CN111950807A - Comprehensive energy system optimization operation method considering uncertainty and demand response - Google Patents
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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
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:
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;representing a robust scene; wherein the content of the first and second substances,
(1.1) cost of energy purchase
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;andthe electricity purchase price, the gas purchase price and the heat purchase price in the time period t are respectively;andrespectively 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
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;the output of the unit i in the time period t is optimized in the day ahead;
(1.3) cost to environmental protection
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;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:
in the formula (I), the compound is shown in the specification,andoutput electric power of a transformer, a photovoltaic, a fan and a micro-combustion engine in a period t in optimization before the day;the power consumption of the electric refrigerator in the period t in the optimization before the day is achieved;respectively optimizing the output thermal power of the heat exchanger and the micro-combustion engine in the period t in the day-ahead optimization;optimizing medium time t for day aheadHeat consumption power of the segment absorption chiller;respectively outputting cold power of the electric refrigerator and the absorption refrigerator in the period t in optimization in the day ahead;optimizing the gas purchasing quantity of the system in the period t in the day ahead;the method comprises the following steps of optimizing the air consumption of the micro-combustion engine at a time t in the day ahead;andrespectively electric load, heat load, cold load and air load in the period t in the day-ahead optimization;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
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
In the formula, subscript x represents an energy storage type;respectively storing energy output power and energy storage state in the period t in the day-ahead optimization;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
In the formula (I), the compound is shown in the specification,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
In the formula (I), the compound is shown in the specification,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;andrespectively 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
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;the conservative parameter of the robust interval of the uncertain variable of x energy types in the t period isIs 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:
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
In the formula, T is a scheduling period cycle of 24h, and the time step is 1 h;andthe electricity purchase price, the gas purchase price and the heat purchase price in the time period t are respectively;andrespectively 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
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,iFor unit transport of unit iMaintenance cost;the output of the unit i in the time period t under the scene s in the optimization in the day;
(1.3) cost to environmental protection
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;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
In the formula (I), the compound is shown in the specification,andoutput 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;the power consumption of the electric refrigerator at the t time period under the scene s in the optimization in the day is calculated;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;the heat consumption power of the absorption refrigerator at the time t under the scene s in the optimization in the day is calculated;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;the gas purchasing quantity of the system at the t time period under the scene s in the optimization in the day is calculated;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;andrespectively 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;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
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
In the formula, subscript x represents an energy storage type;respectively storing energy output power and energy storage state at t time period under a scene s in optimization in the day;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
In the formula (I), the compound is shown in the specification,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
In the formula (I), the compound is shown in the specification,the load quantity and the upper limit thereof which are replaced at the t time period under the scene s in the day optimization; andrespectively 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;andrespectively 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:
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 environment protectionThis CceThree parts;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
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 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 optimizing the decision increases with the decrease, but the daily comprehensive operation cost of the garden gradually decreases. Therefore, the parameters are selected reasonably considering the economic cost and the decision risk, and the coordination and optimization between the operation economy and the reliability of the park are realized.
TABLE 2 comparison of park operating costs under different conditions
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
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 (9)
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;
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.
2. The method as claimed in claim 1, wherein the integrated energy system day-ahead robust optimization operation model taking the robustness uncertainty and the time-shift type demand response into account in step 1) is based on a minimum day-ahead integrated operation cost of the integrated energy system in a robust scene as an objective function, and is based on a constraint of day-ahead energy balance, a constraint of day-ahead energy conversion equipment, a constraint of day-ahead energy storage, a constraint of day-ahead energy purchase, a constraint of day-ahead time-shift type demand response, and a constraint of day-ahead robust.
3. The method of claim 2, wherein the objective function is expressed as follows:
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;representing a robust scene; wherein the content of the first and second substances,
(1) cost of energy purchase
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;andthe electricity purchase price, the gas purchase price and the heat purchase price in the time period t are respectively;andrespectively 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;
(2) cost of operation and maintenance
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;the output of the unit i in the time period t is optimized in the day ahead;
(3) cost of environmental protection
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;the output electric power of the micro-combustion engine in the period t in the optimization before the day.
4. The method of claim 2, wherein the constraints are specifically expressed as:
(1) day-ahead energy balance constraint:
in the formula (I), the compound is shown in the specification,andoutput electric power of a transformer, a photovoltaic, a fan and a micro-combustion engine in a period t in optimization before the day;the power consumption of the electric refrigerator in the period t in the optimization before the day is achieved;respectively optimizing the output thermal power of the heat exchanger and the micro-combustion engine in the period t in the day-ahead optimization;the heat consumption power of the absorption refrigerator in the period t in the optimization before the day;respectively outputting cold power of the electric refrigerator and the absorption refrigerator in the period t in optimization in the day ahead;optimizing the gas purchasing quantity of the system in the period t in the day ahead;the method comprises the following steps of optimizing the air consumption of the micro-combustion engine at a time t in the day ahead;andrespectively electric load, heat load, cold load and air load in the period t in the day-ahead optimization;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
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
In the formula, subscript x represents an energy storage type;respectively storing energy output power and energy storage state in the period t in the day-ahead optimization;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
In the formula (I), the compound is shown in the specification,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
In the formula (I), the compound is shown in the specification,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;andrespectively 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
5. The method as claimed in claim 1, wherein the model for the day-to-day stochastic optimal operation of the integrated energy system taking into account the uncertainty and the demand response in step 2) is based on a minimum day-to-day synthetic operation cost of the integrated energy system as an objective function, and on a constraint condition of a day-to-day energy balance constraint, a day-to-day energy conversion device constraint, a day-to-day energy storage constraint, a day-to-day energy purchase constraint and a day-to-day alternative demand response constraint.
6. The method of claim 5, wherein the objective function is expressed as follows:
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
In the formula, T is a scheduling period cycle of 24h, and the time step is 1 h;andthe electricity purchase price, the gas purchase price and the heat purchase price in the time period t are respectively;andrespectively 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
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;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
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;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.
7. The method of claim 5, wherein the constraints are specifically expressed as:
(1) energy balance constraint in the day
In the formula (I), the compound is shown in the specification,andoutput 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;the power consumption of the electric refrigerator at the t time period under the scene s in the optimization in the day is calculated;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;the heat consumption power of the absorption refrigerator at the time t under the scene s in the optimization in the day is calculated;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;the gas purchasing quantity of the system at the t time period under the scene s in the optimization in the day is calculated;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;andrespectively 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;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
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
In the formula, subscript x represents an energy storage type;respectively storing energy output power and energy storage state at t time period under a scene s in optimization in the day;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
In the formula (I), the compound is shown in the specification,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
In the formula (I), the compound is shown in the specification,the load quantity and the upper limit thereof which are replaced at the t time period under the scene s in the day optimization; andrespectively 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;andrespectively 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.
8. 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:
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;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.
9. 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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