CN110533225B - Business park comprehensive energy system optimal scheduling method based on opportunity constraint planning - Google Patents
Business park comprehensive energy system optimal scheduling method based on opportunity constraint planning Download PDFInfo
- Publication number
- CN110533225B CN110533225B CN201910723934.0A CN201910723934A CN110533225B CN 110533225 B CN110533225 B CN 110533225B CN 201910723934 A CN201910723934 A CN 201910723934A CN 110533225 B CN110533225 B CN 110533225B
- Authority
- CN
- China
- Prior art keywords
- power
- constraint
- time
- electric
- ice
- 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 40
- 238000005457 optimization Methods 0.000 claims abstract description 34
- 230000002068 genetic effect Effects 0.000 claims abstract description 13
- 238000004088 simulation Methods 0.000 claims abstract description 10
- 238000006243 chemical reaction Methods 0.000 claims abstract description 8
- 230000005611 electricity Effects 0.000 claims description 64
- 238000003860 storage Methods 0.000 claims description 38
- 239000007789 gas Substances 0.000 claims description 27
- 238000002844 melting Methods 0.000 claims description 22
- 238000005338 heat storage Methods 0.000 claims description 20
- 230000003993 interaction Effects 0.000 claims description 20
- 230000008018 melting Effects 0.000 claims description 19
- 238000005057 refrigeration Methods 0.000 claims description 17
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 17
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 14
- 230000035772 mutation Effects 0.000 claims description 11
- 238000010521 absorption reaction Methods 0.000 claims description 10
- 238000001816 cooling Methods 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 10
- 238000013178 mathematical model Methods 0.000 claims description 10
- 238000010248 power generation Methods 0.000 claims description 9
- 238000007599 discharging Methods 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 7
- 238000004519 manufacturing process Methods 0.000 claims description 7
- 239000003345 natural gas Substances 0.000 claims description 7
- 238000009395 breeding Methods 0.000 claims description 6
- 230000001488 breeding effect Effects 0.000 claims description 6
- 230000008878 coupling Effects 0.000 claims description 6
- 238000010168 coupling process Methods 0.000 claims description 6
- 238000005859 coupling reaction Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 5
- 238000002948 stochastic simulation Methods 0.000 claims description 4
- 239000002918 waste heat Substances 0.000 claims description 4
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 229960005486 vaccine Drugs 0.000 claims description 3
- 238000013139 quantization Methods 0.000 abstract 1
- 230000008901 benefit Effects 0.000 description 3
- 230000005284 excitation Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000013519 translation 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
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a business park comprehensive energy system optimal scheduling method based on opportunity constraint planning. Firstly, a commercial park comprehensive energy system model is built, and on the basis of modeling energy conversion of each element of the park, uncertainty of new energy output and load is considered, and a commercial park energy optimization scheduling model based on opportunity constraint planning is built. The model aims at minimum running cost, adopts a hybrid algorithm of an improved immune genetic algorithm and random simulation to solve, establishes a quantization index aiming at the accompanying unbalance risk in the opportunity constraint planning model, so as to provide reference for comprehensive energy system running scheduling for balancing economy and reliability, and has important guiding significance for actual scheduling of the system.
Description
Technical Field
The invention relates to the technical field of energy-saving power generation scheduling, in particular to a business park comprehensive energy system optimization scheduling method based on opportunity constraint planning.
Background
The traditional energy system is limited to single energy forms such as electricity, gas, heat, cold and the like, can not fully exert the complementary advantages among the energy sources, and solves the problems of energy utilization, environmental protection, renewable energy consumption and the like. Aiming at the problem, the concepts of the energy Internet and the comprehensive energy system are generated, the barriers among the energy subsystems can be broken, the complementation and the collaborative optimization of various energy sources are realized in the region and among the cross regions, and a new energy revolution is being promoted.
The regional comprehensive energy system facing the park is an energy production and supply marketing integrated system which is positioned on the load side of energy and can meet various energy consumption requirements of energy end users in a certain region, and the regional comprehensive energy system has been successfully applied in many European and American countries. The national energy agency in 2015 clearly indicates "enhanced energy interconnection, promotion of multi-energy fusion and system complementation", and approves construction and promotes the first 23 "multi-energy complementation integration optimization demonstration projects" in 2017. Compared with a single energy supply mode, the multi-energy coordination has higher efficiency and energy supply flexibility, but the complexity of an energy system is greatly increased, and higher requirements are put on optimal scheduling.
It is worth noting that the commercial park comprehensive energy system gathers more load side resources, the coupling between multiple energy sources is more serious, and the energy sources are easily influenced by the energy utilization rules of users. The existing research is too single on a refrigeration model, does not consider participation of complex cooling equipment such as ice storage air conditioners and the like, and is insufficient in utilization of flexible load resources in regional traffic networks. Meanwhile, the influence on the power generation planning caused by low penetration of new energy and low load prediction accuracy is increasingly remarkable, and the difficulty of system scheduling is increased. Therefore, there is an urgent need for a method for optimizing and scheduling a comprehensive energy system in a commercial park to solve the above problems.
Disclosure of Invention
The invention aims to provide an optimal scheduling method for a comprehensive energy system of a commercial park based on opportunistic constraint planning, which is used for solving the problems in the background technology.
In order to achieve the above purpose, the invention relates to a business park comprehensive energy system optimizing and scheduling method based on opportunity constraint planning, wherein the modeling of comprehensive energy system equipment is based on the characteristics of tight coupling of an intelligent power distribution network, a natural gas network, a heat supply/cold network and a traffic network in the business park, and each energy module in the park is mainly modeled from the aspects of energy sources, energy source conversion and storage, end user demands and the like;
the establishment of the business park comprehensive energy system optimization scheduling mathematical model based on opportunity constraint planning aims at optimizing the system operation total cost with the minimum as a target under a series of constraint conditions. Uncertainty factors such as photovoltaic output, load prediction deviation and the like are included in the model, and an opportunity constraint planning model taking the lowest running cost as an objective function is established;
the method is characterized in that the solution of the business park comprehensive energy system optimization scheduling mathematical model based on opportunity constraint planning is realized by considering the complexity of the model and the problem that an optimization algorithm is easy to fall into local optimum and efficiency, processing opportunity constraint conditions through a random simulation technology, and then utilizing an improved immune genetic algorithm to solve.
Preferably, the comprehensive energy system energy module mainly comprises photovoltaic power generation equipment, a combined cooling, heating and power system, an electric boiler, an ice storage air conditioner, a heat storage water tank, an electric automobile, a bus power exchange station and the like, and various energy modules are modeled as follows:
1) The combined heat and power system consists of a gas turbine, a waste heat boiler and an absorption refrigerator, the output cold, heat and electricity of the combined heat and power system have strong coupling relation, and a model can be expressed as follows:
wherein:output, electrical efficiency and rated power of the gas turbine at time t, respectively, < >> Respectively representing the heat power output by the co-production system, the heat consumed by the absorption refrigerator and the cold power output by the co-production system at the moment t; a. b, c and d are gas turbine correlation coefficients; η (eta) L 、η abs The heat loss coefficient and the absorption chiller efficiency, respectively.
2) The photovoltaic model is as follows:
wherein:for the photovoltaic power generation unit t moment giving out force, < >>For its rated power.
3) The electric boiler model is as follows:
wherein: η (eta) EH The conversion efficiency of the electric boiler;the electricity consumed by the electric boiler at the time t and the generated heat are respectively.
4) The ice cold-storage air conditioner model is as follows:
the daytime refrigeration mode, the constraint of the ice cold storage air conditioner is as follows:
the power constraint of the refrigerator, the electric power consumed by refrigeration and the working time constraint of the refrigerator are respectively;the refrigerating power at the moment t of the refrigerator; />For t moment refrigeration work sign->Indicating that the device is in a working state, otherwise, indicating that the device stops working; />The electric power consumed by refrigeration at the time t is represented; COP of E Is the refrigerating energy efficiency ratio of the unit; t (T) valley Refers to the electricity price valley period.
The ice making mode at night is characterized in that the ice storage air conditioner is constrained as follows:
the above are respectively the ice making amount of the refrigerator, the power consumption for making ice and the ice making working time constraint, and the ice making needs to be continuously carried out in the electricity price valley period, wherein:the power of ice making and the power of ice making consumption at the moment t are respectively; />For the ice making work mark at time t, < >>Indicating that the device is in an operating state, and otherwise, indicating that the device is out of operation.
Ice melting refrigeration constraint in daytime:
the above are respectively ice melting power constraint, ice melting time constraint and ice amount expression in the ice storage tank, wherein,for the ice melting power at time t, < > and>for the ice melting work mark at time t, < >>Indicating that the device is in a working state, otherwise, indicating that the device stops working; IS t+1 Representing the ice amount of the ice storage tank at time t+1, sigma i Is the self-loss coefficient of the ice storage tank, eta is And eta im The ice storage efficiency and the ice melting efficiency are obtained.
5) The model of the heat storage water tank is as follows:
in the method, in the process of the invention,the heat storage quantity in the water tank at the time t+1 is shown; η (eta) m 、η w The heat storage and release efficiency is achieved; />The heat release mark is that 1 represents heat release and 0 represents heat storage; />The maximum value of the heat storage and release power of the water tank at the time t is +.>When the water tank releases heat +.>For positive, heat accumulation->Negative;
6) The electric automobile model is as follows:
each EV power and charge variation can be expressed as:
wherein:for the power of the kth electric vehicle at time t, < >>The electric quantity of the kth electric automobile at the time t+1,and->The rated power of charging and discharging of the kth electric automobile is respectively; η (eta) c 、η d Is charge and discharge efficiency; />For the discharge sign of the kth electric vehicle at time t, < >>Indicating discharge and vice versa.
Its operation satisfies the following constraints:
the electric quantity constraint of the electric automobile, the time period constraint capable of being scheduled and the electric quantity requirement when the electric automobile leaves the garden area are respectively set; wherein the battery capacity is the minimumMaximum->Respectively taking 20% E bat And 90% E bat ，E bat The rated electric quantity of the battery of the electric automobile is obtained; />The time when the kth electric automobile arrives at the park and leaves the park is respectively; electric quantity of kth electric automobile leaving garden area +.>Needs to satisfy travel distance d klea Requirement d max The maximum endurance mileage of the electric automobile.
7) The electric bus power exchange station model is as follows:
the power and electric quantity expression of the electric bus battery replacement station is as follows:
wherein:is the power of the electric bus power exchange station at the moment t, < >>The electric quantity of the electric bus power exchange station at the time t+1 is the charging time quantity; p is p cBSS 、p dBSS Is the charge/discharge power of the charge potential; />Taking 1 as discharge and 0 as charge for a discharge mark of an ith charge potential at a t moment; />Charging/discharging efficiency, respectively; n (N) c 、N s,t The number of the charging bits and the number of the battery replacement at the time t are respectively; />Replacing the residual electric quantity value of the old battery for the time t; />The capacity of each battery newly replaced at the moment t; it runs under the following constraints:
the above respectively represent the power constraint and the electric quantity constraint of the power exchange station, and the minimum electric quantityThe battery replacement requirement at the moment and the minimum electric quantity of other batteries are required to be met, and the SOC of the new battery to be replaced is set to be 0.9; maximum electric quantity->Taking the total electric quantity when the SOC is 0.9 for all the batteries, E bss The SOC of each battery is between 0.2 and 0.9 for the rated electric quantity of each battery; n (N) z Is the total number of cells.
Preferably, the system optimization scheduling mathematical model includes an objective function, constraints, and an opportunistic constraint planning model. The model aims at minimizing the cost of the system in a scheduling period, and comprises the gas purchase cost, the electricity purchase cost and the compensation cost, and the objective function is expressed as follows:
wherein:the gas purchase cost, the electricity purchase cost and the compensation cost are respectively at the moment t; c (C) gas 、CV gas Is the price of natural gas and the heat value thereof; />The interaction power of the system and the power grid at the moment t is represented, wherein positive values represent electricity buying, and negative values represent electricity selling; />The electricity purchase and selling prices at the moment t are respectively; η (eta) b For buying electricity, 1 represents buying electricity, and 0 represents selling electricity; />The method is to compensate the ice-melting and cooling of the ice-storage air conditioner, the discharge of the electric automobile and the power exchange station, N E Is the number of electric automobiles lambda 1 For the cold compensation coefficient, the electric compensation coefficient is related to the electricity price at time t, which is taken as +.>
Preferably, considering uncertainty of photovoltaic and cold, hot and electric loads, photovoltaic output and load demands in M scenes are obtained through Latin hypercube sampling, and load loss risks in different scenes are quantified. In a certain scene, when the unit output is greater than the load demand, the load loss is 0; conversely, when the supply is not in demand, the power-loss load electricity in the ith scene at the time t is expressed as:
wherein:desired electrical load and desired photovoltaic output at time t, respectively,/->The method comprises the steps of predicting an electrical load prediction error and a photovoltaic output prediction error in an ith scene at a moment t;
the system loss load at time t is the average of M scenes and can be expressed as:
note that the thermal and cold load losses are similar to the electrical load losses, and are average losses in M scenarios, and are not listed here in any way. The risk costs faced by a business park during a dispatch period can be expressed as:
wherein: QNS t 、CNS t The heat load loss and the cold load loss at the time t are respectively, C er,t 、C qr,t 、C cr,t The unit cost corresponding to the power failure, heat and cold load of the system at the moment t is obtained.
Preferably, the model constraint conditions include, in addition to equipment operation constraints, power balance, reliability, interaction constraints with a power grid, and the like:
1) Power balance constraint
Wherein:and the requirements of electricity, heat and cold load at the moment t are respectively.
2) Interaction constraint with power grid
Wherein:for the power interaction between the system and the power grid at time t, < >>Is the maximum interaction power;
3) Electrical, thermal, cold reliability constraints of the system:
wherein: beta is the confidence level, set to 0.95.
Preferably, because the model decision variables are more, the relationships among the variables are complex and mutually coupled, the opportunity constraint conditions can be processed only through a random simulation technology, and then the improved immune genetic algorithm is utilized to solve the business park comprehensive energy system optimization scheduling model based on the opportunity constraint planning.
For a given set of decision variables, stochastic simulation is used to verify whether the opportunity constraints are met. For time t, a pre-counter N' =0; then generating random quantity by Latin hypercube simulation And substituted into the left of the reliability constraint inequality along with the decision variables, if the inequality is true, N '=n' +1, and so on, repeated M times. If M is large enough, according to the big number theorem, if and only if N'/M is larger than or equal to beta, the reliability constraint is established, a group of decision variables at the moment are feasible solutions, and then the optimal solution is selected from the feasible solutions.
The flow of the immune vaccine algorithm is as follows:
(1) The optimization objective and its constraint are analyzed to determine the appropriate coding form, which is herein real-number coding.
(2) Under the condition of meeting the output constraint of the unit and the transaction constraint with the power grid, N antibodies are randomly generated, and m antibodies are extracted from a memory bank to form an initial population.
(3) The expected breeding rate of the population antibody is evaluated, and compared with the adaptability evaluation of the traditional immune genetic algorithm, the index encourages the antibody with high adaptability (low scheduling cost), inhibits the antibody with high concentration (high similarity) and ensures the antibody diversity.
(4) The population is sorted in descending order according to the expected reproduction rate, the first N excellent antibodies are taken to form a parent population, and the first m elite antibodies are stored in a memory bank.
(5) Judging whether an ending condition is met, and ending if yes; and otherwise, carrying out the next operation.
(6) And (3) carrying out selection, crossing and mutation operation on the antibody based on the result of the step (4) to obtain a mutated population.
(7) And (3) evaluating the average expected reproduction rate of the mutated population, carrying out population segmentation operation on the population which does not reach the preset value, sequencing the antibodies according to the expected reproduction rate, carrying out mutation operation again on the antibodies which are lower than the average expected reproduction rate of the population, and merging the antibodies subjected to the mutation operation and the antibodies which do not carry out the mutation operation into a new population.
(8) And (3) executing the step (3) until the result is output.
The business park comprehensive energy system optimization scheduling method based on opportunity constraint planning has the following benefits:
(1) The electric automobile and the bus power exchange station can be charged in a plurality of electricity price valley time periods and discharged in a peak time period as much as possible, and the charging and discharging strategy can improve the economy and flexibility of dispatching on the premise of not influencing the travelling comfort of users, so that a huge space is provided for the development of flexible resources of an electric power system.
(2) Through the optimal control of the ice storage air conditioner, the running cost of the system can be reduced, and the demand response potential of the system is improved. The ice cold-storage air conditioner can play a role in peak clipping and valley filling on the power grid side, and can improve electricity economy on the user side.
(3) The confidence level affects both the running cost of the system and its risk level. The smaller β represents a lower requirement for reliability constraints, at which time the running cost is reduced. But the risk increases with less control over uncertainty factors. A suitable beta can be provided herein that combines reliability and economy requirements.
Description of the drawings:
FIG. 1 is a schematic diagram of a commercial campus integrated energy system architecture based on opportunistic constraint planning.
Fig. 2 is a plot of expected values of photovoltaic output and thermal-electrical load in a business park comprehensive energy system optimization scheduling method based on opportunity constraint planning.
FIG. 3 is a flow chart of a business park comprehensive energy system optimization scheduling method based on opportunity constraint planning.
Fig. 4 is a graph of scheduling results of electric vehicles and power exchange stations in two modes in a business park comprehensive energy system optimization scheduling method based on opportunity constraint planning.
Fig. 5 is a graph of power interaction with a power grid in two modes in a business park comprehensive energy system optimization scheduling method based on opportunity constraint planning.
Fig. 6 is a graph of the results of scheduling electric, thermal and cold loads in a business park comprehensive energy system optimization scheduling method based on opportunity constraint planning.
The specific embodiment is as follows:
in order to make the objects, technical solutions and advantages of the present invention become more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of the invention. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
According to the business park comprehensive energy system scheduling method based on the opportunity constraint planning, the business park comprehensive energy system optimization scheduling method comprises modeling of comprehensive energy system equipment, establishment of an optimization scheduling mathematical model and solving of the optimization scheduling mathematical model. The equipment modeling of the business park comprehensive energy system based on the opportunity constraint planning combines the characteristics of close coupling of a business park intelligent power distribution network, a natural gas network, a heat supply/cooling network and a traffic network, and models each energy module of the park mainly from the aspects of energy sources, energy conversion and storage, end user demands and the like;
and establishing an optimal scheduling model of the comprehensive energy system of the commercial park based on the opportunity constraint planning, wherein the optimal scheduling model is optimized by taking the minimum total cost of system operation as a target under a series of constraint conditions. In addition, uncertainty factors such as photovoltaic output, load prediction deviation and the like are included in the model, and an opportunity constraint planning model taking the lowest running cost of the comprehensive energy system as an objective function is established;
the method is characterized in that the optimization scheduling mathematical model of the business park comprehensive energy system based on opportunity constraint planning is solved, decision variables of the optimization scheduling model are more, relations among the variables are complex and mutually coupled, the complexity of the model and the probability that an optimization algorithm is trapped in a local optimal and efficient problem are considered, opportunity constraint conditions are processed through a random simulation technology, and then the improved immune genetic algorithm is utilized for solving.
The business park comprehensive energy system optimization scheduling method based on opportunity constraint planning aims at achieving the aim of minimizing the total cost of the system, comprehensively considers the risk and economy in scheduling, and performs peak clipping and valley filling on a power grid load curve.
As shown in fig. 1, the device in the optimization scheduling of the comprehensive energy system of the commercial park based on the opportunity constraint planning mainly comprises a photovoltaic power generation system, a combined cooling, heating and power system, an electric boiler, an ice cold storage air conditioner, a heat storage water tank, an electric automobile, a bus power exchange station and the like, and various energy modules are modeled as follows: .
1) The combined heat and power system consists of a gas turbine, a waste heat boiler and an absorption refrigerator, the output cold, heat and electricity of the combined heat and power system have strong coupling relation, and a model can be expressed as follows:
wherein:output, electrical efficiency and rated power of the gas turbine at time t, respectively, < >> Respectively represent the thermal power output by the co-production system at the time tThe heat consumed by the absorption refrigerator and the cold power output by the co-production system; a. b, c and d are gas turbine correlation coefficients; η (eta) L 、η abs The heat loss coefficient and the absorption chiller efficiency, respectively.
2) The photovoltaic model is as follows:
wherein:for the photovoltaic power generation unit t moment giving out force, < >>For its rated power.
3) The electric boiler model is as follows:
wherein: η (eta) EH The conversion efficiency of the electric boiler;the electricity consumed by the electric boiler at the time t and the generated heat are respectively.
4) The ice cold-storage air conditioner model is as follows:
the daytime refrigeration mode, the constraint of the ice cold storage air conditioner is as follows:
the power constraint of the refrigerator, the electric power consumed by refrigeration and the working time constraint of the refrigerator are respectively;the refrigerating power at the moment t of the refrigerator; />The refrigeration working mark is the refrigeration working mark at the moment t,/>indicating that the device is in a working state, otherwise, indicating that the device stops working; />The electric power consumed by refrigeration at the time t is represented; COP of E Is the refrigerating energy efficiency ratio of the unit; t (T) valley Refers to the electricity price valley period.
The ice making mode at night is characterized in that the ice storage air conditioner is constrained as follows:
the above are respectively the ice making amount of the refrigerator, the power consumption for making ice and the ice making working time constraint, and the ice making needs to be continuously carried out in the electricity price valley period, wherein:the power of ice making and the power of ice making consumption at the moment t are respectively; />For the ice making work mark at time t, < >>Indicating that the device is in an operating state, and otherwise, indicating that the device is out of operation.
Ice melting refrigeration constraint in daytime:
the above are respectively ice melting power constraint, ice melting time constraint and ice amount expression in the ice storage tank, wherein,for the ice melting power at time t, < > and>for the ice melting work mark at time t, < >>Indicating that the device is in a working state, otherwise, indicating that the device stops working; IS t+1 Representing the ice amount of the ice storage tank at time t+1, sigma i Is the self-loss coefficient of the ice storage tank, eta is And eta im The ice storage efficiency and the ice melting efficiency are obtained.
5) The model of the heat storage water tank is as follows:
in the method, in the process of the invention,the heat storage quantity in the water tank at the time t+1 is shown; η (eta) m 、η w The heat storage and release efficiency is achieved; />The heat release mark is that 1 represents heat release and 0 represents heat storage; />The maximum value of the heat storage and release power of the water tank at the time t is +.>When the water tank releases heat +.>For positive, heat accumulation->Is negative.
6) The electric automobile model is as follows:
each EV power and charge variation can be expressed as:
wherein:for the power of the kth electric vehicle at time t, < >>The electric quantity of the kth electric automobile at the time t+1,and->The rated power of charging and discharging of the kth electric automobile is respectively; η (eta) c 、η d Is charge and discharge efficiency; />For the discharge sign of the kth electric vehicle at time t, < >>Indicating discharge and vice versa.
Its operation satisfies the following constraints:
the electric quantity constraint of the electric automobile, the time period constraint capable of being scheduled and the electric quantity requirement when the electric automobile leaves the garden area are respectively set; wherein the battery capacity is the minimumMaximum->Respectively taking 20% E bat And 90% E bat ，E bat The rated electric quantity of the battery of the electric automobile is obtained; />The time when the kth electric automobile arrives at the park and leaves the park is respectively; electric quantity of kth electric automobile leaving garden area +.>Needs to meet travel distanced klea Requirement d max The maximum endurance mileage of the electric automobile.
7) The electric bus power exchange station model is as follows:
the power and electric quantity expression of the electric bus battery replacement station is as follows:
wherein:is the power of the electric bus power exchange station at the moment t, < >>The electric quantity of the electric bus power exchange station at the time t+1 is the charging time quantity; p is p cBSS 、p dBSS Is the charge/discharge power of the charge potential; />Taking 1 as discharge and 0 as charge for a discharge mark of an ith charge potential at a t moment; />Charging/discharging efficiency, respectively; n (N) c 、N s,t The number of the charging bits and the number of the battery replacement at the time t are respectively; />Replacing the residual electric quantity value of the old battery for the time t; />The capacity of each battery newly replaced at the moment t; it runs under the following constraints:
the above respectively represent the power constraint and the electric quantity constraint of the power exchange station, and the minimum electric quantityThe battery replacement requirement at the moment and the minimum electric quantity of other batteries are required to be met, and the SOC of the new battery to be replaced is set to be 0.9; maximum electric quantity->Taking the total electric quantity when the SOC is 0.9 for all the batteries, E bss The SOC of each battery is between 0.2 and 0.9 for the rated electric quantity of each battery; n (N) z Is the total number of cells.
As shown in fig. 2, expected curves of cold, heat, electric load and photovoltaic output required by the commercial park are obtained by predicting deviation and photovoltaic output deviation through Latin hypercube simulation, and the deviation is overlapped with corresponding expected values, namely actual cold, heat, electric load and photovoltaic output.
The optimal scheduling model aims at minimizing the cost of the system in a scheduling period, and comprises the gas purchase cost, the electricity purchase cost and the compensation cost, and the objective function is expressed as follows:
wherein:the gas purchase cost, the electricity purchase cost and the compensation cost are respectively at the moment t; c (C) gas 、CV gas Is the price of natural gas and the heat value thereof; />The interaction power of the system and the power grid at the moment t is represented, wherein positive values represent electricity buying, and negative values represent electricity selling; />The electricity purchase and selling prices at the moment t are respectively; η (eta) b For buying electricity, 1 represents buying electricity, and 0 represents selling electricity; />The method is to compensate the ice-melting and cooling of the ice-storage air conditioner, the discharge of the electric automobile and the power exchange station, N E Is the number of electric automobiles lambda 1 For the cold compensation coefficient, the electric compensation coefficient is related to the electricity price at time t, which is taken as +.>
And taking uncertainty of photovoltaic and cold, hot and electric loads into consideration, obtaining photovoltaic output and load demands under M scenes through Latin hypercube sampling, and quantifying the load loss risks under different scenes. In a certain scene, when the unit output is greater than the load demand, the load loss is 0; conversely, when the supply is not in demand, the power-loss load electricity in the ith scene at the time t can be expressed as:
wherein:desired electrical load and desired photovoltaic output at time t, respectively,/->The method comprises the steps of predicting an electrical load prediction error and a photovoltaic output prediction error in an ith scene at a moment t;
the system loss load at time t is the average of M scenes and can be expressed as:
note that the thermal and cold load losses are similar to the electrical load losses, and are average losses in M scenarios, and are not listed here in any way. The risk costs faced by a business park during a dispatch period can be expressed as:
wherein: QNS t 、CNS t The heat load loss and the cold load loss at the time t are respectively, C er,t 、C qr,t 、C cr,t The unit cost corresponding to the power failure, heat and cold load of the system at the moment t is obtained.
Besides equipment operation constraint, the model constraint conditions also comprise power balance, reliability, interaction constraint with a power grid and the like:
1) Power balance constraint
Wherein:and the requirements of electricity, heat and cold load at the moment t are respectively.
2) Interaction constraint with power grid
Wherein:for the power interaction between the system and the power grid at time t, < >>Is the maximum interaction power;
3) Electrical, thermal, cold reliability constraints of the system:
wherein: beta is the confidence level, set to 0.95.
The model is difficult to convert into deterministic model solving, so that opportunity constraint conditions are processed through a stochastic simulation technology, and then an improved immune genetic algorithm is utilized to solve a business park comprehensive energy system optimization scheduling model based on opportunity constraint planning.
For a given set of decision variables, stochastic simulation is used to verify whether the opportunity constraints are met. For period t, a pre-counter N' =0; then generating random quantity by Latin hypercube simulation And substituted into the left of the reliability constraint inequality along with the decision variables, if the inequality is true, N '=n' +1, and so on, repeated M times. If M is large enough, according to the big theorem, if and only if N'/M is larger than or equal to beta, the reliability constraint is established, a group of decision variables at the moment are feasible solutions, and then an optimal solution is selected from the feasible solutions by utilizing an improved immune genetic algorithm.
As shown in fig. 3, the flow of the immune vaccine algorithm is as follows:
(1) The optimization objective and its constraint are analyzed to determine the appropriate coding form, which is herein real-number coding.
(2) Under the condition of meeting the output constraint of the unit and the transaction constraint with the power grid, N antibodies are randomly generated, and m antibodies are extracted from a memory bank to form an initial population.
(3) The expected breeding rate of the population antibody is evaluated, and compared with the adaptability evaluation of the traditional immune genetic algorithm, the index encourages the antibody with high adaptability (low scheduling cost), inhibits the antibody with high concentration (high similarity) and ensures the antibody diversity.
(4) The population is sorted in descending order according to the expected reproduction rate, the first N excellent antibodies are taken to form a parent population, and the first m elite antibodies are stored in a memory bank.
(5) Judging whether an ending condition is met, and ending if yes; and otherwise, carrying out the next operation.
(6) And (3) carrying out selection, crossing and mutation operation on the antibody based on the result of the step (4) to obtain a mutated population.
(7) And (3) evaluating the average expected reproduction rate of the mutated population, carrying out population segmentation operation on the population which does not reach the preset value, sequencing the antibodies according to the expected reproduction rate, carrying out mutation operation again on the antibodies which are lower than the average expected reproduction rate of the population, and merging the antibodies subjected to the mutation operation and the antibodies which do not carry out the mutation operation into a new population.
(8) And (3) executing the step (3) until the result is output.
In one embodiment, the commercial park consists of 1 3000KW gas turbine, 1 1300KW photovoltaic installation, 1 3000KWh hot water storage tank, 1 1000KW electric boiler, 1 ice storage air conditioner, 1 bus battery exchange station and 100 electric cars of the same model. The values of the gas turbine parameters a, b, c, d are-0.3144, 0.076554, 0.4285 and 0.09122 respectively; suppose that the number of battery changes during night time (1-6, 23-24 hours) obeys a normal distribution with a mean of 2.5 and a standard deviation of 1, i.e., N s ～N(2.5,1 2 ) The power change requirement in daytime is high, and N is satisfied s ～N(5,1.5 2 ) The SOC of the battery under change is [0.25,0.35 ]]The inner parts are uniformly distributed; time of EV arrival at parkSOC at arrival [0.2,0.4]The inner parts are uniformly distributed; leave time +.> And distance d of going out from work klea Obeys the log-normal distribution and meets lnd klea ～N(3.65,0.4 2 )，d max 80km; the number of power exchanging requirements per hour of a power exchanging station, the SOC of each battery under exchanging, the time and SOC value of each EV reaching a park, the time of leaving the park and the travel distance from work are all obtained by Monte Carlo simulation, the maximum interaction power of the system and a power grid is 800KW, and the related energy prices are shown in a table 1; the standard deviation of the predicted deviation of the photovoltaic output and the cold-hot electrical load is 0.06; the device parameters are shown in tables 2 and 3.
TABLE 1 general Industrial energy prices in Beijing area
Wherein, the peak time period is (11:00-15:00; 19:00-21:00), the usual time period is (8:00-10:00; 16:00-18:00; 22:00-23:00), the valley time period is (24:00-7:00), and the selling price is 0.8 times of the buying price; the natural gas calorific value is 9.5KWh/m 3 。
Table 2 device parameters
/>
TABLE 3 energy storage system device parameters
Solving by adopting an improved immune genetic algorithm to obtain a decision variable To explore the effect of different Demand Responses (DR) on the optimization results in the campus, a DR model was constructed as follows: the electric automobile and the power exchange station are stimulated to orderly charge and discharge according to the time-of-use electricity price, and the ice storage air conditioner bears part of cold load. And comparing and analyzing the result of the non-started DR (the electric automobile and the power exchange station are in disordered charge and discharge and the ice storage air conditioner does not work). Fig. 4 shows the dispatching results of the electric automobile and the power exchange station in different modes, and fig. 5 shows the interaction results with the power grid in different modes. The scheduling results of electric, thermal and cold loads in the DR participation mode are shown in FIG. 6.
In fig. 4, positive values of electric vehicle and battery-exchange station power represent discharge, and negative values represent charge. It can be seen that under the time-of-use electricity price mechanism and compensation excitation, the charge and discharge of the time-of-use electricity price mechanism and the compensation excitation are optimized. In the DR-free mode, the electric automobile starts to charge after reaching the park and stops after reaching the electric quantity required by traveling, so that the charging power is concentrated at 7-10, and is always in an idle state at the later time, and the unordered charging and discharging limit the space of the electric automobile for participating in the flexible adjustment of the electric power system. And in the DR mode, the electric automobile is charged more when the electricity price is low on the basis of meeting the travel rule of the user, and the surplus electric quantity is discharged when the electricity price is high, so long as the electric quantity reaches the required electric quantity when leaving the park. Similarly, under the condition of no DR, the electric power of the power exchange station is the power exchange demand power at the moment, and in the DR mode, the power exchange station can also orderly charge and discharge according to the time-of-use electricity price, and the load characteristic of the power grid is improved on the premise of meeting the power exchange demand of the next hour.
Fig. 5 is a graph showing the interaction amount of the system and the power grid, wherein positive values represent electricity buying and negative values represent electricity selling. The total interaction quantity of the system and the power grid in one day is basically consistent in the two modes, and the electric automobile and the power exchange station load in the DR mode are shifted due to electricity price excitation, so that the buying electricity quantity is changed based on the electricity price. The main expression is as follows: in the DR mode, due to the orderly charge and discharge regulation of the electric automobile and the power exchange station, the system realizes the conversion from electricity buying to electricity selling in the electricity price peak period, and the total electricity buying amount in the valley period is increased. The peak clipping and valley filling functions are achieved on the power grid load curve. Table 4 compares the operating costs in the two modes, and it can be seen that the DR mode not only improves the load curve, but also improves the scheduling economy.
Table 4 cost of operation in two modes
Fig. 6 is a scheduling result of an electric load, a thermal load, and a cooling load in a DR mode. The gas turbine power output in electrical load scheduling is at a higher level, bearing most of the electrical load demand. The electric automobile and the bus power exchange station are used as mobile energy storage, so that the economy and flexibility of system scheduling are improved on the premise of not affecting the travelling comfort of users, and a huge space is provided for the development of flexible resources of an electric power system. The waste heat of the gas turbine in the heat load scheduling bears most of heat load demands, the heat storage water tank can store heat when the heat is excessive, and the heat is released when the heat is insufficient, so that a good heat translation effect is achieved. The electric heating and heat storage improve the thermoelectric decoupling capacity of the gas turbine, and the supply of heat load can be realized more flexibly. The absorption refrigerator in the cold load scheduling bears most of the cold load demand, the ice storage air conditioner is in an ice making state at the time of 24-7 days, the cold energy is stored in the form of ice, and the ice melting is carried out for cooling at the time of cold load peak and higher electricity price (mainly 11-15 times), so that the air conditioner load at the electricity consumption peak is reduced. At 24, the ice volume of the ice bank was 324KWh on average with the initial ice volume.
To investigate the influence of confidence level on the dispatching cost of the comprehensive energy system and the risk level of the system, a risk penalty coefficient C is adopted er,t 、C qr,t 、C cr,t Are respectively set as1.3, 2, the confidence level is reduced from 1 to 0.8 in sequence with the amplitude of 0.05, the running cost and risk of the system under different confidence levels are solved, and the results are shown in table 5.
Table 5 system scheduling costs and risk at different confidence levels
As can be seen from table 5: the economic dispatching cost of the comprehensive energy system is reduced along with the reduction of the confidence level beta, the risk cost is increased along with the reduction of beta, and the total cost after risk is considered shows a trend of descending and then ascending. It can be seen that the low cost and low risk of the system cannot be achieved under the condition of opportunistic constraint planning. In a sense, 1- β is the maximum load shedding probability allowed by the system. Therefore, a proper beta needs to be selected, so that the system is reasonably balanced between economy and risk. In table 2, when β=0.9, the cost after accounting for risk is lowest, and the economy and reliability after trade-off are best.
It is to be understood that the above examples of the present invention are provided by way of illustration only and are not intended to limit the scope of the invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While remaining within the scope of the invention, obvious variations or modifications are incorporated by reference herein.
Claims (5)
1. The utility model provides a business park comprehensive energy system optimal scheduling method based on opportunity constraint planning, which is characterized by comprising the following steps:
step 1, modeling of comprehensive energy system equipment: the intelligent power distribution network, the natural gas network, the heat supply/cold supply network and the traffic network are tightly coupled, and modeling is carried out on each energy system device in the park in terms of sources, conversion and storage of energy and requirements of end users; the comprehensive energy system equipment comprises photovoltaic power generation equipment, a combined cooling, heating and power system, an electric boiler, an ice cold storage air conditioner, a heat storage water tank, an electric automobile and bus power exchange station equipment;
the modeling of the comprehensive energy system equipment is as follows:
1) The combined heat and power system consists of a gas turbine, a waste heat boiler and an absorption refrigerator, and the output cold, heat and electricity energies have a coupling relation, and the model is expressed as follows:
wherein:the output, the electric efficiency and the rated power of the gas turbine at the time t are respectively,respectively representing the heat power output by the co-production system, the heat consumed by the absorption refrigerator and the cold power output by the co-production system at the moment t; a. b, c and d are gas turbine correlation coefficients; η (eta) L 、η abs The heat loss coefficient and the absorption refrigerator efficiency are respectively;
2) The model of the photovoltaic power generation apparatus is as follows:
wherein:for the photovoltaic power generation unit t moment giving out force, < >>Rated for its power;
3) The electric boiler model is as follows:
wherein: η (eta) EH The conversion efficiency of the electric boiler;the electricity consumed by the electric boiler at the time t and the generated heat are respectively;
4) The ice cold-storage air conditioner model is as follows:
the daytime refrigeration mode, the constraint of the ice cold storage air conditioner is as follows:
the power constraint of the refrigerator, the electric power consumed by refrigeration and the working time constraint of the refrigerator are respectively;the refrigerating power at the moment t of the refrigerator; />For t moment refrigeration work sign->Indicating that the device is in a working state, otherwise, indicating that the device stops working; />The electric power consumed by refrigeration at the time t is represented; COP of E Is the refrigerating energy efficiency ratio of the unit; t (T) valley Refers to electricity price valley periods;
the ice making mode at night is characterized in that the ice storage air conditioner is constrained as follows:
the above are respectively the ice making amount of the refrigerating machine, the power consumption for making ice and the ice making working time constraint, and the ice making is neededIs to be continuously carried out in the electricity price valley period, wherein:the power of ice making and the power of ice making consumption at the moment t are respectively; />For the ice making work mark at time t, < >>Indicating that the device is in a working state, otherwise, indicating that the device stops working;
ice melting refrigeration constraint in daytime:
the above are respectively ice melting power constraint, ice melting time constraint and ice amount expression in the ice storage tank, wherein,for the ice melting power at time t, < > and>for the ice melting work mark at time t, < >>Indicating that the device is in a working state, otherwise, indicating that the device stops working; IS t+1 Representing the ice amount of the ice storage tank at time t+1, sigma i Is the self-loss coefficient of the ice storage tank, eta is And eta im The ice storage efficiency and the ice melting efficiency are;
5) The model of the heat storage water tank is as follows:
in the method, in the process of the invention,the heat storage quantity in the water tank at the time t+1; η (eta) m 、η w The heat storage and release efficiency is achieved; />The heat release mark is that 1 represents heat release and 0 represents heat storage; />The maximum value of the heat storage and release power of the water tank at the time t is +.>When the water tank releases heat +.>For positive, heat accumulation->Negative;
6) The electric automobile model is as follows:
the power and charge variation of each electric car can be expressed as:
wherein:for the power of the kth electric vehicle at time t, < >>For the electric quantity of the kth electric automobile at the time t+1, < >>And->The rated power of charging and discharging of the kth electric automobile is respectively; η (eta) c 、η d Is charge and discharge efficiency; />For the discharge sign of the kth electric vehicle at time t, < >>Indicating discharge, and conversely, charging;
its operation satisfies the following constraints:
the electric quantity constraint of the electric automobile, the time period constraint capable of being scheduled and the electric quantity requirement when the electric automobile leaves the garden area are respectively set; wherein the battery capacity is the minimumMaximum->Respectively taking 20% E bat And 90% E bat ，E bat The rated electric quantity of the battery of the electric automobile is obtained; /> The time when the kth electric automobile arrives at the park and leaves the park is respectively; electric quantity of kth electric automobile leaving garden area +.>Needs to satisfy travel distance d klea Requirement d max The maximum endurance mileage of the electric automobile is obtained;
7) The electric bus power exchange station model is as follows:
the power and electric quantity expression of the electric bus battery replacement station is as follows:
wherein:is the power of the electric bus power exchange station at the moment t, < >>The electric quantity of the electric bus power exchange station at the time t+1 is the charging time quantity; p is p cBSS 、p dBSS Is the charge/discharge power of the charge potential; />Taking 1 as discharge and 0 as charge for a discharge mark of an ith charge potential at a t moment; />Charging/discharging efficiency, respectively; n (N) c 、N s,t The number of the charging bits and the number of the battery replacement at the time t are respectively; />Replacing the residual electric quantity value of the old battery for the time t; />The capacity of each battery newly replaced at the moment t; the following constraints need to be satisfied during operation:
the above respectively represent the power constraint and the electric quantity constraint of the power exchange station, and the minimum electric quantityThe battery replacement requirement at the moment and the minimum electric quantity of other batteries are required to be met, and the SOC of the new battery to be replaced is set to be 0.9; maximum electric quantity->Taking the total electric quantity when the SOC is 0.9 for all the batteries, E bss The SOC of each battery is between 0.2 and 0.9 for the rated electric quantity of each battery; n (N) z Is the total number of cells;
step 2, establishing an optimized dispatching mathematical model: the optimal scheduling model is optimized by taking the lowest total cost of system operation as a target; the method comprises the steps of incorporating photovoltaic output and load prediction deviation uncertainty factors into a mathematical model, and establishing an opportunity constraint planning model taking the lowest running cost of a comprehensive energy system as an objective function; the system optimization scheduling mathematical model comprises an objective function, constraint conditions and an opportunity constraint planning model; the model aims at minimizing the cost of the system in a scheduling period, and comprises the gas purchase cost, the electricity purchase cost and the compensation cost, and the objective function is expressed as follows:
wherein:the gas purchase cost, the electricity purchase cost and the compensation cost are respectively at the moment t; c (C) gas 、CV gas Is the price of natural gas and the heat value thereof; />The interaction power of the system and the power grid at the moment t is represented, wherein positive values represent electricity buying, and negative values represent electricity selling;the electricity purchase and selling prices at the moment t are respectively; η (eta) b For buying electricity, 1 represents buying electricity, and 0 represents selling electricity; />The method is to compensate the ice-melting and cooling of the ice-storage air conditioner, the discharge of the electric automobile and the power exchange station, N E Is the number of electric automobiles lambda 1 For the cold compensation coefficient, the electric compensation coefficient is related to the electricity price at time t, which is taken as +.>
Step 3, solving an optimized dispatching mathematical model: the optimal scheduling model has a plurality of decision variables, and the variables are mutually coupled; the opportunity constraint conditions are processed through a random simulation technology, and then the improved immune genetic algorithm is utilized to solve; the opportunity constraint condition is processed through a random simulation technology, and then an improved immune genetic algorithm is utilized to solve a business park comprehensive energy system optimization scheduling model based on opportunity constraint planning; for a given set of decision variables, stochastic simulation is used to verify whether the opportunity constraints are met: for time t, a pre-counter N' =0; then generating random quantity by Latin hypercube simulationAnd substituted into the left of the reliability constraint inequality along with the decision variables, if the inequality is true, N '=n' +1, and so on, repeated M times. If M is large enough, a set of decision variables at this time are established according to the big-number theorem if and only if N'/M is greater than or equal to betaThe feasible solution is then selected as the optimal solution.
2. The business park comprehensive energy system optimization scheduling method based on opportunity constraint planning according to claim 1, wherein the method comprises the following steps: the energy terminal requirements include cold, heat, and electrical loads required by the business park.
3. The business park comprehensive energy system optimization scheduling method based on opportunity constraint planning according to claim 1, wherein the method comprises the following steps: and taking uncertainty of photovoltaic and cold, hot and electric loads into consideration, obtaining photovoltaic output and load demands under M scenes through Latin hypercube sampling, and quantifying the load loss risks under different scenes. In a certain scene, when the unit output is greater than the load demand, the load loss is 0; conversely, when there is a supply or a shortage, the amount of load loss in the ith scene at time t is expressed as:
wherein:desired electrical load and desired photovoltaic output at time t, respectively,/->The method comprises the steps of predicting an electrical load prediction error and a photovoltaic output prediction error in an ith scene at a moment t;
the system loss load at time t is the average of M scenes and is expressed as:
the risk costs faced by a business park during a dispatch period are expressed as:
wherein: QNS t 、CNS t The heat load loss and the cold load loss at the time t are respectively, C er,t 、C qr,t 、C cr,t The unit cost corresponding to the power failure, heat and cold load of the system at the moment t is obtained.
4. The business park comprehensive energy system optimization scheduling method based on opportunity constraint planning according to claim 1, wherein the method comprises the following steps: besides equipment operation constraint, the model constraint conditions also comprise power balance, reliability and interaction constraint with a power grid:
1) Power balance constraint
Wherein:the requirements of electricity, heat and cold load at the moment t are respectively;
2) Interaction constraint with power grid
Wherein:for the power interaction between the system and the power grid at time t, < >>Is the maximum interaction power;
3) Electrical, thermal, cold reliability constraints of the system:
wherein: beta is the confidence level, set to 0.95.
5. The business park comprehensive energy system optimization scheduling method based on opportunity constraint planning according to claim 1, wherein the method comprises the following steps: solving an optimal solution of the optimal scheduling model by adopting an improved immune vaccine algorithm, wherein the flow of the immune genetic algorithm is as follows:
(1) Analyzing the optimization target and the constraint condition expression thereof, determining a proper coding form, and adopting real number coding;
(2) Under the condition that the output constraint of the unit and the transaction constraint condition with a power grid are met, randomly generating N antibodies and extracting m antibodies from a memory bank to form an initial population;
(3) The expected breeding rate of the population antibodies is evaluated, compared with the adaptability evaluation of the traditional immune genetic algorithm, the index encourages the antibodies with high adaptability, inhibits the antibodies with high concentration, ensures the diversity of the antibodies, and has high adaptability, namely high scheduling cost and high concentration, namely high similarity;
(4) Sequencing the population in a descending order according to the expected reproduction rate, taking the first N excellent antibodies to form a parent population, and storing the first m elite antibodies into a memory bank;
(5) Judging whether an ending condition is met, and ending if yes; otherwise, performing the next operation;
(6) Selecting, crossing and mutating the antibody based on the result of the step (4) to obtain a mutated population;
(7) The average expected breeding rate of the mutated population is evaluated, population segmentation operation is carried out on the population which does not reach the preset value, antibodies are ordered according to the expected breeding rate, mutation operation is carried out on the antibodies which are lower than the average expected breeding rate of the population again, and the antibodies after the mutation operation and the antibodies which do not carry out the mutation operation are combined into a new population;
(8) And (3) executing the step (3) until the result is output.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910723934.0A CN110533225B (en) | 2019-08-07 | 2019-08-07 | Business park comprehensive energy system optimal scheduling method based on opportunity constraint planning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910723934.0A CN110533225B (en) | 2019-08-07 | 2019-08-07 | Business park comprehensive energy system optimal scheduling method based on opportunity constraint planning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110533225A CN110533225A (en) | 2019-12-03 |
| CN110533225B true CN110533225B (en) | 2023-10-20 |
Family
ID=68660476
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910723934.0A Active CN110533225B (en) | 2019-08-07 | 2019-08-07 | Business park comprehensive energy system optimal scheduling method based on opportunity constraint planning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110533225B (en) |
Families Citing this family (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111191835B (en) * | 2019-12-27 | 2023-06-23 | 国网辽宁省电力有限公司阜新供电公司 | IES incomplete data load prediction method and system based on C-GAN migration learning |
| CN110943487B (en) * | 2019-12-31 | 2021-07-27 | 四川大学 | Energy optimization method and device for park energy system |
| CN111178636A (en) * | 2019-12-31 | 2020-05-19 | 新奥数能科技有限公司 | Comprehensive energy system optimization method and device considering new energy uncertainty |
| CN111815068B (en) * | 2020-07-22 | 2023-01-06 | 福州大学 | Optimization method for solving urban comprehensive energy network by two-stage constraint boundary tightening algorithm |
| CN111864749A (en) * | 2020-08-18 | 2020-10-30 | 国网湖南省电力有限公司 | Photovoltaic system power park electric vehicle charging method and system and storage medium |
| CN112103955B (en) * | 2020-09-16 | 2022-02-08 | 湖南大学 | Electric energy storage accident reserve capacity optimal utilization method of comprehensive energy system |
| CN112507507B (en) * | 2020-10-12 | 2022-06-17 | 上海电力大学 | Comprehensive energy equipment optimal configuration method based on economy and reliability |
| CN112366704B (en) * | 2020-11-13 | 2022-09-27 | 中国科学院电工研究所 | Comprehensive energy system tie line power control method based on excitation demand response |
| CN112600253B (en) * | 2020-11-18 | 2022-12-02 | 国网浙江省电力有限公司台州供电公司 | Park comprehensive energy collaborative optimization method and equipment based on optimal energy utilization efficiency |
| CN112446616B (en) * | 2020-11-26 | 2022-07-29 | 国网山东省电力公司经济技术研究院 | Modeling method for optimal operation strategy and load characteristic of park type comprehensive energy system |
| CN112562377A (en) * | 2020-12-01 | 2021-03-26 | 厦门大学 | Passenger vehicle real-time scheduling method based on random opportunity constraint |
| CN112949976A (en) * | 2021-01-14 | 2021-06-11 | 台州宏远电力设计院有限公司 | Optimal scheduling method for microgrid energy in commercial park |
| CN113158450A (en) * | 2021-04-08 | 2021-07-23 | 国网河南省电力公司电力科学研究院 | Building energy management system-based economic scheduling method and system |
| CN113344736B (en) * | 2021-05-21 | 2023-08-18 | 温州电力设计有限公司 | Park-level comprehensive energy system and control method thereof |
| CN113517690A (en) * | 2021-07-02 | 2021-10-19 | 东北电力大学 | Double-layer dispatching method for community comprehensive energy system with electric vehicle charging station |
| CN113822572B (en) * | 2021-09-22 | 2024-02-13 | 国网内蒙古东部电力有限公司 | Park comprehensive energy system optimal scheduling method considering energy sharing and multiple risks |
| CN114022046B (en) * | 2021-11-30 | 2023-04-18 | 国网四川省电力公司电力科学研究院 | Comprehensive energy system optimization scheduling method considering traffic balance |
| CN115693643B (en) * | 2022-08-31 | 2023-08-11 | 中国矿业大学 | Power distribution network bilateral risk management and control method considering fault time uncertainty |
| CN117040028B (en) * | 2023-09-28 | 2024-01-09 | 泰豪科技(深圳)电力技术有限公司 | Control strategy optimization method and system for optical storage and charging micro-grid of industrial and commercial park |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107918811A (en) * | 2017-11-30 | 2018-04-17 | 国网天津市电力公司 | A kind of energy internet construction method for improving efficiency of energy utilization |
| CN108537409A (en) * | 2018-03-15 | 2018-09-14 | 广东电网有限责任公司电网规划研究中心 | A kind of industrial park power distribution network collaborative planning method considering multiple-energy-source coupled characteristic |
| WO2018196456A1 (en) * | 2017-04-28 | 2018-11-01 | 东南大学 | Modeling method for heat and electricity combined optimized scheduling model |
| CN109002941A (en) * | 2018-09-28 | 2018-12-14 | 南方电网科学研究院有限责任公司 | Consider integrated energy system lectotype selection and the method for planning capacity of heat accumulation link |
| CN109063992A (en) * | 2018-07-18 | 2018-12-21 | 国网重庆市电力公司经济技术研究院 | Consider the power distribution network Expansion Planning method of regional complex energy resource system optimization operation |
| CN109658005A (en) * | 2018-12-29 | 2019-04-19 | 天津大学 | Consider garden integrated energy system lectotype selection and the method for planning capacity of electric energy storage |
| CN109783909A (en) * | 2018-12-29 | 2019-05-21 | 天津大学 | Consider the comprehensive energy station equipment type selecting and method for planning capacity of a variety of energy storage devices |
| CN109784569A (en) * | 2019-01-23 | 2019-05-21 | 华北电力大学 | A kind of regional complex energy resource system optimal control method |
| CN109885009A (en) * | 2019-03-19 | 2019-06-14 | 广东电网有限责任公司电网规划研究中心 | Meter and electricity turn the garden energy source optimization configuration method of providing multiple forms of energy to complement each other of gas planning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2013397866A1 (en) * | 2013-08-13 | 2016-03-03 | Accenture Global Services Limited | System, method and apparatus for integrated multi-energy scheduling in a micro-grid and a tangible computer readable medium |
-
2019
- 2019-08-07 CN CN201910723934.0A patent/CN110533225B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018196456A1 (en) * | 2017-04-28 | 2018-11-01 | 东南大学 | Modeling method for heat and electricity combined optimized scheduling model |
| CN107918811A (en) * | 2017-11-30 | 2018-04-17 | 国网天津市电力公司 | A kind of energy internet construction method for improving efficiency of energy utilization |
| CN108537409A (en) * | 2018-03-15 | 2018-09-14 | 广东电网有限责任公司电网规划研究中心 | A kind of industrial park power distribution network collaborative planning method considering multiple-energy-source coupled characteristic |
| CN109063992A (en) * | 2018-07-18 | 2018-12-21 | 国网重庆市电力公司经济技术研究院 | Consider the power distribution network Expansion Planning method of regional complex energy resource system optimization operation |
| CN109002941A (en) * | 2018-09-28 | 2018-12-14 | 南方电网科学研究院有限责任公司 | Consider integrated energy system lectotype selection and the method for planning capacity of heat accumulation link |
| CN109658005A (en) * | 2018-12-29 | 2019-04-19 | 天津大学 | Consider garden integrated energy system lectotype selection and the method for planning capacity of electric energy storage |
| CN109783909A (en) * | 2018-12-29 | 2019-05-21 | 天津大学 | Consider the comprehensive energy station equipment type selecting and method for planning capacity of a variety of energy storage devices |
| CN109784569A (en) * | 2019-01-23 | 2019-05-21 | 华北电力大学 | A kind of regional complex energy resource system optimal control method |
| CN109885009A (en) * | 2019-03-19 | 2019-06-14 | 广东电网有限责任公司电网规划研究中心 | Meter and electricity turn the garden energy source optimization configuration method of providing multiple forms of energy to complement each other of gas planning |
Non-Patent Citations (2)
| Title |
|---|
| 多能互补系统关键技术综述;谭忠富等;《分布式能源》;20171015(第05期);全文 * |
| 综合能源系统建模及效益评价体系综述与展望;曾鸣等;《电网技术》;20180411(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110533225A (en) | 2019-12-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110533225B (en) | Business park comprehensive energy system optimal scheduling method based on opportunity constraint planning | |
| CN109659927B (en) | Energy storage capacity configuration method of comprehensive energy microgrid considering energy storage participation degree | |
| CN106228258B (en) | It is a kind of meter and demand side management home energy source local area network energy optimal control method | |
| CN109492791B (en) | Inter-city expressway network light storage charging station constant volume planning method based on charging guidance | |
| CN105811409B (en) | A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile | |
| CN109449973B (en) | Energy optimization method for rail transit power supply system containing photovoltaic and energy storage | |
| Alabi et al. | Improved hybrid inexact optimal scheduling of virtual powerplant (VPP) for zero-carbon multi-energy system (ZCMES) incorporating Electric Vehicle (EV) multi-flexible approach | |
| CN110378548B (en) | Electric automobile virtual power plant multi-time scale response capability assessment model construction method | |
| CN111626527B (en) | Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle | |
| WO2022193794A1 (en) | Multi-objective energy management method for smart community microgrid that takes into consideration decommissioned batteries | |
| CN105811397A (en) | Multi-energy complementation microgrid scheduling method based on multi-time scales | |
| CN112183882B (en) | Intelligent charging station charging optimization method based on electric vehicle quick charging requirement | |
| CN110245810B (en) | Regional energy Internet coordinated optimization operation method | |
| CN116151486B (en) | Multi-time-scale random optimization method and device for photovoltaic charging station with energy storage system | |
| CN113902182A (en) | Robust charging optimization method for electric bus fleet considering energy consumption uncertainty | |
| CN114022046B (en) | Comprehensive energy system optimization scheduling method considering traffic balance | |
| CN114583729A (en) | Light-storage electric vehicle charging station scheduling method considering full-life-cycle carbon emission | |
| Kumar et al. | Leveraging energy flexibilities for enhancing the cost-effectiveness and grid-responsiveness of net-zero-energy metro railway and station systems | |
| CN116167573A (en) | Comprehensive energy demand response optimization scheduling method and related device for high-speed rail station | |
| CN114723284A (en) | Reliability evaluation method for power distribution network comprising distributed power supply and electric automobile | |
| Yang et al. | Intraday Rolling Optimization Strategy of PV-Energy Storage-Integrated Charging Station Serving Multiple Electrical Bus Lines | |
| CN110992206B (en) | Optimal scheduling method and system for multi-source electric field | |
| CN115239032B (en) | Highway service area microgrid planning method and system considering energy self-consistency rate | |
| Hasankhani et al. | Optimal charge scheduling of electric vehicles in smart homes | |
| CN116054286A (en) | Residential area capacity optimal configuration method considering multiple elastic resources |
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 |