CN110544175A - Household intelligent power utilization-oriented multi-energy comprehensive optimization scheduling method - Google Patents
Household intelligent power utilization-oriented multi-energy comprehensive optimization scheduling method Download PDFInfo
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Abstract
The invention relates to a multi-energy comprehensive optimization scheduling method for household intelligent power utilization, which comprises the following steps: 1) aiming at heating equipment in a family, considering the energy characteristics and the thermal coupling relation of a household gas device and temperature control type load equipment, and establishing a dynamic heat transfer mathematical model in the family; 2) aiming at the energy utilization characteristics of a distributed power supply, energy storage equipment and other various power load equipment in a family, establishing a corresponding equipment mathematical model; 3) establishing a household multi-energy comprehensive optimization scheduling model, wherein the model aims at minimizing energy consumption cost and comfort loss of resident users, and simultaneously considers necessary constraint conditions of system operation; 4) various household energy-using devices and user set parameters are input into a CPLEX optimization solver, optimization is carried out by combining predicted temperature data, hot water consumption and photovoltaic output conditions, and a household multi-energy comprehensive optimization scheduling result is solved.
Description
Technical Field
the invention belongs to the technical field of intelligent power utilization, and relates to a multi-energy comprehensive optimization scheduling method.
background
in recent years, due to the rapid development of renewable energy sources and the emergence of the problems of wind and light abandonment, more and more researches have considered that surplus between Power supply and demand is stored in a simpler manner by converting electric energy into suitable energy carriers (such as hydrogen, natural Gas, etc.) for balancing load demand fluctuations, reducing impact on the Power grid, and Power-Gas technology (P2G) is rapidly emerging. Meanwhile, the household intelligent power utilization system can actively respond to flexible power price mechanisms including time-of-use power price, real-time power price, peak power price and the like, help users manage household electrical equipment, optimize household load scheduling, and achieve the purposes of power utilization economy, comfort and the like. The household gas device is taken as one of household heat supply equipment to participate in the household intelligent power utilization system for carrying out optimization scheduling together, powerful support can be provided for the sustainable renewable development of energy, and the economy, the environmental protection and the comfort of the household energy can be further improved.
Disclosure of Invention
The invention aims to provide a multi-energy comprehensive optimization scheduling method which can promote local consumption of distributed photovoltaic and load peak clipping and valley filling and realize a comfort level target of household energy, and the technical scheme is as follows:
a household intelligent power utilization-oriented multi-energy comprehensive optimization scheduling method comprises the following steps:
1) Aiming at heating equipment in a family, considering the energy characteristics and the thermal coupling relation of a household gas device and temperature control type load equipment, and establishing a dynamic heat transfer mathematical model in the family;
2) Aiming at the energy utilization characteristics of a distributed power supply, energy storage equipment and other various power load equipment in a family, establishing a corresponding equipment mathematical model;
3) establishing a household multi-energy comprehensive optimization scheduling model, wherein the model aims at minimizing energy consumption cost and comfort loss of resident users, and simultaneously considers necessary constraint conditions of system operation;
4) Various household energy-using equipment and user set parameters are input into a CPLEX optimization solver, optimization is carried out by combining predicted temperature data, hot water consumption and photovoltaic output conditions, and a household multi-energy comprehensive optimization scheduling result is solved;
In the step 1, the heat load device of the dynamic heat transfer mathematical model in the family mainly comprises two parts, namely a family heating device and a hot water device, and the mathematical models are respectively established:
(1) heating equipment model: establishing a thermodynamic model of the device by means of a first order differential equation representing the course of temperature variation;
(2) A hot water equipment model: and establishing a mathematical model of the water heating equipment according to the operation characteristics of the water heating equipment.
Establishing mathematical models of the household electrical load equipment in the step 2) respectively as follows:
(1) an energy storage device: setting the charging and discharging power of the energy storage equipment as the charging and discharging state of the energy storage equipment, wherein 1 is in the corresponding charging and discharging state, and otherwise 0 is not in the corresponding charging and discharging state; eta ch and eta dch are respectively charge and discharge efficiency, respectively charge and discharge maximum power, and epsilon represents self-discharge rate; the charge and discharge operation constraints of the energy storage device are as follows:
SOC≤SOC≤SOC
SOC≥SOC
(2) distributed power generation equipment: for solar photovoltaic, the solar photovoltaic is directly mounted on a household comprehensive energy management system through a DC/AC inverter, and the output of the solar photovoltaic can be supplied to the load and the stored energy in the system and can also be reversely supplied to a power grid;
(3) Interruptible load: setting xISL (i) as the working state of the interruptible load in the ith time interval, 0 as non-working, 1 as working, and [ bISL, eISL ] as the allowed working time interval of the load; IISL and pISL are the desired operating duration and rated power of the load, respectively, the interruptible load operating constraint can be expressed as follows:
P(i)＝x(i)·p
(4) uninterruptible load: let xNISL (i) be the working state of the uninterruptible load in the i-th period, 0 be out of work, 1 be in work, and [ bNISL, eNISL ] be the allowed working period of the load; lNISL and pNISL are the desired operating duration and rated power of the load, respectively, the uninterruptible load operating constraint is expressed by:
P(i)＝x(i)·p。
in the step 3), the family multi-energy comprehensive optimization scheduling model comprises two parts, namely an objective function and a constraint condition, wherein the objective function is selected to minimize family energy cost in a scheduling time range, including gas cost and electricity cost, and reduce comfort loss of a user as much as possible, and is divided into two parts, namely an economic objective and a comfort objective; the constraint conditions mainly include the operation constraint of equipment and the comfort constraint of a user, and the gas-type wall-mounted boiler is ensured to be in a normal operation state and mainly includes the temperature upper and lower limit constraint of an aqueous medium, the hot water flow constraint and the total output constraint of the gas furnace.
Drawings
FIG. 1 Power Curve of photovoltaic output Power and non-dispatchable load demand for the family on the following day
FIG. 2 example 1 plan for scheduling electrical devices in a household
FIG. 3, example 1, room temperature and water temperature optimization result curve
FIG. 4 scheduling plan for each electric device in family according to embodiment 3
FIG. 5, example 3 Room temperature and Water temperature optimization result curves
FIG. 6, examples 2 and 3, fuel gas consumption curves
Detailed Description
in order to realize comprehensive optimized dispatching of electric and gas energy in a family, a mathematical model of the family gas type wall-mounted boiler is established, a thermodynamic coupling relation between electric power and gas in the family is considered, and a family multi-energy comprehensive optimized dispatching model is established so as to realize optimized energy management under participation of multiple energy sources; the provided household gas wall-mounted boiler model can fully reflect household energy utilization characteristics, and the household multi-energy comprehensive optimization scheduling model can cooperatively schedule various household electrical equipment and gas devices, so that the local consumption of distributed photovoltaic is promoted, load peak clipping and valley filling are promoted, and the comfort level target of household energy utilization is realized.
The patent provides a household intelligent power utilization-oriented multi-energy comprehensive optimization scheduling method, which comprises the following steps:
1) aiming at heating equipment in a family, a dynamic heat transfer mathematical model in the family is established by considering the energy characteristics and the thermal coupling relation of a household gas device and temperature control type load equipment.
2) and establishing a mathematical model of corresponding equipment aiming at the energy utilization characteristics of a distributed power supply, energy storage equipment and other various power load equipment in a family.
3) and establishing a household multi-energy comprehensive optimization scheduling model, wherein the model aims at minimizing the energy consumption cost and comfort loss of resident users, and simultaneously considers necessary constraint conditions of system operation.
4) Various household energy-using devices and user set parameters are input into a CPLEX optimization solver, optimization is carried out by combining predicted temperature data, hot water consumption and photovoltaic output conditions, and a household multi-energy comprehensive optimization scheduling result is solved.
In the step 1, the heat load device of the household multi-energy comprehensive optimization scheduling model mainly comprises a household heating device and a hot water device, and the mathematical model is established as follows:
The household heat load considered by the method mainly comprises temperature control type electric load equipment such as an air conditioner and a water heater and a gas type wall-mounted boiler, and the household heat condition is analyzed by modeling the two types of equipment. Wherein, gas formula hanging stove has heating and two kinds of functions of hot water, and its heating function uses "gas furnace + radiator" integrated mode to realize: the water in the heat exchanger is heated and heated by absorbing the heat energy released by the combustion of the natural gas, and then the heat energy is transmitted to the indoor air through a radiator in the communicated house by taking the water as a heat transmission medium; the hot water function is to heat water directly through the heat exchanger and output hot water for users to use. According to the energy utilization characteristics and the thermodynamic coupling relation of the gas type wall-mounted furnace and two temperature control type load devices, mathematical models are respectively established:
(1) And (3) a heating equipment model. Because the gas furnace heats the indoor environment through the water medium, the change of the room temperature and the water medium temperature and the heat transmission rate of the radiator and the indoor air need to be calculated, so that the gas consumption is determined, and the room temperature is controlled. The air conditioning equipment can consume electric energy to generate a large amount of heat energy, and the indoor temperature can be quickly adjusted. The thermodynamic model of the plant can be established by a first order differential equation representing the course of the temperature variation:
P(i)＝n·P·[T(i)-T(i)]/ΔT
T(i+1)＝t·[P(i)-P(i)]/V.to·C+T(i)
Unit represents the heat conduction power of a single group of radiators under the standard temperature difference, delta Ts represents the standard temperature difference, n represents the group number of the radiators, and t represents the operation step length; pin (i) represents the heat exchange power between the aqueous medium and the heat exchanger of the gas device at the moment i, Prad (i) represents the heat released to a room by the aqueous medium for heating at the moment i, vin.total represents the total volume of the aqueous medium, and Cw represents the specific heat capacity of water and has the value of 4.185kJ/kg DEG C; pac (i) represents heating power of the air conditioner, tin (i) represents room temperature at the moment i, the change of the room temperature is related to outdoor temperature and heat conduction power of the heat radiator, and R, C are equivalent thermal resistance and equivalent thermal capacitance of the room respectively.
(2) a hot water plant model. The hot water function of the gas stove is similar to the heating function, the water flowing through the heat exchanger is heated by the heat generated by the combustion of the natural gas and is output to the water storage tank of the electric water heater, wherein the main reason for the change of the hot water temperature is that a user uses the continuous supplement of hot water and cold water. Establishing a mathematical model according to the operating characteristics of the hot water equipment:
T(i+1)＝[(V(i)-V(i))·T+(V-V(i))·T(i)+V(i)·T]/
V+P(i)·t/VC
P(i)＝CV(i)·(T-T)/t
Pwater (i) shows the real-time heat exchange power of the water heated by the gas-fired device, vhot (i) shows the volume of the hot water output at the moment, vcold (i) shows the amount of the hot water used at the moment, pw (i) shows the output power of the electric water heater, tw (i), Tset and Tcold show the real-time temperature of the hot water, the set temperature of the gas-fired device and the initial temperature of the cold water, respectively, and Vtotal shows the volume of the water storage device.
in the step 2, a mathematical model of the household electrical load equipment is established as follows:
(1) An energy storage device. Setting the charging and discharging power of the energy storage equipment as the charging and discharging state (1 is in the corresponding charging and discharging state, otherwise 0 means not in the corresponding charging and discharging state); eta ch and eta dch are respectively charge and discharge efficiency, respectively charge and discharge maximum power, and epsilon represents self-discharge rate; the charge and discharge operation constraints of the energy storage device are as follows:
SOC≤SOC≤SOC
SOC≥SOC
(2) a distributed power generation apparatus. Taking solar photovoltaic as an example, the solar photovoltaic is directly mounted on a household integrated energy management system through a DC/AC inverter. The output of the power transmission system can be supplied to the load and the stored energy in the system, and can also be reversely transmitted to the power grid.
(3) an interruptible load is set as xISL (i) which is the working state (0 represents non-working and 1 represents working) of the interruptible load in the ith time interval, and [ bISL, eISL ] is the allowed working time interval of the load; the lISL and pISL are the required operating time and rated power of the load, respectively. The interruptible load workload constraint may be represented by:
P(i)＝x(i)·p
(4) Non-interruptible load, let xNISL (i) be the working state of the non-interruptible load in the i-th period (0 represents not working, 1 represents working), and [ bNISL, eNISL ] be the allowed working period of the load; lNISL and pNISL are the required operating duration and the rated power of the load, respectively. The uninterruptible load workload constraint may be expressed as:
P(i)＝x(i)·p
in the step 3, the family multi-energy comprehensive optimization scheduling model comprises two parts, namely an objective function and a constraint condition. The objective function is selected to minimize the household energy cost (including gas cost and electricity cost) in the scheduling time range and reduce the comfort loss of the user as much as possible, and is divided into an economical objective and a comfort objective. Can be expressed as:
min F＝ωF+(1-ω)F
privebuy (i) and privacell (i) are respectively the electricity purchasing price and the electricity selling price of the system interacting with the power grid in the ith period, Vgas (i) and privegas respectively represent the consumption and the unit price of natural gas, Qn represents the heat energy generated by unit volume of the natural gas, 9.8kWh/m3, and eta represents the energy conversion efficiency of a gas device. User set values respectively representing the indoor temperature and the hot water temperature represent the upper and lower limit temperature difference of the two temperatures. Cin and Cw represent loss coefficients under standard temperature difference, a weighting parameter omega is introduced into the model, and a user can set parameter values by himself to balance economical efficiency and comfort targets.
The constraints are mainly the operation constraints of the device and the comfort constraints of the user, wherein the operation constraints of the interruptible load, the non-interruptible compliance and the energy storage device are described in steps 1 and 2, respectively. User comfort constraints for air conditioners and water heaters are embodied in the range of room temperature and hot water temperature that is within a user's satisfaction, as follows:
in addition, the decision model should also ensure that the gas-fired wall-mounted boiler is in a normal operation state, and mainly includes the upper and lower temperature limits of the aqueous medium, the hot water flow constraint and the total output constraint of the gas-fired boiler, which can be expressed as:
And 4, solving the household multi-energy comprehensive optimization scheduling model established in the step 3. Considering that a large number of decision variables and constraint conditions are involved in the model, a mature and efficient CPLEX solver is adopted to solve the problem. The specific solving process is as follows: 1. inputting the operating parameters and user set parameters of the electrical equipment model into a CPLEX solver; 2. inputting a model constraint condition and a target function, and predicting the obtained photovoltaic output data, an unscheduled load curve and environmental temperature data; 3. and solving by using a CPLEX solver to obtain an optimized scheduling result.
The simulation calculates the energy utilization plan of the future day by taking 15 minutes as a step, and the energy utilization plan is divided into 96 periods. The model only considers the daily energy utilization condition of the household user in winter, and the output condition, the non-dispatchable load curve and the environment temperature of the household photovoltaic cell on the next day obtained by the prediction means are shown in figure 1. Under a flexible electric power market environment, the simulation adopts peak-valley time-of-use electricity price as the purchase and sale electricity price, and the gas price is 2.28 yuan/m 3.
The scheduling involves various loads, wherein the operation parameters of the interruptible and non-interruptible scheduling loads are shown in table 1. In thermodynamic parameters of the temperature control type dispatching load, the residential thermal resistance and the heat capacity value are respectively 18 ℃/kW and 0.525 kWh/DEG C, the total capacity of a water tank of an electric water heater is 100L, and the rated powers of an air conditioner and the electric water heater are respectively 1.8kW and 3.6 kW. The ideal indoor temperature is set to 18 ℃, the upper and lower floating ranges are 2 ℃, the ideal hot water temperature is 60 ℃, the upper and lower floating ranges are 10 ℃, and the cold water temperature in winter is assumed to be 5 ℃. The parameters of the energy storage device and the gas wall-hanging furnace are respectively shown in the table 2 and the table 3.
In order to evaluate the optimization effect of the household multi-energy comprehensive optimization model containing the gas furnace, three calculation examples are designed for research, and the respective optimization results are compared. In the first example, only the optimal scheduling of the electric equipment is considered, and the gas-fired device problem, namely the typical household energy management problem, is not considered; in the second calculation example, heat supply is provided by gas equipment, and the air conditioner and the electric water heater are not considered to participate in optimized scheduling, because families equipped with the gas equipment tend to the energy supply mode, coordination between the electric power equipment and the gas equipment is omitted, and potential value of electric power heat supply is wasted; in the embodiment 3, the electric power equipment and the gas device are considered to participate in the optimal scheduling, namely the exemplary embodiment of the invention.
fig. 2 and 4 are respectively optimized home power equipment optimal scheduling plans, and it can be seen that in the example 1, in order to ensure that the room temperature and the water temperature are within the constraint range, the air conditioner and the water heater are still in an on state in the peak electricity price stage, and in the example 3, more gas is adopted for heat supply in the peak electricity utilization stage, so that the energy utilization economy is improved. In addition, comparing fig. 3 and 5, under the operation plan of the example 3, the room temperature and the water temperature are both within the temperature limit range, and the difference between the operation room temperature and the set temperature is small, the fluctuation of the hot water temperature is well suppressed, while the temperature fluctuation is relatively obvious in the example 1. Therefore, the addition of the household gas device has a good optimization effect on the operation of the whole household intelligent power utilization system.
The gas consumption of each fifteen minutes in the embodiments 2 and 3 is shown in fig. 6, and in the valley period and the middle peak period, the gas consumption of the embodiment 3 is smaller than that of the embodiment 2, and more economical electric energy is used for temperature control. Table 3 describes the comparison of the operation results of the three examples in detail by numerical values, in terms of daily average energy consumption, the combination of the two energies results in lower energy consumption cost, the purchasing power of example 3 is lower and the sales power is stronger, the daily average cost of comparative example 1 is reduced by about 26.29%, and the economic cost of comparative example 2 is reduced by 16.83% and the gas consumption of 44.5% under the condition of losing little comfort. Compared with the prior art, the family multi-energy comprehensive optimization operation mode has higher economical efficiency, which embodies the superiority of the family multi-energy comprehensive optimization scheduling method.
TABLE 1 interruptible, uninterruptible load operating parameters
TABLE 2 energy storage device operating parameters
Table 3 gas wall-hanging stove operating parameters
TABLE 4 three example optimization run results
Claims (4)
1. a household intelligent power utilization-oriented multi-energy comprehensive optimization scheduling method comprises the following steps:
1) aiming at heating equipment in a family, considering the energy characteristics and the thermal coupling relation of a household gas device and temperature control type load equipment, and establishing a dynamic heat transfer mathematical model in the family;
2) aiming at the energy utilization characteristics of a distributed power supply, energy storage equipment and other various power load equipment in a family, establishing a corresponding equipment mathematical model;
3) establishing a household multi-energy comprehensive optimization scheduling model, wherein the model aims at minimizing energy consumption cost and comfort loss of resident users, and simultaneously considers necessary constraint conditions of system operation;
4) various household energy-using devices and user set parameters are input into a CPLEX optimization solver, optimization is carried out by combining predicted temperature data, hot water consumption and photovoltaic output conditions, and a household multi-energy comprehensive optimization scheduling result is solved.
2. The dispatching method according to claim 1, wherein in the step 1, the heat load equipment of the dynamic heat transfer mathematical model in the household mainly comprises two parts of household heating equipment and hot water equipment, and the mathematical models are respectively established as follows:
(1) Heating equipment model: establishing a thermodynamic model of the device by means of a first order differential equation representing the course of temperature variation;
(2) a hot water equipment model: and establishing a mathematical model of the water heating equipment according to the operation characteristics of the water heating equipment.
3. The dispatching method according to claim 1, wherein the mathematical models of the household electrical load devices are established in the step 2) as follows:
(1) an energy storage device: setting the charging and discharging power of the energy storage equipment as the charging and discharging state of the energy storage equipment, wherein 1 is in the corresponding charging and discharging state, and otherwise 0 is not in the corresponding charging and discharging state; eta ch and eta dch are respectively charge and discharge efficiency, respectively charge and discharge maximum power, and epsilon represents self-discharge rate; the charge and discharge operation constraints of the energy storage device are as follows:
SOC≤SOC≤SOC
SOC≥SOC
(2) distributed power generation equipment: for solar photovoltaic, the solar photovoltaic is directly mounted on a household comprehensive energy management system through a DC/AC inverter, and the output of the solar photovoltaic can be supplied to the load and the stored energy in the system and can also be reversely supplied to a power grid;
(3) interruptible load: setting xISL (i) as the working state of the interruptible load in the ith time interval, 0 as non-working, 1 as working, and [ bISL, eISL ] as the allowed working time interval of the load; IISL and pISL are the desired operating duration and rated power of the load, respectively, the interruptible load operating constraint can be expressed as follows:
P(i)＝x(i)·p
(4) uninterruptible load: let xNISL (i) be the working state of the uninterruptible load in the i-th period, 0 be out of work, 1 be in work, and [ bNISL, eNISL ] be the allowed working period of the load; lNISL and pNISL are the desired operating duration and rated power of the load, respectively, the uninterruptible load operating constraint is expressed by:
P(i)＝x(i)·p。
4. The dispatching method according to claim 1, wherein in the step 3), the family multi-energy comprehensive optimization dispatching model comprises two parts, namely an objective function and a constraint condition, the objective function is selected to minimize family energy cost in a dispatching time range, including gas cost and electricity cost, and reduce comfort loss of a user as much as possible, and the objective function is divided into two parts, namely an economical objective and a comfort objective; the constraint conditions mainly include the operation constraint of equipment and the comfort constraint of a user, and the gas-type wall-mounted boiler is ensured to be in a normal operation state and mainly includes the temperature upper and lower limit constraint of an aqueous medium, the hot water flow constraint and the total output constraint of the gas furnace.
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