CN107767074B - Energy hub planning method considering comprehensive demand response resources - Google Patents

Energy hub planning method considering comprehensive demand response resources Download PDF

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CN107767074B
CN107767074B CN201711096560.1A CN201711096560A CN107767074B CN 107767074 B CN107767074 B CN 107767074B CN 201711096560 A CN201711096560 A CN 201711096560A CN 107767074 B CN107767074 B CN 107767074B
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CN107767074A (en
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仇知
王蓓蓓
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Southeast University
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
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    • Y04S50/16—Energy services, e.g. dispersed generation or demand or load or energy savings aggregation

Abstract

The invention discloses an energy hub planning method considering comprehensive demand response resources, which comprises the following steps: (1) data acquisition, including weather temperature, electric load, wind power output, capacity to be selected of equipment to be planned, investment cost and various parameters of normal operation of the system in different seasons; (2) specifically classifying the demand response technology according to the controllability of various loads and establishing a model; (3) modeling the energy hub according to the coupling conversion relation of each energy source in the energy hub, and solving by adopting cplex; (4) and judging whether the planning result is more superior after various types of demand responses participate, and outputting an optimal result. The method provided by the invention fully utilizes the characteristic that various loads can be translated, can be transferred and can reduce the demand response to participate in the planning and the operation of the energy hub, optimizes the model selection configuration of various devices, reduces the total cost of the planning and the operation of the energy hub, and provides decision support for the planning work of the energy hub.

Description

Energy hub planning method considering comprehensive demand response resources
Technical Field
The invention belongs to the field of energy hub planning, and particularly relates to an energy hub planning method considering comprehensive demand response resources.
Background
With the deterioration of the environment and the increasing shortage of fossil energy, the development of an integrated energy system organically coordinated in each stage of planning, operation and construction is a necessary way for realizing sustainable development of energy.
The energy hub serves as a coupling point to achieve economic dispatching and overall regulation and control of optimal power flow of the hybrid energy system. The energy hub is a conversion station of each energy carrier, and the mutual conversion of different energies is realized in the energy hub through corresponding elements. The existing planning method takes various devices on the energy supply side into consideration, but does not take the flexible controllability of the load on the user side into consideration, so that the phenomena of redundant device capacity and low resource utilization rate are caused in the planning result inevitably. With the development of demand response technology, the demand side resource adjustment potential is efficiently, economically and reasonably utilized, so that the reduction of the total cost of the whole planning system on the premise of maintaining the energy service level becomes possible.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the prior art, the invention provides an energy hub planning method considering comprehensive demand response resources, which constructs equipment model selection planning of the comprehensive demand response resources participating in an energy hub, embodies the coordination function of demand response in planning operation, optimizes model selection and output of various equipment, reduces the total cost of system planning operation, and promotes reasonable configuration and planning of the energy hub.
The technical scheme is as follows: an energy hub planning method considering comprehensive demand response resources comprises the following steps:
(1) data acquisition: the data comprises electricity, heat and cold loads, wind power generation capacity, unit electricity and gas purchase price, various demand response resource incentive prices, candidate capacity of power supply and heating and energy storage equipment, installation investment cost and operation and maintenance cost in different seasons in a planning year;
(2) classifying various loads: dividing the load in the step (1) into a rigid load, a translatable load and a reducible load according to the energy utilization characteristics of the cold/heat/electric load, and establishing various demand response models;
(3) modeling an energy hub: according to the coupling conversion relation of each energy source in the energy hub, the optimal total cost is taken as a target, the power balance of various loads and the planning logic and the upper and lower operation limits of each device are taken as constraint conditions;
(4) the model was solved by cplex optimization tool: and judging whether the planning result is superior after the demand response technology is adopted or not through the total construction scheduling cost of the energy hub, the saving amount of electrical resources and the utilization rate of equipment, and outputting an optimal result.
Further, the step (2) includes dividing the load control management means of the electric, heat and cold loads into 3 types of demand responses of a translatable load, a translatable load and a flexible load in the central heating and cooling system, wherein the control management means is the load management means in the demand side management. It can improve the load characteristics by giving the user an incentive compensating stimulus; or the load is controlled by a terminal device such as a timer switch or a batcher installed at the user through a control channel in contact with the customer.
The demand response resource classification and corresponding models and formulas are as follows:
(2a) translatable load (transfer type DR): the shift of the user who can translate the load is fixed, and after translating the load to a fixed period, the daily load quantity is kept unchanged, and the calculation expression is as follows:
Figure GDA0003109591250000021
(2b) transferable load (shift type DR): the load can be transferred within one day, the daily load quantity is kept unchanged, and the formula is as follows:
Figure GDA0003109591250000022
(2c) flexible load (clip type DR): the load can be flexibly reduced, but load rebound can occur after the load is reduced, the rebound value is related to the load change value in the first 3 periods, and the expression is as follows:
Figure GDA0003109591250000023
in the formula: in formulae (1), (2), and (3):
Figure GDA0003109591250000024
0-1 variable providing positive and negative DR output for class i loads at time t,
Figure GDA0003109591250000025
Respectively providing a 0-1 variable for reflecting whether the positive output of DR is called and a 0-1 variable for reflecting whether the negative output of DR is called for the ith load at the time t; t denotes a period of time during which the i-th class load provides a demand response.
The constraint of the adjustable capacity upper and lower limits of the ith type load is shown as a formula (4); equation (5) is the coupling constraint of the positive and negative contributions of DR; equation (6) is the invocation time constraint of DR; calculating the unit electric quantity cost of DR by the formula (7), wherein the unit electric quantity cost is increased gradually along with the calling quantity;
Figure GDA0003109591250000026
Figure GDA0003109591250000031
Figure GDA0003109591250000032
Figure GDA0003109591250000033
in the formula: c. CDRoprThe DR unit electricity cost,
Figure GDA0003109591250000034
The maximum dosage of DR, NDRThe total number of devices that can provide DR.
Further, the energy hub model building of step 3 includes the following steps:
3.1 establish the objective function: selecting the total cost in the planning period as a minimized objective function, wherein the total cost comprises the investment cost of an investor and the operation cost in the planning period, and the total cost comprises the investment cost, the operation maintenance cost and the DR calling cost; the objective function of the minimization is:
minCtot＝Cinv+Cope+CDR (8)
in the formula: ctotRepresents the total cost in the planned year, Cinv、CopeAnd CDRRespectively representing the total investment cost, the operation and maintenance cost and the DR calling cost in the planning year.
Investment cost: the investment cost comprises the investment cost of newly-added CCHP units, gas boilers, PtG stations, electric air conditioners and air storage devices of all comprehensive energy centers in a planning period, and specifically comprises the following steps:
Figure GDA0003109591250000035
in the formula:
Figure GDA0003109591250000036
is a newly added CCHP unit, a gas boiler, PtG station, and a candidate model set of an electric air conditioner, CωInvestment and installation costs, x, for the omega model of the different apparatusωAnd the variable is a 0-1 variable installed for the omega model of each type of equipment.
The operation and maintenance cost is as follows:
Figure GDA0003109591250000037
in the formula: y is the planning year and r is the discount rate; s represents different typical days in the planning period, wherein c is the cooling season, h is the heating season, t is the transition season, e is the high temperature extreme weather, epsilonsRepresenting the occurrence probability of each typical day; p is a radical ofGAS、peleRespectively represents the unit electricity and gas purchasing costs,
Figure GDA0003109591250000038
respectively representing gas purchase and electric quantity purchase.
DR invocation cost: cDRThe cost of electric power of DR and the cost of electric power of DR per unit cDRoprAnd positive force of ith DR at time t
Figure GDA0003109591250000041
Or negative output
Figure GDA0003109591250000042
In connection with, namely:
Figure GDA0003109591250000043
3.2 the constraints are specifically as follows:
planning logic constraint: suppose that at most one new energy center can be selected from each type of equipment in each energy center in the planning period, namely:
Figure GDA0003109591250000044
the calculation formula of the cold-heat-electricity power balance constraint is as follows:
Figure GDA0003109591250000045
in the formula: and sequentially giving out electric load-power balance, cold load-power balance and heat load-power balance constraints. The load can be regulated and controlled by setting electric, hot and cold loads,
Figure GDA0003109591250000046
Figure GDA0003109591250000047
the sum of positive and negative output of electric, cold and hot DR respectively and load rebound caused by flexible DR are called;
Figure GDA0003109591250000048
respectively representing the electricity purchasing quantity, the CCHP unit power supply quantity, the actual output of the wind power plant and the air abandoning quantity of the energy center at each moment in a scene s,
Figure GDA0003109591250000049
loads、
Figure GDA00031095912500000410
respectively representing the electric load demand, the load loss amount, the air conditioner power consumption and the PtG station power consumption of the energy center at each moment under a scene s; secondly, the constraint of cold load balance,
Figure GDA00031095912500000411
respectively the cold load demand of the energy center at each moment under the scene s and the heat supply of the CCHP unit and the electric air conditioner; and finally, the thermal load balance constraint is adopted,
Figure GDA00031095912500000412
and respectively representing the heat load demand of the energy center at each moment and the heat supply amount of the CCHP unit and the GB unit under the scene s.
And (3) planning and operating constraints of the CCHP unit: the combined cooling heating and power generation system integrates refrigeration, heat supply and power generation. The electrical load is supplied by the power generation equipment; providing a portion of the heat load from a heat recovery system of the power plant; the absorption refrigerator supplies part of the cooling load.
Figure GDA0003109591250000051
In the formula:
Figure GDA0003109591250000052
for gas consumption, HgasIs the heat value of the natural gas,
Figure GDA0003109591250000053
the power is generated and output for the gas internal combustion engine,
Figure GDA0003109591250000054
respectively recoverable waste heat in the power generation process;
Figure GDA0003109591250000055
refrigeration and heating;
Figure GDA0003109591250000056
the refrigeration and heating efficiency is improved;
Figure GDA0003109591250000057
respectively with minimum refrigeration/heat and maximum refrigeration/heat; alpha and beta are performance parameters of different types of internal combustion engines.
And (3) operating constraints of the gas boiler, and calculating the expression as follows:
Figure GDA0003109591250000058
in the formula: etaGHBOf gas-fired boilersOperating efficiency;
Figure GDA0003109591250000059
is the gas consumption of the omega type gas boiler under typical days.
And (3) electric air conditioner operation constraint:
Figure GDA00031095912500000510
in the formula: etaACThe operation efficiency of the electric refrigeration air conditioner is improved;
Figure GDA00031095912500000511
is the power consumption of the air conditioner in s typical days.
P2G runs constraints and calculates the expression as follows:
Figure GDA00031095912500000512
in the formula: etaPtGPtG plant operating efficiency;
Figure GDA00031095912500000513
PtG gas production at each moment which is omega model under s typical days;
Figure GDA00031095912500000514
is the power consumption.
The operation upper and lower limit constraint expressions of each device are as follows:
Figure GDA00031095912500000515
in the formula:
Figure GDA00031095912500000516
respectively form an upper limit and a lower limit of CCHP power;
Figure GDA00031095912500000517
respectively of type GHB powerA lower limit;
Figure GDA0003109591250000061
respectively, the upper and lower power limits of type PtG.
And (3) restraining the gas storage device: for the gas storage equipment in the energy center, the constraint conditions of mutually exclusive charging and discharging states, the actual gas storage amount between the upper limit and the lower limit of the gas storage amount of the equipment and the balance of gas storage and gas discharge in the whole scheduling period are met, and the calculation expression is as follows:
Figure GDA0003109591250000062
in the formula:
Figure GDA0003109591250000063
and
Figure GDA0003109591250000064
respectively charging and discharging air power and efficiency for the air storage equipment; sGASminAnd SGASmaxRespectively representing the lower limit and the upper limit of the gas storage device; ss,GAS(t) represents the gas storage capacity of the gas storage apparatus. Considering that the gas storage device cannot be inflated and deflated simultaneously in a scheduling period, the constraint corresponding to the inflation and deflation mutual exclusion constraint needs to be introduced.
Has the advantages that: compared with the prior art, the method has the obvious effects that after the DR participates in the planning output result, the total cost of the energy hub including investment cost, operation maintenance cost and DR calling cost is obviously reduced, the air volume abandoning and load losing quantity is improved, and the utilization rate of electricity and gas resources is greatly improved; the invention optimizes the type selection configuration of various devices in the energy hub, reduces the total cost of planning and operating the system, and promotes the reasonable configuration and planning of the energy hub.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of a coupling model of an internal device of an energy hub according to the present invention.
Detailed Description
For the purpose of illustrating the technical solutions disclosed in the present invention in detail, the following description is further provided with reference to the accompanying drawings and specific embodiments.
First, the parameter setting case of the embodiment is explained. The model parameters and the price in the method are obtained by reference and actual research, and are taken as priority, so that the technical characteristics and the protection scope of the invention are not limited.
1) Preparing data: according to the actual situation of a certain area, 4 typical scene types are set: the probability of occurrence in all seasons is as follows: 0.245, 0.333, 0.417, 0.005; the electricity, heat and cold loads of each typical season are obtained by the actual load prediction of the region, see table 1, and the planning period is 5 years. The parameters of the installation investment cost and the operation maintenance cost of the electric gas conversion unit, the gas boiler, the combined cooling heating and power unit, the energy storage device and the electric air conditioning equipment in the energy hub are shown in the table 2, and the electricity and gas purchase prices are 0.92 & lty/kW & h and 2.37 & lty/m & gt respectively3。
TABLE 1 electric heating and cooling load and wind power output in each typical season
Figure GDA0003109591250000071
TABLE 2 investment and operation cost parameters of various equipments
Figure GDA0003109591250000072
2) Dividing various loads into rigid loads, translatable loads, transferable loads and reducible loads, wherein the rigid loads are basic loads and can not be adjusted, the translatable loads, the transferable loads and the reducible loads can provide demand response technical support, establishing a demand response model according to the characteristics of each type of loads, and the incentive prices of various demand response resources are shown in a table 3, and assuming that the adjustable loads capable of providing demand response in the electric/heat/cold loads account for 30% of the total loads:
TABLE 3 demand response incentive price parameter for various types of loads
Figure GDA0003109591250000081
3) According to the coupling transformation relation of each energy in the energy hub, modeling the energy hub by taking the optimal total cost including installation investment cost, operation maintenance cost, wind abandoning punishment cost, load loss punishment cost and demand response scheduling cost as a target and taking various load power balances, equipment planning logics and operation upper and lower limits as constraint conditions;
4) solving the model through the cplex optimization tool, and providing planning results after various demand responses participate and model selection capacities of various devices in the energy hub, wherein the results are shown in the following table:
it can be seen that after DR participates, the total cost is reduced by about 4.41%, the electricity and gas purchase cost in the operation and maintenance cost is reduced by about 2.56%, which indicates that the total amount of electricity and gas purchased is reduced and the resource utilization rate is improved; for equipment type selection, the capacity of various types of equipment is reduced, the redundancy of the capacity of the equipment is reduced, and the operation efficiency is improved.
TABLE 4 cost and equipment type comparison
Figure GDA0003109591250000082

Claims (4)

1. An energy hub planning method considering comprehensive demand response resources is characterized by comprising the following steps of:
(1) data acquisition: the data comprises electricity, heat and cold loads, wind power generation capacity, unit electricity and gas purchase price, various demand response resource incentive prices, candidate capacity of power supply and heating and energy storage equipment, installation investment cost and operation and maintenance cost in different seasons in a planning year;
(2) classifying various loads: in a central heating and cooling system, controllable loads of electric loads, heat loads and cooling loads are divided into 3 demand response categories of translatable loads, transferable loads and flexible loads, and the demand response resource classification and the corresponding models are as follows:
(2a) translatable loads, namely transfer type DR: the shift of the user who can translate the load is fixed, and after the load is translated to a fixed period, the daily load quantity is kept unchanged, and the calculation formula is as follows:
Figure FDA0003109591240000011
(2b) transferable load, shift-type DR: the load can be transferred within one day, the daily load quantity is kept unchanged, and the calculation formula is as follows:
Figure FDA0003109591240000012
(2c) flexible load, i.e. clip type DR: the load can be flexibly reduced, but load rebound can occur after reduction, the rebound value is related to the load change value in the first 3 periods, and the calculation expression is as follows:
Figure FDA0003109591240000013
in formulae (1), (2), and (3):
Figure FDA0003109591240000014
0-1 variable providing positive and negative DR output for class i loads at time t,
Figure FDA0003109591240000015
Respectively providing a 0-1 variable for reflecting whether the positive output of DR is called and a 0-1 variable for reflecting whether the negative output of DR is called for the ith load at the time t; t represents the time period for the ith type load to provide the demand response;
the constraint of the adjustable capacity upper and lower limits of the ith type load is shown as a formula (4); equation (5) is the coupling constraint of the positive and negative contributions of DR; equation (6) is the invocation time constraint of DR; calculating the unit electric quantity cost of DR by the formula (7), wherein the unit electric quantity cost is increased gradually along with the calling quantity;
Figure FDA0003109591240000016
Figure FDA0003109591240000021
Figure FDA0003109591240000022
Figure FDA0003109591240000023
in formulae (4), (5), (6), and (7): c. CDRoprThe DR unit electricity cost,
Figure FDA0003109591240000024
The maximum dosage of DR, NDRThe total number of devices that can provide DR;
(3) modeling an energy hub: according to the coupling conversion relation of each energy source in the energy hub, the optimal total cost is taken as a target, the power balance of various loads and the planning logic and the upper and lower operation limits of each device are taken as constraint conditions;
(4) the model was solved by cplex optimization tool: and judging whether the planning result is superior after the demand response technology is adopted or not through the total construction scheduling cost of the energy hub, the saving amount of electrical resources and the utilization rate of equipment, and outputting an optimal result.
2. The method of claim 1, wherein the step (3) of building the energy hub model comprises the steps of:
(3.1) determining an objective function: selecting the total cost in the planning period as a minimized objective function, wherein the total cost comprises investment cost of an investor and operation cost in the planning period, the total cost comprises the investment cost, the operation maintenance cost and DR calling cost, and the minimum objective expression of the total cost is as follows:
minCtot＝Cinv+Cope+CDR (8)
in the formula: ctotRepresents the total cost in the planned year, Cinv、CopeAnd CDRRespectively representing the total investment cost, the operation maintenance cost and the DR calling cost in a planning year;
(3.2) establishing a constraint condition: the constraint conditions comprise planning logic constraint, cooling, heating and power balance constraint, CCHP unit planning operation constraint, gas boiler operation constraint, P2G operation constraint, upper and lower limit constraint of each equipment operation and gas storage device constraint.
3. The method as claimed in claim 2, wherein the investment cost, the operation and maintenance cost, and the DR invoking cost in step (3.1) are as follows:
(3a) investment cost: the investment cost comprises the investment cost of newly added CCHP units, gas boilers, PtG stations, electric air conditioners and air storage devices of all comprehensive energy centers in a planning period, and the calculation expression is as follows:
Figure FDA0003109591240000031
in the formula:
Figure FDA0003109591240000032
is a newly added CCHP unit, a gas boiler, PtG station, and a candidate model set of an electric air conditioner, CωInvestment and installation costs, x, for the omega model of the different apparatusωA variable of 0-1 installed for the omega model of each type of equipment;
(3b) the operation and maintenance cost calculation expression is as follows:
Figure FDA0003109591240000033
in the formula: y is the planning year and r is the discount rate; s represents different typical days in the planning period, wherein c is the cooling season, h is the heating season, t is the transition season, e is the high temperature extreme weather, epsilonsRepresenting the occurrence probability of each typical day; p is a radical ofGAS、peleRespectively represents the unit electricity and gas purchasing costs,
Figure DEST_PATH_IMAGE001
respectively representing gas purchase and electric quantity purchase;
(3c) the DR call cost calculation expression is:
Figure FDA0003109591240000035
in the formula: cDRThe cost of electric power of DR and the cost of electric power of DR per unit cDRoprAnd positive force of ith DR at time t
Figure FDA0003109591240000036
Or negative output
Figure FDA0003109591240000037
It is related.
4. The method of claim 3, wherein the constraints of step (3.2) are as follows:
planning logic constraint: suppose that at most one new energy center can be selected from each type of equipment in each energy center in the planning period, namely:
Figure FDA0003109591240000038
and (3) power balance constraint of cold, heat and electricity:
Figure FDA0003109591240000039
in the formula: sequentially gives out electric load-power balance, cold load-power balance and heat load-power balance constraints, and can regulate and control the load by setting the electric load, the heat load and the cold load,
Figure FDA00031095912400000310
Figure FDA0003109591240000041
the sum of positive and negative output of electric, cold and hot DR respectively and load rebound caused by flexible DR are called; ps ele、Ps CCHP、Ps Wind、Ps cur_windRespectively representing the electricity purchasing quantity, the CCHP unit power supply quantity, the actual output of the wind power plant and the air abandoning quantity of the energy center at each moment in a scene s,
Figure FDA0003109591240000042
loads、
Figure FDA0003109591240000043
Ps PtGrespectively representing the electric load demand, the load loss amount, the air conditioner power consumption and the PtG station power consumption of the energy center at each moment under a scene s; secondly, the constraint of cold load balance,
Figure FDA0003109591240000044
respectively the cold load demand of the energy center at each moment under the scene s and the heat supply of the CCHP unit and the electric air conditioner; and finally, the thermal load balance constraint is adopted,
Figure FDA0003109591240000045
representing the energy centre at each moment in the scene s separatelyHeat load demand, heat supply of CCHP unit and GB unit;
and (3) planning and operating constraints of the CCHP unit: the combined cooling heating and power generation system integrates refrigeration, heat supply and power generation, and the power load is supplied by power generation equipment; providing a portion of the heat load from a heat recovery system of the power plant; absorbing part of the cold load supplied by the refrigerator;
Figure FDA0003109591240000046
in the formula:
Figure FDA0003109591240000047
for gas consumption, HgasIs the heat value of the natural gas,
Figure FDA0003109591240000048
the power is generated and output for the gas internal combustion engine,
Figure FDA0003109591240000049
respectively recoverable waste heat in the power generation process;
Figure FDA00031095912400000410
refrigeration and heating;
Figure FDA00031095912400000411
the refrigeration and heating efficiency is improved;
Figure FDA00031095912400000412
respectively with minimum refrigeration/heat and maximum refrigeration/heat; alpha and beta are performance parameters of internal combustion engines of different models;
and (3) operation constraint of the gas boiler:
Figure FDA00031095912400000413
in the formula: etaGHBThe operation efficiency of the gas boiler;
Figure FDA00031095912400000414
the gas consumption of a omega-type gas boiler under typical days;
and (3) electric air conditioner operation constraint:
Figure FDA00031095912400000415
in the formula: etaACThe operation efficiency of the electric refrigeration air conditioner is improved;
Figure FDA00031095912400000416
is the power consumption of the air conditioner in s typical days;
P2G operating constraints:
Figure FDA0003109591240000051
in the formula: etaPtGPtG plant operating efficiency;
Figure FDA0003109591240000052
PtG gas production at each moment which is omega model under s typical days;
Figure FDA0003109591240000053
power consumption;
and (3) restricting the operation upper limit and the operation lower limit of each device:
Figure FDA0003109591240000054
in the formula:
Figure FDA0003109591240000055
respectively, the upper limit and the lower limit of the power of the type CCHP;
Figure FDA0003109591240000056
respectively an upper limit and a lower limit of type GHB power;
Figure FDA0003109591240000057
respectively, type PtG upper and lower power limits;
and (3) restraining the gas storage device:
for the gas storage equipment in the energy center, constraint conditions of mutually exclusive charging and discharging states, actual gas storage amount between the upper limit and the lower limit of the gas storage amount of the equipment and gas storage and gas discharge balance in the whole scheduling period are met;
Figure FDA0003109591240000058
in the formula: ps Gcha、
Figure FDA0003109591240000059
And Ps Gdis、
Figure FDA00031095912400000510
Respectively charging power, charging efficiency, discharging power and discharging efficiency for the gas storage equipment; sGASminAnd SGASmaxRespectively representing the lower limit and the upper limit of the gas storage device; ss,GASAnd (t) the gas storage capacity of the gas storage device is shown, the gas storage device cannot be simultaneously inflated and deflated in a scheduling period, and the restraint corresponding to the inflation and deflation mutual exclusion restraint needs to be introduced.
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