CN111950807B - Comprehensive energy system optimization operation method considering uncertainty and demand response - Google Patents

Comprehensive energy system optimization operation method considering uncertainty and demand response Download PDF

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CN111950807B
CN111950807B CN202010871022.0A CN202010871022A CN111950807B CN 111950807 B CN111950807 B CN 111950807B CN 202010871022 A CN202010871022 A CN 202010871022A CN 111950807 B CN111950807 B CN 111950807B
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李鹏
王子轩
郭天宇
王加浩
陈博
夏辉
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North China Electric Power University
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Abstract

An integrated energy system optimization operation method considering uncertainty and demand response comprises the following steps: in a day-ahead optimization stage, establishing a comprehensive energy system day-ahead robust optimization operation model considering robust uncertainty and time-shifting type demand response according to day-ahead prediction data of various distributed energy sources and loads and energy price fluctuation factors; in the in-day optimization stage, establishing a comprehensive energy system in-day random optimization operation model considering random uncertainty and alternative demand response according to in-day prediction data of various distributed energy and loads and a system in-day optimization result; and forming a day-ahead-day two-stage collaborative optimization operation model of the comprehensive energy system considering multiple uncertainties and comprehensive demand response by the two models, and calling a Gurobi solver to solve the model by the Yalmip tool box. The invention can effectively exert the complementary and cooperative advantages among various energy sources, further reduce the comprehensive operation cost of the park and realize the economic, environment-friendly, flexible and efficient operation of a comprehensive energy system.

Description

Comprehensive energy system optimization operation method considering uncertainty and demand response
Technical Field
The invention relates to an optimized operation method of a comprehensive energy system. In particular to an optimization operation method of a comprehensive energy system considering uncertainty and demand response.
Background
With the increasing crisis of energy exhaustion and the problem of environmental pollution, the existing energy production and consumption mode is difficult to meet the requirements of rapid development of the current economic society, the limitations of various types of energy and the diversity of the requirements of human beings on terminal energy determine that any one energy is difficult to independently bear the important role of energy transformation. Under the background, the energy internet is produced, and the comprehensive energy system is taken as an important development direction of the energy internet, is an important means for improving the social energy utilization efficiency and promoting the large-scale utilization of renewable energy sources, and on one hand, the comprehensive scheduling and the cooperative optimization of various energy sources can be realized through multi-energy complementation, so that the utilization rate of the renewable energy sources is improved; on the other hand, the energy supply device can supply energy according to the requirements of users on energy with different tastes, and realizes the cascade utilization of energy, thereby improving the comprehensive utilization efficiency of the energy, and the optimization operation of the energy supply device becomes a hotspot of the research in the current energy field.
A large number of uncertain factors (such as randomness and volatility of various types of renewable energy sources, cold and hot electrical loads and the like) exist in the comprehensive energy system, and certain challenges are brought to the optimized operation of the comprehensive energy system; in addition, with the complementation and the coordination of various energy sources, the traditional power demand response is gradually expanded into comprehensive demand response, and a new optimized regulation and control means is introduced for a comprehensive energy source system. Therefore, the comprehensive energy system optimization operation research considering multiple uncertainties and comprehensive demand response is of great significance.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a comprehensive energy system optimization operation method which can give full play to complementary and cooperative advantages among various energy sources and takes uncertainty and demand response into account.
The technical scheme adopted by the invention is as follows: an integrated energy system optimization operation method considering uncertainty and demand response comprises the following steps:
1) in a day-ahead optimization stage, establishing a comprehensive energy system day-ahead robust optimization operation model considering robust uncertainty and time-shifting type demand response according to day-ahead prediction data of various distributed energy sources and loads and energy price fluctuation factors;
2) in the in-day optimization stage, establishing a comprehensive energy system in-day random optimization operation model considering random uncertainty and alternative demand response according to in-day prediction data of various distributed energy sources and loads and a system in-day optimization result;
3) and the day-ahead robust optimization operation model of the comprehensive energy system considering the robust uncertainty and the time-shifting type demand response and the day-interior random optimization operation model of the comprehensive energy system considering the random uncertainty and the alternative type demand response jointly form a day-ahead and day-interior two-stage collaborative optimization operation model of the comprehensive energy system considering the multiple uncertainties and the comprehensive demand response, and a Gurobi solver is called through a Yalmip tool box to carry out model solution.
The comprehensive energy system optimization operation method considering uncertainty and demand response has the following advantages:
1. the invention represents the uncertainty of the distributed power supply and the load by two time scales in day-before-day, can effectively reduce the influence of the randomness and the volatility of the source load power on the system, and improves the accuracy of the scheduling plan.
2. According to the invention, time-shifting type demand response and substitution type demand response are optimized in two stages before and in day, so that the regulation and control function of a user side in the system optimization operation can be fully exerted, and the supply and demand balance of the multi-energy flow is further promoted.
3. The invention can effectively exert the complementary and cooperative advantages among various energy sources, further reduce the comprehensive operation cost of the park and realize the economic, environment-friendly, flexible and efficient operation of a comprehensive energy system.
Drawings
FIG. 1 is a block diagram of a typical campus energy complex system in accordance with an embodiment of the present invention;
FIG. 2 is a graph of wind, photovoltaic and load power predictions in an example of the invention;
FIG. 3 is a comparison of operating costs for time-shifted demand responses before and after periods of the campus in accordance with an embodiment of the present invention;
FIG. 4a is a plot power flow optimization scheduling result during the intraday optimization phase in an example of the present invention;
FIG. 4b is a plot energy flow optimization scheduling result of the intraday optimization stage for the example of the present invention;
FIG. 4c is the result of energy flow optimization scheduling of the campus concentrator during the intraday optimization stage in the example of the present invention.
Detailed Description
The method for optimizing the operation of the integrated energy system considering uncertainty and demand response according to the present invention is described in detail with reference to the following embodiments and the accompanying drawings.
The invention relates to an uncertainty and demand response considered comprehensive energy system optimization operation method, which comprises the following steps:
1) in a day-ahead optimization stage, establishing a comprehensive energy system day-ahead robust optimization operation model considering robust uncertainty and time-shifting type demand response according to day-ahead prediction data of various distributed energy sources and loads and energy price fluctuation factors;
the comprehensive energy system day-ahead robust optimization operation model considering the robustness uncertainty and the time-shifting type demand response takes the minimum day-ahead comprehensive operation cost of the comprehensive energy system in a robust scene as an objective function and takes the day-ahead energy balance constraint, the day-ahead energy conversion equipment constraint, the day-ahead energy storage constraint, the day-ahead energy purchase constraint, the day-ahead time-shifting type demand response constraint and the day-ahead robust constraint as constraint conditions. Wherein the content of the first and second substances,
(1) the expression of the target function is as follows:
Figure BDA0002651118780000021
wherein, CaheadFor the day-ahead integrated operating costs of the integrated energy system, including the energy purchase cost CpeAnd operation and maintenance cost ComAnd environmental cost CceThree partsDividing;
Figure BDA0002651118780000022
representing a robust scene; wherein the content of the first and second substances,
(1.1) cost of energy purchase
Figure BDA0002651118780000023
In the formula, T is a scheduling period cycle of 24h, and the time step is 1 h; subscript a is a day-ahead optimization designation;
Figure BDA0002651118780000024
and
Figure BDA0002651118780000025
the electricity purchase price, the gas purchase price and the heat purchase price in the time period t are respectively;
Figure BDA0002651118780000026
and
Figure BDA0002651118780000027
respectively optimizing the electricity purchasing power, the gas purchasing power and the heat purchasing power of the system at the time t in the day-ahead optimization; for convenience of calculation, the price units are yuan/(kW.h), and the power units are kW; Δ t is the time interval;
(1.2) operation and maintenance costs
Figure BDA0002651118780000028
In the formula, M is the number of operation and maintenance units in the park, and comprises a photovoltaic unit, a fan, a micro-combustion engine, an electric refrigerator, an absorption refrigerator, and electricity storage and heat storage equipment; k is a radical ofom,iThe unit operation and maintenance cost of the unit i is obtained;
Figure BDA0002651118780000031
the output of the unit i in the time period t is optimized in the day ahead;
(1.3) cost to environmental protection
Figure BDA0002651118780000032
In the formula, alpha is unit CO2The cost of treatment of; beta is ae、βg、βhAnd betaMTRespectively obtaining equivalent carbon emission coefficients of system electricity purchase, gas purchase, heat purchase and micro-combustion engine operation;
Figure BDA0002651118780000033
the output electric power of the micro-combustion engine in the period t in the optimization before the day.
(2) The constraint condition is specifically expressed as:
(2.1) energy balance constraint before day:
Figure BDA0002651118780000034
in the formula (I), the compound is shown in the specification,
Figure BDA0002651118780000035
and
Figure BDA0002651118780000036
output electric power of a transformer, a photovoltaic, a fan and a micro-combustion engine in a period t in optimization before the day;
Figure BDA0002651118780000037
the power consumption of the electric refrigerator in the period t in the optimization before the day is achieved;
Figure BDA0002651118780000038
respectively optimizing the output thermal power of the heat exchanger and the micro-combustion engine in the period t in the day-ahead optimization;
Figure BDA0002651118780000039
the heat consumption power of the absorption refrigerator in the period t in the optimization before the day;
Figure BDA00026511187800000310
respectively outputting cold power of the electric refrigerator and the absorption refrigerator in the period t in optimization in the day ahead;
Figure BDA00026511187800000311
optimizing the gas purchasing quantity of the system in the period t in the day ahead;
Figure BDA00026511187800000312
the method comprises the following steps of optimizing the air consumption of the micro-combustion engine at a time t in the day ahead;
Figure BDA00026511187800000313
and
Figure BDA00026511187800000314
respectively electric load, heat load, cold load and air load in the period t in the day-ahead optimization;
Figure BDA00026511187800000315
respectively outputting power for electricity storage and heat storage in the period t in optimization before the day;
(2.2) day ahead energy conversion device constraints
Figure BDA00026511187800000316
In the formula, PT,max、HHE,max、PPV,max、PWT,max、PMT,max、PEC,maxAnd HAC,maxThe maximum power of the transformer, the heat exchanger, the photovoltaic, the wind power, the micro-combustion engine, the electric refrigerator and the absorption refrigerator respectively;
(2.3) energy storage restraint before day
Figure BDA00026511187800000317
In the formula, subscript x represents an energy storage type;
Figure BDA00026511187800000318
respectively optimizing the t period in the day-aheadThe output power and energy storage state of the stored energy;
Figure BDA00026511187800000319
respectively storing the initial energy storage state and the final energy storage state of the energy storage equipment in the optimization in the day ahead; px,c,max、Px,d,maxRespectively storing the maximum energy charging and discharging power of the energy; ex,min、Ex,maxMinimum and maximum states of the energy storage device, respectively;
(2.4) Provisioning of energy purchase
Figure BDA0002651118780000041
In the formula (I), the compound is shown in the specification,
Figure BDA0002651118780000042
respectively optimizing the electricity purchasing, heat purchasing and gas purchasing quantity of the system at the time t in the day-ahead optimization; pe,max、Hh,max、Fg,maxRespectively carrying out system electricity purchasing, heat purchasing and gas purchasing upper limits on an external network;
(2.5) time-of-day-shift type demand response constraint
Figure BDA0002651118780000043
Figure BDA0002651118780000044
In the formula (I), the compound is shown in the specification,
Figure BDA0002651118780000045
respectively representing the variation and the variation upper limit of the time-shifting type demand response load; pL,a、HL,a、CL,a、FL,aRespectively carrying out electric, hot, cold and air loads after time-shifting type demand response is implemented;
Figure BDA0002651118780000046
and
Figure BDA0002651118780000047
respectively representing the electric, heat, cold and air loads before implementing the time-shifting type demand response; ep、Eh、EcAnd EfRespectively are energy price elastic matrixes of electricity, heat, cold and gas; Δ pp、Δph、ΔpcAnd Δ pfRespectively are energy price change rate matrixes of electricity, heat, cold and gas;
(2.6) day-ahead robust constraint
Figure BDA0002651118780000048
In the formula, T is a scheduling period cycle of 24 h; subscript x represents energy type, and p, c, h and f are respectively electric, cold, hot and gas energy marks;
Figure BDA0002651118780000049
a robust interval conservative parameter of uncertain variables of x energy types in a t period, wherein gamma is
Figure BDA00026511187800000410
Is measured.
2) In the in-day optimization stage, establishing a comprehensive energy system in-day random optimization operation model considering random uncertainty and alternative demand response according to in-day prediction data of various distributed energy sources and loads and a system in-day optimization result; the comprehensive energy system day-interior random optimization operation model considering random uncertainty and alternative demand response takes the minimum day-interior comprehensive operation cost of the comprehensive energy system as an objective function and takes day-interior energy balance constraint, day-interior energy conversion equipment constraint, day-interior energy storage constraint, day-interior energy purchasing constraint and day-interior alternative demand response constraint as constraint conditions. Wherein the content of the first and second substances,
(1) the expression of the target function is as follows:
Figure BDA00026511187800000411
Cintrafor the daily integrated operating costs of the integrated energy system, including the energy purchase cost Cpe,sAnd operation and maintenance cost Com,sAnd environmental cost Cce,sThree moieties, s, PrsRespectively representing an uncertain typical scene and the probability of occurrence thereof; wherein:
(1.1) cost of energy purchase
Figure BDA00026511187800000412
In the formula, T is a scheduling period cycle of 24h, and the time step is 1 h;
Figure BDA0002651118780000051
and
Figure BDA0002651118780000052
the electricity purchase price, the gas purchase price and the heat purchase price in the time period t are respectively;
Figure BDA0002651118780000053
and
Figure BDA0002651118780000054
respectively the electricity purchasing power, the gas purchasing power and the heat purchasing power of a system at the time t under a scene s in optimization in the day, wherein for convenience of calculation, the price units are all yuan/(kW.h), and the power units are all kW; Δ t is the time interval;
(1.2) operation and maintenance costs
Figure BDA0002651118780000055
In the formula, M is the number of operation and maintenance units in the park, and comprises a photovoltaic unit, a fan, a micro-combustion engine, an electric refrigerator, an absorption refrigerator, and electricity storage and heat storage equipment; k is a radical ofom,iThe unit operation and maintenance cost of the unit i is obtained;
Figure BDA0002651118780000056
is a dayThe output of the unit i in the time period t under the scene s in the internal optimization;
(1.3) cost to environmental protection
Figure BDA0002651118780000057
In the formula, alpha is unit CO2The cost of treatment of; beta is ae、βg、βhAnd betaMTRespectively obtaining equivalent carbon emission coefficients of system electricity purchase, gas purchase, heat purchase and micro-combustion engine operation;
Figure BDA0002651118780000058
the output electric power of the micro-combustion engine in the time t under the scene s in the optimization in the day is obtained.
(2) The constraint condition is specifically expressed as:
(2.1) energy balance constraint in the day
Figure BDA0002651118780000059
In the formula (I), the compound is shown in the specification,
Figure BDA00026511187800000510
and
Figure BDA00026511187800000511
output electric power of a transformer, a photovoltaic, a fan and a micro-combustion engine at a time t under a scene s in optimization in the day is respectively;
Figure BDA00026511187800000512
the power consumption of the electric refrigerator at the t time period under the scene s in the optimization in the day is calculated;
Figure BDA00026511187800000513
respectively outputting thermal powers of the heat exchanger and the micro-combustion engine at a time t under a scene s in optimization in the day;
Figure BDA00026511187800000514
optimizing midrange for the dayThe heat consumption power of the absorption refrigerator in the t time period under the scene s;
Figure BDA00026511187800000515
respectively outputting cold power of the electric refrigerator and the absorption refrigerator at a time period t under a scene s in optimization in the day;
Figure BDA00026511187800000516
the gas purchasing quantity of the system at the t time period under the scene s in the optimization in the day is calculated;
Figure BDA00026511187800000517
the air consumption of the micro-combustion engine at the t time period under the scene s in the optimization in the day is measured;
Figure BDA00026511187800000518
and
Figure BDA00026511187800000519
respectively the electric load, the heat load, the cold load and the air load of a scene s in the optimization in the day at a time period t;
Figure BDA00026511187800000520
respectively outputting power for electricity storage and heat storage at a time period t under a scene s in optimization in the day;
(2.2) day-to-day energy conversion device constraints
Figure BDA0002651118780000061
In the formula, PT,max、HHE,max、PPV,max、PWT,max、PMT,max、PEC,maxAnd HAC,maxThe maximum power of the transformer, the heat exchanger, the photovoltaic, the wind power, the micro-combustion engine, the electric refrigerator and the absorption refrigerator respectively;
(2.3) energy storage restraint in the day
Figure BDA0002651118780000062
In the formula, subscript x represents an energy storage type;
Figure BDA0002651118780000063
respectively storing energy output power and energy storage state at t time period under a scene s in optimization in the day;
Figure BDA0002651118780000064
respectively storing the initial energy storage state and the final energy storage state of the energy storage equipment under a scene s in the optimization in the day; px,c,max、Px,d,maxRespectively storing the maximum energy charging and discharging power of the energy; ex,min、Ex,maxMinimum and maximum states of the energy storage device, respectively;
(2.4) restriction of daily energy purchase
Figure BDA0002651118780000065
In the formula (I), the compound is shown in the specification,
Figure BDA0002651118780000066
respectively the electricity purchasing, the heat purchasing and the gas purchasing quantity of the system at the time period t under the scene s in the optimization in the day; pe,max、Hh,max、Fg,maxRespectively carrying out system electricity purchasing, heat purchasing and gas purchasing upper limits on an external network;
(2.5) Intra-day alternative demand response constraints
Figure BDA0002651118780000067
Figure BDA0002651118780000068
In the formula (I), the compound is shown in the specification,
Figure BDA0002651118780000069
the load quantity and the upper limit thereof which are replaced at the t time period under the scene s in the day optimization;
Figure BDA00026511187800000610
Figure BDA00026511187800000611
and
Figure BDA00026511187800000612
respectively representing the electric load, the heat load, the cold load and the air load after implementing the alternative demand response at the t time period under a scene s in the optimization in the day;
Figure BDA00026511187800000613
and
Figure BDA00026511187800000614
respectively representing the electrical, thermal, cold, and gas loads prior to implementing the alternative demand response; k is a radical ofijRepresents the substitution conversion efficiency of energy sources i and j, i, j belongs to { p, h, c, f }, i is not equal to j, and p, h, i and f respectively represent electricity, heat, cold and gas.
3) And the day-ahead robust optimization operation model of the comprehensive energy system considering the robust uncertainty and the time-shifting type demand response and the day-interior random optimization operation model of the comprehensive energy system considering the random uncertainty and the alternative type demand response jointly form a day-ahead and day-interior two-stage collaborative optimization operation model of the comprehensive energy system considering the multiple uncertainties and the comprehensive demand response, and a Gurobi solver is called through a Yalmip tool box to carry out model solution.
The comprehensive energy system day-ahead-day two-stage collaborative optimization operation model considering multiple uncertainties and comprehensive demand response is comprehensively expressed as follows:
Figure BDA0002651118780000071
in the formula, CaheadFor the day-ahead integrated operating costs of the integrated energy system, including the energy purchase cost CpeAnd operation and maintenance cost ComAnd environmental cost CceThree parts;
Figure BDA0002651118780000072
representing a robust scene; cintraFor the daily integrated operating costs of the integrated energy system, including the energy purchase cost Cpe,sAnd operation and maintenance cost Com,sAnd environmental cost Cce,sThree moieties, s, PrsRespectively, representing the uncertain representative scene and its probability of occurrence.
The calling of the Gurobi solver through the Yalmip toolbox to carry out model solution comprises the following steps:
(1) in a day-ahead optimization stage, based on an MATLAB platform, calling a Gurobi solver to solve the established comprehensive energy system day-ahead robust optimization operation model considering robust uncertainty and time-shifting type demand response through a Yalmip tool box to obtain a day-ahead optimization operation scheme of the comprehensive energy system;
(2) and in the in-day optimization stage, based on the obtained day-ahead optimization operation scheme of the comprehensive energy system, based on the MATLAB platform, calling a Gurobi solver to solve the established day-ahead random optimization operation model of the comprehensive energy system, which takes account of random uncertainty and alternative demand response, through a Yalmip toolbox, and correcting the day-ahead optimization operation scheme of the comprehensive energy system in real time to obtain the day-ahead optimization operation scheme of the comprehensive energy system.
Specific examples are given below.
The following simulation analysis is performed based on a typical park integrated energy system as an example, and the specific structure is shown in fig. 1. The wind power and the photovoltaic adopt an MPPT mode, one day is divided into 24 time intervals by calculation examples, the power prediction error intervals before the day of the distributed power supply/load are respectively +/-20%/+/-10%, the power prediction error distribution in the day follows normal distribution with a predicted value as a mean value and 0.1/0.03 time of the mean value as a standard deviation, the wind and light output and electricity, gas, heat and cold load prediction curves are shown in figure 2, and the energy price is shown in table 1.
TABLE 1 energy prices
Figure BDA0002651118780000073
Figure BDA0002651118780000081
For the optimization of the day-ahead stage of the campus, the conservative degree of the robust interval is taken as 5 by way of example, a model without considering the time-shift type demand response and a model with considering the time-shift type demand response are compared, the running cost of each time period is as shown in fig. 3, and the daily comprehensive running cost is 10331.1 yuan and 10150.4 yuan respectively. It can be seen that the implementation of the transfer-type DR strategy can transfer the energy consumption curve and the operating cost of the energy purchase price in the peak time period to the valley time period, so that the daily comprehensive operating cost of the park is reduced, and the energy consumption pressure of the park in a severe and uncertain scene is further relieved.
In order to further analyze the influence of the selection of the robust interval on the optimization result, the conservation degree gamma is respectively 5, 4.5, 4, 3.5 and 3 for simulation analysis, and the comparison result is shown in table 2. By comparison, the risk of optimization decision increases with the decrease of Γ, but the comprehensive operation cost of the campus day gradually decreases. Therefore, the parameter gamma is selected reasonably considering both the economic cost and the decision risk, and the coordination and optimization between the economy and the reliability of the park operation are realized.
TABLE 2 comparison of park operating costs at different gamma
Figure BDA0002651118780000082
In the day-to-day optimization stage, in order to verify the influence of source load uncertainty and a demand response strategy on day-to-day stage optimization decision, a comprehensive energy system day-to-day random optimization operation model which is established based on the method and takes random uncertainty and alternative demand response into account is used for carrying out comparative analysis on the following scenes.
Scene 1: deterministic, regardless of demand response.
Scene 2: determinism, taking into account demand response.
Scene 3: uncertainty, regardless of demand response.
Scene 4: uncertainty, consider demand response.
The optimization results under different scenarios are shown in table 3.
TABLE 3 comparison of park operating costs under different scenarios
Figure BDA0002651118780000091
Comparing scenarios 1 and 3 with scenarios 2 and 4, respectively, it can be seen that the implementation of the integrated demand response strategy effectively reduces the integrated operating cost of the campus from 8789.5 yuan and 8835.9 yuan to 8406.5 yuan and 8454.7 yuan, respectively. On one hand, due to the 'peak clipping and valley filling' function of the transfer type demand response, partial load of the electricity and gas load curve at the peak time period is transferred to the valley time period, and the vertical time sequence transfer of the user energy demand is realized; on the other hand, due to the 'energy use substitution' function of the substitution type demand response, various energy prices are used as excitation signals, the energy use mode of the multi-energy user is promoted to be autonomously adjusted, and the transverse multi-energy complementation of the energy use demand of the user is realized.
For scenario 4, the result of the campus operation in the optimization stage in the day is shown in fig. 4, and fig. 4a, 4b, and 4c are the results of the optimal scheduling of the power flows on the collector, the heat collector, and the heat collector, respectively, where the upper part of the horizontal axis represents energy input and the lower part of the horizontal axis represents energy output. As can be seen from fig. 4(a), during the low price period of electricity, the electrical load is mainly satisfied by the power purchased by the power grid, and the insufficient part is supplied by the wind power and the micro-combustion engine; in a time period with higher electricity price, the electricity purchasing of the power grid is reduced, the output of the micro-combustion engine is increased, and the insufficient part is supplied by wind power, photovoltaic and a storage battery. As can be seen from fig. 4(b), during the off-peak period of electricity prices, the heat load is mainly satisfied by the heat supply network purchasing heat, and the insufficient part is supplied by the micro-combustion engine; during the peak period of electricity price, along with the increase of the output of the micro-combustion engine, part of heat load and the heat consumption of the absorption refrigerator are supplied by the micro-combustion engine and the heat storage device, and the rest is still satisfied by heat purchased by the heat supply network. As can be seen from fig. 4(c), during the off-peak period of electricity prices, the cooling load is mainly satisfied by the electric refrigerator, and the shortage is partially supplied by the absorption refrigerator; in the time period of higher electricity price, as the output of the micro-combustion engine increases, the cooling load is supplied by the absorption refrigerator preferentially, and the shortage is supplied by the electric refrigerator. In conclusion, the park can independently adjust the energy conversion mode and purchase energy according to the changes of electricity, gas, heat and cold loads and energy purchase prices, fully exerts the comprehensive response of multi-load and the complementary and complementary advantages of various energies, further reduces the comprehensive operation cost of the park, realizes the economic, environment-friendly, flexible and efficient operation of a comprehensive energy system, and confirms the correctness and the effectiveness of the comprehensive energy system optimized operation method considering uncertainty and demand response.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. An integrated energy system optimization operation method considering uncertainty and demand response is characterized by comprising the following steps:
1) in a day-ahead optimization stage, establishing a comprehensive energy system day-ahead robust optimization operation model considering robust uncertainty and time-shifting type demand response according to day-ahead prediction data of various distributed energy sources and loads and energy price fluctuation factors;
the comprehensive energy system day-ahead robust optimization operation model considering the robustness uncertainty and the time-shifting type demand response takes the minimum day-ahead comprehensive operation cost of the comprehensive energy system in a robust scene as an objective function and takes a day-ahead energy balance constraint, a day-ahead energy conversion equipment constraint, a day-ahead energy storage constraint, a day-ahead energy purchase constraint, a day-ahead time-shifting type demand response constraint and a day-ahead robust constraint as constraint conditions; wherein, the constraint condition is specifically expressed as:
(1) day-ahead energy balance constraint:
Figure FDA0003424280590000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003424280590000012
and
Figure FDA0003424280590000013
output electric power of a transformer, a photovoltaic, a fan and a micro-combustion engine in a period t in optimization before the day;
Figure FDA0003424280590000014
the power consumption of the electric refrigerator in the period t in the optimization before the day is achieved;
Figure FDA0003424280590000015
respectively optimizing the output thermal power of the heat exchanger and the micro-combustion engine in the period t in the day-ahead optimization;
Figure FDA0003424280590000016
the heat consumption power of the absorption refrigerator in the period t in the optimization before the day;
Figure FDA0003424280590000017
respectively outputting cold power of the electric refrigerator and the absorption refrigerator in the period t in optimization in the day ahead;
Figure FDA0003424280590000018
optimizing the gas purchasing quantity of the system in the period t in the day ahead;
Figure FDA0003424280590000019
the method comprises the following steps of optimizing the air consumption of the micro-combustion engine at a time t in the day ahead;
Figure FDA00034242805900000110
and
Figure FDA00034242805900000111
respectively electric load, heat load, cold load and air load in the period t in the day-ahead optimization;
Figure FDA00034242805900000112
respectively outputting power for electricity storage and heat storage in the period t in optimization before the day;
(2) day-ahead energy conversion equipment constraints
Figure FDA00034242805900000113
In the formula, PT,max、HHE,max、PPV,max、PWT,max、PMT,max、PEC,maxAnd HAC,maxThe maximum power of the transformer, the heat exchanger, the photovoltaic, the wind power, the micro-combustion engine, the electric refrigerator and the absorption refrigerator respectively;
(3) day-ahead energy storage restraint
Figure FDA0003424280590000021
In the formula, subscript x represents an energy storage type;
Figure FDA0003424280590000022
respectively storing energy output power and energy storage state in the period t in the day-ahead optimization;
Figure FDA0003424280590000023
respectively storing the initial energy storage state and the final energy storage state of the energy storage equipment in the optimization in the day ahead; px,c,max、Px,d,maxRespectively storing the maximum energy charging and discharging power of the energy; ex,min、Ex,maxMinimum and maximum states of the energy storage device, respectively;
(4) energy purchasing restriction before day
Figure FDA0003424280590000024
In the formula (I), the compound is shown in the specification,
Figure FDA0003424280590000025
respectively optimizing the electricity purchasing, heat purchasing and gas purchasing quantity of the system at the time t in the day-ahead optimization; pe,max、Hh,max、Fg,maxRespectively carrying out system electricity purchasing, heat purchasing and gas purchasing upper limits on an external network;
(5) time-of-day demand response constraints
Figure FDA0003424280590000026
Figure FDA0003424280590000027
In the formula (I), the compound is shown in the specification,
Figure FDA0003424280590000028
respectively representing the variation and the variation upper limit of the time-shifting type demand response load; pL,a、HL,a、CL,a、FL,aRespectively carrying out electric, hot, cold and air loads after time-shifting type demand response is implemented;
Figure FDA0003424280590000029
and
Figure FDA00034242805900000210
respectively representing the electric, heat, cold and air loads before implementing the time-shifting type demand response; ep、Eh、EcAnd EfRespectively are energy price elastic matrixes of electricity, heat, cold and gas; Δ pp、Δph、ΔpcAnd Δ pfRespectively are energy price change rate matrixes of electricity, heat, cold and gas;
(6) day-ahead robust constraint
Figure FDA00034242805900000211
In the formula, T is a scheduling period cycle of 24 h; subscript x represents energy type, and p, c, h and f are respectively electric, cold, hot and gas energy marks;
Figure FDA00034242805900000212
a robust interval conservative parameter of uncertain variables of x energy types in a t period, wherein gamma is
Figure FDA00034242805900000213
Maximum value of (d);
2) in the in-day optimization stage, establishing a comprehensive energy system in-day random optimization operation model considering random uncertainty and alternative demand response according to in-day prediction data of various distributed energy sources and loads and a system in-day optimization result;
the comprehensive energy system day-interior random optimization operation model considering random uncertainty and substitution type demand response takes the minimum day-interior comprehensive operation cost of the comprehensive energy system as an objective function and takes day-interior energy balance constraint, day-interior energy conversion equipment constraint, day-interior energy storage constraint, day-interior energy purchasing constraint and day-interior substitution type demand response constraint as constraint conditions; wherein, the constraint condition is specifically expressed as:
(1) energy balance constraint in the day
Figure FDA0003424280590000031
In the formula (I), the compound is shown in the specification,
Figure FDA0003424280590000032
and
Figure FDA0003424280590000033
output electric power of a transformer, a photovoltaic, a fan and a micro-combustion engine at a time t under a scene s in optimization in the day is respectively;
Figure FDA0003424280590000034
the power consumption of the electric refrigerator at the t time period under the scene s in the optimization in the day is calculated;
Figure FDA0003424280590000035
respectively outputting thermal powers of the heat exchanger and the micro-combustion engine at a time t under a scene s in optimization in the day;
Figure FDA0003424280590000036
the heat consumption power of the absorption refrigerator at the time t under the scene s in the optimization in the day is calculated;
Figure FDA0003424280590000037
respectively outputting cold power of the electric refrigerator and the absorption refrigerator at a time period t under a scene s in optimization in the day;
Figure FDA0003424280590000038
the gas purchasing quantity of the system at the t time period under the scene s in the optimization in the day is calculated;
Figure FDA0003424280590000039
the air consumption of the micro-combustion engine at the t time period under the scene s in the optimization in the day is measured;
Figure FDA00034242805900000310
and
Figure FDA00034242805900000311
respectively the electric load, the heat load, the cold load and the air load of a scene s in the optimization in the day at a time period t;
Figure FDA00034242805900000312
respectively outputting power for electricity storage and heat storage at a time period t under a scene s in optimization in the day;
(2) energy conversion equipment constraint in the daytime
Figure FDA00034242805900000313
In the formula, PT,max、HHE,max、PPV,max、PWT,max、PMT,max、PEC,maxAnd HAC,maxThe maximum power of the transformer, the heat exchanger, the photovoltaic, the wind power, the micro-combustion engine, the electric refrigerator and the absorption refrigerator respectively;
(3) energy storage restraint in the day
Figure FDA00034242805900000314
In the formula, subscript x represents an energy storage type;
Figure FDA00034242805900000315
respectively storing energy output power and energy storage state at t time period under a scene s in optimization in the day;
Figure FDA00034242805900000316
respectively storing the initial energy storage state and the final energy storage state of the energy storage equipment under a scene s in the optimization in the day; px,c,max、Px,d,maxRespectively storing the maximum energy charging and discharging power of the energy; ex,min、Ex,maxMinimum and maximum states of the energy storage device, respectively;
(4) restriction of energy purchase within day
Figure FDA0003424280590000041
In the formula (I), the compound is shown in the specification,
Figure FDA0003424280590000042
respectively the electricity purchasing, the heat purchasing and the gas purchasing quantity of the system at the time period t under the scene s in the optimization in the day; pe,max、Hh,max、Fg,maxRespectively carrying out system electricity purchasing, heat purchasing and gas purchasing upper limits on an external network;
(5) day alternate demand response constraints
Figure FDA0003424280590000043
Figure FDA0003424280590000044
In the formula (I), the compound is shown in the specification,
Figure FDA0003424280590000045
the load quantity and the upper limit thereof which are replaced at the t time period under the scene s in the day optimization;
Figure FDA0003424280590000046
Figure FDA0003424280590000047
and
Figure FDA0003424280590000048
respectively representing the electric load, the heat load, the cold load and the air load after implementing the alternative demand response at the t time period under a scene s in the optimization in the day;
Figure FDA0003424280590000049
and
Figure FDA00034242805900000410
respectively representing the electrical, thermal, cold, and gas loads prior to implementing the alternative demand response; k is a radical ofijRepresenting the substitution conversion efficiency of energy i and j, wherein i, j belongs to { p, h, c, f }, i is not equal to j, and p, h, i and f respectively represent electricity, heat, cold and gas;
3) and the day-ahead robust optimization operation model of the comprehensive energy system considering the robust uncertainty and the time-shifting type demand response and the day-interior random optimization operation model of the comprehensive energy system considering the random uncertainty and the alternative type demand response jointly form a day-ahead and day-interior two-stage collaborative optimization operation model of the comprehensive energy system considering the multiple uncertainties and the comprehensive demand response, and a Gurobi solver is called through a Yalmip tool box to carry out model solution.
2. The method of claim 1, wherein the objective function is expressed as follows:
Figure FDA00034242805900000411
wherein, CaheadFor the day-ahead integrated operating costs of the integrated energy system, including the energy purchase cost CpeAnd operation and maintenance cost ComAnd environmental cost CceThree parts;
Figure FDA00034242805900000412
representing a robust scene; wherein the content of the first and second substances,
(1) cost of energy purchase
Figure FDA00034242805900000413
In the formula, T is a scheduling period cycle of 24h, and the time step is 1 h; subscript a is a day-ahead optimization designation;
Figure FDA00034242805900000414
and
Figure FDA00034242805900000415
the electricity purchase price, the gas purchase price and the heat purchase price in the time period t are respectively;
Figure FDA00034242805900000416
and
Figure FDA00034242805900000417
respectively optimizing the electricity purchasing power, the gas purchasing power and the heat purchasing power of the system at the time t in the day-ahead optimization; to facilitate the defecationIn the calculation, the price units are yuan/(kW.h), and the power units are kW; Δ t is the time interval;
(2) cost of operation and maintenance
Figure FDA0003424280590000051
In the formula, M is the number of operation and maintenance units in the park, and comprises a photovoltaic unit, a fan, a micro-combustion engine, an electric refrigerator, an absorption refrigerator, and electricity storage and heat storage equipment; k is a radical ofom,iThe unit operation and maintenance cost of the unit i is obtained;
Figure FDA0003424280590000052
the output of the unit i in the time period t is optimized in the day ahead;
(3) cost of environmental protection
Figure FDA0003424280590000053
In the formula, alpha is unit CO2The cost of treatment of; beta is ae、βg、βhAnd betaMTRespectively obtaining equivalent carbon emission coefficients of system electricity purchase, gas purchase, heat purchase and micro-combustion engine operation;
Figure FDA0003424280590000054
the output electric power of the micro-combustion engine in the period t in the optimization before the day.
3. The method of claim 1, wherein the objective function is expressed as follows:
Figure FDA0003424280590000055
Cintrafor the daily integrated operating costs of the integrated energy system, including the energy purchase cost Cpe,sAnd operation and maintenance cost Com,sAnd environmental cost Cce,sThree moieties, s, PrsRespectively representing an uncertain typical scene and the probability of occurrence thereof; wherein:
(1) cost of energy purchase
Figure FDA0003424280590000056
In the formula, T is a scheduling period cycle of 24h, and the time step is 1 h;
Figure FDA0003424280590000057
and
Figure FDA0003424280590000058
the electricity purchase price, the gas purchase price and the heat purchase price in the time period t are respectively;
Figure FDA0003424280590000059
and
Figure FDA00034242805900000510
respectively the electricity purchasing power, the gas purchasing power and the heat purchasing power of a system at the time t under a scene s in optimization in the day, wherein for convenience of calculation, the price units are all yuan/(kW.h), and the power units are all kW; Δ t is the time interval;
(2) cost of operation and maintenance
Figure FDA00034242805900000511
In the formula, M is the number of operation and maintenance units in the park, and comprises a photovoltaic unit, a fan, a micro-combustion engine, an electric refrigerator, an absorption refrigerator, and electricity storage and heat storage equipment; k is a radical ofom,iThe unit operation and maintenance cost of the unit i is obtained;
Figure FDA00034242805900000512
the output of the unit i in the time period t under the scene s in the optimization in the day;
(3) cost of environmental protection
Figure FDA00034242805900000513
In the formula, alpha is unit CO2The cost of treatment of; beta is ae、βg、βhAnd betaMTRespectively obtaining equivalent carbon emission coefficients of system electricity purchase, gas purchase, heat purchase and micro-combustion engine operation;
Figure FDA00034242805900000514
the output electric power of the micro-combustion engine in the time t under the scene s in the optimization in the day is obtained.
4. The method as claimed in claim 1, wherein the integrated energy system optimization operation model considering the uncertainty and the demand response in step 3) is represented by the following steps:
Figure FDA0003424280590000061
in the formula, CaheadFor the day-ahead integrated operating costs of the integrated energy system, including the energy purchase cost CpeAnd operation and maintenance cost ComAnd environmental cost CceThree parts;
Figure FDA0003424280590000062
representing a robust scene; cintraFor the daily integrated operating costs of the integrated energy system, including the energy purchase cost Cpe,sAnd operation and maintenance cost Com,sAnd environmental cost Cce,sThree moieties, s, PrsRespectively, representing the uncertain representative scene and its probability of occurrence.
5. The method for optimizing the operation of the comprehensive energy system with consideration of uncertainty and demand response according to claim 1, wherein the step 3) of calling a Gurobi solver to perform model solution by using a Yalmip toolbox comprises:
(1) in a day-ahead optimization stage, based on an MATLAB platform, calling a Gurobi solver to solve the established comprehensive energy system day-ahead robust optimization operation model considering robust uncertainty and time-shifting type demand response through a Yalmip tool box to obtain a day-ahead optimization operation scheme of the comprehensive energy system;
(2) and in the in-day optimization stage, based on the obtained day-ahead optimization operation scheme of the comprehensive energy system, based on the MATLAB platform, calling a Gurobi solver to solve the established day-ahead random optimization operation model of the comprehensive energy system, which takes account of random uncertainty and alternative demand response, through a Yalmip toolbox, and correcting the day-ahead optimization operation scheme of the comprehensive energy system in real time to obtain the day-ahead optimization operation scheme of the comprehensive energy system.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112583021B (en) * 2020-11-23 2022-12-09 国家电网有限公司 Comprehensive energy system optimal scheduling method and device considering comprehensive demand response
CN112580938B (en) * 2020-12-03 2024-05-14 国家电网有限公司 Multi-uncertainty-oriented comprehensive energy system optimal scheduling method and device
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CN113673739B (en) * 2021-02-22 2023-04-18 华北电力大学（保定） Multi-time-space scale collaborative optimization operation method of distributed comprehensive energy system
CN112837181B (en) * 2021-02-23 2022-10-04 国网山东省电力公司经济技术研究院 Scheduling method of comprehensive energy system considering demand response uncertainty
CN113112082B (en) * 2021-04-21 2023-04-28 上海电力大学 Two-stage operation optimization method for distributed system
CN113435659B (en) * 2021-07-09 2023-01-24 中国电力工程顾问集团中南电力设计院有限公司 Scene analysis-based two-stage optimized operation method and system for comprehensive energy system
CN116780649B (en) * 2023-06-16 2024-03-01 国网浙江省电力有限公司嘉兴供电公司 Multi-energy complementary utilization distributed robust optimization operation method

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107341601A (en) * 2017-06-26 2017-11-10 南方电网科学研究院有限责任公司 Based on a few days ago, the electric power resource dispatching method of time of day yardstick and system
CN107563676A (en) * 2017-10-11 2018-01-09 华中科技大学 Consider the source lotus coordinated operation dispatching method of Multiple Time Scales polymorphic type demand response
CN107958300A (en) * 2017-08-21 2018-04-24 浙江工业大学 A kind of more microgrid interconnected operation coordinated scheduling optimization methods for considering interactive response
CN108667012A (en) * 2018-05-21 2018-10-16 国网山东省电力公司电力科学研究院 Regional Energy the Internet sources lotus based on more scenes stores up dual-stage coordination optimizing method
CN109412158A (en) * 2018-11-30 2019-03-01 国家电网公司西南分部 A kind of sending end power grid Unit Combination progress control method for considering to abandon energy cost constraint
CN109474022A (en) * 2019-01-22 2019-03-15 合肥工业大学 The power distribution network optimization regulating method of the interaction of consideration source lotus and distributed generation resource power output randomness
CN110009152A (en) * 2019-04-03 2019-07-12 东南大学 A kind of consideration electricity turns gas and probabilistic regional complex energy system operation robust Optimal methods
CN110417062A (en) * 2019-07-31 2019-11-05 广东电网有限责任公司 A kind of electrical integrated energy system Optimization Scheduling
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization
CN111291942A (en) * 2020-03-05 2020-06-16 燕山大学 Two-stage scheduling method and system of comprehensive energy system considering demand response

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106845701B (en) * 2017-01-11 2019-11-08 东南大学 A kind of integrated energy system optimization method based on heat supply network and house thermal inertia
CN109858711B (en) * 2019-03-07 2022-05-24 东北电力大学 Wind power consumption day-ahead scheduling method considering price type demand response and participation of CSP power station
CN110807560B (en) * 2019-11-12 2022-10-04 国网重庆市电力公司电力科学研究院 Method and device for scheduling hydrogen-mixed natural gas energy system and readable storage medium

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107341601A (en) * 2017-06-26 2017-11-10 南方电网科学研究院有限责任公司 Based on a few days ago, the electric power resource dispatching method of time of day yardstick and system
CN107958300A (en) * 2017-08-21 2018-04-24 浙江工业大学 A kind of more microgrid interconnected operation coordinated scheduling optimization methods for considering interactive response
CN107563676A (en) * 2017-10-11 2018-01-09 华中科技大学 Consider the source lotus coordinated operation dispatching method of Multiple Time Scales polymorphic type demand response
CN108667012A (en) * 2018-05-21 2018-10-16 国网山东省电力公司电力科学研究院 Regional Energy the Internet sources lotus based on more scenes stores up dual-stage coordination optimizing method
CN109412158A (en) * 2018-11-30 2019-03-01 国家电网公司西南分部 A kind of sending end power grid Unit Combination progress control method for considering to abandon energy cost constraint
CN109474022A (en) * 2019-01-22 2019-03-15 合肥工业大学 The power distribution network optimization regulating method of the interaction of consideration source lotus and distributed generation resource power output randomness
CN110009152A (en) * 2019-04-03 2019-07-12 东南大学 A kind of consideration electricity turns gas and probabilistic regional complex energy system operation robust Optimal methods
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization
CN110417062A (en) * 2019-07-31 2019-11-05 广东电网有限责任公司 A kind of electrical integrated energy system Optimization Scheduling
CN111291942A (en) * 2020-03-05 2020-06-16 燕山大学 Two-stage scheduling method and system of comprehensive energy system considering demand response

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Study on Optimal Operation of Integrated Energy System Considering New Energy Incentive Mechanism;Chaofan Jiang;《 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2)》;20200409;参见第301-306页 *
基于双层优化的综合能源服务商博弈策略;李鹏 等;《电网技术》;20200818;第45卷(第2期);参见第460-469页 *
计及多能源多需求响应手段的园区综合能源系统优化调度模型;李鹏 等;《电力建设》;20200531;第41卷(第5期);参见第45-57页 *
计及需求响应不确定性的综合能源系统协同优化配置;刘文霞 等;《电力系统自动化》;20200531;第44卷(第10期);参见第41-49页 *

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