CN113141005B - New energy consumption-oriented comprehensive energy system multi-time scale scheduling method - Google Patents

New energy consumption-oriented comprehensive energy system multi-time scale scheduling method Download PDF

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CN113141005B
CN113141005B CN202110398265.1A CN202110398265A CN113141005B CN 113141005 B CN113141005 B CN 113141005B CN 202110398265 A CN202110398265 A CN 202110398265A CN 113141005 B CN113141005 B CN 113141005B
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gas
load
energy
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CN113141005A (en
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郑涛
曹敬
陆晓
胡伟
徐玮
杨梓俊
杨宇峰
荆江平
孙延平
许学荣
程炜
寇潇文
龚广京
孙伟伟
金玉龙
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State Grid Jiangsu Electric Power Co Ltd
Nari Technology Co Ltd
State Grid Electric Power Research Institute
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State Grid Jiangsu Electric Power Co Ltd
Nari Technology Co Ltd
State Grid Electric Power Research Institute
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

The invention provides a new energy consumption-oriented comprehensive energy system multi-time scale scheduling method, which specifically comprises the following steps: s1, inputting basic data of each energy device required by optimal scheduling of an electric and thermal integrated energy system and wind and solar load output; s2, constructing a target function of minimizing the operation cost of the electric and thermal comprehensive energy system in the day-ahead stage; s3, constructing a target function of minimizing deviation of an electric heat seal energy system in a rolling stage in a day; s4, constructing a mathematical model for coordinated operation of the source network load storage interactive electric and heat comprehensive energy system; and S5, performing linear transformation on the model constructed in the step S4 based on a second-order cone relaxation and increment piecewise linearization theory to obtain an optimal solution for coordinated operation of the electric and thermal integrated energy system.

Description

New energy consumption-oriented comprehensive energy system multi-time scale scheduling method
Technical Field
The invention belongs to the field of energy optimization and scheduling, and relates to a new energy consumption-oriented comprehensive energy system multi-time scale scheduling method.
Background
With the rapid development of economic society, the demand of traditional fossil energy represented by coal and oil is increased rapidly, and the problems of resource waste and environmental pollution are caused while large-scale exploitation and utilization are carried out. In the face of the double severe situation of energy and environment, sustainable clean energy is developed, and the sustainable development of economy and society is well-known. The idea of energy internet is produced, and the electric power network, the natural gas network and the thermal power network are used as main energy carriers, and the mutual dependency relationship among the electric power network, the natural gas network and the thermal power network has attracted great attention in domestic and foreign research, so that the traditional electric power system gradually evolves into a complex comprehensive energy system which takes electricity as a core and is coupled with a natural gas system and a thermal power system.
How to ensure the safety and the economy of the coordinated operation of the electricity-gas-heat comprehensive energy system while improving the consumption capability of new energy becomes a problem with great challenge and practical significance.
Disclosure of Invention
The purpose of the invention is as follows: in order to improve the new energy consumption capability and ensure the coordinated operation of an electricity-gas-heat comprehensive energy system, the invention provides a method for optimizing and scheduling based on the mixed time scale of the new energy consumption system.
The technical scheme is as follows: a comprehensive energy system multi-time scale scheduling method for new energy consumption is disclosed, wherein the comprehensive energy system takes an electric power system as a core and is coupled with an energy system of a natural gas system and a thermodynamic system; the method comprises the following steps:
step 1: acquiring basic data of each energy device and wind-light load output;
step 2: constructing an objective function of minimizing the operation cost of the integrated energy system in the day-ahead stage based on the step 1;
and step 3: establishing a double-layer intraday rolling optimization model of an integrated energy system in an intraday rolling stage based on a double-layer intraday rolling optimization architecture of a mixed instruction cycle, wherein the double-layer intraday rolling optimization model comprises an upper layer model taking minimization of the interactive power deviation between the current day and the intraday electric energy as a target and a lower layer model taking minimization of the intraday deviation between the current day and the natural gas as a target;
and 4, step 4: and (3) solving the model established in the step (3) based on the demand side response constraint condition, the energy equipment safe operation constraint condition and the energy transmission network constraint condition to obtain a coordinated operation scheme of the power system, the natural gas system and the thermal system.
Has the beneficial effects that: the invention has the following advantages:
1. aiming at the electricity-gas-heat multi-energy-flow comprehensive energy system, demand response is introduced, the demand response is an effective measure for the load side to participate in system optimization scheduling, and an electricity-gas-heat comprehensive demand response model is constructed so as to effectively exert the potential of the load side to participate in the comprehensive energy system optimization scheduling;
2. the source network load storage coordination optimization mathematical model constructed by the invention can effectively improve the running economy and new energy consumption level of the comprehensive energy system;
3. the method is used for linearly converting the complex model based on the second-order cone relaxation and incremental piecewise linearization theory, so that the optimal solution for coordinated operation of the electricity-heat-gas comprehensive energy system is obtained, the difficulty in solving the model is greatly reduced, and scientific reference is provided for engineering application.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of incremental piecewise linearization in the present invention;
FIG. 3 is a schematic structural diagram of a coordinated operation system of the integrated energy system according to an embodiment of the invention;
FIG. 4 is a wind power 24h absorption curve obtained through optimization in the embodiment of the invention;
FIGS. 5-7 illustrate the electrical, gas and thermal loads before and after demand response optimized in the examples;
FIG. 8 shows the interaction electric quantity with the upper-level power grid in the day-ahead and day-in stages obtained by optimization in the embodiment;
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings and the embodiment.
Fig. 1 shows a method for optimizing scheduling based on a hybrid time scale of a new energy consumption system, which is directed to a complex multi-energy-flow comprehensive energy system that uses an electric power system as a core and couples a natural gas system and a thermal power system, and includes the following steps:
step 1: basic data of each energy device required by optimized dispatching of the electric-gas-heat integrated energy system and wind-light charge output are input;
step 2: when a coordinated dispatching strategy is formulated, in order to improve the economical efficiency of system operation and the new energy consumption level, an objective function of minimizing the operation cost of the electricity-gas-heat comprehensive energy system in the previous stage is constructed, as shown in formula (1).
Figure BDA0003019398200000021
In the formula (1), F is the total operating cost,
Figure BDA0003019398200000022
the operating cost of energy production and conversion equipment;
Figure BDA0003019398200000023
a penalty function is introduced for promoting the wind-solar energy consumption; c E 、C NG Respectively representing the interaction cost with an upper-level power grid and an upper-level natural gas network. Specifically, the formula is shown in (2) to (5).
Figure BDA0003019398200000024
Figure BDA0003019398200000025
Figure BDA0003019398200000026
Figure BDA0003019398200000027
Wherein T is an optimized scheduling period;
Figure BDA0003019398200000031
the energy equipment related to the invention is the unit operation cost of the energy equipment, and the energy equipment comprises but is not limited to a gas turbine, a gas boiler, an electric gas conversion device, a heat pump, an electricity storage device, a heat storage device and an air storage device; p t,m The power of the device m at time t before the day;
Figure BDA0003019398200000032
the unit penalty cost of the new energy sources, the new energy sources of the invention include but are not limited to wind power and photovoltaic;
Figure BDA0003019398200000033
abandoning the optical power for the abandoned wind of the new energy power generation equipment i at the moment t; f. of ex,E 、f ex,NG Respectively representing interaction cost functions with a superior power grid and a superior gas grid; p t ex 、
Figure BDA0003019398200000034
And the interactive power of the day-ahead comprehensive energy system and a superior power grid and the interactive flow of the gas grid are respectively represented at the moment t.
And 3, step 3: the wind-solar output and load requirements have volatility and uncertainty, the smaller the prediction time scale is, the more accurate the prediction information is, and the prediction accuracy degree of the rolling stage in the day is higher compared with the prediction result of the previous stage. In addition, the transmission characteristics of various energy flows of electricity, heat and gas have obvious difference, so the invention provides a double-layer intraday rolling optimization architecture based on a mixed instruction cycle. The electric energy is transmitted at the speed of light, so that the upper layer aims at minimizing the interactive power deviation between the electric energy in the day and the electric energy in the day; the transmission of energy flows in the heat supply network and the gas network has a time delay characteristic, so that the lower layer aims at minimizing the day-ahead and day-ahead deviation of heat energy and natural gas to construct a complete day-ahead rolling stage double-layer optimization target. The method specifically comprises the following steps:
the upper layer takes the minimization of the interactive power deviation between the day-ahead electric energy and the day-inside electric energy as an optimization target: in order to accurately reflect the fluctuation of wind, light and power loads and reduce the deviation between the day-ahead rolling stage and the day-in rolling stage, the cost of the electric energy interactive power deviation between the day-ahead rolling stage and the day-in rolling stage is minimized as a target, and an upper-layer objective function is specifically shown as a formula (6).
Figure BDA0003019398200000035
In the formula, c ex，E Is the unit electric energy interaction cost; p t ex 、
Figure BDA0003019398200000036
Respectively representing the electric energy interactive power of the rolling stage in the day and the day ahead;
the lower layer takes the minimization of the deviation of heat energy and natural gas in the day before as an optimization target: in order to gradually reduce the influence of uncertainty of heat load and gas load on system optimization scheduling and effectively coordinate scheduling strategies of rolling stages before and in the day, a lower-layer objective function is specifically shown as a formula (7).
Figure BDA0003019398200000037
In the formula, c ex，NG Is the unit natural gas flow interaction cost;
Figure BDA0003019398200000038
respectively representing natural gas interaction values of rolling stages in the day ahead and the day;
Figure BDA0003019398200000039
penalty cost per unit deviation, P, for energy equipment t，m 、
Figure BDA00030193982000000310
Representing the power of the device m at time t of the day-ahead and day-in scrolling phases, respectively.
And 4, step 4: the method comprises the steps of introducing electricity, heat and gas demand response, comprehensively considering the randomness of source charge and the transmission capacity of an energy transmission network, and establishing a mixed time scale scheduling model considering the comprehensive demand response, so that a power generation side, a load side, a network side and energy storage can participate in the optimized scheduling of a comprehensive energy system, the safety of system operation is ensured, and the new energy consumption level is improved. In the optimization process, the constraint conditions to be considered are specifically as follows:
i) demand response
The demand response is an effective measure for the load side to participate in the system optimization scheduling, and aiming at the electric-heating-gas multi-energy-flow comprehensive energy system, the electric-heating-gas multi-energy-flow comprehensive demand response is introduced to effectively play the potential of the load side to participate in the comprehensive energy system optimization scheduling.
1) Demand side responsive electrical load constraints
The equality constraint is used to characterize the electrical load relationship before and after demand response that is satisfactory in terms of electricity, and is expressed as:
P t E,DR ＝P t E +ΔP t E (8)
Figure BDA0003019398200000041
Figure BDA0003019398200000042
in the formula, P t E,DR Representing the electrical load t time after the demand response; p t E 、ΔP t E Respectively representing the electric load and the variation thereof in the t period before the demand response; chi-type food processing machine US Representing the degree of user demand response for the satisfaction degree of the power utilization mode of the user; lambda [ alpha ] E Is the electricity price elastic coefficient;
Figure BDA0003019398200000043
respectively representing the electricity prices and the amount of change thereof during the t period before the demand response.
The inequality constraint is mainly constructed from three aspects of satisfaction limit of power utilization modes, transfer power load limit at any time interval and peak-valley electricity price limit, and is expressed as follows:
Figure BDA0003019398200000044
Figure BDA0003019398200000045
Figure BDA0003019398200000046
in the formula (I), the compound is shown in the specification,
Figure BDA0003019398200000047
a minimum limit representing a satisfaction of the electricity mode;
Figure BDA0003019398200000048
represents a maximum limit for transferring electrical load for any period of time;
Figure BDA0003019398200000049
respectively representing peak-to-valley electricity prices after demand response;
Figure BDA00030193982000000410
a quantitative relationship characterizing peak-to-valley electricity prices.
2) Demand side responsive thermal load constraints
The equality constraint is used to characterize the relationship of the heat load to the indoor temperature after demand response taking into account the user comfort, expressed as:
Figure BDA00030193982000000411
in the formula (I), the compound is shown in the specification,
Figure BDA00030193982000000412
representing a post-demand response thermal load;
Figure BDA00030193982000000413
respectively representing the inside and outside temperature of the building; r B Represents the equivalent thermal resistance of a building; and N is the number of heating users.
The inequality constraint is mainly constructed from two aspects of indoor temperature limitation and total heat load limitation before and after demand response, and is expressed as follows:
Figure BDA00030193982000000414
Figure BDA0003019398200000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003019398200000052
respectively representing the upper limit and the lower limit of the comfortable temperature of the user;
Figure BDA0003019398200000053
and characterizing the quantitative relation of the total heat loads before and after the demand response.
3) Demand side responsive gas load restraint
Similar to demand side response electrical load, the equality constraint is used to characterize the demand response pre-post air load relationship that is satisfied in view of air usage, and is expressed as:
V t LOAD,DR ＝V t LOAD +ΔV t LOAD (17)
Figure BDA0003019398200000054
Figure BDA0003019398200000055
in the formula, V t LOAD,DR Representing the air load t time after the demand response; v t LOAD 、ΔV t LOAD Respectively representing the air load and the variation thereof in the t period before the demand response; gamma ray US Representing the degree of user demand response for the satisfaction degree of the gas using mode of the user; lambda NG Is the electricity price elastic coefficient;
Figure BDA0003019398200000056
respectively representing the electricity price and the variation thereof t time before the demand response.
Inequality constraints are mainly constructed from three aspects of satisfaction limit of gas utilization mode, transfer gas load limit in any time period and total gas utilization load before and after demand response, and are expressed as follows:
Figure BDA0003019398200000057
Figure BDA0003019398200000058
Figure BDA0003019398200000059
in the formula (I), the compound is shown in the specification,
Figure BDA00030193982000000510
a minimum limit representing a satisfaction degree of an electric mode;
Figure BDA00030193982000000511
representing the maximum limit of the transferred air load for any period of time.
II) energy equipment safe operation restraint
The energy equipment mainly comprises a gas turbine, a bromine refrigerator, a gas boiler, a ground source heat pump and an energy storage device, and the safe operation constraint is as follows.
1) A typical combined cooling heating and power unit mainly comprises a gas turbine and a bromine cooling machine, realizes cascade utilization of energy through waste heat recovery, and has the advantages of reliability, high efficiency, energy conservation and the like.
P t GT ＝V t GT ·η GT ·LHV (23)
Figure BDA00030193982000000512
Figure BDA00030193982000000513
Figure BDA0003019398200000061
In the formula, P t GT 、η GT Respectively, the power generation power and the power generation efficiency of the gas turbine, V t GT The natural gas flow rate consumed for it; LHV is the low heating value of natural gas;
Figure BDA0003019398200000062
λ BC respectively represents the heating power and the heating efficiency of the bromine refrigerator, lambda re 、η loss Respectively representing the recovery rate of the waste heat of the flue gas and the heat dissipation loss coefficient of the gas turbine; u. of GT The variable is a 0-1 variable and represents the starting and stopping states of the gas turbine;
Figure BDA0003019398200000063
the maximum generated power of the gas turbine;
Figure BDA0003019398200000064
respectively representing the upper and lower limits of the climbing power of the gas turbine.
2) Gas boiler
Q t GB ＝V t GB ·η GB ·LHV (27)
Figure BDA0003019398200000065
Figure BDA0003019398200000066
In the formula (I), the compound is shown in the specification,
Figure BDA0003019398200000067
η GB respectively showing heating power and heating efficiency of the gas boiler, V t GB The natural gas flow rate consumed for it; u. of GB The variable is a 0-1 variable and represents the start-stop state of the gas boiler;
Figure BDA0003019398200000068
the maximum heating power of the gas boiler;
Figure BDA0003019398200000069
respectively representing the upper and lower limits of the climbing power of the gas boiler.
3) A ground source heat pump:
H t HP ＝P t HP ·η HP (30)
Figure BDA00030193982000000610
Figure BDA00030193982000000611
in the formula (I), the compound is shown in the specification,
Figure BDA00030193982000000612
η HP respectively represents the heating power and the heating efficiency of the ground source heat pump, P t HP Consuming power for it;
Figure BDA00030193982000000613
Figure BDA00030193982000000614
respectively representing the maximum value and the minimum value of the heating power of the ground source heat pump; u. of HP The variable is 0-1, and the starting and stopping states of the ground source heat pump are represented;
Figure BDA00030193982000000615
respectively representing the upper limit and the lower limit of the climbing power of the ground source heat pump.
4) Energy storage device
The energy storage device can flexibly and quickly throughput power and effectively stabilize the fluctuation of new energy and load. The general mathematical model is as follows.
Figure BDA00030193982000000616
In the formula (I), the compound is shown in the specification,
Figure BDA00030193982000000617
respectively representing the charging and discharging power of the stored energy at the moment t;
Figure BDA00030193982000000618
the variable is 0-1, and represents the charge-discharge state of energy storage;
Figure BDA0003019398200000071
rated charging and discharging power for energy storage respectively; lambda [ alpha ] S Is the self-loss rate;
Figure BDA0003019398200000072
Figure BDA0003019398200000073
respectively the charge and discharge efficiency of the stored energy;
Figure BDA0003019398200000074
the energy of the energy storage device at time t.
Figure BDA0003019398200000075
And the energy regression of stored energy is represented, and the sustainability of the scheduling strategy is ensured.
5) Electric gas conversion equipment
Figure BDA0003019398200000076
Figure BDA0003019398200000077
In the formula, P t P2G 、V t P2G Respectively representing the power consumption and the generated natural gas quantity of the electric gas conversion equipment; eta P2G Indicating the operating efficiency of the electric gas conversion equipment.
III) energy transport network constraints
Due to differences in energy characteristics, steady state transmission models of different energy networks are different. The invention relates to a multi-energy flow system which is an electricity-gas-heat comprehensive energy system, and mathematical models of a power network, a heat power network and a natural gas network are specifically shown as follows.
1) Power network
The time required for power transmission is very short, the power is transmitted at the speed of light, and a power flow equation is usually adopted to describe the distribution condition of the power in the network. Based on the distflow branch trend theory, the mathematical model of the radial distribution network is as follows:
Figure BDA0003019398200000078
Figure BDA0003019398200000079
Figure BDA00030193982000000710
in the formula, u (j) and v (j) respectively represent a branch first (last) node set taking a node j as a last (first) node; r is ij 、 x ij Respectively representing the resistance and reactance, P, of branch ij ij,t 、P ij,t Respectively representing the active power and the reactive power; p j,t 、Q j,t Respectively representing net active power and net reactive power of the node j; u shape i,t Representing the voltage value at node i at time t.
2) Thermodynamic network
The heat supply network realizes energy transmission depending on steam or hot water flow, certain thermal inertia exists in the energy transmission process, and a thermal network model shown as the following is adopted in the invention.
Figure BDA00030193982000000711
H t,k ＝C w m w (T t S -T t E ) (40)
B·m＝m w (41)
Figure BDA0003019398200000081
In the formula (I), the compound is shown in the specification,
Figure BDA0003019398200000082
respectively representing the charging power and the discharging power of the heat storage device; h t,k Represents the thermal power at time t at thermal load node k; c w Represents the specific heat capacity of water; m is w Indicating node injection flow; t is t S 、T t E Respectively representing the water supply temperature and the water return temperature at the node; b represents a heat supply network node-pipeline incidence matrix, and m is the flow velocity of each pipeline;
Figure BDA0003019398200000083
respectively representing the maximum value and the minimum value of the water supply temperature;
Figure BDA0003019398200000084
respectively representing the maximum value and the minimum value of the return water temperature;
3) Natural gas network
Compared with electric energy, natural gas transmission speed is relatively slow (several meters to dozens of meters per second), but the two still have certain similarity: energy is transmitted from a supply side to a consumption side; the energy transmission direction is the direction of node state quantity (air pressure and voltage) reduction; the inflow energy and the outflow energy of each node are equal. Similar to a power flow analysis method, the relationship between the pipeline flow and the node pressure of the gas network is described by means of the Weymouth equation.
Figure BDA0003019398200000085
Figure BDA0003019398200000086
In the formula (I), the compound is shown in the specification,
Figure BDA0003019398200000087
natural gas flow rate of the natural gas pipeline ij;
Figure BDA0003019398200000088
air pressures at two ends of the pipeline are respectively;
Figure BDA0003019398200000089
is a constant related to the length of the pipe, the radius, and the gas density.
Natural gas flow balance constraint:
Figure BDA00030193982000000810
in the formula, V t SOURSE,NG And the natural gas interaction amount of the gas source node is represented.
And 5: and (4) carrying out linear transformation on the model constructed in the step (4) based on a second-order cone relaxation and increment piecewise linearization theory, and further obtaining an optimal solution for coordinated operation of the electricity-gas-heat comprehensive energy system.
The power grid flow constraint based on the distflow theory is a non-convex and non-linear equation set, and is inconvenient for direct solution of the comprehensive energy system model. Therefore, the method carries out linear transformation on the power flow equation constraint based on the second-order cone relaxation theory.
Intermediate variables are first defined:
Figure BDA00030193982000000811
Figure BDA00030193982000000812
the power network flow constraint in step S4 may be expressed as:
Figure BDA0003019398200000091
Figure BDA0003019398200000092
relaxing equation (45) to inequality constraint
Figure BDA0003019398200000093
Finally, the power flow constraint of the standard second-order cone form is obtained as follows:
Figure BDA0003019398200000094
the branch current constraint and the node voltage constraint are as follows:
Figure BDA0003019398200000095
in the formula of U i,max 、U i,min Respectively representing the upper limit and the lower limit of the node voltage; I.C. A ij,max Representing the upper limit of the branch current.
Similar to the power flow constraint of the power network, the steady-state transmission model of the natural gas network is also a group of non-convex and non-linear equations, and is inconvenient for direct solution of the comprehensive energy system model. In order to reduce the solving difficulty and ensure the accuracy of the solution, the method is used for carrying out linear transformation on the natural gas flow equality constraint on the basis of the piecewise linearization theory.
First, the intermediate variables are defined:
Figure BDA0003019398200000096
the natural gas network flow constraint in step S4 can be expressed as:
Figure BDA0003019398200000097
the nonlinear terms in the form of f (y) = y | y | exist on the left side of the equal sign, the functions can be linearly converted based on the increment piecewise linearization theory, the linearization formulas are shown in formulas (54) to (56), and the specific process is shown in the flowchart 2.
Figure BDA0003019398200000098
Figure BDA0003019398200000099
Figure BDA00030193982000000910
ξ n 、ζ n For the introduced intermediate variable, ξ n ∈[0,1]Characterizing a location on the nth segment; zeta n Is a variable of 0-1, and ensures that the linearization process is continuously distributed in the whole interval from left to right without jumping.
Example (b):
the technical solution of the present invention will now be described by taking the integrated energy system coordinated operation system shown in fig. 3 as an example. The integrated energy system coordinated operation system shown in fig. 3 is composed of a modified IEEE33 node power system, a belgium 20 node natural gas system and a 6 node thermal system. In an IEEE33 node power system, an electric energy storage BS is connected into a node 5, and a new energy power generation device DG is connected into nodes 11 and 31; in a 6-node thermodynamic system, a heat energy storage HS is accessed to a node 2; in a 20-node natural gas system in Belgium, gas sources G1 and G2 are connected to nodes 1 and 6, and gas energy storage S1-S4 are connected to nodes 2, 5, 12 and 13; coupling equipment between the power system and the natural gas system comprises a gas turbine GT and an electric gas conversion P2G; coupling equipment between the power system and the thermodynamic system is a ground source heat pump HP; the coupling device between the natural gas system and the thermodynamic system is a gas boiler GB.
Wind power and load prediction information of the rolling stage in the day is simulated by superposing the power prediction value of the previous stage with the prediction error of normal distribution. The time-of-use electricity price was set to Gu Shiduan (1 point-6 points, 23 points-24 points): 0.42 yuan/kWh; plateau period (7-16 points): 0.88 yuan/kWh; peak period (17 point-22 point): 1.35 yuan/kWh.
In order to prove the effectiveness of the method provided by the invention on the coordinated operation of the new energy-oriented electric-gas-heat comprehensive energy system, the simulation result of the embodiment is explained and analyzed below. Fig. 4 shows a day-ahead consumption curve of new energy, fig. 5, 6, and 7 show comparison graphs of electric load, gas load, and thermal load before and after participation in demand response, and fig. 8 shows optimization results of interaction electric quantity with a superior grid at day-ahead and day-in stages. Therefore, the coordinated operation of the power network, the natural gas network and the heat power network can fully exert the potential of source, network, load and storage coordination and optimization, various devices and resources in the comprehensive energy system are scheduled, new energy is fully consumed, and scientific guidance is provided for the actual operation scheduling of the complex multi-energy system which takes electricity as a core and is coupled with the gas network and the heat supply network.

Claims (7)

1. A new energy consumption-oriented comprehensive energy system multi-time scale scheduling method is disclosed, wherein the comprehensive energy system is an energy system which takes an electric power system as a core and is coupled with a natural gas system and a thermodynamic system; the method is characterized in that: the method comprises the following steps:
step 1: acquiring basic data of each energy device and wind-light load output;
step 2: constructing an objective function of minimizing the operation cost of the integrated energy system in the day-ahead stage based on the step 1;
and step 3: establishing a double-layer intraday rolling optimization model of an integrated energy system in an intraday rolling stage based on a double-layer intraday rolling optimization architecture of a mixed instruction cycle, wherein the double-layer intraday rolling optimization model comprises an upper layer model taking minimization of the interactive power deviation between the current day and the intraday electric energy as a target and a lower layer model taking minimization of the intraday deviation between the current day and the natural gas as a target;
and 4, step 4: solving the model established in the step 3 based on the demand side response constraint condition, the energy equipment safe operation constraint condition and the energy transmission network constraint condition to obtain a coordinated operation scheme of the power system, the natural gas system and the thermal system;
the energy device comprises: the system comprises a combined cooling heating and power supply unit, a gas boiler, a ground source heat pump, an energy storage device and electric gas conversion equipment;
in step 4, the demand side response constraints comprise demand side response electric load constraints, demand side response heat load constraints and demand side response gas load constraints;
the demand side responsive electrical load constraints include:
to characterize the equality constraints of the electrical load relationship before and after demand response considering satisfactory electrical usage:
P t E,DR ＝P t E +ΔP t E (8)
Figure FDA00037628141800000110
Figure FDA0003762814180000011
in the formula, P t E,DR Representing the electrical load t time after the demand response; p is t E 、ΔP t E Respectively representing the electric load and the variation thereof in the t period before the demand response; chi shape US Representing the degree of user demand response for the satisfaction degree of the power utilization mode of the user; lambda [ alpha ] E Is the electricity price elastic coefficient;
Figure FDA0003762814180000012
respectively representing the electricity price and the variation thereof in the t period before the demand response;
inequality constraints to characterize the electricity utility satisfaction limit, the transferred electricity load limit at any time period, and the peak-to-valley electricity price limit:
Figure FDA0003762814180000013
Figure FDA0003762814180000014
Figure FDA0003762814180000015
in the formula (I), the compound is shown in the specification,
Figure FDA0003762814180000016
a minimum limit representing a satisfaction degree of an electric mode;
Figure FDA0003762814180000017
represents a maximum limit for transferring electrical load for any period of time;
Figure FDA0003762814180000018
respectively representing peak-to-valley electricity prices after demand response;
Figure FDA0003762814180000019
representing the quantity relation of peak-valley electricity prices;
the demand side response thermal load constraints include:
the equality constraint to characterize the relationship of heat load to indoor temperature after demand response considering user comfort:
Figure FDA0003762814180000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003762814180000022
representing a post-demand response thermal load;
Figure FDA0003762814180000023
respectively representing the inside and outside temperature of the building; r B Represents the equivalent thermal resistance of a building; n is the number of heating users;
the inequality constraints used to characterize the indoor temperature limit, the total heat load limit before and after the demand response:
Figure FDA0003762814180000024
Figure FDA0003762814180000025
in the formula (I), the compound is shown in the specification,
Figure FDA0003762814180000026
respectively representing the upper limit and the lower limit of the comfortable temperature of the user;
Figure FDA0003762814180000027
representing the quantity relation of the total heat loads before and after the demand response;
the demand side responsive air load constraints include:
to characterize the equality constraint that considers the gas demand response versus pre-and post-demand satisfactory:
V t LOAD,DR ＝V t LOAD +ΔV t LOAD (17)
Figure FDA00037628141800000215
Figure FDA0003762814180000028
in the formula, V t LOAD,DR Representing the air load t time after the demand response; v t LOAD 、ΔV t LOAD Respectively representing the air load and the variation thereof in the t period before the demand response; gamma ray US Representing the degree of user demand response for the satisfaction degree of the gas using mode of the user; lambda NG Is the electricity price elastic coefficient;
Figure FDA0003762814180000029
respectively representing the electricity price and the variation thereof in the t period before the demand response;
to characterize the gas usage satisfaction limit, the transfer gas load limit for any period of time, and the constraints on the total gas usage before and after demand response:
Figure FDA00037628141800000210
Figure FDA00037628141800000211
Figure FDA00037628141800000212
in the formula (I), the compound is shown in the specification,
Figure FDA00037628141800000213
a minimum limit representing a satisfaction of the electricity mode;
Figure FDA00037628141800000214
representing the maximum limit of the transferred gas load at any time period.
2. The new energy consumption-oriented comprehensive energy system multi-time scale scheduling method according to claim 1, characterized in that: in the step 3, the step of the method is that,
the upper layer model is represented as:
Figure FDA0003762814180000031
in the formula, c ex，E Is the unit electric energy interaction cost; p t ex Expressed as the interactive power of the integrated energy system and the superior power grid at the moment t in the day-ahead stage,
Figure FDA0003762814180000032
the interactive power of the comprehensive energy system and a superior power grid at the moment t in the rolling stage in the day is represented;
the underlying model is represented as:
Figure FDA0003762814180000033
in the formula, c ex,NG Is the unit natural gas flow interaction cost;
Figure FDA0003762814180000034
the interactive flow between the comprehensive energy system and the superior air network at the moment t in the day-ahead stage is shown,
Figure FDA0003762814180000035
the interactive flow between the comprehensive energy system and a superior air network at the moment t in the rolling stage in the day is represented;
Figure FDA0003762814180000036
penalty cost per unit deviation, P, for energy equipment t,m Representing the power of the device m at time t of the integrated energy system at the day-ahead stage,
Figure FDA0003762814180000037
representing the power of the device m at the moment t of the rolling phase of the day.
3. The new energy consumption-oriented comprehensive energy system multi-time scale scheduling method according to claim 1, characterized in that: in step 4, the energy device safe operation constraint includes: the method comprises the following steps of (1) performing combined cooling heating power supply unit safety operation constraint, gas boiler safety operation constraint, ground source heat pump safety operation constraint, energy storage device safety operation constraint and electric gas conversion equipment safety operation constraint;
the combined cooling heating and power unit comprises a gas turbine and a bromine cooler, and the safe operation constraint of the combined cooling heating and power unit is expressed as follows:
P t GT ＝V t GT ·η GT ·LHV (23)
Figure FDA0003762814180000038
Figure FDA0003762814180000039
Figure FDA00037628141800000310
in the formula, P t GT 、η GT Respectively, the power generation power and the power generation efficiency of the gas turbine, V t GT The natural gas flow rate consumed for it; LHV is the low heating value of natural gas;
Figure FDA00037628141800000311
λ BC respectively represents the heating power and the heating efficiency of the bromine refrigerator, lambda re 、η loss Respectively the recovery rate of the waste heat of the flue gas and the heat dissipation loss coefficient of the gas turbine; u. of GT The variable is a 0-1 variable and represents the starting and stopping states of the gas turbine;
Figure FDA00037628141800000312
the maximum generated power of the gas turbine;
Figure FDA00037628141800000313
respectively representing the upper limit and the lower limit of the climbing power of the gas turbine;
the safe operation constraint of the gas boiler is expressed as:
Figure FDA00037628141800000314
Figure FDA00037628141800000315
Figure FDA00037628141800000316
in the formula, Q t GB 、η GB Respectively, the heating power and the heating efficiency of the gas boiler, V t GB The natural gas flow rate consumed for it; u. of GB The variable is a 0-1 variable and represents the start-stop state of the gas boiler;
Figure FDA0003762814180000041
the maximum heating power of the gas boiler;
Figure FDA0003762814180000042
respectively representing the upper limit and the lower limit of the climbing power of the gas boiler;
the safe operation constraint of the ground source heat pump is represented as:
H t HP ＝P t HP ·η HP (30)
Figure FDA0003762814180000043
Figure FDA0003762814180000044
in the formula (I), the compound is shown in the specification,
Figure FDA0003762814180000045
η HP respectively represents the heating power and the heating efficiency of the ground source heat pump, P t HP Consuming power for it;
Figure FDA0003762814180000046
Figure FDA0003762814180000047
respectively representing the maximum value and the minimum value of the heating power of the ground source heat pump; u. of HP The variable is 0-1, and the starting and stopping states of the ground source heat pump are represented;
Figure FDA0003762814180000048
respectively representing the upper limit and the lower limit of the climbing power of the ground source heat pump;
the energy storage device safe operation constraint is expressed as:
Figure FDA0003762814180000049
in the formula (I), the compound is shown in the specification,
Figure FDA00037628141800000410
respectively representing the charging power and the discharging power of the energy storage device at the moment t;
Figure FDA00037628141800000411
the variable is 0-1, and represents the charge-discharge state of the energy storage device;
Figure FDA00037628141800000412
rated charging and discharging power of the energy storage device are respectively set; lambda S Is the self-loss rate;
Figure FDA00037628141800000413
respectively the charge and discharge efficiency of the energy storage device;
Figure FDA00037628141800000414
the energy of the energy storage device at the moment t;
Figure FDA00037628141800000415
characterizing an energy regression of the energy storage device;
the electrical transfer equipment safe operation constraint is expressed as:
Figure FDA00037628141800000416
Figure FDA00037628141800000417
in the formula, P t P2G 、V t P2G Respectively representing the power consumption and the generated natural gas quantity of the electric gas conversion equipment; eta P2G The working efficiency of the electric gas conversion equipment is shown.
4. The new energy consumption-oriented integrated energy system multi-time scale scheduling method according to claim 1, characterized in that: in step 4, the energy transmission network constraints comprise electric power transmission network constraints, thermal power transmission network constraints and natural gas transmission network constraints;
the power transmission network constraint is expressed as:
Figure FDA0003762814180000051
Figure FDA0003762814180000052
Figure FDA0003762814180000053
in the formula, u (j) and v (j) respectively represent a branch head/tail node set taking a node j as a tail/head node; r is a radical of hydrogen ij 、x ij Respectively representing the resistance and reactance, P, of branch ij ij,t 、P ij,t Respectively representing the active power and the reactive power; p j,t 、Q j,t Respectively representing net active power and net reactive power of the node j; u shape i,t Represents the voltage value of the node i at the time t;
the thermal transport network constraint is expressed as:
Figure FDA0003762814180000054
H t,k ＝C w m w (T t S -T t E ) (40)
B·m＝m w (41)
Figure FDA0003762814180000055
in the formula (I), the compound is shown in the specification,
Figure FDA0003762814180000056
respectively representing the charging and discharging power of the heat storage device; h t,k Represents the thermal power at time t at thermal load node k; c w Represents the specific heat capacity of water; m is w Indicating node injection flow; t is t S 、T t E Respectively representing the water supply temperature and the water return temperature at the node; b represents a heat supply network node-pipeline incidence matrix, and m is the flow velocity of each pipeline;
Figure FDA0003762814180000057
respectively representing the maximum value and the minimum value of the water supply temperature;
Figure FDA0003762814180000058
respectively representing the maximum value and the minimum value of the return water temperature;
the natural gas transmission network constraint is expressed as:
Figure FDA0003762814180000059
Figure FDA00037628141800000510
Figure FDA00037628141800000511
in the formula (I), the compound is shown in the specification,
Figure FDA00037628141800000512
indicating the natural gas flow rate of the natural gas pipeline ij;
Figure FDA00037628141800000513
air pressures at two ends of the pipeline are respectively;
Figure FDA00037628141800000514
is a constant, V, related to the length, radius, gas density of the pipe t SOURSE,NG And the natural gas interaction quantity of the gas source node is represented.
5. The new energy consumption-oriented integrated energy system multi-time scale scheduling method according to claim 4, characterized in that: in step 4, the solution is performed on the scheduling model established in step 3 to obtain a coordinated operation scheme of the power system, the natural gas system and the thermodynamic system, and the method comprises the following steps:
based on a second-order cone relaxation theory, carrying out linear transformation on the constraint of the power transmission network;
based on a piecewise linearization theory, carrying out linear transformation on the natural gas transmission network constraint;
and solving the scheduling model based on the power transmission network constraint after linear conversion, the power transmission network constraint after linear conversion and other constraints to obtain a coordinated operation scheme of the power system, the natural gas system and the thermodynamic system.
6. The new energy consumption-oriented integrated energy system multi-time scale scheduling method according to claim 5, characterized in that: the method for linearly converting the power transmission network constraint based on the second-order cone relaxation theory specifically comprises the following steps:
defining the intermediate variables:
Figure FDA0003762814180000061
Figure FDA0003762814180000062
expressions (36), (37), and (38) are expressed as follows based on the intermediate variables:
Figure FDA0003762814180000063
Figure FDA0003762814180000064
relaxing equation (45) into an inequality constraint:
Figure FDA0003762814180000065
obtaining a standard second-order cone-shaped power transmission network constraint:
Figure FDA0003762814180000066
Figure FDA0003762814180000067
in the formula of U i,max 、U i,min Respectively representing the upper limit and the lower limit of the node voltage; i is ij,max Representing the upper limit of the branch current.
7. The new energy consumption-oriented integrated energy system multi-time scale scheduling method according to claim 5, characterized in that: the method for linearly transforming the natural gas transmission network constraint based on the piecewise linearization theory specifically comprises the following steps:
defining the intermediate variables:
Figure FDA0003762814180000068
based on the intermediate variables defined by equation (52), equations (43) and (44) are expressed as follows:
Figure FDA0003762814180000069
based on the theory of incremental piecewise linearization, equation (53) is linearly transformed:
Figure FDA0003762814180000071
Figure FDA0003762814180000072
Figure FDA0003762814180000073
ξ n 、ζ n for the introduced intermediate variable, ξ n ∈[0,1]Characterizing a location on the nth segment; zeta n Is a variable from 0 to 1.
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