CN112837181A - Scheduling method of comprehensive energy system considering demand response uncertainty - Google Patents
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Abstract
The invention discloses a scheduling method of a comprehensive energy system considering the uncertainty of demand response, which comprises the following steps: calculating four energy sources of cold, heat and electricity, and constructing an energy hub equipment model of the comprehensive energy system; combining price type demand response and incentive type demand response to construct a demand response model, wherein the demand response model has uncertainty; constructing a scheduling model of the comprehensive energy system based on an energy hub equipment model and a demand response model and aiming at the lowest operation cost and the lowest carbon emission of the comprehensive energy system; and calculating to obtain a scheduling result based on the scheduling model. The invention also discloses a computing device for executing the method.
Description
Technical Field
The invention relates to the technical field of energy and power, in particular to an optimal scheduling method of a comprehensive energy system considering the uncertainty of demand response.
Background
With the increasing prominence of the problems of environmental pollution, energy crisis and the like and the transformation of energy production and consumption modes, the traditional energy system mainly based on the construction of a single energy system gradually changes to a comprehensive energy system. The comprehensive energy system effectively improves the energy utilization efficiency while meeting the diversified energy utilization requirements of users through integrating various energy resources such as electricity, heat, gas, cold and the like and through multi-energy complementation and coordinated optimization.
At present, the research on the comprehensive energy system at home and abroad mainly focuses on the aspects of optimizing scheduling, capacity allocation and the like. For example, some schemes consider wind power and load prediction deviation, and construct a "day ahead-day in-real time" multi-time scale rolling scheduling plan model to analyze the influence of uncertainty factors on a scheduling plan; for another example, the scheme also provides a cold/heat/electricity flexibility index and a system overall flexibility evaluation index from the perspective of energy allowable fluctuation rate by calling multiple flexible resources on an energy supply side and a demand side, and a multi-objective optimization scheduling model with the lowest daily operation cost and the highest flexibility is constructed.
In addition, under the development of the power market, Demand Response (DR) is gradually changed into system resources equal to or even prioritized to the supply side, and becomes an important means for economic dispatch of operators, so that researchers have considered demand Response in the dispatch and planning of the integrated energy system. For example, in some schemes, an integrated energy system optimization scheduling model considering demand response based on real-time electricity prices is constructed, and the influence of price uncertainty on scheduling results is analyzed.
In summary, in the existing research results, there is less research on the operation and scheduling of the integrated energy system, considering the influence of implementing price type and incentive type demand response. Therefore, an optimal scheduling scheme which takes economic benefits and environmental benefits of the integrated energy system into consideration is needed.
Disclosure of Invention
To this end, the present invention provides a scheduling method for an integrated energy system that accounts for demand response uncertainty in an effort to solve or at least alleviate at least one of the problems identified above.
According to one aspect of the invention, a scheduling method of an integrated energy system considering uncertainty of demand response is provided, which comprises the following steps: calculating four energy sources of cold, heat and electricity, and constructing an energy hub equipment model of the comprehensive energy system; combining price type demand response and incentive type demand response to construct a demand response model, wherein the demand response model has uncertainty; constructing a scheduling model of the comprehensive energy system based on an energy hub equipment model and a demand response model and aiming at the lowest operation cost and the lowest carbon emission of the comprehensive energy system; and calculating to obtain a scheduling result based on the scheduling model.
Optionally, in the method according to the invention, the demand response model is represented as:
wherein, pitFor the total benefit after the user participates in the demand response at time t, qtFor the amount of electricity used after the user participates in the demand response at time t, B (q)t) Consuming electric quantity q for user at t timetUtility of, InctFor the benefit of the user participating in the incentive type demand response at the time t, PentThe penalty standard of the user not meeting the contract to participate in the demand response at the t moment,
and (4) response quantity at the t moment agreed by the contract of the user and the operator.
Optionally, in the method according to the present invention, the step of building a demand response model by combining the price type demand response and the incentive type demand response further includes: using a triangular fuzzy number to represent uncertainty in the demand response model, wherein the uncertainty comprises uncertainty of actual power consumption of a user and uncertainty of renewable energy output; the uncertainty is expressed as:
wherein,
is the actual demand response at time t, k1、k3The lower limit coefficient and the upper limit coefficient of the deviation range of the predicted value of the demand response quantity are respectively.
Optionally, in the method according to the invention, the energy hub device model comprises an energy production device model, an energy storage device model and an energy conversion device model.
Optionally, in the method according to the present invention, the step of constructing a scheduling model of the integrated energy system based on the energy hub device model and the demand response model with the goals of lowest operation cost and minimum carbon emission of the integrated energy system includes: determining two objective functions of the comprehensive energy system according to the running cost and the carbon emission of the comprehensive energy system; determining at least one constraint based on the energy hub device model and the demand response model; and synthesizing the two objective functions and the determined constraint conditions to obtain a scheduling model.
Optionally, in the method according to the invention, the objective function comprises an objective function for the operating cost and an objective function for the carbon emissions: min f1＝C1+C2+C3+C4-R, and
wherein f is1For operating costs, f2To carbon emission, C1Cost for energy purchase, C2Maintenance costs for the operation of the apparatus, C3Implementation cost for demand response, C4For equipment start-stop cost, R for energy sales revenue,
CO for nth device in integrated energy system2The amount of the discharged water is reduced,
in an integrated energy systemThe output power of the nth device at time t,
for CO of the grid when purchasing energy2The discharge amount of (c); and the constraint condition includes at least an electric power balance constraint, the electric power balance constraint being an indeterminate constraint.
Optionally, in the method according to the present invention, the step of calculating a scheduling result based on the scheduling model includes: processing the uncertain constraints in the scheduling model and converting the uncertain constraints into the deterministic constraints; solving the scheduling model by adopting an epsilon constraint method to obtain an optimal solution set; and obtaining an optimal solution from the optimal solution set by using a fuzzy decision method as a scheduling result.
Optionally, in the method according to the present invention, the step of solving the scheduling model by using an epsilon constraint method to obtain an optimal solution set includes: converting a target function aiming at carbon emission into a constraint condition to obtain a scheduling model of a single target; obtaining different objective function values by changing the value of epsilon, wherein the value of epsilon is determined by the value of an objective function aiming at the carbon emission; and constructing an optimal solution set by using the obtained objective function values.
According to yet another aspect of the present invention, there is provided a computing device comprising: one or more processors; and a memory; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described above.
According to a further aspect of the invention there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described above.
In conclusion, according to the scheme of the invention, a demand response model comprising price type demand response and incentive type demand response is established, and uncertainty in the power utilization process is considered at the same time. On the basis, a comprehensive energy system multi-objective optimization scheduling model considering the uncertainty of demand response is established by taking the lowest system operation cost and the lowest carbon emission as optimization targets. Then, solving the multi-target scheduling model to obtain a series of optimal solutions as an optimal solution set; and performing optimal compromise selection from the optimal solution set to obtain an optimal solution which is used as an optimal operation strategy of the comprehensive energy system. Example analysis results show that peak clipping and valley filling can be realized by combining price type demand response and incentive type demand response, and the operation cost and the carbon emission of a system are effectively reduced.
Drawings
To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.
FIG. 1 shows a schematic diagram of a configuration of a computing device 100 according to one embodiment of the invention;
FIG. 2 illustrates a flow diagram of a scheduling method 200 of an integrated energy system that accounts for demand response uncertainty, according to one embodiment of the present invention;
FIG. 3 shows a schematic diagram of an energy flow process within an integrated energy system according to one embodiment of the invention;
FIG. 4 shows a schematic of typical daily electricity, heat, cold load curves and renewable energy (wind, photovoltaic) output curves according to one embodiment of the invention;
FIG. 5 is a diagram illustrating an optimal solution for a scheduling model according to one embodiment of the invention;
FIG. 6 illustrates a graph of demand side electrical loads under different scenarios, according to one embodiment of the present invention;
fig. 7A to 7F are diagrams illustrating the electric, thermal and cold balance of the integrated energy system corresponding to scenario 1 and scenario 6 according to the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 is a block diagram of an example computing device 100. In a basic configuration 102, computing device 100 typically includes system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between the processor 104 and the system memory 106.
Depending on the desired configuration, the processor 104 may be any type of processor, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as a level one cache 110 and a level two cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations the memory controller 118 may be an internal part of the processor 104.
Depending on the desired configuration, system memory 106 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some embodiments, application 122 may be arranged to operate with program data 124 on an operating system. In some embodiments, computing device 100 is configured to perform a dispatch method 200 for an integrated energy system that accounts for demand response uncertainty, and program data 124 includes instructions for performing the method.
The computing device 100 also includes a storage device 132, the storage device 132 including removable storage 136 and non-removable storage 138, the removable storage 136 and the non-removable storage 138 each connected to the storage interface bus 134.
Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, image input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communications with one or more other computing devices 162 over a network communication link via one or more communication ports 164.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media. In some embodiments, one or more programs are stored in the computer readable medium, the one or more programs including instructions for performing the scheduling method of the integrated energy system accounting for demand response uncertainty according to the present invention.
Computing device 100 may be implemented as part of a small-form factor portable (or mobile) electronic device such as a cellular telephone, a digital camera, a Personal Digital Assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device 100 may also be implemented as a personal computer including both desktop and notebook computer configurations.
FIG. 2 illustrates a flow diagram of a scheduling method 200 of an integrated energy system that accounts for demand response uncertainty, according to one embodiment of the invention. As shown in fig. 2, the method 200 begins at step S210.
In step S210, an energy hub device model of the integrated energy system is constructed in consideration of four energy sources, i.e., cold energy, hot energy, and electric energy.
An Integrated Energy System (IES) is a comprehensive Energy System which utilizes the coupling mechanism of each Energy System in space and time and adopts the operation mechanism of 'spontaneous self-use and surplus internet access' to realize multi-Energy complementation and Energy cascade utilization, and is one of the important ways to break the Energy predicament of china. An Energy Hub (EH) is an interface platform among sources, networks and loads in the IES, and comprises the mutual conversion, distribution and storage of various forms of Energy, so that the optimal configuration of Energy resources is realized, and theoretical support is provided for the planning design and operation optimization of the IES. In an embodiment according to the invention, an EH is constructed by considering four energy sources of cold and hot electricity, and the input and output relations of the EH are shown in formula (1), and based on different optimization targets, the EH can meet the requirements of energy utilization equipment by adjusting the output of the energy supply equipment.
In the formula，ηeFor the average conversion efficiency of the power transformer, Le、Lc、LhRespectively representing the electricity, cold and heat load demands of users; ee、EgasRespectively representing electric energy generated by wind power and photovoltaic and input natural gas; k denotes a distribution coefficient. Fig. 3 shows an energy flow process inside the integrated energy system.
In one embodiment, the energy hub equipment model is formed from three links of energy production, energy storage and energy conversion. In other words, the energy hub device model includes an energy production device model, an energy storage device model, and an energy conversion device model.
(1) Energy production equipment model
The energy production facility includes at least: renewable energy source unit, gas turbine, exhaust-heat boiler, gas boiler. The model of the specific device at least comprises: the system comprises a power generation power model of a renewable energy source unit, a power generation power model of a gas turbine, a heat production power model of a waste heat boiler and a heat production power model of a gas boiler. The specific calculation method is as follows:
in the formula, Ee,tGenerating power of the renewable energy source unit at the t moment;
the unit of the generated power of the wind power and the generated power of the photovoltaic unit are respectively expressed in kW.
In the formula,
for the power generated by the gas turbine, qgasThe heat value of the natural gas is used,
in order to be efficient for the gas turbine,
consuming power for natural gas of a gas turbine;
the heat-generating power of the waste heat boiler,
the efficiency of the heat generating power of the waste heat boiler;
is the heat-producing power of the gas-fired boiler,
for the efficiency of the heat power output of the gas boiler,
consuming power for the natural gas of the gas boiler.
(2) Energy storage device model
The energy storage device comprises at least: an electricity storage device and a heat storage device. And the energy storage device model at least comprises: the system comprises an electricity storage equipment model and a heat storage equipment model. The specific calculation method is as follows:
1) electricity storage equipment model
In the formula,
the unit is the power storage capacity at the time t and is kW.h; alpha is the self-loss rate of the power storage equipment;
is the power storage capacity at the time t-1 and has the unit of kW ·h；
Respectively is the charging and discharging power at the time t, and the unit is kW;
charge-discharge efficiency;
a variable of 0-1 for charge and discharge states; Δ t is the charge and discharge time, alternatively, Δ t is taken to be 1 hour.
2) Heat storage equipment model
In the formula,
the unit is KW.h, which is the heat storage capacity at the moment t; beta is the self-loss rate of the heat storage equipment;
the unit is KW.h, which is the heat storage capacity at the moment of t-1;
respectively is the heat charge and discharge power at the time t, and the unit is KW;
the heat charge and discharge efficiency is shown; Δ t represents the heat charging and discharging time, and is optionally 1 hour. .
(3) Energy conversion equipment model
The energy conversion device includes at least: absorption chiller equipment, electric boiler and electric chiller, and energy conversion equipment model includes at least: an absorption chiller model, a heat pump model, and an electric chiller model.
According to the energy flow diagram of fig. 3, in one embodiment, a model is created for the primary energy coupling devices, such as lithium bromide absorption chiller devices, electric boilers, and electric chillers, to obtain the coupling characteristics between the various energy sources. The specific calculation is as follows.
1) Lithium bromide absorption type refrigerator model
The lithium bromide absorption refrigerator is a cold-heat coupling device that can convert the thermal energy of the system into cold power and supply the cold power to a cold load, and a model of the output is as follows.
In the formula,
the unit of the cold power and the heat absorption power output by the lithium bromide refrigerator at the t moment are respectively kW;
the refrigerating efficiency of the lithium bromide refrigerator is shown.
2) Heat pump model
The heat pump is a typical electro-thermal coupling device, and increases the power consumption in the valley period while satisfying the heat load demand, and the output model thereof is as follows.
In the formula,
respectively outputting thermal power and consumed electric power of the heat pump at the t moment, wherein the unit is kW;
the efficiency of electricity to heat.
3) Electric refrigerator model
The electric refrigerator is a typical electric cold coupling device, and increases the electricity consumption in the valley period while satisfying the cold load demand, and the output model thereof is as follows.
In the formula,
the unit of the cold power output by the electric refrigerator and the unit of the consumed electric power consumed by the electric refrigerator at the t moment are respectively kW;
is the efficiency of the electric refrigerator.
Subsequently, in step S220, a Demand Response model is constructed by combining Price-Based Demand Response (PBDR) and Incentive-Based Demand Response (IBDR).
The price type demand response guides the user to transfer the power load distribution by formulating time-of-use electricity price, real-time electricity price and the like, and the incentive type demand response is directly controlled by a system operator, guides the user to participate in the demand response and pays corresponding compensation.
Price type demand response is mainly achieved by market elasticity, which includes self-elasticity and cross-elasticity according to economic principles, wherein self-elasticity is used to measure the influence of current single-period electricity price change on electricity demand, and cross-elasticity is used to measure the influence of multi-period electricity price change on multi-period electricity demand. The self-elastic coefficient and the cross-elastic coefficient of the electrical load are expressed as:
in the formula, ∈ (t, h) represents the price elasticity at time t to time h, that is, the power load elasticity coefficient, and when t ≠ h, it is the self-elasticity coefficient, and when t ≠ h, it is the cross-elasticity coefficient.
Respectively is a priceElectricity price at h moment and electricity consumption at t moment before type demand response, delta qt、ΔphThe load fluctuation amount at the t-th moment and the price fluctuation amount at the h-th moment after the demand response are respectively.
The incentive type demand response realizes the demand response by signing a demand response contract with the user and appointing response capacity, response time, reward standard, penalty standard and the like.
Therefore, a single-period demand response model that considers both the price-type demand response and the incentive-type demand response is as follows.
In the formula (10), ntFor the total benefit after the user participates in the demand response at time t, B (q)t) Consuming electric quantity q for user at t timetUtility of, InctFor the benefit of the user participating in the incentive type demand response at the time t, PentThe penalty standard of the user not meeting the contract to participate in the demand response at the t moment,
and (4) response quantity at the t moment agreed by the contract of the user and the operator. Taking the derivative of equation (10) across and making it equal to 0, the demand response model of equation (11) is obtained, at which time q istRepresenting the power consumption after the user participates in the demand response at the t moment as the formula (12):
q obtained based on equation (12) without considering uncertaintytCalculating to obtain the user demand response quantity delta qtThe predicted value is shown in formula (13).
However, the uncertainty of the actual power consumption situation of the user is influenced by conditions such as natural environments and behavior habits, and the actual demand response amount has a certain uncertainty, so the demand response model has uncertainty. Wherein the uncertainty comprises: uncertainty of the actual power consumption of the user and uncertainty of the renewable energy output.
Therefore, in one embodiment, the step of constructing the demand response model further comprises: the uncertainty in the demand response model is represented using triangular fuzzy numbers, as shown below.
In the formula,
is the actual demand response at time t, k1、k3The lower limit coefficient and the upper limit coefficient of the deviation range of the predicted value of the demand response quantity are respectively, and the values of the coefficients are determined by historical data.
Similarly, for the uncertainty of the renewable energy output, a triangular fuzzy number is adopted to represent the uncertainty caused by the renewable energy output prediction error formed by wind power and photovoltaic.
In the formula,
predicting fuzzy parameters for renewable energy sources day ahead at time t, Ee,tIs a predicted value, k, of renewable energy sources before the day at the t moment2、k4Respectively the lower limit coefficient and the upper limit coefficient of the predicted value deviation range before the day of the renewable energy sourceAs determined by historical data.
Subsequently, in step S230, a scheduling model of the integrated energy system is constructed based on the energy hub device model and the demand response model, with the objective of minimizing the operation cost and carbon emission of the integrated energy system.
In one embodiment, the process of building the scheduling model may be performed in three steps.
The method comprises the first step of determining two objective functions of the comprehensive energy system according to the running cost and the carbon emission of the comprehensive energy system.
On the one hand, the operating cost of the integrated energy system is determined by the energy purchase cost C1And the equipment operation and maintenance cost C2And a demand response implementation cost C3And the equipment start-stop cost C4And energy sales revenue R. The specific expression is as follows:
min f1＝C1+C2+C3+C4-R (16)
wherein, the calculation mode of each part is as follows:
1) cost of energy purchase C1
In the formula,
the price of the electric power and the natural gas at the t moment respectively, and the value of the heat value of the natural gas is 9.7 kW.h/m3，
And T is the dispatching time length of the electric power purchased from the power grid at the T moment by the system operator.
2) Cost of equipment operation and maintenance C2
In the formula,
for a unit operating maintenance cost of the output power of the device m,
output power for device m at time t[16]。
3) Demand response implementation cost C3
4) Equipment start-stop cost C4
In the formula,
and
respectively representing the starting-up cost and the stopping cost of the unit i; u. ofitAnd vitAnd respectively representing a starting variable and a stopping variable of the unit i at the t moment.
5) Energy sales revenue R
In the formula,
sales earnings of electricity, heat and cold are respectively obtained at the t-th moment,
the price and the amount of electricity sold by the IES to the grid, respectively.
On the other hand, the carbon emission of the integrated energy system is determined by the carbon emission of each equipment and the carbon emission of the grid at the time of purchasing energy.
The IES contaminants mainly come from some equipment and the power grid in the system, in this embodiment, as CO2The environmental protection performance is measured by the emission amount of the carbon element, and the target is the minimum carbon emission, and the specific expression is shown as follows.
In the formula,
CO for nth device in IES2The amount of the discharged water is reduced,
for the output power of the nth device in the IES at the time instant t,
for CO of the grid when purchasing energy2The amount of discharge of (c).
In a second step, at least one constraint is determined based on the energy hub device model and the demand response model.
In an embodiment according to the invention, the at least one constraint comprises an uncertainty constraint. And, each parameter value in the constraint is obtained by the energy hub device model in the foregoing.
The following shows 6 constraints and their calculation according to an embodiment of the present invention.
(1) Power balance constraint
1) The electric power balance constraint is an uncertain constraint and has the expression as follows:
wherein Cr {. is {. DEG }A confidence expression is expressed and used for expressing the confidence,
is a fuzzy parameter of the demand response quantity, alpha is a confidence level of satisfying the electric power balance constraint,
and (4) carrying out power utilization load before demand response is carried out for the user at the time t.
2) Thermal power balance constraint
In the formula,
the actual heat load for the user at time t.
3) Cold power balance constraint
In the formula,
the actual cooling load is used for the user at time t.
(2) Tie line constraint
In the formula, Pgrid,min、Pgrid,maxThe minimum value and the maximum value of the interaction power of the IES and the power distribution network are respectively.
(3) Energy storage device restraint
In the formula,
lower and upper capacity limits for the electricity storage device;
the lower and upper capacity limits of the energy storage device.
(4) Device force constraints
(5) Unit climbing restraint
In the formula,
is the upper limit of the climbing of the device m.
(6) Constraint of starting and stopping of unit
uit+vit≤1 (31)
In the formula u it1 represents that the unit i is in a starting state at the t moment, and otherwise is 0; v. ofitA value of 1 indicates that the unit i is in a shutdown state at the time t, otherwise, the value is 0.
And thirdly, integrating the two objective functions and the determined constraint conditions to obtain a scheduling model.
In one embodiment, the scheduling model is obtained by combining the above equations (16) and (22), and equations (23) -31.
Subsequently, in step S240, a scheduling result is calculated based on the scheduling model.
According to an embodiment of the present invention, the step of solving the scheduling model includes the following three steps.
(1) And processing the uncertain constraints in the scheduling model and converting the uncertain constraints into the deterministic constraints.
And according to an uncertain planning theory, converting uncertain constraints in the scheduling model into deterministic constraints and then solving the deterministic constraints. In this way, the uncertainty constraint (23) is transformed into the form:
(2) and solving the scheduling model by adopting an epsilon constraint method to obtain an optimal solution set.
In this embodiment, the scheduling model has two targets, the solving method for the multi-target model includes a weighting method, an epsilon constraint method, and the like, and in one embodiment, the epsilon constraint method is adopted for solving to obtain a Pareto solution set.
The idea of the epsilon constraint method is to convert an objective function in a multi-objective model into a constraint condition, and convert a multi-objective optimization problem into a series of single-objective optimization problems for solving by gradually modifying the value range of the constraint condition. In this embodiment, the objective function for the carbon emission is converted into the constraint condition, so that a scheduling model of a single objective is obtained, as shown in the following formula.
Wherein the value of epsilon is determined by the value of the objective function aiming at the carbon emission, namely the value interval of epsilon is f2From a minimum value to a maximum value. Different objective function values can be obtained by changing the value of epsilon, and the different objective function values jointly form an optimal solution set (or called Pareto optimal solution set) of the multi-objective problem.
(3) And obtaining an optimal solution from the optimal solution set by using a fuzzy decision method as a scheduling result.
After the Pareto optimal solution set is obtained, a fuzzy decision method is utilized to obtain an optimal solution from the Pareto front compromise.
First, the jth operating scenario (j ═ 1,2, …, N) of the kth objective function (according to an exemplary embodiment of the invention, k ═ 1,2) is defined0，N0Number of Pareto solutions) as shown below.
In the formula,
respectively the minimum value and the maximum value of the k-th objective function in the Pareto solution set,
is the value of the jth operating scenario of the kth objective function.
Next, the membership function value for each run is selected, as shown below.
And finally, selecting a maximum value from the formula (36), wherein a solution scheme corresponding to the value is the final compromise decision solution, and a specific expression is shown as follows.
Thus, the integrated energy system dispatch method 200 in accordance with the present invention that accounts for the uncertainty of demand response is complete. To further illustrate the effectiveness of the method 200, an example is used herein for the analysis.
According to the calculation example, actual data of a certain industrial park in the north in summer on a typical day is selected, the time scale is 1 hour (h), CPLEX is called under the MATLAB environment to solve, and the rationality and the effectiveness of the scheduling model established in the embodiment are verified.
Fig. 4 is a typical daily electricity, heat, cold load curve and renewable energy (wind, photovoltaic) output curve. In order to fully utilize renewable energy and reduce the phenomenon of wind and light abandonment, the embodiment sets priority to ensure the full consumption of renewable energy. The electricity purchasing price connected with the power grid is the same as the electricity selling price on the same day, and the time-of-use electricity price is adopted and is specifically shown in table 1.
TABLE 1 energy prices
Self elasticity and cross elasticity as shown in table 2,
TABLE 2 self-elasticity and Cross-elasticity in Peak, Flat, Valley time periods
| Peak period | Flat time period | In the valley period | |
| Peak period | 40 | -90 | -75 |
| Flat time period | -90 | 40 | -100 |
| In the valley period | -75 | -100 | 40 |
Specific parameters of each device in the energy hub device are shown in tables 3 and 4.
TABLE 3 energy storage device principal parameters
Table 4 other equipment main parameters
In order to ensure the safety of the power grid, the upper limit power and the lower limit power of electricity purchased and sold by the IES to the power grid are respectively 0kW and 200 kW. The error interval of the predicted value of the demand response quantity is [ -20%, 20% ], and the confidence coefficient alpha is 0.95.
In order to verify the effectiveness of the constructed scheduling model, six scenes shown in table 5 are respectively constructed according to different demand response measures for comparison, and the two aspects of the IES benefit and the operation result under different scenes are analyzed.
TABLE 56 example scenarios
(1) IES benefit analysis under different scenes
The optimal scheduling strategy is sought with the goal of minimizing the daily operating cost and pollutant emissions (i.e., carbon emissions) of the system operator as the optimization objective.
Comparing the results of the scene 1 and the scene 2, it can be known that after the time-of-use electricity price measure is implemented, the user reduces the electricity consumption in the peak time period and increases the electricity consumption in the valley time period in order to reduce the electricity consumption cost, thereby reducing the operation cost and the carbon emission of the system. Comparing the results of the scenarios 2, 3 and 4, it can be seen that, compared to the implementation of only the IBDR measure, the PBDR measure based on the TOU (time of use electricity price) enables the user to adjust the electricity utilization time and the electricity utilization amount autonomously, so that the economic benefit is more significant. Comparing the results of the scene 2, the scene 5 and the scene 6, it can be known that the implementation of the time-of-use electricity price significantly improves the enthusiasm of the user for participating in demand response and increases the electricity consumption in the valley period by combining the PBDR and the IBDR measures, and the incentive and punishment measures in the peak period have a more obvious effect on reducing the electricity consumption in the peak period, thereby improving the electricity selling profit and reducing the operation cost and the carbon emission of the system. Comparing the results of the scenario 5 and the scenario 6, it can be seen that, compared with the implementation of the incentive price and the penalty price at the same time, increasing the incentive price level can bring more significant economic benefit and environmental benefit, and the pareto optimal solution diagram of the scheduling model corresponding to the scenario 6 is shown in fig. 5.
(2) IES operation result analysis under different scenes
Under different demand response measure scenarios (i.e., the 6 scenarios shown in table 5), the demand side power load graph is shown in fig. 6. It can be seen that, during the high electricity price level period, the electricity load of the scene 6 is significantly reduced, and during the low electricity price period, the electricity load of the scene 6 is most significantly increased, so that the peak clipping and valley filling effects of the scene 6 are optimal.
Comparing the output conditions of the units in the scene 1 and the scene 6, the power load of the park and the power requirements of the electric refrigerator and the heat pump can be met through the cooperative operation of the electric energy storage, the gas turbine, the renewable energy source and the power grid; the heat load of the park and the heat demand of the absorption refrigerator can be met through the waste heat boiler, the heat pump and the heat energy storage equipment; the cooling requirement of the garden can be met by the absorption refrigerator and the electric refrigerator. Fig. 7A to 7F show the electric, thermal, and cold balance diagrams of the integrated energy system corresponding to scenario 1 and scenario 6.
As can be seen from fig. 7A and 7B, the amount of electricity purchased, the heat pump electricity usage, and the electric refrigerator electricity usage at the peak period of scene 6 are significantly reduced compared to scene 1. As can be seen from fig. 7C and 7D, the heat load is supplied mainly by the waste heat boiler, peaking only during periods of higher demand by the heat pump and the heat storage device. And because the increased electricity consumption in the period of low electricity price in scene 6 is mainly supplied by the gas turbine, and the rest heat can meet part of heat requirements through the waste heat boiler, compared with scene 1, the heat production quantity of the waste heat boiler in the period of low electricity price is larger. As can be seen from fig. 7E and 7F, since the coefficient of performance of the electric refrigeration is much higher than that of the absorption refrigerator, most of the refrigeration capacity is provided by the electric refrigerator, and compared to scenario 1, during the peak time of electricity consumption, scenario 6 increases the refrigeration capacity of the absorption refrigerator, reduces the refrigeration capacity of the electric refrigerator, thereby improving the economic efficiency of the system.
According to the scheme of the invention, a demand response model containing price and incentive measures is established based on an elastic theory, and on the basis, a comprehensive energy system multi-objective optimization scheduling model considering the uncertainty of demand response is established by taking the lowest system operation cost and the lowest carbon emission as optimization targets. Then, solving the multi-target scheduling model to obtain a series of optimal solutions as an optimal solution set; and performing optimal compromise selection from the optimal solution set to obtain an optimal solution which is used as an optimal operation strategy of the comprehensive energy system.
By combining with example analysis, the scheme has the following advantages: 1) by combining price type demand response and excitation type demand response measures, peak clipping and valley filling can be obviously realized, and the operation cost and the carbon emission of the system are effectively reduced; 2) compared with the method for simultaneously making the incentive price and the punishment price, the method for making the incentive price has the advantages that the incentive price level is improved, the enthusiasm of the user for participating in demand response can be mobilized, and good economic benefits and environmental protection benefits are achieved.
It should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention.
Claims (10)
1. A method for scheduling an integrated energy system that accounts for demand response uncertainty, the method adapted to be executed in a computing device, comprising the steps of:
calculating four energy sources of cold, heat and electricity, and constructing an energy hub equipment model of the comprehensive energy system;
combining price type demand response and incentive type demand response to construct a demand response model, wherein the demand response model has uncertainty;
constructing a scheduling model of the integrated energy system based on the energy hub equipment model and the demand response model and aiming at the lowest operation cost and the lowest carbon emission of the integrated energy system;
and calculating to obtain a scheduling result based on the scheduling model.
2. The method of claim 1, wherein the demand response model is represented as:
wherein, pitFor the total benefit after the user participates in the demand response at time t, qtFor the amount of electricity used after the user participates in the demand response at time t, B (q)t) Consuming electric quantity q for user at t timetUtility of, InctFor the benefit of the user participating in the incentive type demand response at the time t, PentThe penalty standard of the user not meeting the contract to participate in the demand response at the t moment,
and (4) response quantity at the t moment agreed by the contract of the user and the operator.
3. The method of claim 2, wherein the step of building a demand response model in combination with a pricing type demand response and an incentive type demand response further comprises:
employing triangular fuzzy numbers to represent uncertainties in the demand response model, wherein the uncertainties comprise: uncertainty of actual power consumption of a user and uncertainty of output of renewable energy;
the uncertainty is represented as:
wherein,
is the actual demand response at time t, k1、k3The lower limit coefficient and the upper limit coefficient of the deviation range of the predicted value of the demand response quantity are respectively.
4. The method of any one of claims 1-3,
the energy hub equipment model comprises an energy production equipment model, an energy storage equipment model and an energy conversion equipment model.
5. The method according to any one of claims 1-4, wherein the step of constructing a scheduling model of the integrated energy system with the goal of lowest cost of operation and minimum carbon emissions of the integrated energy system based on the energy hub device model and the demand response model comprises:
determining two objective functions of the comprehensive energy system according to the running cost and the carbon emission of the comprehensive energy system;
determining at least one constraint based on the energy hub device model and the demand response model;
and synthesizing the two objective functions and the determined constraint conditions to obtain the scheduling model.
6. The method of claim 5, wherein,
the objective function includes an objective function for operating cost and an objective function for carbon emissions:
min f1＝C1+C2+C3+C4-R, and
wherein f is1For operating costs, f2To carbon emission, C1Cost for energy purchase, C2Maintenance costs for the operation of the apparatus, C3Implementation cost for demand response, C4For equipment start-stop cost, R for energy sales revenue,
CO for nth device in integrated energy system2The amount of the discharged water is reduced,
for the output power of the nth device in the integrated energy system at the time t,
for CO of the grid when purchasing energy2The discharge amount of (c); and
the constraints include at least an electric power balance constraint, which is an indeterminate constraint.
7. The method of claim 6, wherein the step of calculating the scheduling result based on the scheduling model comprises:
processing the uncertain constraints in the scheduling model and converting the uncertain constraints into the deterministic constraints;
solving the scheduling model by adopting an epsilon constraint method to obtain an optimal solution set;
and obtaining an optimal solution from the optimal solution set by using a fuzzy decision method as a scheduling result.
8. The method of claim 7, wherein solving the scheduling model using an epsilon constraint method to obtain an optimal solution set comprises:
converting a target function aiming at carbon emission into a constraint condition to obtain a scheduling model of a single target;
obtaining different objective function values by changing the value of epsilon, wherein the value of epsilon is determined by the value of an objective function aiming at the carbon emission; and
and forming an optimal solution set by using the obtained objective function values.
9. A computing device, comprising:
one or more processors; and
a memory;
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-8.
10. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-8.
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