This application claims priority to Chinese patent application No. 2020111155872 filed on Oct. 19, 2020, and the disclosure of which is incorporated herein by reference in its entirety.
The present invention relates to the technical field of reliability management of integrated energy system, in particular to a method for programming the energy storage device in power-gas coupling system based on reliability constraints.
In recent years, with the advantages of flexibility and efficiency, natural gas units are widely used, which make the coupling between natural gas system and electric power system closer, together constituting the power-gas coupling system. In this context, natural gas units can only produce electric energy by obtaining natural gas from the natural gas system to meet the demand of power load in the electric power system. Therefore, the failure of natural gas system may affect the operation of electric power system and threaten the safe and stable operation of electric power system; in order to ensure the reliability of the power-gas coupling system, the unified programming of the energy storage devices in the electric power system and the natural gas system is necessary; the coupling characteristics of the two systems are fully considered, to ensure sufficient capacity in the system.
The present invention is to solve the technical problem that: effectively ensure the reliability and the sufficient long-term reserve capacity of the power-gas coupling system. The present invention is proposed in view of the above possible problems.
The present invention has the following beneficial effects that: considering not only the economy but also the reliability of the system in the programming process, the present invention determines more effectively and accurately the programming scheme of the energy storage device in power-gas coupling system, which is more accurate, comprehensive and effective than the previous programming method; In addition, the present invention can be directly applied in the power programming software at current stage, to accurately and efficiently programme the energy storage device in the power-gas coupling system through the study of the influence of natural gas system failure on electric power system, which is of great significance to improve the reliability of the electric power system and ensure the safe and reliable operation of the electric power system.
As shown in
Monitoring equipment is used to get the parameters and operation condition of each equipment in the power-gas coupling system in a year and input the parameters and operation condition in the programming model. Monitoring equipment includes pressure sensor, flow sensor, voltage sensor, current sensor.
Based on the input information, the Monte Carlo sampling strategy is used to determine the different operating states of the system according to the reliability parameters of the components in the system and the fault and repair of the components.
It should be noted that the different operating states of the system comprise,
Ot=[Ot1,L,Otb,L,OtN
where, Ot refers to the state matrix of the system of year t, Otb refers to the vector of the system under state b of year t, and Nb refers to the total number of states of the system.
The Monte Carlo sampling strategy is to determine the operating state of components by sampling method according to the failure rate and repair rate of components.
Specifically, the operating state of the system and the random failures of the components in the system are determined based on component faults. Firstly, the operating states of different components are obtained through Monte Carlo sampling. The different states of the system can be expressed as:
Ot=[Ot1,L,Otb,L,OtN
where, Ot refers to the state matrix of the system of year t, Otb refers to the vector of the system under state b of year t, and Nb refers to the total number of states of the system.
Where, the state vector Otb of system refers to the collection of states of all components of the system.
Taking the electric power system as an example, Otb can be expressed as:
Otb=[O1tb,L,Oktb],
where, Oktb refers to the running state of the generator set k under state b of year t.
Corresponding duration for different states b can be expressed as:
D=[Dt1,L,Dtb,L,DtN
where, D refers to the collection of duration of each state, Dtb refers to the duration under state b of year t.
A programming model of the energy storage device based on reliability constraints is constructed based on the operating state of the system; benders decomposition algorithm is adopted to calculate the programming model, so that the programming scheme of the energy storage device is obtained.
Wherein, the programming model of the energy storage device comprises the establishment of objective functions and constraint conditions.
Specifically, the objective functions comprise,
where, TC refers to the total variables programmed for the system, IC refers to the variables programmed for energy storage device, OC refers to the variables of power-gas coupling system in operation, EENSt and EGNSt refer to the average power load loss and average gas load loss of year t respectively, CtE and CtG refer to the variables of power load loss and gas load loss of year t respectively, d refers to discount rate.
More specifically, the power-gas coupling system is mainly composed of the natural gas system and the electric power system coupled by the natural gas unit; natural gas unit produces electricity by consuming natural gas; the input end of natural gas unit is connected to the natural gas system as the natural gas load, while the output end is connected to the electric power system as the generator set; the natural gas system is mainly composed of compressor, pipeline, gas source and natural gas load; each natural gas node comprises gas source and natural gas load, and the natural gas nodes are connected with each other through pipelines; the electric power system is mainly composed of coal-fired units, natural gas units, lines and power loads; each power node comprises coal-fired units, natural gas units and power loads, and the power nodes are connected with each other through lines.
Secondly, the constraint conditions comprise the constraint for energy storage device programming, natural gas system, electric power system and reliability.
Wherein, the reliability standards comprise average power load loss and average gas load loss.
Specifically, the average power load loss comprises,
where, EENSt refers to the average power load loss of year t; i refers to the index of power node; PLCitb refers to the power load loss at power node i under state b of year t; and Dtb refers to the duration under state b.
The average gas load loss comprises,
where, EGNSt refers to the average gas load loss of year t.
Further, the constraints for the energy storage device programming comprise,
ze(t−1)≤zet ∀e∈CS, ∀t;
zg(t−1)≤zgt ∀g∈CG, ∀t,
where, zet and zgt refer to the programmed state of battery t and gas storage device of year e respectively, wherein CS and CG refer to a collection of batteries and a collection of gas storage devices respectively.
Secondly, the constraints for natural gas system and electric power system comprise,
The process of benders decomposition algorithm solution model comprises decomposing the energy storage device programming model into one programming master problem and adding two reliability problems for quick solution.
More specifically, A and B, in the above formula, can be calculated respectively by the following formula:
where, Petmax and Wgtmax refer to the capacity of battery e and gas storage device W of year t respectively; zet and zgt refer to the programmed state of battery e and gas storage device g of year t respectively; CS and CG refer to the collection of batteries and gas storage devices respectively; Pktb and Ck refer to the variable of the output capacity and corresponding supply capacity of coal-fired unit k under state b of year t respectively; Petb and Ce refer to the variable of the output capacity and corresponding supply capacity of battery e under state b of year t respectively; Wwtb and Cw refer to the variable of the output capacity and corresponding supply capacity of gas source w under state b of year t respectively; Wgtb and Cg refer to the variable of the gas output and corresponding supply capacity of gas storage device g under state b of year t respectively; Dtb refers to the duration under state b of year t; EG and EW refer to the collection core-fired unit k and gas source w.
The following constraints are established simultaneously:
I. Programming Constraints for Energy Storage Device:
During the programming process, the programming state of the energy storage device must satisfy the following constraints:
ze(t-1)≤zet ∀e∈CS,∀t;
zg(t-1)≤zgt ∀g∈CG,∀t,
In addition, the generation capacity and air source capacity in the system of each year t after the energy storage device is programmed shall be larger than the load, which meets the following constraints:
where, Pietmax refers to the capacity of battery e at power node i; Pikmax refers to the generating capacity of generator set k at power node i; Wmgtmax refers to the capacity of gas storage device g at natural gas node m; Wmwmax refers to the gas production capacity of gas source W at natural gas node m; PDtb and GDtb refer to the power load and the natural gas load under state b of year t respectively; PRtb and GRtb refer to the power reserve capacity and the natural gas reserve capacity under state b of year t respectively.
II. Constraints for Natural Gas System:
During operation, the natural gas system needs to meet the following constraints, specifically as follows:
a. Constraints for Gas Flow Balance at Node:
In the system operation, the natural gas inflow and outflow at any node is the same, specifically expressed as:
where, Wmwtb refers to the gas output of gas source w at natural gas node m under state b of year t; Wmwtb refers to the gas output of gas storage device g at natural gas node m under state b of year t; τptb refers to the amount of natural gas flowing through pipeline p under state b of year t; τctb refers to the amount of natural gas flowing through compressor C under state b of year t; GDmtb refers to the natural gas load at node m under state b of year t; GLCmtb refers to the natural gas load loss at node m under state b of year t; GC and GL refer to the collection of compressor and pipeline respectively.
b. Constraints for Pipeline Flow:
The amount of natural gas flowing through the pipeline is related to the air pressure at both ends, specifically expressed as:
(σptb+−σptb−)·(πmtb−πntb)=τptb2/Mp ∀p∈GL,∀b,∀t,
where, σptb+ and σptb− refer to the marker for the flow direction of natural gas in pipeline p; σptb+=1 means that the natural gas in pipeline p flows from node m to node n; σptb−=1 means that the natural gas in pipeline flows from node n to node m; πmtb and πntb refer to the gas pressure at node m and n under state b of year t respectively; Mp refers to the fixed parameters of pipeline p.
The positions of markers for pipeline flow are binary variables and meet the following constraints:
σptb++σptb−=1 ∀p∈GL,∀b,∀t.
In addition, the pipeline flow needs to meet the following constraints:
−(1−σptb+)·τpmax≤τptb≤(1−σptb−)·τpmax ∀p∈GL,∀b,∀t,
where, τpmax refers to the maximum natural gas flow in pipeline p.
c. Constraints for Compressor:
The air pressure at both ends of the compressor is related to the coefficient of compressor, specifically expressed as:
Γctb=πcmtb/πcntb ∀c∈GC,∀m,∀b,∀t,
where, Γctb refers to the compressibility of compressor C under state b of year t; πcmtb and πctnb refer to the air pressure at node m and node n of compressor c under state b of year t respectively.
In addition, the compressibility of compressor shall meet the following constraints:
Γcmin≤Γctb≤Γcmax ∀c∈GC,∀m,∀b,∀t,
where, rcmax and rcmin refers to the maximum and minimum compressibility of compressor C respectively.
d. Constraints for the Output Capacity of Air Source:
The output capacity of air source shall meet the following constraints:
O≤Wmwtb≤Wmvmax·owtb ∀w∈EW,∀m,∀b,∀t,
where, Wmwmax refers to the maximum output capacity of air source W at node m; owtb refers to the running state of air source w under state b of year t.
e. Constraints for the Output of Gas Storage Device:
Gas storage device shall meet the following constraints:
−Wmgmax·zgt≤Wmgtb≤Wmgmax·zgt ∀g∈CG,∀m,∀b,∀t,
where, Wmgmax refers to the maximum output of gas storage device g at node m.
f. Constraints for Natural Gas Load Loss:
The natural gas load loss at each node shall meet the following constraints:
GLCmtb≤GDmtb ∀m,∀b,∀t.
III. Constraints for Electric Power System:
During operation, the electric power shall meet the following constraints, specifically as follows:
a. Constraints for Power Balance at Node:
At any power node, the power inflow is equal to the outflow, which is specifically expressed as:
where, Pietb refers to the output power of battery e at power node i under state b of year t; PiktbGG refers to the output power of natural gas unit k at power node i under state b of year t; PiktbCG refers to the output power of coal-fired unit k at power node i under state b of year t; PLCitb refers to the power load loss at power node i under state b of year t; fltb refers to the power of line l under state b of year t; EL refers to the collection of power line.
b. Constraints for Line Power:
The power flowing through the power line shall meet the following constraints:
fltb≤(θitb−θjtb)/xl ∀l∈EL,∀b,∀t,
where, θitb and θjtb refer to the directional angle of power node i and node j under state b of year t; xl refers to the impedance of line l.
In addition, the power flowing through the power line shall meet the following constraints:
−flmax≤fltb≤flmax ∀l∈EL,∀b,∀t,
where, flmax refers to the power transmission capacity of line l.
c. Constraints for the Directional Angle of Node:
The directional angle of node shall meet the following constraints:
−θimax≤θitb≤θimax ∀i,∀b,∀t,
where, θimax refers to the maximum value of the directional angle of node i.
d. Constraints for Battery Output Power:
The output power of battery shall meet the following constraints:
−Piemax·zet≤Pietb≤Piemax·et ∀e∈CS,∀i,∀b,∀t.
e. Constraints for Generator Set Output Power:
The output power of coal-fired unit shall meet the following constraints:
O≤PiktbCG≤Pikmax·oktb ∀k∈EG,∀i,∀b,∀t,
where, Pikmax refers to the generating capacity of coal-fired unit at node i.
The output power of natural gas unit is related to the injected natural gas power, specifically expressed as:
PiktbGG=(GDmtb−GLCmtb)·ϑ·oktb ∀b,∀t,
where, ϑ refers to the caloricity of natural gas.
f. Constraints for Power Load Loss:
The power load loss at each node shall meet the following constraints:
PLCitb≤PDitb ∀i,∀b,∀t.
IV. Constraints for Reliability:
The reliability of power-gas coupling system is usually measured by the average power load loss and the average natural gas load loss. The average power load loss can be calculated by the following formula:
where, refers to the average power load loss of year. Average power load loss shall meet the following constraints:
EENSt≤EENSlimit,
where, refers to the upper limit of average power load loss of year, which can be given by the programmer.
The average natural load loss can be calculated by the following formula:
where, EGNSt refers to the average natural gas load loss of year t. Average natural gas load loss shall meet the following constraints:
EGNSt≤EGNSlimit,
where, EGNSlimit refers to the upper limit of average natural gas load loss of year t, which can be given by the programmer.
In order to verify and illustrate the technical effect adopted in this method, this embodiment compare the method for programming the energy storage device in power-gas coupling system in traditional technical solution with the present invention, so as to verify the real effect of this method.
Taking the power-gas coupling system as an example, we define the reliability constraints EENSlimit and EGNSlimit and are respectively 10000 MWh and 2×105 m3. The model proposed by the present invention and the model of the traditional method are used to programme the system. Based on the programming results, the reliability analysis of the programmed system is carried out, and the analysis results are shown in the following table.
As shown from the above analysis results, the reliability indexes EENS and EGNS obtained by the programming through traditional method are much higher than the reliability constraint indexes EENSlimit and EGNSlimit, indicating that the programming results of the traditional method cannot guarantee the reliable operation of the system. On the contrary, the reliability indexes EENS and EGNS obtained by the present invention are far less than the reliability constraint indexes, indicating that the method proposed by the present invention can ensure the reliability requirements of the system.
Number | Date | Country | Kind |
---|---|---|---|
202011115587.2 | Oct 2020 | CN | national |
Number | Name | Date | Kind |
---|---|---|---|
8768810 | Infanger | Jul 2014 | B2 |
9043163 | Mezic | May 2015 | B2 |
11249121 | Wong | Feb 2022 | B2 |
Entry |
---|
Dantzig et al.; “Large-Scale Stochastic Linear Programs: Importance Sampling and Benders Decomposition”; 1991; Stanford University; pp. 1-11. (Year: 1991). |
Infanger; “Monte Carlo (Importance) Sampling within a Benders Decomposition Algorithm for Stochastic Linear Programs”; 1992; Stanford University; Annals of Operations Research; pp. 69-95. (Year: 1992). |
Number | Date | Country | |
---|---|---|---|
20220122198 A1 | Apr 2022 | US |