CN116187648A - Virtual power plant thermoelectric combination optimization scheduling method based on thermal decoupling - Google Patents
Virtual power plant thermoelectric combination optimization scheduling method based on thermal decoupling Download PDFInfo
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Abstract
A virtual power plant combined heat and power optimization scheduling method based on thermal decoupling comprises the following steps: step 1: constructing a virtual power plant system containing cogeneration; step 2: formulating an operation strategy of distributed resources in the virtual power plant; step 3: formulating a two-stage scheduling strategy of the virtual power plant; step 4: establishing a two-stage scheduling model and constraint conditions considering environmental cost; step 5: and (3) quantitatively analyzing the influence of the aggregated cogeneration unit of the virtual power plant on the virtual power plant by solving the two-stage scheduling model. According to the virtual power plant thermoelectric combined optimization scheduling method based on the thermal decoupling, the operation flexibility of the gas turbine is improved while the waste heat of the gas turbine is fully utilized, the output deviation cost of the virtual power plant is reduced, and the thermoelectric coordinated scheduling of the virtual power plant is realized.
Description
Technical Field
The invention relates to the technical field of comprehensive energy optimization scheduling, in particular to a virtual power plant thermoelectric combination optimization scheduling method based on thermal decoupling.
Background
At present, scholars at home and abroad have conducted a great deal of research on VPP energy scheduling. For example:
aiming at energy management at a source side of a virtual power plant, documents [1] Fan Songli, ai and He Xing ] virtual power plant scheduling risk analysis [ J ] based on opportunity constraint planning, china motor engineering report, 2015,35 (16): 4025-4034. An opportunity constraint planning model is established for uncertainty factors such as wind and light output, load prediction errors, unit fault outage and the like, and balance between risks and economy is discussed. Document [2] Lv Mengxuan, louis, liu Jianqin, etc.. A virtual power plant with high proportion of wind power is provided with multiple types of standby coordination optimization [ J ]. Chinese motor engineering report, 2018,38 (10): 2874-2882+3138. A multi-element standby capacity decision method considering risk loss is established from the source-load-storage of the virtual power plant, and the absorption rate of renewable energy sources is improved.
For virtual power plant source-load side energy management, literature [3] should fly auspicious, xu Tianji, li, and the like, electric power system protection and control is researched by a day-ahead dispatching optimization strategy of commercial virtual power plants containing electric vehicle charging stations [ J ], 2020,48 (21): 92-100, and the electric vehicle charging stations are introduced into the virtual power plants, so that ordered charging and discharging of electric vehicles are realized. Document [4] Liu Lijun, roning, wu Tong, etc.. Consider demand side response virtual plant optimization scheduling based on mixed integer second order cone planning [ J ]. Solar school report, 2021,42 (08): 96-104. Load peak-valley-difference is reduced by guiding users to respond to power market price changes based on price demand response
However, the above research only involves the participation of virtual power plants in the main power market, and as the energy market progresses, the energy forms of virtual power plants aggregate and the external market environments available for participation are also gradually diversified. For example:
literature [5] Sun Hui, fan Xuanxuan, hu Shubo, etc. ] virtual power plant participates in the internal and external coordination bidding strategy [ J ] of the daily electric power market, power grid technology, 2022,46 (04): 1248-1262. Internal and external coordination models of the virtual power plant participating in the electric energy market and auxiliary peak shaving market are established, the bid amounts of the virtual power plant in two markets at the daily time are obtained through price-quantity game between the virtual power plant and distributed resources, and the decision flexibility of the virtual power plant is improved, but the real-time feasibility of the scheme is not verified. Document [6] Sun Guojiang, zhou Yizhou, wei Zhinong, etc. A virtual power plant combined heat and power dispatching robust optimization model under energy and rotation reserve market [ J ]. Chinese motor engineering report, 2017,37 (11): 3118-3128+3367. Excavate flexibility of cogeneration unit, consider virtual power plant to participate in electric energy market and rotation reserve market, improve the economic benefits of virtual power plant, but cause the waste of cogeneration unit heat energy.
In summary, the research on virtual power plants has the following disadvantages:
(1): the existing literature mostly considers that the virtual power plant participates in the main power market, and the virtual power plant is less involved in the heat market and the peak shaving market, so that the coordination scheduling of the electricity, heat and peak shaving market can not be realized;
(2): a single day-ahead schedule does not reflect the uncertainty and volatility of wind, light output, and the availability of day-ahead scheduling strategies well.
Disclosure of Invention
In order to solve the technical problems, the invention provides a thermal-electric coupling-based virtual power plant combined heat and power optimization scheduling method. The invention fully utilizes the waste heat of the gas turbine, improves the operation flexibility of the gas turbine, reduces the output deviation cost of the virtual power plant and realizes the thermoelectric coordination scheduling of the virtual power plant.
The technical scheme adopted by the invention is as follows:
a virtual power plant combined heat and power optimization scheduling method based on thermal decoupling comprises the following steps:
step 1: constructing a virtual power plant system containing cogeneration;
step 2: formulating an operation strategy of distributed resources in the virtual power plant;
step 3: formulating a two-stage scheduling strategy of the virtual power plant;
step 4: establishing a two-stage scheduling model and constraint conditions considering environmental cost;
step 5: and (3) quantitatively analyzing the influence of the aggregated cogeneration unit of the virtual power plant on the virtual power plant by solving the two-stage scheduling model.
In the step 1, the single distributed resource has small capacity and cannot reach the market admission threshold, so the invention considers that the resource on the demand side is aggregated into a whole with larger capacity through the virtual power plant and is combined with energy storage to participate in peak regulation market, thereby achieving the purpose of improving the whole income. In addition, the virtual power plant can participate in the electricity and heat market through the aggregate heat and power cogeneration. The virtual power plant participates in various markets of electricity, heat and peak shaving at the same time, so that the decision flexibility of the virtual power plant is improved, and the overall benefit of the virtual power plant is further improved. Virtual power plant aggregate resources include wind power plants, photovoltaic power plants, electricity/heat storage, electricity/heat load, cogeneration, and gas boilers.
In the step 2, the operation strategy of the distributed resource in the virtual power plant includes: a thermal decoupling strategy of a cogeneration unit, an electric energy storage and flexible load operation strategy and a wind power and photovoltaic total absorption strategy;
(1) The thermal decoupling strategy of the cogeneration unit is to reduce the thermal coupling of the cogeneration unit by introducing a gas boiler and heat energy storage. When the cogeneration unit operates in a 'fixed heat power' mode, the power output of the cogeneration unit can be improved when the power supply of the system is insufficient, and the surplus heat is stored through heat energy storage; when the power supply of the system is excessive, the output of the cogeneration unit is downwards adjusted, insufficient heat is supplied by the gas boiler, and the operation flexibility of the cogeneration unit is improved through the synergistic effect of the heat energy storage and the gas boiler.
The micro gas turbine (MT) operates in a single power generation state, the efficiency is about 30%, and if the high-temperature waste heat flue gas generated by the micro gas turbine power generation is utilized, the energy utilization rate can reach more than 70%. The invention takes the waste heat recovery device into consideration, and collects the waste heat of the micro gas turbine to form cogeneration, and the output models of the micro gas turbine and the waste heat recovery device are as follows:
Q hrs,t ＝η hrs Q mt,t (2)
wherein t is a scheduling period; p (P) gmt,t 、P mt,t 、Q mt,t Respectively inputting natural gas power, power generation power and waste heat power of the micro gas turbine for a period t; q (Q) hrs,t The heat supply power of the waste heat recovery device is t time periods; η (eta) mt 、
η hrs The heat recovery efficiency of the waste heat recovery device is respectively the power generation efficiency of the micro gas turbine, the heat dissipation loss rate of the micro gas turbine.
The cogeneration has higher energy efficiency and lower operation energy consumption compared with the micro gas turbine, but the strong thermoelectric coupling characteristic of the cogeneration reduces the system operation flexibility and further compresses the internet space of the renewable energy sources. According to the invention, the thermoelectric coupling of the cogeneration unit is reduced by introducing a gas boiler and heat energy storage modes, so that the operation flexibility of the cogeneration unit is improved, and the gas boiler and the heat energy storage modes are shown in formulas (3) and (4):
Q gb,t ＝η gb Q ggb,t (3)
in which Q gb,t 、η gb 、Q ggb,t The heat supply power and the heat production efficiency of the boiler at the period t and the power of the natural gas input into the boiler at the period t are respectively provided; q (Q) HS,c,t 、Q HS,d,t Respectively charging and discharging power of the thermal energy storage;
the heat charging and discharging efficiency for heat energy storage; e (E) HS,t 、E HS,t-1 The heat storage quantity of the heat energy storage is respectively t time period and t-1 time period.
(2) Electrical energy storage and flexible load operation strategies:
flexible load operation strategy: the flexible load actively stabilizes peak period load to valley-flat period or reduces peak period load to participate in peak regulation market by responding to load electricity purchasing electricity price formulated by the virtual power plant;
electric energy storage operation strategy: the electric energy storage actively responds to the time-sharing electricity price of the external electric power market, and simultaneously responds to the peak shaving compensation electricity price of the external electric power market in combination with the flexible load to participate in the peak shaving market while participating in the main electric power market.
The aggregated electrical load of the virtual power plant of the present invention includes fixed loads and translatable loads, such as: electric vehicles, washing machines, disinfection cabinets, etc.
Wherein P is L,e,t 、P Lbase,t 、P Lts,t Actual load, fixed load and load adjustment amount, P, respectively, of t period Lts,in,t 、P Lts,out,t And t time periods of load in and out respectively. P (P) Lts,in,t+1 A shift load representing a t+1 period;
because the electricity purchase price of the virtual power plant is generally greater than the electricity selling price, the user preferentially utilizes the electric energy generated inside the virtual power plant. In addition, when the user participates in the peak shaving market, the difference between the electricity purchasing price of the power grid and the electricity selling price of the virtual power plant is born by the virtual power plant, and the flexible load participating in the peak shaving market is compensated.
Wherein C is Lec,t Compensation cost for the user to participate in peak shaving; beta pf,t 、β vf,t The marks of peak clipping and valley filling are respectively 1, which indicates that peak clipping and valley filling are allowed, 0, which indicates that peak clipping and valley filling are not allowed, beta pf,t 、β vf,t Not 1 at the same time; mu (mu) pf,t 、μ vf,t Compensating the price for the unit electric quantity of the user participating in peak clipping and valley filling respectively; p (P) ES,pf,t 、P ES,vf,t Respectively storing the competitive bidding electric quantity of the electric energy to participate in peak clipping and valley filling markets; p (P) pf 、P vf And the access capacity of the peak clipping and valley filling markets is respectively.
Electricity cost C of electrical load in t-period virtual power plant Le,t The method comprises the following steps:
C Le,t ＝λ se,t P L,e,t -C Lec,t (7)
wherein lambda is se,t And selling electricity prices for the virtual power plants.
The energy storage is used as an important flexible resource, the external combined flexible load participates in the peak shaving market to obtain peak shaving income, and the internal combined flexible load can realize energy transfer to participate in the electric energy market. Revenue f of t-period energy storage combined user participating in peak shaving market 1,t The method comprises the following steps:
f 1,t ＝λ pf,t [P ES,pf,t +P DR,pf,t ]+λ vf,t [P ES,vf,t +P DR,vf,t ] (8)
wherein P is DR,pf,t 、P DR,vf,t Respectively participating in competitive bidding electric quantity of peak clipping and valley filling markets for flexible loads; lambda (lambda) pf,t 、λ vf,t The peak clipping and valley filling prices of the unit electric quantity in the peak shaving market are respectively.
The peak shaving period requires the combined competitive power of the electric energy storage and the flexible load to meet the admission capacity of the peak shaving market, and the competitive power of the non-peak shaving period is zero.
Wherein P is ES,max And (5) storing the maximum charge and discharge power for electricity.
The output of the electric energy storage participating in the electric market must not exceed the upper and lower power limits, and the constraint is as follows:
-P ES,max ≤P ES,e,t ≤P ES,max (12)
wherein P is ES,e,t And (5) the charge and discharge quantity of the electric energy stored in the electric power market for the period t.
The energy storage participation two market electric quantity must not exceed the maximum capacity, and the initial and final electric quantity in one scheduling period are the same:
wherein, the subscript T is a scheduling period; e (E) ES,t Charge amount for t period of energy storage E ES,min 、E ES,max Respectively the minimum charge and the maximum charge of energy storage, P ES,c,t 、P ES,d,t And
and the charging and discharging power and the charging and discharging efficiency of the electric energy storage participating in the electric power market in the t period are respectively. E (E) ES,t0 、E ES,T 、E ES,t-1 E representing different time periods ES,t A value;
(3) And the wind power and photovoltaic full-consumption strategy is that wind power and photovoltaic are all on the network according to power prediction before the day, and the standby capacity of the cogeneration unit is determined according to wind-solar prediction deviation. And in the real-time stage, the thermal decoupling is carried out on the cogeneration unit by utilizing the flexibility brought by the gas boiler and the thermal energy storage, so that the full wind-solar energy absorption is realized. Because the heat and power cogeneration unit in the real-time stage can downwards adjust the output through the new heat source, the heat and power cogeneration unit in the day-ahead can provide positive reserve, and the constraint of the reserve heat and power cogeneration is as follows:
R chp,up,t ≥△P w-,t +△P pv-,t (14)
wherein R is chp,up,t For the positive standby of the cogeneration unit, delta P w-,t 、△P pv-,t Wind power respectively,Negative deviation of the photovoltaic output.
In the step 3, in the day-ahead stage, the gas boiler and the heat energy storage are not involved in day-ahead scheduling as flexible resources, and the day-ahead bidding strategy is as follows:
(1) The virtual power plant coordination scheduling center predicts the output force according to wind and light, and preferably schedules surfing;
(2) The cogeneration output participates in the electricity and heat market, and declares the day-ahead electricity and heat bid amount;
(3) The electric energy storage participates in the electric energy market and the flexible load is combined to participate in peak shaving market energy reporting.
Due to uncertainty of wind and light output, the declaration output of the virtual power plant before the day needs to be adjusted in real time in the day. Real-time scheduling is as follows:
(1) If the output deviation is larger than 0 (actual output > planned output), the heat energy storage and the gas boiler increase the heat output so as to reduce the cogeneration power output, and if the deviation is not enough to be stabilized, the heat energy storage and the gas boiler are sold at low price;
(2) If the output deviation is smaller than 0 (actual output < planned output), the heat energy storage is used for storing heat so as to increase the electric output of the cogeneration unit; if the deviation is not sufficiently stabilized, the electric energy is purchased at a high price.
In the step 4, a two-stage scheduling model considering the environmental cost is specifically as follows:
the virtual power plant declares the output to each energy market with the maximum profit as the target before the day, and the net profit F of the virtual power plant is obtained in the period of t vpp,t ：
F vpp,t ＝f 1,t +f 2,t +f 3,t -f 4,t +C Le,t +C Lh,t (15)
Wherein f 2,t 、f 3,t 、f 4,t The electricity market selling benefits, the heat market selling benefits and the running cost of the virtual power plant in the period t are respectively; c (C) Lh,t And purchasing heat cost for the internal heat load of the virtual power plant.
Wherein lambda is be,t 、λ se,t The electricity purchase price of the virtual power plant; p (P) s,t Selling electric power for participating in electric market of t-period virtual power plant, P b,t 、P vf,t The virtual power plants participate in the electricity market and the electricity purchasing power of the valley filling market in the period t respectively; lambda (lambda) sh,t 、Q L,h,t And selling heat price and heat load power in the virtual power plant for the period t.
The virtual power plant operating costs include:
(1) Cogeneration power generation cost f 41,t ：
The electricity generation cost of the cogeneration unit can be reflected by the following gas purchase cost:
wherein lambda is g Is the price of natural gas, Q gas For the low calorific value of natural gas, 9.97kWh/m was taken 3 。
(2) Cost f of electric energy storage operation 42,t ：
The energy storage is lost in the charging and discharging processes, so the operation cost of the energy storage can be given by the following formula
f 42,t ＝a|P ES,t | 2 +b|P ES,t |+c (18)；
Wherein a, b and c are consumption characteristic parameters of electric energy storage.
(3) Wind power and photovoltaic power generation cost f 43,t ：
f 43,t ＝λ w P w,t +λ pv P pv,t (19)；
Wherein lambda is w 、λ pv The wind and light unit electric quantity power generation cost coefficients are respectively; p (P) w,t 、P pv,t The predicted output of wind and light in the t period is respectively.
(4) Environmental cost f 44,t ：
In the method, in the process of the invention,
the j-th waste gas amount generated for the unit electric energy of the i-th unit; p (P) mt,i,t Generating power for the t period of the ith gas turbine; n and m are the number of cogeneration units and the number of exhaust gas types, respectively, where m=3, and include NO x 、SO 2 And CO 2 Three gases, k j Is the unit cost coefficient of the j-th gas.
Real-time scheduling model:
in the real-time dispatching stage, the virtual power plant comprehensively considers ultra-short-term wind and light power prediction according to the day-ahead clear result, timely adjusts the gas boiler and the heat energy storage output, and adjusts the electric output of the cogeneration unit by adjusting the cogeneration heat output so as to achieve the purpose of following the wind and light output change and maximize the system benefit. In addition, the penalty cost caused by the output deviation, the gas cost of the gas boiler and the operation cost of the heat energy storage should be considered in the real-time scheduling model, and the penalty cost and the gas cost and the operation cost of the heat energy storage are expressed as follows:
where all bands' represent real-time output or cost, as scheduled prior to day.
Introducing a new heat source gas boiler and heat energy storage output;
Respectively adding the gas purchasing cost of the gas boiler and the operation cost of heat energy storage;
The environmental cost of the boiler is increased;
Cost is penalized for virtual power plant output deviation.
Because the purchase heat price of the virtual power plant is larger than the sale heat price, the heat energy storage does not consider the purchase heat from the outside, but starts to transfer the internal heat output of the virtual power plant to follow the change of wind and light output, namely
The expression is as follows:
wherein C is chu Cost of investment and construction for converting heat energy storage into daily, Q chure 、ρ chure The heat energy storage capacity and the unit capacity cost are respectively; n represents the service life, the invention takes n as 40 years,
the j-th exhaust gas amount generated for the unit capacity of the a-th gas boiler;
The heat generation amount is the heat generation amount of the a-th gas boiler in the t period;
when the actual output deviates from the planned output, punishment is given to prompt the virtual power plant to improve the wind-light prediction precision and optimize the day-ahead reporting strategy, so that the safe operation of the power system is ensured. With the deviation of the electric output of the virtual power plant in the period t
For example, specific penalties are as follows:
punishment electricity price setting:
wherein lambda is ep,t And (5) the electricity output deviation punishment electricity price is in t period, and the electricity output deviation punishment electricity price is respectively adjusted up or down by 50% by taking the electricity selling price as a reference. Punishment price lambda for peak clipping and peak shaving, valley filling and peak shaving and heat energy output deviation pfp,t 、λ vfp,t 、λ hp,t Set and lambda ep,t Similarly, the description is omitted here.
Virtual power plant output deviation penalty cost
Represented by the formula:
in the method, in the process of the invention,
and respectively scheduling peak clipping, peak shaving, valley filling, peak shaving and heat output deviation in real time in the t period. Constraint conditions: />
(1) Power balance constraint:
day-ahead power balance constraint:
wherein P is e,t 、Q h,t 、P pf,t The bidding amounts of electricity, heat and peak clipping and peak shaving before the day, P e,t ＝P s,t -P b,t 。
Real-time power balance constraint:
(2) Climbing constraint and power constraint of cogeneration unit:
wherein P is mt,min 、P mt,max Respectively minimum power and maximum power of the cogeneration unit, P mt,up 、P mt,down And the upper limit and the lower limit of the climbing power of the cogeneration are respectively set.
(3) Thermal energy storage capacity and charge-discharge thermal constraints:
in which Q HS,t Charging and discharging power for t-period heat energy storage; q (Q) HS,max 、Q HS,min Upper and lower limits of charging and discharging power for heat energy storage E HS,max 、E HS,min Is the upper and lower limit of the capacity for thermal energy storage.
(4) Boiler power and climbing constraints:
in which Q gb,max 、Q gb,min The upper and lower limits of boiler power are respectively; q (Q) gb,up 、Q gb,down The upper limit and the lower limit of the climbing power of the boiler are respectively.
In the step 5, the Yalmip is an optimization tool kit based on the Matlab platform, which provides a programming interface besides self-contained solving algorithms (linprog, bnb, etc.), and can realize calling of almost all optimization software solvers, so that the Yalmip is widely applied to solving of optimization problems. The invention adopts a mode of calling a solver Cplex by Yalmip to solve a scheduling model, and comprises the following specific steps:
5.1: initial data such as wind before day, predicted light output, time-of-use electricity price, load data and the like are input.
5.2: decision variables, such as the day-ahead and day-in-day output of the unit, are defined using the sdpvar command.
5.3: defining objective function, yalmip defaults to solve minimum value, so that it is necessary to define the objective of the invention as-F in algorithm vpp,t 。
5.4: constraint is added and defined using [ ] commands.
5.5: setting solving parameters and solving, firstly setting the parameters by an sdsetting command;
according to the invention, a Cplex solver is called through an sdsetting ('solver', 'Cplex') command; and secondly solving through an optimize command.
5.6: and outputting an optimization result, and obtaining the optimization result by using the value command.
The invention discloses a virtual power plant thermoelectric combination optimization scheduling method based on thermal decoupling, which has the following technical effects:
1) Compared with the participation in a single electric power market, the virtual power plant participation in multiple markets realizes the efficient utilization of resources, and improves the decision flexibility of the virtual power plant and the overall benefit of the virtual power plant;
2) The aggregated heat and power cogeneration unit of the virtual power plant reduces the power generation cost of the virtual power plant and improves the competitive bidding electric quantity and income of the virtual power plant in the electric power market. Compared with a virtual power plant without a cogeneration unit under the condition of the same capacity, the virtual power plant has the advantages of lower gas purchasing cost, higher energy utilization rate and lower environmental cost;
3) The invention effectively improves the enthusiasm of the flexible load to participate in peak shaving through the operation compensation strategy of the flexible load, reduces the risk that the market admittance threshold cannot be reached due to the too low flexible load capacity when the electric energy storage is combined with the flexible load to participate in the peak shaving market, and improves the competitive bidding electric quantity of the virtual power plant to participate in the peak shaving market;
4) The heat and power cogeneration unit provided by the invention is provided with the heat energy storage and gas boiler, so that the operation flexibility of the heat and power cogeneration unit is effectively improved, the real-time output deviation is reduced, and the full consumption of wind and light is realized;
5) According to the invention, the virtual power plant is expanded from participating in a main power market to participating in a heat and peak shaving market simultaneously through the aggregate cogeneration unit, so that the trading channel of the virtual power plant is widened, the thermoelectric collaborative optimization scheduling of the virtual power plant is realized, and the method has important significance in improving the operation flexibility and the energy utilization rate of a power system.
Drawings
FIG. 1 is a block diagram of a Virtual Power Plant (VPP) according to the present invention.
Fig. 2 is a graph of equivalent thermoelectric characteristics of a cogeneration unit (CHP) comprising a heat storage and gas fired boiler (GB).
FIG. 3 is a two-stage virtual power plant decision flow diagram.
Fig. 4 shows a wind and light output diagram.
Fig. 5 is a load base value diagram.
FIG. 6 (a) is a graph of the result of the set output adjustment in the real-time scheduling phase of scenario 6;
FIG. 6 (b) is a graph of the result of the set output adjustment in the real-time scheduling phase of scenario 7;
fig. 6 (c) is a graph of the result of the real-time scheduling stage unit output adjustment in scenario 8.
Detailed Description
A virtual power plant combined heat and power optimization scheduling method based on thermal decoupling comprises the following steps:
step one: constructing a virtual power plant system containing cogeneration:
as shown in fig. 1, the virtual power plant aggregate resources comprise wind power plants, photovoltaic power stations, electric/thermal energy storage, electric/thermal load, cogeneration and gas boilers, and the virtual power plant coordination scheduling center obtains prices of electric, thermal and peak shaving markets to the outside to participate in bidding of the outside market, and coordinates the output of each distributed resource to the inside.
Step two: formulating an operation strategy of distributed resources in the virtual power plant:
the operation strategy mainly comprises the following steps: a thermoelectric coupling strategy, an electric energy storage and flexible load operation strategy and a wind power and photovoltaic total absorption strategy. As the electrical force increases, as shown in fig. 2, the thermal force gradually increases along ABThe increase is externally manifested as unadjustable heat output. When the thermal energy storage device is introduced, the equivalent output interval is converted into CDFE, and the external output is adjustable when the electric output is unchanged. Such as the electric force is P mt (equation is changed to mathtype), the heat output is equal to Q hrs1 ～Q hrs2 (formula is changed to mathtype). When the gas boiler is continuously introduced, the upper limit of the heat output of the gas boiler is continuously increased, and the equivalent output interval is converted into CDHJ. Therefore, the heat energy storage and the gas boiler are introduced, so that the operation flexibility of the cogeneration unit can be effectively improved.
Step three: formulating a two-stage scheduling strategy of the virtual power plant:
the decision flow of the virtual power plant participating in the multi-energy market is shown in fig. 3, and in the day-ahead stage, the gas boiler and the thermal energy storage do not participate in day-ahead scheduling as flexible resources, and the day-ahead bidding strategy is as follows:
(1) The virtual power plant coordination scheduling center predicts the output force according to wind and light, and preferably schedules surfing;
(2) The cogeneration output participates in the electricity and heat market, and declares the day-ahead electricity and heat bid amount;
(3) The electric energy storage participates in the electric energy market and the flexible load is combined to participate in peak shaving market energy reporting.
Due to uncertainty of wind and light output, the declaration output of the virtual power plant before the day needs to be adjusted in real time in the day. Real-time scheduling is as follows:
(1) If the output deviation is larger than 0 (actual output > planned output), the heat energy storage and the gas boiler increase the heat output so as to reduce the cogeneration power output, and if the deviation is not enough to be stabilized, the heat energy storage and the gas boiler are sold at low price;
(2) If the output deviation is smaller than 0 (actual output < planned output), the heat energy storage is used for storing heat so as to increase the electric output of the cogeneration unit; if the deviation is not sufficiently stabilized, the electric energy is purchased at a high price.
Step four: establishing a two-stage scheduling model and constraint conditions considering environmental cost:
virtual power plants report output to various energy markets with maximum revenue targets in the day-ahead. In the real-time dispatching stage, the virtual power plant comprehensively considers ultra-short-term wind and light power prediction according to the day-ahead clear result, timely adjusts the gas boiler and the heat energy storage output, and adjusts the electric output of the cogeneration unit by adjusting the cogeneration heat output so as to achieve the purpose of following the wind and light output change and maximize the system benefit. In addition, the penalty cost caused by the output deviation, the gas cost of the gas boiler and the operation cost of the heat energy storage are considered in the real-time scheduling model. The constraints include: power balance constraint, cogeneration unit climbing constraint, power constraint, heat energy storage capacity and charging and discharging constraint, boiler power and climbing constraint, peak shaving constraint and standby constraint.
Step five: by solving the model, the influence of the virtual power plant aggregate cogeneration unit on the virtual power plant is quantitatively analyzed:
the virtual power plant consists of 1 wind power plant with 6MWh, 1 photovoltaic power station with 4MWh, 4 cogeneration units with 5MWh, 5MWh of electric energy storage, and 3MWh of heat energy storage and gas boiler. The wind-solar prediction error is 20%, and the maximum adjustment amounts of the interruptible load and the transferable load are 15% of the load base value of each period. The heat load in the heating season is basically unchanged, and the set value of the heat load in the virtual power plant is set to be 6MWh, and the selling price of the heat is 300 yuan/MWh. The virtual power plant participates in the peak shaving market with minimum competitive capacity of 2MWh, and can participate in the valley filling market at 0:00-8:00. The peak clipping market can be participated in 8:00-12:00 and 17:00-21:00, the peak clipping compensation price can be 1000 yuan/MWh, and the valley filling compensation price can be 350 yuan/MWh. The peak-valley period setting and the VPP electricity purchase price are shown in table 1, the unit parameters are shown in table 2, the day-ahead scene setting is shown in table 3, the real-time scene setting is shown in table 4, and the wind-solar power generation capacity and the load base value are shown in fig. 4 and 5.
Table 1 Peak-to-valley time period setting virtual Power plant purchase and sale prices
TABLE 2 Unit operating parameters
| Parameters (parameters) | Numerical value | Unit (B) | Parameters (parameters) | Numerical value | Unit (B) |
| η mt | 30 | ％ | λ pv | 0.5 | Yuan/KW.h |
| ηl m o t ss | 10 | ％ | λ chp | 0.018 | Yuan/KW.h |
| η hrs | 75 | ％ | λ gb | 0.017 | Yuan/KW.h |
| η gb | 80 | ％ | Q gas | 9.97 | KW·h/m3 |
| λ sh | 300 | meta/MW.h | λ w | 0.43 | Yuan/ |
| λ | |||||
| g | 2 | Meta/m 3 |
TABLE 3 day front scene settings
Note that: in table 3, 'v' means containing, 'x' means not containing; the cogeneration refers to that the micro gas turbine combined waste heat recovery device works in a cogeneration state, and at the moment, when the virtual power plant does not participate in the heat energy market, the surplus heat is discharged through heat dissipation of the unit; the non-cogeneration means that the micro gas turbine works in a single power generation state and supplies heat by the gas boiler, and the capacity of the gas boiler is set to be 27MWh.
TABLE 4 intra-day scene settings
| Scene(s) | Thermal energy storage | Gas boiler |
| 6 | Ⅹ | Ⅹ |
| 7 | Ⅹ | √ |
| 8 | √ | √ |
Note that: in table 4, "v" means containing and "x" means not containing.
(1) Analyzing the daily bidding results of the virtual power plant:
the energy storage charging, the flexible load increasing and the virtual power plant purchasing electricity to the power grid are negative, and vice versa.
TABLE 5 virtual plant revenue/cost conditions for different scenarios
As can be seen from the comparison of the gains and the costs of the scenes 1-3 in the table 5, the gains of the virtual power plants are gradually increased along with the increase of the external markets in which the virtual power plants can participate, and the total gain of the virtual power plants is greatly increased when the virtual power plants participate in multiple markets of electricity, heat and peak shaving, and is increased by 16% compared with the total gain of the virtual power plants participating in single electric power markets. The virtual power plant has greatly enhanced decision flexibility when participating in the combined market of electricity, heat and auxiliary peak shaving markets, and the virtual power plant can adjust bidding electric quantity of participating in each market by comparing prices of different markets so as to achieve the purpose of maximizing benefits of the virtual power plant.
The micro gas turbines in the scenes 1-3 work under a single power generation working condition, and the gas boilers serve as the heat sources of the virtual power plants in the market in the day-ahead, when the virtual power plants do not participate in the heat energy market, the gas boilers generate a small amount of heat for the heat loads in the virtual power plants, and the heat selling benefits of the scenes 1 and 2 in the table 5 are the same. When the virtual power plant participates in the heat energy market, the gas boiler operates at maximum power to obtain higher heat selling benefits. This is manifested in Table 5 by an increase in heat sales and gas purchase costs for scenario 3 as compared to scenarios 1, 2, which further results in an increase in environmental costs. From the comparison of scenes 2 and 4 and scenes 3 and 5 in table 5, it can be seen that the virtual power plant containing cogeneration has larger profit and lower environmental cost than the virtual power plant without cogeneration under the same external market condition.
(2) Real-time scheduling result analysis of the virtual power plant:
the heat energy storage and heat and power cogeneration reduced electric output is negative, the heat energy storage and heat release and power cogeneration increased electric output is positive, the real-time wind and light output is > predicted output before day, the deviation is positive, and the real-time wind and light output is < predicted output before day, the deviation is negative.
TABLE 6 offset cost
| Scene(s) | 6 | 7 | 8 |
| Peak shaving market output deviation cost | 601 | 0 | 0 |
| Electric energy market output deviation book | 1026 | 550 | 0 |
| Thermal energy market output deviation book | 6524 | 1959 | 0 |
| Total output deviation cost | 8151 | 2509 | 0 |
As can be seen from table 6, when the real-time dispatching stage does not thermally decouple the cogeneration unit, a larger output deviation cost is generated due to the lack of the dispatchable resource of the system, which is shown as higher output deviation cost of scenario 6. In addition, because the peak shaving and off-valley period electric energy market unit electric quantity deviation cost set by the system is large, the real-time virtual power plant can preferentially meet the peak shaving and the power output of the electric energy market. The performance is that the output deviation cost of the thermal energy markets of scenes 6 and 7 in table 6 is maximum, and the combined heat and power generation unit in fig. 6 (a) can downwards adjust the output to meet the power requirement preferentially when the deviation of the non-valley period is greater than 0, which also causes insufficient heat supply and increases the output deviation of the thermal market.
The cost of the output deviation after the gas boiler is introduced in the scene 7 is reduced, but the output deviation in the real-time dispatching stage cannot be completely stabilized, because the gas boiler only can assist the cogeneration unit to downwards adjust the output. As shown in fig. 6 (b), when the deviation of wind and light output is greater than 0, the output is increased, and the cogeneration adjusts the output downwards; the output is reduced when the deviation of wind and light output is less than 0. After the gas boiler and the heat energy storage are introduced in the scene 8, the operation flexibility of the cogeneration unit is greatly enhanced, and the cogeneration unit can utilize the heat of charging (discharging) of the heat energy storage to adjust the output upwards (downwards) so as to meet the output requirement. The overall cost of the output deviation for scenario 8 shown in table 6 is 0, and the cogeneration unit of fig. 6 (c) produces more upward regulated output than that of fig. 6 (a), 6 (b) during the off-peak period.
Claims (7)
1. The virtual power plant combined heat and power optimization scheduling method based on thermal decoupling is characterized by comprising the following steps of:
step 1: constructing a virtual power plant system containing cogeneration;
step 2: formulating an operation strategy of distributed resources in the virtual power plant;
step 3: formulating a two-stage scheduling strategy of the virtual power plant;
step 4: establishing a two-stage scheduling model and constraint conditions considering environmental cost;
step 5: and (3) quantitatively analyzing the influence of the aggregated cogeneration unit of the virtual power plant on the virtual power plant by solving the two-stage scheduling model.
2. The virtual power plant combined heat and power optimization scheduling method based on thermal decoupling according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 1, the virtual power plant aggregate resources comprise wind power plants, photovoltaic power stations, electric/thermal energy storage, electric/thermal load, cogeneration and gas boilers.
3. The virtual power plant combined heat and power optimization scheduling method based on thermal decoupling according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 2, the operation strategy of the distributed resource in the virtual power plant includes: a thermal decoupling strategy of a cogeneration unit, an electric energy storage and flexible load operation strategy and a wind power and photovoltaic total absorption strategy;
(1) The thermal decoupling strategy of the cogeneration unit comprises the following specific steps:
and the waste heat of the micro gas turbine is collected by the waste heat recovery device to form cogeneration, and the output models of the micro gas turbine and the waste heat recovery device are as follows:
Q hrs,t ＝η hrs Q mt,t (2)
wherein t is a scheduling period; p (P) gmt,t 、P mt,t 、Q mt,t Respectively inputting natural gas power, power generation power and waste heat power of the micro gas turbine for a period t; q (Q) hrs,t The heat supply power of the waste heat recovery device is t time periods; η (eta) mt 、
η hrs The heat recovery efficiency of the waste heat recovery device is respectively the power generation efficiency of the micro gas turbine, the heat dissipation loss rate of the micro gas turbine;
the gas boiler and the thermal energy storage model are shown as (3) and (4):
Q gb,t ＝η gb Q ggb,t (3)
in which Q gb,t 、η gb 、Q ggb,t The heat supply power and the heat production efficiency of the boiler at the period t and the power of the natural gas input into the boiler at the period t are respectively provided; q (Q) HS,c,t 、Q HS,d,t Respectively charging and discharging power of the thermal energy storage;
the heat charging and discharging efficiency for heat energy storage; e (E) HS,t 、E HS,t-1 The heat storage quantity of the heat energy storage is respectively t time period and t-1 time period;
(2) The electric energy storage and flexible load operation strategy is as follows:
the aggregated electrical load of the virtual power plant includes a fixed load and a translatable load,
wherein P is L,e,t 、P Lbase,t 、P Lts,t Actual load, fixed load and load adjustment amount, P, respectively, of t period Lts,in,t 、P Lts,out,t Load transferred in and out in t time periods respectively; p (P) Lts,in,t+1 A shift load representing a t+1 period;
because the electricity purchasing price of the virtual power plant is larger than the electricity selling price, the user preferentially utilizes the electric energy generated in the virtual power plant; in addition, when the user participates in the peak shaving market, the difference between the electricity purchasing price of the power grid and the electricity selling price of the virtual power plant is born by the virtual power plant, and the flexible load participating in the peak shaving market is compensated;
wherein C is Lec,t Compensation cost for the user to participate in peak shaving; beta pf,t 、β vf,t The marks of peak clipping and valley filling are respectively 1, which indicates that peak clipping and valley filling are allowed, 0, which indicates that peak clipping and valley filling are not allowed, beta pf,t 、β vf,t Not 1 at the same time; mu (mu) pf,t 、μ vf,t Compensating the price for the unit electric quantity of the user participating in peak clipping and valley filling respectively; p (P) ES,pf,t 、P ES,vf,t Respectively storing the competitive bidding electric quantity of the electric energy to participate in peak clipping and valley filling markets; p (P) pf 、P vf The admission capacity of peak clipping and valley filling markets respectively;
electricity cost C of electrical load in t-period virtual power plant Le,t The method comprises the following steps:
C Le,t ＝λ se,t P L,e,t -C Lec,t (7)
wherein lambda is se,t Selling electricity prices for the virtual power plants;
revenue f of t-period energy storage combined user participating in peak shaving market 1,t The method comprises the following steps:
f 1,t ＝λ pf,t [P ES,pf,t +P DR,pf,t ]+λ vf,t [P ES,vf,t +P DR,vf,t ] (8)
wherein P is DR,pf,t 、P DR,vf,t Respectively participating in competitive bidding electric quantity of peak clipping and valley filling markets for flexible loads; lambda (lambda) pf,t 、λ vf,t Peak clipping and valley filling prices of the unit electric quantity in the peak shaving market are respectively set;
the peak shaving period requires the combined competitive power of the electric energy storage and the flexible load to meet the admission capacity of the peak shaving market, and the competitive power of the non-peak shaving period is zero;
wherein P is ES,max Maximum charge and discharge power for the electric energy storage;
the output of the electric energy storage participating in the electric market must not exceed the upper and lower power limits, and the constraint is as follows:
-P ES,max ≤P ES,e,t ≤P ES,max (12)
wherein P is ES,e,t The charge and discharge quantity of the electric energy stored in the electric power market is t time period;
the energy storage participation two market electric quantity must not exceed the maximum capacity, and the initial and final electric quantity in one scheduling period are the same:
wherein, the subscript T is a scheduling period; e (E) ES,t Charge amount for t period of energy storage E ES,min 、E ES,max Respectively the minimum charge and the maximum charge of energy storage, P ES,c,t 、P ES,d,t And
the charging and discharging power and the charging and discharging efficiency of the electric energy storage participating in the electric power market in the t period are respectively; e (E) ES,t0 、E ES,T 、E ES,t-1 E representing different time periods ES,t A value; />
(3) The wind power and photovoltaic total-absorption strategy is as follows:
because the heat and power cogeneration unit in the real-time stage can downwards adjust the output through the new heat source, the heat and power cogeneration unit in the day-ahead can provide positive reserve, and the constraint of the reserve heat and power cogeneration is as follows:
R chp,up,t ≥△P w-,t +△P pv-,t (14)
wherein R is chp,up,t For the positive standby of the cogeneration unit, delta P w-,t 、△P pv-,t Negative deviations of wind power and photovoltaic output are respectively obtained.
4. The virtual power plant combined heat and power optimization scheduling method based on thermal decoupling according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 3, in the day-ahead stage, the gas boiler and the heat energy storage are not involved in day-ahead scheduling as flexible resources, and the day-ahead bidding strategy is as follows:
(1) The virtual power plant coordination scheduling center predicts the output force according to wind and light, and preferably schedules surfing;
(2) The cogeneration output participates in the electricity and heat market, and declares the day-ahead electricity and heat bid amount;
(3) The electric energy storage participates in the electric energy market and the flexible load is combined to participate in peak shaving market energy reporting;
because of uncertainty of wind and light output, the declaration output of the virtual power plant before the day needs to be adjusted in real time in the day, and real-time scheduling is as follows:
(1) if the output deviation is larger than 0, namely the actual output is larger than the planned output, the heat energy storage and the gas boiler increase the heat output so as to reduce the cogeneration power output, and if the deviation is not enough to be stabilized, the power is sold at low price;
(2) if the output deviation is smaller than 0, namely the actual output is smaller than the planned output, the heat energy is stored to increase the electric output of the cogeneration unit; if the deviation is not sufficiently stabilized, the electric energy is purchased at a high price.
5. The virtual power plant combined heat and power optimization scheduling method based on thermal decoupling according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 4, the two-stage scheduling model is established, including the following steps:
step 4.1: the virtual power plant declares the output to each energy market with the maximum profit as the target before the day, and the net profit F of the virtual power plant is obtained in the period of t vpp,t ：
F vpp,t ＝f 1,t +f 2,t +f 3,t -f 4,t +C Le,t +C Lh,t (15)
Wherein f 2,t 、f 3,t 、f 4,t The electricity market selling benefits, the heat market selling benefits and the running cost of the virtual power plant in the period t are respectively; c (C) Lh,t The method comprises the steps of purchasing heat cost for the internal heat load of the virtual power plant;
wherein lambda is be,t 、λ se,t The electricity purchase price of the virtual power plant; p (P) s,t Selling electric power for participating in electric market of t-period virtual power plant, P b,t 、P vf,t The virtual power plants participate in the electricity market and the electricity purchasing power of the valley filling market in the period t respectively; lambda (lambda) sh,t 、Q L,h,t Selling heat price and heat load power in the virtual power plant for a period t;
step 4.2: the virtual power plant operating costs include:
(1) Cogeneration power generation cost f 41,t ：
The electricity generation cost of the cogeneration unit can be reflected by the following gas purchase cost:
wherein lambda is g Is the price of natural gas, Q gas For the low calorific value of natural gas, 9.97kWh/m was taken 3 ；
(2) Cost f of electric energy storage operation 42,t ：
The energy storage is lost in the charging and discharging processes, so the operation cost of the energy storage can be given by the following formula
f 42,t ＝a|P ES,t | 2 +b|P ES,t |+c (18)；
Wherein a, b and c are consumption characteristic parameters of electric energy storage;
(3) Wind power and photovoltaic power generation cost f 43,t ：
f 43,t ＝λ w P w,t +λ pv P pv,t (19)；
Wherein lambda is w 、λ pv The wind and light unit electric quantity power generation cost coefficients are respectively; p (P) w,t 、P pv,t The predicted output of wind and light in the t period is respectively;
(4) Environmental cost f 44,t ：
In the method, in the process of the invention,
the j-th waste gas amount generated for the unit electric energy of the i-th unit; p (P) mt,i,t Generating power for the t period of the ith gas turbine; n and m are the number of cogeneration units and the number of exhaust gas types, respectively, where m=3, and include NO x 、SO 2 And CO 2 Three gases, k j A unit cost coefficient for the j-th gas;
step 4.3: the penalty cost caused by the output deviation, the gas cost of the gas boiler and the operation cost of the thermal energy storage are considered in the real-time scheduling model, and the method is expressed as follows:
wherein, all the zones' represent real-time output or cost, and the same day of schedule is adopted;
introducing a new heat source gas boiler and heat energy storage output;
Respectively adding the gas purchasing cost of the gas boiler and the operation cost of heat energy storage;
The environmental cost of the boiler is increased;
Penalty cost for virtual power plant output bias;
because the purchase heat price of the virtual power plant is larger than the sale heat price, the heat energy storage does not consider the purchase heat from the outside, but starts to transfer the internal heat output of the virtual power plant to follow the change of wind and light output, namely
The expression is as follows:
wherein C is chu Cost of investment and construction for converting heat energy storage into daily, Q chure 、ρ chure The heat energy storage capacity and the unit capacity cost are respectively; n represents the service life;
the j-th exhaust gas amount generated for the unit capacity of the a-th gas boiler;
The heat generation amount is the heat generation amount of the a-th gas boiler in the t period;
the electric output deviation of the virtual power plant in the period t is
The specific penalty is as follows: />
Punishment electricity price setting:
wherein lambda is ep,t The electricity output deviation punishment electricity price is used for t time period, and the electricity output deviation punishment electricity price is respectively adjusted up or down by 50% by taking electricity selling price as a reference;
virtual power plant output deviation penalty cost
Represented by the formula:
wherein DeltaP t pf 、△P t vf 、△P t h And respectively scheduling peak clipping, peak shaving, valley filling, peak shaving and heat output deviation in real time in the t period.
6. The virtual power plant combined heat and power optimization scheduling method based on thermal decoupling according to claim 5, wherein the method is characterized by comprising the following steps of: in the step 4, the scheduling model includes the following constraints:
(1) Power balance constraint:
day-ahead power balance constraint:
wherein P is e,t 、Q h,t 、P pf,t Respectively is the day-ahead electricity,Heat and peak clipping and regulating bidding quantity, P e,t ＝P s,t -P b,t ；
Real-time power balance constraint:
(2) Climbing constraint and power constraint of cogeneration unit:
wherein P is mt，min 、P mt，max Respectively minimum power and maximum power of the cogeneration unit, P mt，up 、P mt，down Respectively the upper limit and the lower limit of the climbing power of the cogeneration;
(3) Thermal energy storage capacity and charge-discharge thermal constraints:
in which Q HS,t Charging and discharging power for t-period heat energy storage; q (Q) HS,max 、Q HS,min Upper and lower limits of charging and discharging power for heat energy storage E HS,max 、E HS,min Upper and lower limits of capacity for thermal energy storage;
(4) Boiler power and climbing constraints:
in which Q gb,max 、Q gb,min The upper and lower limits of boiler power are respectively; q (Q) gb,up 、Q gb,down The upper limit and the lower limit of the climbing power of the boiler are respectively.
7. The virtual power plant combined heat and power optimization scheduling method based on thermal decoupling according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 5, the scheduling model is solved by adopting a mode of calling a solver Cplex by a Yalmip, and the specific steps are as follows:
5.1: inputting initial data such as wind before day, light forecast output, time-of-use electricity price, load data and the like;
5.2: defining decision variables such as the day-ahead and day-in-day output of the unit by using the sdpvar command;
5.3: defining an objective function, yalmip solves for minima by default, thus defining an objective in the algorithm as-F vpp,t ；
5.4: adding constraint conditions, and defining constraint conditions by adopting [ ] commands;
5.5: setting solving parameters and solving, firstly setting the parameters by an sdsetting command;
invoking the Cplex solver by an sdsetting ('solver', 'Cplex') command; secondly, solving through an optimize command;
5.6: and outputting an optimization result, and obtaining the optimization result by using the value command.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117096874A (en) * | 2023-09-27 | 2023-11-21 | 华中科技大学 | Modeling method and application of power system scheduling model |
| CN118249422A (en) * | 2024-05-27 | 2024-06-25 | 国网吉林省电力有限公司经济技术研究院 | Industrial virtual power plant optimal scheduling method considering hydrogen production and energy storage |
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| CN117096874A (en) * | 2023-09-27 | 2023-11-21 | 华中科技大学 | Modeling method and application of power system scheduling model |
| CN117096874B (en) * | 2023-09-27 | 2024-01-05 | 华中科技大学 | Modeling method and application of power system scheduling model |
| CN118249422A (en) * | 2024-05-27 | 2024-06-25 | 国网吉林省电力有限公司经济技术研究院 | Industrial virtual power plant optimal scheduling method considering hydrogen production and energy storage |
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