CN112950410B - Energy hub system planning method considering wind-solar correlation and preventive maintenance - Google Patents
Energy hub system planning method considering wind-solar correlation and preventive maintenance Download PDFInfo
- Publication number
- CN112950410B CN112950410B CN202110483271.7A CN202110483271A CN112950410B CN 112950410 B CN112950410 B CN 112950410B CN 202110483271 A CN202110483271 A CN 202110483271A CN 112950410 B CN112950410 B CN 112950410B
- Authority
- CN
- China
- Prior art keywords
- scene
- energy hub
- time
- ees
- maintenance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 95
- 238000012423 maintenance Methods 0.000 title claims abstract description 94
- 230000003449 preventive effect Effects 0.000 title claims abstract description 33
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 32
- 230000005611 electricity Effects 0.000 claims description 26
- 238000005315 distribution function Methods 0.000 claims description 18
- 239000007789 gas Substances 0.000 claims description 16
- 239000003345 natural gas Substances 0.000 claims description 16
- 238000007599 discharging Methods 0.000 claims description 14
- 238000009826 distribution Methods 0.000 claims description 13
- 238000004146 energy storage Methods 0.000 claims description 11
- 230000001186 cumulative effect Effects 0.000 claims description 9
- 238000010521 absorption reaction Methods 0.000 claims description 6
- 238000004134 energy conservation Methods 0.000 claims description 6
- 239000000446 fuel Substances 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 238000010977 unit operation Methods 0.000 claims description 6
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 239000013256 coordination polymer Substances 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 239000002918 waste heat Substances 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000001816 cooling Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an energy hub system planning method considering wind-solar correlation and preventive maintenance, and relates to the technical field of power systems. According to the invention, a typical solar-wind light output sequence is obtained by acquiring basic parameters required by planning an energy hub system and utilizing a non-parameter kernel density estimation and a Frank-Copula function wind-light output scene generation method; calculating maintenance cost and usable capacity of each device in the energy hub based on the random failure rate of the device; establishing an energy hub system double-layer multi-scene planning model which takes into account wind-light output correlation and preventive maintenance plan by using a random two-layer planning method, wherein the model comprises the objective function and constraint conditions; substituting the basic parameters into a constructed model, solving the model by adopting a Cplex solver with the aim of minimizing the annual total cost of energy hub planning, and outputting the model, the number, the capacity, the maintenance plan, the maintenance cost and the optimal output data of the selected equipment in the energy hub.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to an energy hub system planning method for accounting for wind-solar correlation and preventive maintenance.
Background
The energy is the motive power for economic development, is the material foundation of modern civilization, and the safe and reliable energy supply is the basic guarantee for realizing the sustainable development of socioeconomic. The environmental pollution, the energy crisis and the energy consumption in the human production and living process are increasing increasingly, so that various kinds of energy such as electricity, natural gas, heat and the like are planned in a collaborative mode to run and develop research in various countries. In the operation process of a multi-energy system, one of the most important problems is how to realize accurate modeling of the multi-energy system, including links of energy production, conversion, distribution, storage and the like in the system. Some researchers have developed research around multi-energy systems, where the concept of energy hubs proposed by the zurich federal regulation institute is a typical representative, plays a vital role in solving the problem of multi-energy carriers, and has received a great deal of attention from academia and industry.
An energy hub is an interface between an energy producer and a consumer that combines direct connection, energy conversion, and energy storage technologies to couple multiple energy sources such as electricity, natural gas, heat, etc. to meet load demands. If the energy hub is required to realize the full utilization of various energy sources under the condition of meeting basic technical and economic constraint conditions, scientific selection and capacity configuration of components in the energy hub are required. Planning and operation of such a system would be more complex and challenging than separate power and natural gas networks.
At present, energy hub planning models which account for renewable energy output of fans, photovoltaics and the like are studied in the prior art, the fan and the photovoltaic output are described by using Weibull distribution and beta distribution respectively, and the energy hub planning problem is modeled on the basis. However, the existing energy hub planning model does not consider the correlation of wind and light output, in fact, the wind and light output of renewable energy sources such as wind and light has uncertainty and the same area wind and light output has correlation. On the other hand, the existing research adopts a simplified calculation formula aiming at the maintenance cost of components in the energy hub, namely the maintenance cost coefficient is multiplied by the standard capacity of the maintenance components. The simple calculation mode assumes that the components in the energy hub can play the maximum role in the life cycle, namely the standard capacity of the components is not changed after the components are in fault and maintained, and the standard capacity is greatly different from the actual operation condition of the components.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an energy hub system planning method for accounting for wind-solar correlation and preventive maintenance.
The technical proposal of the invention is that,
the planning method of the energy hub system for accounting for wind-solar correlation and preventive maintenance specifically comprises the following steps:
step 1: characterizing uncertainty and correlation of wind and light output in an energy hub system;
step 1.1: acquiring n-day fan and photovoltaic output historical data of an area needing to be planned by an energy hub system, and selecting a Gaussian kernel function by adopting a nonparametric kernel density method to respectively generate probability density functions of fan and photovoltaic output in each period of 24 hours of the energy hub optimal configuration areaAnd->
Where t=1, 2,..24, 24 hours; PW (pseudo wire) t And PI (proportional integral) t The output of the fan and the photovoltaic at the moment t and PW are respectively i t And PI (proportional integral) i t The output of the fan and the photovoltaic at the moment t of the ith day are respectively; h is bandwidth, K (·) is a gaussian kernel function, expressed as follows:
step 1.2: probability density function for generated fan and photovoltaic outputAnd->Integrating to obtain cumulative distribution functions Phi (PW) and Phi (PI) of fans and photovoltaic output of each period of time within 24 hours of the energy hub optimal configuration area, wherein the cumulative distribution functions are expressed as follows:
selecting a Frank-Copula function as a joint distribution function C F Obtaining a combined distribution function of the fan and the photovoltaic output in each period:
in the formula, theta is a related parameter, and theta is E (-1, 1).
Step 1.3: inversion operation is carried out on the obtained combined distribution function of the fan and the photovoltaic output in each period based on a Monte Carlo simulation method, and wind and light output scene data considering randomness and relativity are generated;
step 2: calculating maintenance cost and usable capacity of each device in the energy hub system based on the random failure rate of the device;
the maintenance cost and the usable capacity method are calculated as follows:
component failure rate function:
wherein f j(t) And F j (t) is a probability density function and a cumulative distribution function of the occurrence of faults of the jth component respectively, J is a collection of components in the energy hub, J= { EHP, CHP, AB, AB, ESS }, J ε J, EHP, CHP, AB, AB, EES respectively represent an electric heat pump, a cogeneration unit, a gas boiler, an absorption refrigerator and electric energy storage.
Equipment maintenance cost function:
wherein n is j A maintenance period for the j-th component; n is the number of fixed maintenance times τ;and->Preventive maintenance costs and corrective maintenance costs for the j-th component, respectively; τ is the period length in months, days or weeks.
Wherein k is j Standard capacity for the j-th component; n is the number of maintenance cycles; u is an auxiliary variable, and has no practical significance;and->The device capacity lost after preventive maintenance and corrective maintenance for the j-th component, respectively, is:
step 3: the wind-solar power output scene data obtained in the step 1, the maintenance cost and usable capacity data obtained in the step 2, electricity price, natural gas price, equipment parameters and cold/heat/electricity load demands are used as input parameters, a double-layer multi-scene planning model of the energy hub system for wind-solar correlation and preventive maintenance is established, and the model, the number, the capacity, the maintenance plan and the optimal output data of the equipment meeting the load side cold/heat/electricity demands and the constraint of the energy hub system are determined;
the double-layer multi-scene planning model of the energy hub system for accounting for wind-solar correlation and preventive maintenance comprises an upper layer and a lower layer, wherein the upper layer model is used for configuring energy hub equipment, decision variables are the model number and the number of each component, and an objective function is that the annual investment cost and the preventive maintenance cost of the energy hub system are the lowest; the lower layer is used for calculating the optimal output of components in the energy hub system, decision variables are natural gas distribution coefficients, waste heat distribution coefficients and the optimal output of equipment hours, and an objective function is that the annual operation cost of the energy hub system is the lowest, and the annual operation cost comprises the energy hub gas purchasing cost and the energy hub and power distribution network interaction cost; the energy hub system is planned to have the total cost of the annual total system, including the annual investment cost and the preventive maintenance cost in the upper objective function and the annual operation cost in the lower objective function, and the formula is as follows:
Min{ZIC+ZOC+ZMC}
wherein ZIC, ZOC and ZMC represent the total annual cost, the annual operating cost and the preventative maintenance cost, respectively, in the energy hub system planning model;
wherein the method comprises the steps of
In the method, in the process of the invention,construction costs for configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; m, n, i, j, h, v and k are respectively the serial numbers of the configuration CHP, WT, PV, AB, EES, EHP, ab in the energy hub; CH, CW, CP, CB, CH, CE, CA are each a set of candidates CHP, WT, PV, AB, EES, EHP, ab within an energy hub; CRF is the capital recovery factor.
Wherein r is the discount rate; and l is the operating period of the equipment in the energy hub planning process.
In the method, in the process of the invention,rated capacity of configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; />The unit capacity costs of configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; i CHP ，I WT ，I PV ，I AB ，I EES ，I EHP ，I Ab The binary variable of CHP, WT, PV, AB, EES, EHP, ab is configured or not, 1 is configured, otherwise, 0 is configured.
Wherein N is s Is the total number of scenes; p is p s Probability of occurrence for the s-th scene;for the amount of electricity purchased from the grid at a time t at a scene s; />To be the price of purchasing electricity from the grid at a time t at a scene s; />CHP, AB, EHP, ab, respectively.
In the method, in the process of the invention,the CHP, denoted m, consumes fuel costs at scene s, time t; />Representing the electrical power generated by CHP numbered m in scene s at time t; lambda (lambda) g,t Is the price of natural gas at time t; />AB, denoted j, is in scene s, time t consuming fuel costs; />Representing the thermal power generated by the AB with the number j in a scene s and the time t; lambda (lambda) g,t Is the price of natural gas at time t; />Representing the cost of using electricity to generate at scene s, time t, for EHP numbered v; />Representing the electric power generated by EHP numbered v in scene s at time t; lambda (lambda) e,t The price of electricity at time t; />The cost of using electricity generation at time t for Ab numbered v in scenario k; />Represents the electrical power generated by the EHP numbered k at scene s, time t.
ZMC＝CM j ×Z j
In CM j For maintenance cost of jth device, Z j A binary variable of whether the j-th device is serviced.
Wherein the constraints of the overall objective function include: energy conservation constraints, tie line power constraints, scenario constraints, equipment operation constraints, and maintenance planning constraints.
The energy conservation constraint includes an electric balance power constraint, a thermal balance power constraint, and a cold balance power constraint:
wherein the power constraints are electrically balanced:
in the method, in the process of the invention,respectively representing the charging and discharging power of EES with the number h in a scene s and at the time t; />Respectively are provided withCHP, WT, PV, which are numbered m, n and i, show the power output at scene s and time t; />For scenario s, the electrical load demand at time t.
Thermal equilibrium power constraint:
in the method, in the process of the invention,CHP, AB, EHP, which are respectively numbered m, j and v, have heat output in a scene s and at time t; />Scene s, thermal load demand at time t.
Cold balance power constraint:
in the method, in the process of the invention,EHP and Ab respectively represent the cold output of the scene s and the time t; />For scenario s, the cold load demand at time t.
The tie power constraint is:
in the method, in the process of the invention,representing the minimum and maximum values numbered link power, respectively.
The scene constraint is:
wherein N is s Is the total number of scenes; ρ s Is the scene probability.
The equipment operation approximately comprises a cogeneration unit operation constraint, a gas boiler operation constraint and a storage battery operation constraint:
wherein the cogeneration unit operation constraint:
in the method, in the process of the invention,the upper and lower limits of CHP output electric power are denoted by the number m, respectively.
Gas boiler operation constraints:
in the method, in the process of the invention,the upper limit and the lower limit of the output heat power of the gas boiler are respectively indicated by the number j.
Battery operation constraints:
in the method, in the process of the invention,respectively representing the energy storage of EES with the number h in the scene s, time t and time t-1; respectively representing the charging power and the discharging power of the EES with the number h in a scene s and at the time t; η (eta) EES,Ch 、η EES,Dis Respectively representing the charging power and the discharging efficiency of the EES with the number h in a scene s and at the time t; e (E) EES,Max 、E EES,Min Respectively representing the upper limit and the lower limit of energy storage of the EES with the number h in a scene s and at the time t; p (P) EES,Ch,Max 、P EES,Dis,Max Respectively represent EES charging power and discharging powerA power maximum; />The EES, which is numbered h, charges or discharges 1 at scene s, time t, or 0, respectively.
The maintenance plan constraints are:
where S is a maintenance period set, S= {1, …, N }
This constraint ensures that each device within the energy hub can only select one maintenance strategy according to the alternative cycle.
The beneficial effects generated by adopting the technical method are as follows:
the invention provides a planning method of an energy hub system for considering wind-solar correlation and preventive maintenance, which aims at the minimum total annual cost of the planning operation of the energy hub system, utilizes a wind-solar output scene generation method of a non-parameter kernel density estimation and a Frank-Copula function to obtain a typical solar-wind-solar output sequence, calculates the maintenance cost and usable capacity of each device based on the random failure rate of the devices in the energy hub, and constructs a double-layer multi-scene planning model of the energy hub system for considering wind-solar correlation and preventive maintenance. In addition, the invention calls the Cplex solver to solve the model under the Yalmip in Matlab to solve the model. And finally, determining the model number, the capacity, the maintenance plan and the optimal output of the equipment in the energy hub system through calculation example analysis, analyzing the aspects of annual total cost, maintenance plan rationality and the like, and verifying the economy, the effectiveness and the engineering practicability of the model.
Drawings
FIG. 1 is a flow chart of a method for planning an energy hub system according to the present invention;
fig. 2 is a schematic diagram of the structure of an energy hub system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the electricity price of the energy hub system according to the present invention;
FIG. 4 is a schematic diagram of the load side cooling/heating/electrical demand of the energy hub system of the present invention;
FIG. 5 is a schematic view of a wind power generation scenario of the present invention;
FIG. 6 is a schematic view of a photovoltaic power generation scenario of the present invention;
FIG. 7 is a schematic diagram of an exemplary solar junction electrical power balance curve according to the present invention;
FIG. 8 is a schematic diagram of a thermal power balance curve of an exemplary solar junction according to the present invention;
FIG. 9 is a graph showing the cold power balance of an exemplary solar junction according to the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
An energy hub system planning method for accounting for wind-solar correlation and preventive maintenance is shown in fig. 1, and specifically comprises the following steps:
step 1: characterizing uncertainty and correlation of wind and light output in an energy hub system;
step 1.1: the method comprises the steps of acquiring fan and Photovoltaic output historical data of an area needing to be planned by an energy hub system, wherein the connection mode between equipment and equipment contained in the energy hub system is shown as figure 2, and the energy hub system in figure 2 comprises a cogeneration unit (Combined heat and power, CHP), a fan (WT), a Photovoltaic (PV), an Auxiliary boiler (A.B), a storage battery (Energy storage battery, ESB), an Electric heating pump (Electric heat pump, EHP), an Electric refrigerator (EC) and an absorption refrigerator (absorption chiller, ab.chiler). In the energy hub system, the electric requirements required by a user side can be met by a power distribution network, a fan, a photovoltaic, a storage battery and a cogeneration unit; the heat requirement required by the user side can be met by a cogeneration unit and a gas boiler; the cold requirement required on the user side can be met by both electric and absorption refrigerators.
Selecting Gaussian kernel functions by adopting a non-parametric kernel density method to respectively generate probability density functions of fans and photovoltaic output in each period of 24 hours in an energy hub optimal configuration areaAnd->
Where t=1, 2,..24, 24 hours; PW (pseudo wire) t And PI (proportional integral) t The output of the fan and the photovoltaic at the moment t and PW are respectively i t And PI (proportional integral) i t The output of the fan and the photovoltaic at the moment t of the ith day are respectively; h is bandwidth, K (·) is a gaussian kernel function, expressed as follows:
step 1.2: probability density function for generated fan and photovoltaic outputAnd->Integrating to obtain cumulative distribution functions Phi (PW) and Phi (PI) of fans and photovoltaic output of each period of time within 24 hours of the energy hub optimal configuration area, wherein the cumulative distribution functions are expressed as follows:
considering that the Frank-Copula function can simultaneously give consideration to the non-negative and negative correlations of the parameters, the Frank-Copula function is selected as the joint distribution function C F Obtaining a combined distribution function of the fan and the photovoltaic output in each period:
in the formula, theta is a related parameter, and theta is E (-1, 1).
Step 1.3: inversion operation is carried out on the obtained combined distribution function of the fan and the photovoltaic output in each period based on a Monte Carlo simulation method, and wind and light output scene data considering randomness and relativity are generated;
step 2: calculating maintenance cost and usable capacity of each device in the energy hub system based on the random failure rate of the device;
the maintenance cost and the usable capacity method are calculated as follows:
component failure rate function:
wherein f j(t) And F j (t) is a probability density function and a cumulative distribution function of the occurrence of faults of the jth component respectively, J is a collection of components in the energy hub, J= { EHP, CHP, AB, AB, ESS }, J ε J, EHP, CHP, AB, AB, EES respectively represent an electric heat pump, a cogeneration unit, a gas boiler, an absorption refrigerator and electric energy storage.
Equipment maintenance cost function:
wherein n is j A maintenance period for the j-th component; n is the number of fixed maintenance times τ;and->Preventive maintenance costs and corrective maintenance costs for the j-th component, respectively; τ is the period length in months, days or weeks.
Wherein k is j Standard capacity for the j-th component; n is the number of maintenance cycles; u is an auxiliary variable, and has no practical significance;and->The device capacity lost after preventive maintenance and corrective maintenance for the j-th component, respectively, is:
step 3: setting up a double-layer multi-scene planning model of the energy hub system for wind-solar correlation and preventive maintenance according to wind-solar output scene data obtained in the step 1, as shown in fig. 5 and 6, maintenance cost and usable capacity data obtained in the step 2, electricity price, natural gas price, equipment parameters and cold/heat/electric load demands shown in fig. 3 and as input parameters shown in fig. 4, and determining model, quantity, capacity, maintenance plan and optimal output data of equipment meeting the load side cold/heat/electric demands and the energy hub system constraint;
the double-layer multi-scene planning model of the energy hub system for accounting for wind-solar correlation and preventive maintenance comprises an upper layer and a lower layer, wherein the upper layer model is used for configuring energy hub equipment, decision variables are the model number and the number of each component, and an objective function is that the annual investment cost and the preventive maintenance cost of the energy hub system are the lowest; the lower layer is used for calculating the optimal output of components in the energy hub system, decision variables are natural gas distribution coefficients, waste heat distribution coefficients and the optimal output of equipment hours, and an objective function is that the annual operation cost of the energy hub system is the lowest, and the annual operation cost comprises the energy hub gas purchasing cost and the energy hub and power distribution network interaction cost; the energy hub system is planned to have the total cost of the annual total system, including the annual investment cost and the preventive maintenance cost in the upper objective function and the annual operation cost in the lower objective function, and the formula is as follows:
Min{ZIC+ZOC+ZMC}
wherein ZIC, ZOC and ZMC represent the total annual cost, the annual operating cost and the preventative maintenance cost, respectively, in the energy hub system planning model;
wherein the method comprises the steps of
In the method, in the process of the invention,construction costs for configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; m, n, i, j, h, v and k are respectively the serial numbers of the configuration CHP, WT, PV, AB, EES, EHP, ab in the energy hub; CH, CW, CP, CB, CH, CE, CA are each a set of candidates CHP, WT, PV, AB, EES, EHP, ab within an energy hub; CRF is the capital recovery factor.
Wherein r is the discount rate; and l is the operating period of the equipment in the energy hub planning process.
In the method, in the process of the invention,rated capacity of configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; />The unit capacity costs of configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; i CHP ，I WT ，I PV ，I AB ，I EES ，I EHP ，I Ab The binary variable of CHP, WT, PV, AB, EES, EHP, ab is configured or not, 1 is configured, otherwise, 0 is configured.
Wherein N is s Is the total number of scenes; p is p s Probability of occurrence for the s-th scene;for the amount of electricity purchased from the grid at a time t at a scene s; />To be the price of purchasing electricity from the grid at a time t at a scene s; />CHP, AB, EHP, ab, respectively.
In the method, in the process of the invention,the CHP, denoted m, consumes fuel costs at scene s, time t; />Representing the electrical power generated by CHP numbered m in scene s at time t; lambda (lambda) g,t Is the price of natural gas at time t; />AB, denoted j, is in scene s, time t consuming fuel costs; />Representing the thermal power generated by the AB with the number j in a scene s and the time t; lambda (lambda) g,t Is the price of natural gas at time t; />Representing the cost of using electricity to generate at scene s, time t, for EHP numbered v;the expression number is vElectric power generated by EHP of (a) at scene s and time t; lambda (lambda) e,t The price of electricity at time t; />The cost of using electricity generation at time t for Ab numbered v in scenario k; />Represents the electrical power generated by the EHP numbered k at scene s, time t.
ZMC＝CM j ×Z j
In CM j For maintenance cost of jth device, Z j A binary variable of whether the j-th device is serviced.
Wherein the constraints of the overall objective function include: energy conservation constraints, tie line power constraints, scenario constraints, equipment operation constraints, and maintenance planning constraints.
The energy conservation constraint includes an electric balance power constraint, a thermal balance power constraint, and a cold balance power constraint:
wherein the power constraints are electrically balanced:
in the method, in the process of the invention,respectively representing the charging and discharging power of EES with the number h in a scene s and at the time t; />CHP, WT, PV, which are respectively numbered m, n and i, are the electric power of a scene s and a time t; />For scenario s, the electrical load demand at time t.
Thermal equilibrium power constraint:
in the method, in the process of the invention,CHP, AB, EHP, which are respectively numbered m, j and v, have heat output in a scene s and at time t; />Scene s, thermal load demand at time t.
Cold balance power constraint:
in the method, in the process of the invention,EHP and Ab respectively represent the cold output of the scene s and the time t; />For scenario s, the cold load demand at time t.
The tie power constraint is:
in the method, in the process of the invention,representing the minimum and maximum values numbered link power, respectively.
The scene constraint is:
wherein N is s Is the total number of scenes; ρ s Is the scene probability.
The equipment operation approximately comprises a cogeneration unit operation constraint, a gas boiler operation constraint and a storage battery operation constraint:
wherein the cogeneration unit operation constraint:
/>
in the method, in the process of the invention,the upper and lower limits of CHP output electric power are denoted by the number m, respectively.
Gas boiler operation constraints:
in the method, in the process of the invention,the upper limit and the lower limit of the output heat power of the gas boiler are respectively indicated by the number j.
Battery operation constraints:
in the method, in the process of the invention,respectively representing the energy storage of EES with the number h in the scene s, time t and time t-1; respectively representing the charging power and the discharging power of the EES with the number h in a scene s and at the time t; η (eta) EES,Ch 、η EES,Dis Respectively representing the charging power and the discharging efficiency of the EES with the number h in a scene s and at the time t; e (E) EES,Max 、E EES,Min Respectively representing the upper limit and the lower limit of energy storage of the EES with the number h in a scene s and at the time t; p (P) EES,Ch,Max 、P EES,Dis,Max Respectively representing the maximum value of EES charging power and discharging power; />The EES, which is numbered h, charges or discharges 1 at scene s, time t, or 0, respectively.
The maintenance plan constraints are:
where S is a maintenance period set, S= {1, …, N }
This constraint ensures that each device within the energy hub can only select one maintenance strategy according to the alternative cycle.
The embodiment carries out simulation verification on the planning method of the energy hub system. The present invention utilizes the energy hub system shown in fig. 2 for simulation analysis. In the energy hub system, the technical and economic parameters of various candidate components are shown in table 1, and the fault probability functions of various candidate components are shown in table 2.
Table 1 parameters of components in an energy hub system
Table 2 fault probability functions for different components in an energy junction system
The electricity purchase price of the energy hub system from the upper power grid is shown in fig. 3. The price for purchasing natural gas was set to 2.2 yuan/m 3. The user side cooling/heating/electrical load demand curve is shown in fig. 4. The wind-light output scene generation method using the nonparametric kernel density estimation and the Frank-Copula function can finally obtain 4 typical solar-wind-light output scenes, as shown in fig. 5 and 6, and the probability of each scene is marked in a legend bracket. As can be seen from fig. 5 and 6, the 4 scene wind-light output has obvious seasonality and time sequence, and the wind-light output changes consistently or in correlation at some time intervals, and shows a certain correlation. The necessity of considering wind-light output correlation in the energy hub planning stage is verified.
The maintenance cost results for each device within the energy hub are calculated based on the failure rate are shown in table 3.
Table 3 maintenance costs for each component in the energy hub system under different maintenance periods
/>
Based on the model input data, a calculation result of the model is obtained, and then the calculation result is analyzed.
The result of the optimal planning scheme of the energy hub system calculated by the model constructed by the invention is shown in table 4.
Table 4 optimal planning scheme for energy hub systems
And (3) solving an energy hub double-layer multi-scene planning model which takes the wind-solar output correlation and the preventive maintenance plan into consideration by using a Cplex solver, wherein the annual total cost of the obtained optimal planning scheme is 8440768 yuan, and 675261 yuan (8%) is saved compared with the energy hub system planning scheme which does not adopt the preventive maintenance plan, which reflects the economic performance of the energy hub planning method which takes the preventive maintenance plan into consideration.
Solving an energy hub double-layer multi-scene planning model which takes into account wind-light output correlation and preventive maintenance plan by using a Cplex solver to obtain electric power balance curves of all devices in an energy hub assembly, wherein the electric power balance curves are shown in figure 7; the thermal power balance curve is shown in fig. 8; the cold power balance curve is shown in fig. 9.
The optimal maintenance cycles for each component within the energy hub are shown in table 5.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the invention, which is defined by the following claims.
Claims (1)
1. The energy hub system planning method for accounting for wind-solar correlation and preventive maintenance is characterized by comprising the following steps of:
step 1: characterizing uncertainty and correlation of wind and light output in an energy hub system;
step 1.1: acquiring n-day fan and photovoltaic output historical data of an area needing to be planned by an energy hub system, and selecting a Gaussian kernel function by adopting a nonparametric kernel density method to respectively generate probability density functions of fan and photovoltaic output in each period of 24 hours of the energy hub optimal configuration areaAnd->
Where t=1, 2,..24, 24 hours; PW (pseudo wire) t And PI (proportional integral) t The output of the fan and the photovoltaic at the moment t and PW are respectively i t And PI (proportional integral) i t The output of the fan and the photovoltaic at the moment t of the ith day are respectively; h is bandwidth, K (·) is a gaussian kernel function, expressed as follows:
step 1.2: probability density function for generated fan and photovoltaic outputAnd->Integrating to obtain cumulative distribution functions Phi (PW) and Phi (PI) of fans and photovoltaic output of each period of time within 24 hours of the energy hub optimal configuration area, wherein the cumulative distribution functions are expressed as follows:
selecting a Frank-Copula function as a joint distribution function C F Obtaining a combined distribution function of the fan and the photovoltaic output in each period:
wherein, theta is a relevant parameter, and theta is E (-1, 1);
step 1.3: inversion operation is carried out on the obtained combined distribution function of the fan and the photovoltaic output in each period based on a Monte Carlo simulation method, and wind and light output scene data considering randomness and relativity are generated;
step 2: calculating maintenance cost and usable capacity of each device in the energy hub system based on the random failure rate of the device;
the maintenance cost and the usable capacity method are calculated as follows:
the component failure rate function is:
wherein f j(t) And F j (t) is a probability density function and a cumulative distribution function of the occurrence of faults of the jth component respectively, J is a collection of components in the energy hub, J= { EHP, CHP, AB, AB, ESS }, J ε J, EHP, CHP, AB, AB, EES respectively represent an electric heat pump, a cogeneration unit, a gas boiler, an absorption refrigerator and electric energy storage;
equipment maintenance cost function:
wherein n is j A maintenance period for the j-th component; n is the number of fixed maintenance times τ;and->Preventive maintenance costs and corrective maintenance costs for the j-th component, respectively; τ is the period length in months, days or weeks;
wherein k is j Standard capacity for the j-th component; n is the number of maintenance cycles; u is an auxiliary variable, and has no practical significance;and->The device capacity lost after preventive maintenance and corrective maintenance for the j-th component, respectively, is:
step 3: the wind-solar power output scene data obtained in the step 1, the maintenance cost and usable capacity data obtained in the step 2, electricity price, natural gas price, equipment parameters and cold/heat/electricity load demands are used as input parameters, a double-layer multi-scene planning model of the energy hub system for wind-solar correlation and preventive maintenance is established, and the model, the number, the capacity, the maintenance plan and the optimal output data of the equipment meeting the load side cold/heat/electricity demands and the constraint of the energy hub system are determined;
the double-layer multi-scene planning model of the energy hub system for accounting for wind-solar correlation and preventive maintenance in the step 3 comprises an upper layer and a lower layer, wherein the upper layer model is used for configuring energy hub equipment, decision variables are the model number and the number of each component, and an objective function is that the annual investment cost and the preventive maintenance cost of the energy hub system are the lowest; the lower layer is used for calculating the optimal output of components in the energy hub system, decision variables are natural gas distribution coefficients, waste heat distribution coefficients and the optimal output of equipment hours, and an objective function is that the annual operation cost of the energy hub system is the lowest, and the annual operation cost comprises the energy hub gas purchasing cost and the energy hub and power distribution network interaction cost; the energy hub system is planned to have the total cost of the annual total system, including the annual investment cost and the preventive maintenance cost in the upper objective function and the annual operation cost in the lower objective function, and the formula is as follows:
Min{ZIC+ZOC+ZMC}
wherein ZIC, ZOC and ZMC represent the total annual cost, the annual operating cost and the preventative maintenance cost, respectively, in the energy hub system planning model;
wherein the method comprises the steps of
In the method, in the process of the invention,construction costs for configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; m, n, i, j, h, v and k are respectively the serial numbers of the configuration CHP, WT, PV, AB, EES, EHP, ab in the energy hub; CH, CW, CP, CB, CH, CE, CA are each a set of candidates CHP, WT, PV, AB, EES, EHP, ab within an energy hub; CRF is capital recovery factor;
wherein r is the discount rate; l is the operating age of the equipment in the energy hub planning process;
in the method, in the process of the invention,rated capacity of configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; />The unit capacity costs of configuration CHP, WT, PV, AB, EES, EHP, ab, respectively; i CHP ，I WT ，I PV ，I AB ，I EES ，I EHP ，I Ab Respectively configuring binary variables of CHP, WT, PV, AB, EES, EHP, ab or not, configuring 1, otherwise, 0;
wherein N is s Is the total number of scenes; p is p s Probability of occurrence for the s-th scene;for the amount of electricity purchased from the grid at a time t at a scene s; />To be the price of purchasing electricity from the grid at a time t at a scene s; />The running cost of CHP, AB, EHP, ab respectively;
in the method, in the process of the invention,the CHP, denoted m, consumes fuel costs at scene s, time t; />Representing the electrical power generated by CHP numbered m in scene s at time t; lambda (lambda) g,t Is the price of natural gas at time t; />AB, denoted j, is in scene s, time t consuming fuel costs; />Representing the thermal power generated by the AB with the number j in a scene s and the time t; lambda (lambda) g,t Is the price of natural gas at time t; />Representing the cost of using electricity to generate at scene s, time t, for EHP numbered v; />Representing the electric power generated by EHP numbered v in scene s at time t; lambda (lambda) e,t The price of electricity at time t; />The cost of using electricity generation at time t for Ab numbered v in scenario k; />Represents the electric power generated by EHP numbered k in scene s at time t;
ZMC＝CM j ×Z j
in CM j For maintenance cost of jth device, Z j A binary variable that is whether the j-th device is serviced;
wherein the constraints of the overall objective function include: energy conservation constraints, tie line power constraints, scenario constraints, equipment operation constraints, and maintenance planning constraints;
the energy conservation constraint includes an electric balance power constraint, a thermal balance power constraint, and a cold balance power constraint:
wherein the power constraints are electrically balanced:
in the method, in the process of the invention,respectively representing the charging and discharging power of EES with the number h in a scene s and at the time t; />CHP, WT, PV, which are respectively numbered m, n and i, are the electric power of a scene s and a time t; />For scenario s, electrical load demand at time t;
thermal equilibrium power constraint:
in the method, in the process of the invention,CHP, AB, EHP, which are respectively numbered m, j and v, have heat output in a scene s and at time t; />Scene s, thermal load demand at time t;
cold balance power constraint:
in the method, in the process of the invention,EHP and Ab respectively represent the cold output of the scene s and the time t; />Scene s, the cold load demand at time t;
the tie power constraint is:
in the method, in the process of the invention,respectively representing the minimum value and the maximum value of the power of the connecting line;
the scene constraint is:
wherein N is s Is the total number of scenes; ρ s Is scene probability;
the equipment operation approximately comprises a cogeneration unit operation constraint, a gas boiler operation constraint and a storage battery operation constraint:
wherein the cogeneration unit operation constraint:
in the method, in the process of the invention,respectively representing an upper limit and a lower limit of the CHP output electric power with the number of m;
gas boiler operation constraints:
in the method, in the process of the invention,respectively representing the upper limit and the lower limit of the output heat power of the gas boiler with the number j;
battery operation constraints:
in the method, in the process of the invention,respectively representing the energy storage of EES with the number h in the scene s, time t and time t-1; /> Respectively representing the charging power and the discharging power of the EES with the number h in a scene s and at the time t; η (eta) EES,Ch 、η EES,Dis Respectively representing the charging power and the discharging efficiency of the EES with the number h in a scene s and at the time t; e (E) EES,Max 、E EES,Min Respectively representing the upper limit and the lower limit of energy storage of the EES with the number h in a scene s and at the time t; p (P) EES,Ch,Max 、P EES,Dis,Max Respectively representing the maximum value of EES charging power and discharging power; />Respectively representing binary variables of charging and discharging of EES with the number h in a scene s and time t, wherein the charging or discharging is 1, otherwise, the binary variable is 0;
the maintenance plan constraints are:
where S is a maintenance period set, S= {1, …, N }
This constraint ensures that each device within the energy hub can only select one maintenance strategy according to the alternative cycle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110483271.7A CN112950410B (en) | 2021-04-30 | 2021-04-30 | Energy hub system planning method considering wind-solar correlation and preventive maintenance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110483271.7A CN112950410B (en) | 2021-04-30 | 2021-04-30 | Energy hub system planning method considering wind-solar correlation and preventive maintenance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112950410A CN112950410A (en) | 2021-06-11 |
| CN112950410B true CN112950410B (en) | 2023-10-27 |
Family
ID=76233719
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110483271.7A Active CN112950410B (en) | 2021-04-30 | 2021-04-30 | Energy hub system planning method considering wind-solar correlation and preventive maintenance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112950410B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114169212B (en) * | 2021-12-03 | 2024-05-03 | 南昌大学 | Energy hinge double-layer optimization method considering adjustable thermoelectric ratio |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106058942A (en) * | 2016-08-03 | 2016-10-26 | 长沙理工大学 | Energy hub optimizing model taking wind power nondeterminacy into consideration and including power to gas and CCHP |
| CN108629470A (en) * | 2017-03-17 | 2018-10-09 | 华北电力大学 | System capacity management of providing multiple forms of energy to complement each other based on non-cooperative game is run with optimization |
| CN110941799A (en) * | 2019-11-29 | 2020-03-31 | 国网辽宁省电力有限公司经济技术研究院 | Energy hub random planning method considering system comprehensive uncertainty factors |
| CN111404153A (en) * | 2020-04-16 | 2020-07-10 | 华北电力大学 | Energy hub planning model construction method considering renewable energy and demand response |
-
2021
- 2021-04-30 CN CN202110483271.7A patent/CN112950410B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106058942A (en) * | 2016-08-03 | 2016-10-26 | 长沙理工大学 | Energy hub optimizing model taking wind power nondeterminacy into consideration and including power to gas and CCHP |
| CN108629470A (en) * | 2017-03-17 | 2018-10-09 | 华北电力大学 | System capacity management of providing multiple forms of energy to complement each other based on non-cooperative game is run with optimization |
| CN110941799A (en) * | 2019-11-29 | 2020-03-31 | 国网辽宁省电力有限公司经济技术研究院 | Energy hub random planning method considering system comprehensive uncertainty factors |
| CN111404153A (en) * | 2020-04-16 | 2020-07-10 | 华北电力大学 | Energy hub planning model construction method considering renewable energy and demand response |
Non-Patent Citations (1)
| Title |
|---|
| 融合风光出力场景生成的多能互补微网***优化配置;白凯峰;顾洁;彭虹桥;朱彬若;;电力***自动化(15);139-147 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112950410A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | A two-stage operation optimization method of integrated energy systems with demand response and energy storage | |
| CN113452020B (en) | Scheduling method of electric hydrogen energy system considering flexible hydrogen demand | |
| CN110619110B (en) | Coordinated operation optimization method for comprehensive energy system with heat pump | |
| CN111291963B (en) | Park comprehensive energy system planning method for coordinating economy and reliability | |
| Tabar et al. | Achieving a nearly zero energy structure by a novel framework including energy recovery and conversion, carbon capture and demand response | |
| CN112600253B (en) | Park comprehensive energy collaborative optimization method and equipment based on optimal energy utilization efficiency | |
| CN112446546B (en) | Comprehensive energy system two-stage optimal configuration method considering energy reliability | |
| CN115099007B (en) | Comprehensive energy system optimized operation method based on comprehensive cost-energy consumption curve | |
| CN112785065A (en) | Comprehensive energy system planning method and system based on hybrid artificial fish swarm algorithm | |
| Pezhmani et al. | A centralized stochastic optimal dispatching strategy of networked multi-carrier microgrids considering transactive energy and integrated demand response: Application to water–energy nexus | |
| Qin et al. | Multi-timescale hierarchical scheduling of an integrated energy system considering system inertia | |
| CN113850409A (en) | Comprehensive energy system optimized operation method considering renewable energy access | |
| Ameli et al. | Economical load distribution in power networks that include hybrid solar power plants | |
| Lu et al. | Day‐Ahead Scheduling for Renewable Energy Generation Systems considering Concentrating Solar Power Plants | |
| CN112950410B (en) | Energy hub system planning method considering wind-solar correlation and preventive maintenance | |
| CN113158547B (en) | Regional comprehensive energy system optimal configuration method considering economy and reliability | |
| Ju et al. | Three‐level energy flexible management strategy for micro energy grids considering multiple uncertainties at different time scales | |
| CN107832873A (en) | Integrated energy system Method for optimized planning and device based on double-deck bus-type structure | |
| Liang et al. | Capacity configuration optimization of wind-solar combined power generation system based on improved grasshopper algorithm | |
| Hu et al. | Economic and environmental analysis of coupling waste-to-power technology to integrated energy system (IES) using a two-layer optimization method | |
| Zheng et al. | Comparison and error analysis of off-design and design models of energy hubs | |
| CN114757469B (en) | Regional comprehensive energy system day-ahead optimal scheduling method considering multi-energy flow equipment | |
| CN112465228B (en) | User-side comprehensive energy system optimal configuration method | |
| CN114997460A (en) | Regional micro-energy network operation optimization method considering maximum consumption of renewable energy | |
| CN112736969A (en) | Distributed photovoltaic data processing method and system based on new energy economic dispatching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |