wherein the target load of shifting the cold and hot loads is the product of the shifted electrical load curve and the corresponding rated thermoelectric ratio.
In the model of the translatable load, the load model,
the electrical load translation objective function is:
in the formula, Pload,tIs the electric load value after t time shift, Pmload,tIs a period of tTarget value of electrical load, Pfcload,iThe predicted value of the electric load in the period i is obtained, and K (i) is the electricity selling price of the electric network in the period i;
the heat load translation objective function is:
in the formula, Hload,tIs the electric load value after t time shift, Hmload,tIs a target value of the thermal load for a period of t, FHEThe rated thermoelectric ratio of the gas turbine set;
the cold load translation objective function is:
in the formula, Lload,tIs the value of the cooling load after the translation of the period t, Lmload,tIs a target value of the cooling load for a period of t, FCEThe rated cold-electricity ratio of the gas turbine set.
4. CCHP system optimization operation mathematical model
(1) Objective function
The method is based on the translated cold, heat and power load data, considers the influence of various factors such as economy, energy, environment and the like on the optimized operation of the combined supply system, converts the energy index and the environment index into the economical index, integrates the cost of various aspects as the system optimization objective function, and obviously reduces the system operation cost after load translation.
The optimized scheduling model takes the minimum comprehensive cost of one day as an objective function, and the expression of the objective function is as follows:
in the formula, CtotalFor a total cost of one day, CM(t)、CG(t)、CE(t) the operation and maintenance cost, the purchase energy cost and the pollution gas treatment cost of the combined supply system at the moment t are respectively, and N is the system settingNumber of units, kiFor the operating maintenance cost factor, P, of the ith device of the systemi(t) is the output of the ith device during the period t, KgFor natural gas prices, K for electricity prices from the grid, CP(t) interaction cost of combined supply system and power grid, KBPFor the power price of the distributed energy pole on the internet, 0.4593 yuan/(kWh), P is selectedEXC(t) the interactive power between the system and the power grid at the moment t, wherein the positive time represents the purchase of the power from the power grid, the negative time represents the sale of the power to the power grid, and Eg(t) Natural gas consumption during t, ujDischarge coefficient of j-th gas generated for coal burning, vjEmission coefficient of J-th greenhouse gas produced for burning natural gas, J being the kind of greenhouse gas, alphajThe treatment cost of the jth polluted gas, etaeAnd ηdFor the power generation efficiency and line transmission efficiency, P, of the power plantgridAnd (t) the power purchasing amount of the system to the power grid in the period t.
(2) Constraint conditions
The constraint conditions of the optimized scheduling model include an energy balance constraint, a tie line power constraint, and a controllable unit constraint, wherein,
the energy balance constraint is:
in the formula, Pload(t)、Qload(t) and Lload(t) pure electric load value, thermal load value and cold load value, P, respectively, at time period tGT(t) is the power of the gas internal combustion engine, w (t) is the distribution coefficient of the waste heat in the period t for refrigeration, PEB(t)、QEB(t) electric power and heating power of the electric boiler at time t, PEC(t) and LEC(t) electric refrigerator power and refrigeration power at time t, QTES.ch(t) and QTES.dis(t) input and output powers of the energy storage means for a period of t, Qhrec(t) and LAR(t) the power of the waste heat recovery device and the power of the absorption type refrigerating machine in the period of t;
the tie line power constraint is:
in the formula (I), the compound is shown in the specification,
and
lower and upper values for tie line power;
the controllable unit constraints are:
0≤Pi≤Ni (10)
in the formula, PiThe output power of the ith unit; n is a radical ofiThe capacity of the ith unit.
The load after translation more accords with the running characteristic of the gas internal combustion engine, and when the load value changes in the constraint condition formula (8), the power P of the gas internal combustion engineGT(t) exchanging power P with the gridEXC(t) will also change, thereby affecting the value of the objective function in equation (7).
5. System optimization variable and algorithm flow
The optimization variables solved by the load translation model are the shift-in quantity and the shift-out quantity of each type of translation load in each time period in a typical day, and the number of the optimization variables is the shift-in quantity and the shift-out quantity of each type of translation load in 24 time periods, which are 24 multiplied by K in totaltotalA plurality of; and solving the load translation model by adopting an interior point method. The optimized variables obtained in the optimized operation of the combined supply system are the output of the gas turbine set, the input and output power of the energy storage device and the residual heat distribution coefficient at each moment.
According to the load data before and after translation, the optimization objective function of the combined supply system is solved by utilizing the genetic algorithm. As shown in fig. 2, the optimization solving steps are as follows:
1) inputting predicted thermoelectric load data (predicted load curve) of a typical day, the type and the electricity utilization characteristics of the translational load, and the translatable amount of each type of translational load at each moment.
2) And determining a target load curve under the condition of ensuring that the total amount of the cold, heat and electricity loads in a typical day is not changed. When determining the target electric load curve, the electric load is required to be reduced when the electricity price is high, and the electric load is required to be increased when the electricity price is low; when determining the target thermal load, the thermoelectric ratio at each time is required to be close to the rated thermoelectric ratio on the power supply side; when the target cooling load is determined, the thermoelectric ratio at each time is required to be close to the rated cooling ratio on the power supply side.
3) When the translation load model is solved, the model is known to be a linear constraint quadratic programming problem. Many methods for solving quadratic programming include an interior point method, an ellipsoid algorithm, a Lemke method, an active set method, and the like. Because the translational load model obtained by the invention has more optimization variables, an interior point method is selected for solving.
4) And after the load curve of each season of typical days after translation is obtained through solving, combining the predicted load curve before translation as initial data for solving the optimization operation model of the combined supply system, and inputting operation parameters and cost parameters of various devices of the combined supply system.
5) And solving variables in the optimization operation model of the joint supply system by adopting a genetic algorithm. And initializing the population according to the operating power constraint of various devices.
6) And (4) chromosome decoding, and calculating interactive power equal-power numerical values of a gas internal combustion engine set, an electric boiler, an electric refrigerator, an energy storage device and a system of the combined supply system and the power grid.
7) Calculating the individual fitness value, recording the optimal individual, judging whether the optimal individual meets the termination condition, outputting the optimal result if the optimal individual meets the termination condition, and continuing the following steps if the optimal individual does not meet the termination condition.
8) The steps of roulette selection, crossover and variation are performed and the return to step 6) is made.
6. Examples of the applications
(1) Introduction to the examples
In order to verify the effectiveness of the proposed operation model and optimization method, the present embodiment uses the summer typical daily load and the winter typical daily load of a certain city building, and the load data is shown in fig. 3. The peak value of the electric load in summer is 880kW, and the peak value of the cold load in summer is 1920 kW. The peak value of the electric load in winter is 916kW, and the peak value of the heat load is 2400 kW. In the following examples, 4 gas internal combustion engines having a capacity of 200kW, 1 electric refrigerator having a capacity of 200kW, 1 electric boiler having a capacity of 1600kW, and 1 energy storage device having a capacity of 500kWh were arranged.
Table 1 shows the electrical characteristics and the number of the various types of load devices that can be translated, and this embodiment uses 5 types of load devices that can be translated in common in different types of loads; table 2 shows the number of the various types of electrical equipment capable of translating in each time period, the total number of the 5 types of load equipment capable of translating is 7484, the rated thermoelectric ratio of the power supply side of the combined supply system is 1.29, and the rated cold-to-power ratio is 1.55. The coefficient of the treatment cost of the polluted gas in the invention is detailed in the environmental benefit analysis of distributed power generation in the literature (Qian ScoYuan, Shi Xiao Dang, and the like, China Motor engineering Proc, 2008, 28 (29): 11-15), the time-of-use price data and other parameters related in the calculation example in the literature of the optimal configuration and applicability analysis of a distributed combined cooling, heating and power system (Hurong, Majie, Lizhen, and the like, the power grid technology, 2017, 41 (2): 418-.
TABLE 1 Electrical characteristics for translatable loads
TABLE 2 number of load devices translatable per time period
(2) Analysis of optimization results
1) Load translation results
Fig. 4 and fig. 5 show the shifting result of the summer load and the shifting result of the winter load, respectively, as can be seen from fig. 4, the peak value of the cooling load is reduced from 1920kW to 1585kW and the valley value of the cooling load is increased from 200kW to 646kW in the shifted summer cooling-power load curve compared with the predicted cooling-power load curve; the peak value of the electric load is reduced to 754.4kW from 880kW, and the valley value of the electric load is increased to 444.8kW from 272 kW.
As can be seen from FIG. 5, the shifted winter thermoelectric load curve has a peak value of the thermal load decreased from 2400kW to 1456kW and a valley value of the thermal load increased from 660kW to 991.6kW compared with the predicted thermoelectric load curve; the peak value of the electric load is reduced to 755kW from 916kW, and the valley value of the electric load is increased to 340kW from 196 kW. The load peak-valley difference of each season is obviously reduced, the load translation plays a role in peak clipping and valley filling, and simultaneously the thermoelectric ratio of the load at each moment is closer to that of the energy supply side of the combined supply system.
2) Optimizing scheduling results before and after load translation
Fig. 6 and 7 respectively show the optimized scheduling output results of each device of the system before load translation in summer and after load translation, the output of the gas turbine set is relatively average at each moment after load translation of the system, the average load rate is increased from 0.69 to 0.78, and the utilization rate of the gas turbine set is further increased. The output of the electric refrigerator after load translation is obviously reduced, and the output is only output at individual time, because the cold-electricity ratio of the load side after translation is closer to the energy supply side, the demand of the combined supply system on extra refrigerating capacity is reduced, and the dependence degree on the electric refrigerator is reduced.
Table 3 shows the comparison of the running costs before and after the load translation, and it can be known from the table that the costs of the combined supply system after the load translation are all reduced to a certain extent. The reason is that the cold and electric power generated by the energy supply side of the system is more matched with the load side, so that the electricity purchasing quantity through the external network is reduced, the electricity selling quantity of the system to the external network is increased, and the operation cost of the system is reduced. The total operation cost of the combined supply system after load translation is reduced by 15.6 percent compared with that before load translation.
TABLE 3 comparison of cost before and after load shifting in summer
Fig. 8 and 9 are respectively the optimized scheduling output results of each unit of the system before and after winter load translation. The average load rate of the gas turbine set is improved from 0.71 to 0.80. The peak value of the output force of the electric boiler in the system is reduced to 730kW from 1558.7kW before load shifting, so that the configuration capacity of the electric boiler can be reduced to a certain extent in the configuration of the combined supply system, and the comprehensive operation cost of the system can be further reduced. Meanwhile, the balance of supply and demand in the system can reduce the electric energy demand on the external network, and the interaction power of the system and the external network is greatly reduced, so that the demand on a high-power transmission capacity line can be reduced.
Table 4 shows a comparison of the running costs before and after shifting the load in winter, and it can be seen from the table that the costs of the system after shifting the load are also reduced to some extent. The total running cost of the combined supply system after load translation is reduced by 9.16 percent compared with that before load translation.
TABLE 4 cost comparison before and after load translation in winter
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. A distributed combined cooling heating and power system optimization operation method considering demand side management is characterized by comprising the following steps:
1) the power consumption characteristics of various translatable loads in the combined cooling heating and power system are combined, a translatable load model is established from an angle meeting the matching of thermoelectric ratios of the energy supply side and the demand side at each moment, and load translation is carried out on the cooling heating and power loads respectively, specifically: firstly, translating an electric load, wherein the target load translated by the electric load is in inverse proportion to the electricity price, and then translating a cold-hot load, wherein the target load translated by the cold-hot load is the product of a translated electric load curve and corresponding rated cold-hot ratio and rated hot-hot ratio;
2) on the basis of the combined cooling heating and power system after load translation, an optimized scheduling model is established to carry out optimized solution on the output of each device in the combined cooling, heating and power system to obtain an optimized operation result;
in the step 1), on the premise of ensuring that the total amount of the cold and hot loads in the whole scheduling cycle is not changed, respectively translating the electric loads and the cold and hot loads of the combined supply system, so that the hot-electricity ratio of the demand side at each time interval is matched with the hot-electricity ratio of the supply side, and the optimization variables solved according to the translatable load model are the shifting-out amount and the shifting-in amount of various translated loads at each time interval in a typical day;
in the model of the translatable load, the load model,
the electrical load translation objective function is:
in the formula, Pload,tIs the electric load value after t time shift, Pmload,tFor a target value of the electrical load for a period of t, Pfcload,iThe predicted value of the electric load in the period i is obtained, and K (i) is the electricity selling price of the electric network in the period i;
the heat load translation objective function is:
in the formula, Hload,tIs the electric load value after t time shift, Hmload,tIs a target value of the thermal load for a period of t, FHEThe rated thermoelectric ratio of the gas turbine set;
the cold load translation objective function is:
in the formula, Lload,tIs the value of the cooling load after the translation of the period t, Lmload,tIs a target value of the cooling load for a period of t, FCEThe rated cold-electricity ratio of the gas turbine set.
2. The optimal operation method of the distributed combined cooling heating and power system considering the demand side management as claimed in claim 1, wherein the load translation expression of the combined cooling and heating and power system is as follows:
Sload,t＝Sfcload,t+Sshiftin,t-Sshiftout,t
in the formula, Sload,tIs the load value after t time shift, Sfcload,tFor a predicted value of the load for the period t, Sshiftin,t、Sshiftout,tThe translatable load values that move in and out, respectively, for time t, specifically,
where T is the scheduling period, KtotalOf the kind of translatable load, xk,m,tFor the number of shifts of class k translatable loads from period m into period t, S1,kThe load value of the kth class translation load in the 1 st working period, L is the maximum continuous working time of the translation load, S(l+1),kThe load value of the k-th class translation load in the l + 1-th time period.
3. The optimal operation method of the distributed combined cooling heating and power system with the consideration of the demand side management as claimed in claim 2, wherein in the combined cooling, heating and power system, the moving number of the kth type translatable load satisfies:
in the formula (I), the compound is shown in the specification,xk.tthe number of loads that can be shifted originally in the kth class of loads during the t period, dkShift time margin for class k loads, xk.t.t'The number of shifts from time t into time t' for class k translatable loads.
4. The distributed combined cooling heating and power system optimization operation method considering demand side management according to claim 1, wherein the translatable load model is solved by an interior point method.
5. The method for optimally operating the distributed combined cooling heating and power system with consideration of demand side management according to claim 1, wherein in the step 2), the optimal scheduling model takes the minimum comprehensive cost of one day as an objective function, and the expression of the objective function is as follows:
in the formula, CtotalFor a total cost of one day, CM(t)、CG(t)、CE(t) the operation and maintenance cost, the purchase energy cost and the pollution gas treatment cost of the combined supply system at the moment t respectively, N is the number of system equipment, kiFor the operating maintenance cost factor, P, of the ith device of the systemi(t) is the output of the ith device during the period t, KgFor natural gas prices, K for electricity prices from the grid, CP(t) interaction cost of combined supply system and power grid, KBPFor the power price of the distributed energy pole on the internet, 0.4593 yuan/(kWh), P is selectedEXC(t) the interactive power between the system and the power grid at the moment t, wherein the positive time represents the purchase of the power from the power grid, the negative time represents the sale of the power to the power grid, and Eg(t) Natural gas consumption during t, ujDischarge coefficient of j-th gas generated for coal burning, vjEmission coefficient of J-th greenhouse gas produced for burning natural gas, J being the kind of greenhouse gas, alphajThe treatment cost of the jth polluted gas, etaeAnd ηdFor generating efficiency and line of power plantEfficiency of transmission, PgridAnd (t) the power purchasing amount of the system to the power grid in the period t.
6. The method for optimized operation of a distributed combined cooling, heating and power system with consideration of demand side management according to claim 5, wherein the constraints of the optimized scheduling model comprise energy balance constraints, tie line power constraints and controllable unit constraints, wherein,
the energy balance constraint is:
in the formula, Pload(t)、Qload(t) and Lload(t) pure electric load value, thermal load value and cold load value, P, respectively, at time period tGT(t) is the power of the gas internal combustion engine, w (t) is the distribution coefficient of the waste heat in the period t for refrigeration, PEB(t)、QEB(t) electric power and heating power of the electric boiler at time t, PEC(t) and LEC(t) electric refrigerator power and refrigeration power at time t, QTES.ch(t) and QTES.dis(t) input and output powers of the energy storage means for a period of t, Qhrec(t) and LAR(t) the power of the waste heat recovery device and the power of the absorption type refrigerating machine in the period of t;
the tie line power constraint is:
in the formula (I), the compound is shown in the specification,
and
lower and upper values for tie line power;
the controllable unit constraints are:
0≤Pi≤Ni
in the formula, PiThe output power of the ith unit; n is a radical ofiThe capacity of the ith unit.
7. The distributed combined cooling heating and power system optimization operation method considering demand side management according to claim 1, wherein the optimization scheduling model is solved by a genetic algorithm.
8. The optimal operation method of the distributed combined cooling heating and power system considering the demand side management as claimed in claim 7, wherein when the optimal scheduling model is solved by using a genetic algorithm, a load curve of each season on a typical day after load translation is combined with a predicted load curve before load translation to serve as initial data.
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