CN113256045B - Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty - Google Patents
Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty Download PDFInfo
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Abstract
The invention discloses a park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty. Because the CCG method has better calculation efficiency in processing the robust optimization model, the method adopts the CCG method to solve. And finally, carrying out example simulation by using a commercial solver Gurobi to obtain a two-stage robust optimization scheduling strategy considering the combined thermoelectric demand response, verifying that the method can better treat the uncertainty in the system, and simultaneously can reduce the operation cost of the park and promote the consumption of new energy.
Description
Technical Field
The invention belongs to the technical field of optimization operation of a comprehensive energy system, and particularly relates to a park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty.
Background
The variance of the actual output and the predicted output of the wind power is far larger than the traditional load variance, and the traditional power system operation mode is not enough to ensure the reliability of the system. Due to physical limitations of an electric power system (such as climbing limitation of capacity of a conventional generator and a power transmission line), wind power reduction frequently occurs, so that the wind power utilization rate is low, and the enthusiasm of wind power investment is inhibited in the long run. When a large-scale renewable energy source is connected into a power system, the output of the renewable energy source is difficult to be accurately predicted due to strong randomness and intermittence of the renewable energy source, and in planning and scheduling of the power system, the error of the output predicted value of the renewable energy source causes non-negligible influence, in other words, the random fluctuation of renewable energy source power generation reduces the supply flexibility of the energy source system. Therefore, the traditional deterministic optimal scheduling method cannot meet the requirements, and the uncertainty of renewable energy sources needs to be considered on the basis of the original deterministic optimal scheduling method.
Stochastic optimization and robust optimization are typical methods for solving the problem of optimization with uncertainty. Compared with random optimization, the robust optimization has the advantages of simple data acquisition, high solving speed, suitability for solving large-scale uncertainty problems and the like, and is widely applied to processing uncertainty problems. At present, many scholars research the application of robust optimization in an integrated energy system, but the existing literature does not fully consider the influence of the energy coupling relation of various devices in the system and the response of a load side demand side on the uncertainty of renewable energy in the process of utilizing the robust optimization to process the wind-solar uncertainty of the integrated energy system.
The comprehensive energy system model can adaptively adjust the output of the unit to adapt to the change of the generated energy of the renewable energy source through the energy conversion relation among the devices, and the running safety of the system is ensured. The combined thermoelectric demand response can fully utilize the coupling relation among various devices, further improve the capability of the system for dealing with the uncertainty of renewable energy sources, reduce the abandoned wind and abandoned light and increase the permeability of the renewable energy sources. Therefore, on the basis of the existing research, a two-stage adjustable robust optimization model of the park comprehensive energy system based on the combined heat and power demand response is further considered, and the two-stage adjustable robust optimization model has important significance for the optimization operation of the comprehensive energy system.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty, a two-stage adjustable robust optimization model considering combined thermoelectric demand response is established to process uncertainty problems caused by wind and light output in a park, a dual-layer max-min problem in the model is converted into a single-layer max problem through a dual theory, and a CCG (column and constraint generation) method is adopted to solve, so that the dispatching result is more practical while the solving speed is improved, and the consumption of new energy is further promoted.
In order to solve the technical problems, the invention adopts the technical scheme that: in order to consider the uncertainty of renewable energy sources and aim at the problem of day-ahead economic dispatching of the park integrated energy system, the park integrated energy system day-ahead economic dispatching method considering the uncertainty of wind and light is provided, and a two-stage adjustable robust model considering the combined heat and power demand response is established. The day-ahead economic dispatching of the park comprehensive energy system aims at a basic scene with wind power and photovoltaic output values as predicted values, and when uncertainty occurs in operation, the park comprehensive energy system can adaptively and safely redistribute a generator set, a heat supply unit, P2G equipment, energy storage equipment and energy exchange between a park and a superior network. The optimized scheduling decision completely accords with the idea of a two-stage adjustable robust model. In other words, the start-stop state of the gas turbine and the cogeneration unit is determined in the first stage under the basic scene, and the worst unsafe scene of the system is searched when uncertainty occurs in the second stage. The two-stage adjustable robust scheduling model provided by the invention assumes that the start-stop state of the unit is a first-stage variable, namely when the uncertain variable of the system changes in the fluctuation interval of the uncertain variable, the start-stop state of the unit is kept unchanged, because the physical characteristics of most generator sets limit that the start-stop state of the unit cannot be changed rapidly under the uncertain condition.
A park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty comprises the following steps:
(1) Determining the specific composition of the multi-energy park, including the introduced new energy form and the specific equipment composition;
(2) Establishing models of various energy conversion equipment in the park;
(3) Establishing a demand response model;
(4) On the premise of meeting system safety constraints, establishing a two-stage adjustable robust model considering combined heat and power demand response by taking the minimum running cost of a basic scene as a target function;
(5) Obtaining an abstract expression of a two-stage adjustable robust optimization model of the park comprehensive energy system day-ahead economic dispatching;
(6) Establishing a maximum and minimum subproblem of worst scene identification;
(7) Solving a two-stage adjustable robust model considering combined heat and power demand response by using a CCG method;
(8) Inputting energy access, new energy output data, equipment parameters and operating parameters of the park integrated energy system, and solving a two-stage robust optimization model of the park integrated energy system day-ahead economic dispatching considering wind-solar uncertainty by adopting a commercial solver Gurobi to obtain a dispatching strategy of the park integrated energy system day-ahead economic dispatching.
Further, the specific composition of the park integrated energy system in the step (1) is as follows:
(1) The new energy form of the park integrated energy system is as follows: wind power and photovoltaic power generation;
(2) The energy conversion equipment introduced into the comprehensive energy system of the park comprises: electricity-to-gas equipment, an electric boiler, a gas turbine, a cogeneration unit and gas/heat storage equipment.
The energy conversion equipment models in the park are as follows;
(2.1) model of electric gas-converting apparatus
In the formula: t is scheduling time; m is an index of the electric-to-gas equipment;
respectively gas making power, consumed power and electricity-to-gas efficiency, L, of an electricity-to-gas (P2G) facility HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
The minimum and maximum pneumatic power of the mth station P2G.
(2.2) electric boiler model
In the formula: t is scheduling time; n is an electric boiler index;
and &>
Respectively the power consumption and the heat production of the nth electric boiler in the time period t;
Is the electric heat conversion efficiency of the nth electric boiler>
Dividing into the minimum and maximum heating power of the nth electric boiler;
And (4) starting and stopping states of the nth electric boiler in a time period t (1 represents starting, and 0 represents stopping). />
(2.3) gas turbine model
In the formula: t is scheduling time; q is a gas turbine index;
and &>
Respectively representing the power generation power and the gas consumption power of the gas turbine; f (-) represents the gas turbine energy consumption curve;
And &>
Respectively representing the consumption of natural gas required by the startup and shutdown of the gas turbine; a is a q 、b q And c q Represents the gas coefficient of F (-); l is HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
And dividing the power into the minimum and maximum power generation power of the q gas turbines.
Representing the power generation power of the qth gas turbine in the t period;
The start-stop state (1 represents start-up and 0 represents stop) and the judgment is carried out on the qth gas turbine in the t period>
Starting and stopping states of a qth gas turbine in a time period t;
The upward climbing rate and the downward climbing rate of the qth gas turbine,
for the continuous starting and stopping time of the qth gas turbine within the time period t-1, the pressure is adjusted>
The minimum startup and shutdown time of the qth gas turbine in the time period t.
(2.4) Combined Heat and Power Unit model
In the formula: t is scheduling time; p is an index of the cogeneration unit;
and &>
Respectively representing heat production power and gas consumption power of a combined heat and power generation unit (CHP);
The generating power and the generating efficiency of the micro-combustion engine in the t period are based on the pressure value>
For the heat dissipation loss rate, the device can be used for>
And &>
The heating coefficient and the flue gas recovery rate of the bromine refrigerator are respectively; l is HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
Dividing into the p (th) CHP unit minimum and maximum generating power;
Representing the power generation power of the p-th cogeneration unit in the t period;
The starting and stopping states of the pth CHP unit in the t period (1 represents starting, 0 represents stopping), and/or>
The starting and stopping state of a P-th cogeneration unit in a time period t;
The climbing rate and the descending rate of the pth CHP unit;
For the continuous startup and shutdown time of the pth CHP unit in the t-1 time period,
for the minimum startup of the p-th CHP unit in the time period tAnd downtime.
(2.5) energy storage device model
ESS(t)＝(1-σ S )·ESS(t-1)+ES in (t)·η in -ES out (t)/η out
In the formula: t is scheduling time; ESS (t), ES in (t)、ES out (t) stored energy, stored energy power and released energy power of the energy storage device are respectively in a time period t; ESS (t-1) represents the stored energy of the energy storage device during the t-1 period; sigma S Is the self-consumption rate of the energy storage system; eta in 、η out Respectively storing energy and releasing energy efficiency for the energy storage equipment;
gas storage, gas discharge power, GS, for a period of t in ,max 、GS out,max The maximum gas storage and gas release power of the gas storage device are respectively; c t GS The air storage quantity of the air storage device is greater or less for a time period t>
The gas storage capacity of the gas storage equipment is t-1 time; c GS,min 、C GS,max Respectively the minimum and maximum gas storage capacity, eta, of the gas storage device CGS 、η GS,in 、η GS,out The self-consumption rate, the gas storage efficiency and the gas release efficiency of the gas storage equipment are improved.
For heat-storage, heat-release power for period t, HS in,max 、HS out,max The maximum heat storage power and the maximum heat release power of the heat storage equipment are respectively;
A reserve of heat storage apparatus for a time period t>
The gas storage capacity of the heat storage equipment is t-1 time period; c HS,min 、C HS,max Minimum and maximum heat storage capacity, eta, of the heat storage apparatus CHS 、η HS,in 、η HS,out The self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment are shown.
The demand response model in the step (3) is specifically as follows:
in the formula: t is scheduling time;
P t DR 、P t DR，inte 、P t DR，shif 、P t LD，max predicted value of electric load, demand side response electric load, demand side transfer electric load, maximum electric load allowed by system, P inte,max The maximum interruptible electrical load within the scheduled time.
The maximum interruptible and extractable electrical load proportion for the t period.
Positive means turning out of translatable load, and vice versa.
In the formula: t is scheduling time;
and &>
Respectively obtaining a predicted value of the thermal load, a demand side response thermal load proportion, a maximum thermal load allowed by a system and a thermal load after considering demand response in a time period t; h DR,max The maximum interruptible thermal load within the scheduling time; n is a radical of hydrogen t The time period is scheduled for the whole time.
The two-stage adjustable robust model considering the combined heat and power demand response in the step (4) is specifically as follows:
(4.1) objective function
In the formula: t is scheduling time;
respectively the electricity and gas purchase cost;
The starting-up and shutdown costs of the pth CHP are respectively;
Penalty fee for abandoning wind>
Punishment of cost for light abandonment;
To sell the electricity, C E,cc A compensation cost to interrupt the electrical load for demand side response;
For purchasing electric power>
In order to obtain the gas purchasing power,
wind abandoning power and light abandoning power of the ith fan and the jth group of photovoltaic cells in the t period are respectively set;
For selling electricity power, is selected>
To needAnd the side is asked to interrupt the electric load. Δ t is the scheduling time interval, N t The number of time segments is scheduled.
Respectively the unit prices of purchasing electricity, purchasing gas and selling electricity>
Penalty prices in units of wind abandon and light abandon, respectively>
Is a compensation price per unit demand side response to an interrupted electrical load.
The start-stop state (1 represents the start-up and 0 represents the shutdown) of the pth CHP in the t period, and the judgment is performed>
The cost of starting up and stopping the p-th CHP is respectively. N is a radical of hydrogen WT 、N PV 、N CHP The number of the fan, the photovoltaic cell, the electric boiler and the CHP are respectively.
(4.2) constraint Condition
In the formula: t is scheduling time; (.) u The wind and light output is a corresponding adjusted variable under real-time change; v. of 1,t And v 2,t A slack variable is a power balance constraint; v. of 3,t And v 4,t Constraint relaxation variables for thermal balance; omega WT 、Ω PV Respectively representing uncertain collections of wind power output and photovoltaic output;
deviation of wind power, photovoltaic output and predicted value respectively>
The ratio of the wind power and photovoltaic output deviation value to the predicted value is obtained;
Is a variable from 0 to 1 in the uncertain convergence; delta i 、Δ j Respectively obtaining wind power and photovoltaic output uncertainty precalculated values;
Correcting the climbing and descending of the pth CHP unit;
Correcting the climbing and descending of the qth gas turbine unit; and lambda (-) is a dual variable corresponding to the constraint condition.
The abstract expression of the two-stage adjustable robust optimization model of the park comprehensive energy system economic dispatch in the past is as follows:
in the formula: x represents the starting and stopping states of units related to the CHP and the gas turbine, y and z represent the basic scene and the scheduling output of other units of the system adjusted according to the wind-light output transformation, u is an uncertain variable related to the uncertainty of the wind power output and the photovoltaic output, and c b 、c g A, B, B, F, h, C, D, E, F, G can be derived from the objective function and constraint conditions in 5.
The maximum and minimum subproblems identified in the worst scene in the step (6) are specifically as follows:
the two-stage adjustable robust model set for solving and considering combined heat and power demand response by using the CCG method in the step (7) is as follows:
(7.1) Main problem
Ax+By≤b
(7.2) sub-problem
(7.3) CCG solving step
Step 1: let iteration counter s =0 set the maximum value epsilon of security violation allowed by the system RO 。
Step 2: solving the main problem, if the main problem is solved, obtaining a system unit starting and stopping state x and a unit output arrangement y, and performing the step 3; otherwise, stopping iteration and outputting no solution.
And step 3: and (3) solving the maximum and minimum subproblems according to the x and y obtained by the solution in the step (2), and finding out the wind power and photovoltaic output under the worst scene which causes the maximum possibility of violating the safety specified value.
And 4, step 4: if the maximum possible violation safety-specifying value solved in step 3 is less than epsilon RO Then x and y are the final optimization solution and the iteration is stopped; otherwise, let s = s +1, according to the wind power and photovoltaic output value under the worst scene solved in step 3
The CCG constraint shown below is added to the main question, returning to step 2.
f T v s ≤ε RO
And (8) the data of the park integrated energy system also comprises the specific composition of the park integrated energy system, energy price, equipment parameters and values of each energy conversion equipment, demand response proportion, new energy output fluctuation conditions in a basic scene and a worst scene, maximum violation safety specified value and a predicted value of new energy output and load.
Compared with the prior art, the invention has the beneficial effects that:
(1) The optimization problem containing uncertainty is solved by adopting robust optimization, and a two-stage adjustable robust optimization model is established, so that a result close to reality can be obtained by only acquiring simple data in the scheduling process of the park comprehensive energy system, and the solving speed is high. The improved two-stage adjustable robust optimization model considers the wind and light uncertainty of the comprehensive energy system and simultaneously fully considers the influence of the energy coupling relation of various devices in the system and the response of the load side on the uncertainty of renewable energy. The day-ahead economic dispatching of the park comprehensive energy system aims at a basic scene with wind power and photovoltaic output values as predicted values, and when uncertainty occurs in operation, the park comprehensive energy system can adaptively and safely redistribute a generator set, a heat supply unit, P2G equipment, energy storage equipment and energy exchange between a park and a superior network. The two-stage robust optimization ensures that the system can meet safety constraints under any condition, minimizes the operation cost of a basic scene, and meets the requirements of system safety and economy.
(2) After the combined heat and power demand response is considered, the functions of gas energy storage, heat energy storage and electric heat load demand response are fully exerted, the coupling relation among various devices can be fully utilized by the combined heat and power demand response, the uncertainty capability of the system for coping with renewable energy sources is further improved, the wind and light curtailment is reduced, and the permeability of the renewable energy sources is increased. The comprehensive energy system model can adaptively adjust the output of the unit to adapt to the change of the generated energy of the renewable energy source through the energy conversion relation among the devices, thereby ensuring the safety of the operation of the system, promoting the consumption of the renewable energy source, improving the robustness of the system and improving the economic benefit of the park.
Drawings
FIG. 1 is a flow chart of the steps of the method of the present invention;
FIG. 2 is a diagram of the specific components of the park integrated energy system;
FIG. 3 is a CCG method solving flow chart;
FIG. 4 is a forecast of wind turbines, photovoltaic output, electrical load and thermal load for a typical winter day for a park multi-energy integrated system.
Detailed Description
In order to explain the technical solutions disclosed in the present invention in detail, the present invention is further explained with reference to the accompanying drawings and specific embodiments.
The invention discloses a park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty. The specific implementation step flow is shown in fig. 1, and the technical scheme of the invention comprises the following steps:
step 1: the specific composition of the park integrated energy system is determined, including the new energy form introduced and the specific equipment composition.
(1.1) the new energy form of the park integrated energy system is as follows: wind power and photovoltaic power generation;
(1.2) energy conversion equipment introduced into a park comprehensive energy system comprises: electricity changes gas equipment, electric boiler, gas turbine, combined heat and power generation unit, gas storage/heat-retaining equipment.
Step 2: and establishing various energy conversion equipment models in the park.
(2.1) model of electric gas-converting apparatus
The P2G technology can realize conversion from electric energy to natural gas, the natural gas is transmitted in a large-capacity, long-time and long-distance mode through a natural gas pipeline, powerful technical support is provided for renewable energy consumption, large-range and long-distance space-time transfer of wind power can be achieved, meanwhile, the P2G response is rapid, and the application prospect is strong. The relationship between the P2G equipment gas making power and the power consumption and the limitation of the gas making power are as follows:
in the formula: t is scheduling time; m is an index of the electric-to-gas equipment;
respectively the gas making power, the electricity consumption power and the electricity conversion gas efficiency of the electricity conversion gas (P2G) equipment, L HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
The minimum and maximum pneumatic power of the mth station P2G.
(2.2) electric boiler model
The introduction of the electric boiler can break the electric-thermal coupling hard constraint of the CHP unit and change the traditional 'electricity by heat' scheduling mode. The electric boiler can coordinate the peak valley of the electric heating load, and the relation between the heating capacity and the power consumption and the heating capacity constraint are as follows:
in the formula: t is scheduling time; n is an electric boiler index;
and &>
Respectively the power consumption and the heat production of the nth electric boiler in the time period t;
Is the electric heat conversion efficiency of the nth electric boiler>
Dividing into the minimum and maximum heating power of the nth electric boiler;
The start-stop state of the nth electric boiler in the time period t is shown (1 represents start-up, and 0 represents stop).
(2.3) gas turbine model
The gas turbine converts chemical energy in natural gas into electric energy, and the relation between the consumed natural gas power and the generated power, the generated power limit, the climbing constraint and the minimum on-off time constraint are as follows:
in the formula: t is scheduling time; q is a gas turbine index;
and &>
Respectively representing the power generation power and the gas consumption power of the gas turbine; f (-) represents the gas turbine energy consumption curve;
And &>
Respectively representing the consumption of natural gas required by the startup and shutdown of the gas turbine; a is q 、b q And c q Represents the gas coefficient of F (-); l is HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
Dividing into the q gas turbine minimum and maximum power generation workAnd (4) rate.
Representing the generated power of the qth gas turbine in the t period;
The start-stop state (1 represents start-up and 0 represents stop) and the judgment is carried out on the qth gas turbine in the t period>
Starting and stopping states of a qth gas turbine in a time period t;
The upward climbing rate and the downward climbing rate of the qth gas turbine,
for the continuous starting and stopping time of the qth gas turbine within the time period t-1, the pressure is adjusted>
The minimum startup and shutdown time of the qth gas turbine in the time period t.
(2.4) Combined Heat and Power Unit model
Neglecting the influence of external environment change on the power generation and fuel combustion efficiency, the mathematical relationship of the thermoelectric relationship, the gas-electricity relationship, the relationship between the power generation power, the power generation power limit, the climbing constraint and the minimum on-off time constraint are as follows:
in the formula: t is scheduling time; p is an index of the cogeneration unit;
and &>
Respectively representing heat production power and gas consumption power of a combined heat and power generation unit (CHP);
The generating power and the generating efficiency of the micro-combustion engine in the t period are based on the pressure value>
For heat dissipation loss rate>
And &>
The heating coefficient and the flue gas recovery rate of the bromine refrigerator are respectively; l is HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
Dividing into the p-th CHP unit minimum and maximum generating power;
Representing the power generation power of the p-th cogeneration unit in the t period;
Starting and stopping states (1 represents starting, 0 represents stopping) and/or on/off for the pth CHP unit in a t period>
The starting and stopping state of a P-th cogeneration unit in a time period t;
The climbing rate and the descending rate of the pth CHP unit;
For the continuous startup and shutdown time of the pth CHP unit in the t-1 time period,
the minimum startup and shutdown time of the p-th CHP unit in the time period t is obtained.
(2.5) energy storage device model
The relationship among the energy storage device model, the gas storage/release of the heat storage device, the constraint of the stored/released power and the gas storage at the time t, the constraint of the gas storage and the heat storage capacity of the heat storage device, the gas storage at the time t, the gas storage and the heat storage capacity of the heat storage device, the gas storage at the time t-1, the stored heat quantity, the stored/released gas at the time t and the stored/released power is as follows:
ESS(t)＝(1-σ S )·ESS(t-1)+ES in (t)·η in -ES out (t)/η out
in the formula: t is scheduling time; ESS (t), ES in (t)、ES out (t) storing energy, stored energy power and released energy power of the energy storage device at a time period t, respectively; ESS (t-1) represents the stored energy of the energy storage device during the t-1 period; sigma S Is the self-consumption rate of the energy storage system; eta in 、η out Respectively storing energy and releasing energy efficiency for the energy storage equipment;
gas storage, gas discharge power, GS, for a period of time t in ,max 、GS out,max The maximum gas storage and gas release power of the gas storage device are respectively;
The air storage quantity of the air storage device is greater or less for a time period t>
The gas storage capacity of the gas storage equipment is t-1 time; c GS,min 、C GS,max Respectively the minimum and maximum gas storage capacity, eta, of the gas storage device CGS 、η GS,in 、η GS,out The self-consumption rate, the gas storage efficiency and the gas release efficiency of the gas storage equipment are improved.
For heat-storage, heat-release power for period t, HS in,max 、HS out,max The maximum heat storage power and the maximum heat release power of the heat storage equipment are respectively;
A reserve of heat storage apparatus for a time period t>
The gas storage capacity of the heat storage equipment is t-1 time period; c HS,min 、C HS,max Minimum and maximum heat storage capacity, eta, of the heat storage apparatus CHS 、η HS,in 、η HS,out The self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment are obtained.
And step 3: and establishing a demand response model.
Dividing the demand side electric load capable of responding into a translatable electric load and an interruptible electric load, considering the interrupting cost of the interruptible load, wherein the sum of the demand side electric load capable of responding and the current time interval electric load needs to be less than the maximum electric load allowed in the current time interval, and the relation between the predicted value of the electric load and the electric load after the demand response is as follows:
in the formula: t is scheduling time;
P t DR 、P t DR，inte 、P t DR，shif 、P t LD，max predicted value of electric load, demand side response electric load, demand side transfer electric load, maximum electric load allowed by system, P inte,max The maximum interruptible electrical load during the scheduled time.
The maximum interruptible and extractable electrical load proportion for the t period. P t DR,shif Positive means turning out of translatable load, and vice versa.
The demand side may respond to thermal load constraints as follows:
in the formula: t is scheduling time;
and &>
The predicted value of the thermal load, the response thermal load of the demand side, the response thermal load proportion of the demand side and the system are respectivelyMaximum allowable thermal load and electrical load after considering demand response; h DR,max The maximum interruptible thermal load within the scheduling time; n is a radical of t The entire schedule period.
And 4, step 4: on the premise of meeting the system safety constraint, a two-stage adjustable robust model considering the combined heat and power demand response is established by taking the minimum running cost of a basic scene as an objective function.
(1) Objective function
The two-stage robust optimization model provided by the invention aims to minimize the running cost of a basic scene on the premise of meeting the system safety constraint, and because no load loss is allowed in the basic scene, an objective function does not contain a load loss related variable, and the objective function and a related equation are constrained as follows:
in the formula: t is scheduling time;
respectively the electricity and gas purchase cost;
The starting-up and shutdown costs of the pth CHP are respectively;
Penalty fee for wind abandon, based on the sum of the wind and the wind>
Punishment of cost for light abandonment;
To earn electricity sales, C E,cc A compensation cost to interrupt the electrical load for demand side response;
For purchasing power, is turned on or off>
In order to purchase the gas power, the gas-purchasing power,
wind abandoning power and light abandoning power of the ith fan and the jth group of photovoltaic cells in a time period t are respectively set; p t out To sell electric power, P t DR,inte The electrical load is interrupted for the demand side. Δ t is the scheduling time interval, N t Is the number of scheduling periods.
Respectively the unit purchase electricity, gas and electricity prices phi t WT 、φ t PV Punishment price of wind abandoning and light abandoning respectivelyShelf, or>
Is a compensation price per unit demand side response to an interrupted electrical load.
The start-stop state (1 represents the start-up and 0 represents the shutdown) of the pth CHP in the t period, and the judgment is performed>
The cost of starting up and stopping the p-th CHP is respectively. N is a radical of WT 、N PV 、N CHP The number of the fan, the photovoltaic cell, the electric boiler and the CHP are respectively.
(2) Constraint conditions
In order to ensure the safe operation of the system, the section adopts a double-layer max-min model to identify the worst scene causing the least safe operation of the system, namely the maximum security violation specified value (security operation), wherein the maximum security violation specified value of the system in the worst scene is required to be smaller than a preset value epsilon RO ，ε RO The values are set in relation to predetermined system security levels to ensure safe operation of the campus energy complex. The method comprises the following steps of causing violation of the maximum value of system safety regulation, uncertainty set of wind power output and photovoltaic output, system energy balance constraint, constraint that a slack variable is constantly greater than zero, unit output constraint, unit error correction climbing constraint under uncertainty of renewable energy output, energy storage constraint, power exchange constraint with a superior network, demand side response constraint and wind and light abandoning constraint in the worst scene under an uncertain condition as follows:
v 1t ,v 2t ,v 3t ,v 4t ≥0
P t u,out ≤P out,min ,P t u,out ≤P out,max :(λ 9_3,t ,λ 9_4,t )
in the formula: t is scheduling time;
respectively comprise abandoned wind, abandoned light, electric load after demand response, P2G consumed electric power, electric boiler consumed electric power, park electricity selling power, CHP unit output and gas turbine output according to different wind power outputs>
And photovoltaic output->
An adjusted real-time value; (. Cndot.) u The wind and light output is a corresponding adjusted variable under real-time change; v. of 1,t And v 2,t A slack variable is a power balance constraint; v. of 3,t And v 4,t Constraint relaxation variables for thermal balance; omega WT 、Ω PV Respectively representing uncertain collections of wind power output and photovoltaic output;
Deviation between wind power output and photovoltaic output and a predicted value, respectively>
The ratio of the wind power and photovoltaic output deviation value to the predicted value is obtained;
Is a variable from 0 to 1 in the uncertain convergence; delta of i 、Δ j Respectively calculating uncertainty precalculated values of wind power output and photovoltaic output;
Correcting the climbing and descending of the pth CHP unit;
Correcting the climbing and descending of the qth gas turbine unit; and lambda (-) is a dual variable corresponding to the constraint condition.
And 5, obtaining an abstract expression of a two-stage adjustable robust optimization model of the park comprehensive energy system day-ahead economic dispatching.
The proposed two-stage robust optimization model relates to three systems of electricity, gas and heat, involves more constraints, contains uncertain parameters, and is a nonlinear mixed integer programming problem. For ease of discussion, the two-stage robust optimization scheduling model proposed in this section may take the form of an abstract robust optimization model as follows:
s.t.Ax+By≤b
in the formula: x represents the starting and stopping states of units related to the CHP and the gas turbine, y and z represent the basic scene and the scheduling output of other units of the system adjusted according to the wind-light output transformation, u is an uncertain variable related to the uncertainty of the wind power output and the photovoltaic output, and c b 、c g A, B, B, C, D, E, F, G can be derived from the objective function and constraint conditions in 5.
And 6, establishing the maximum and minimum subproblems of the worst scene identification.
And 4, the sub-problem is the problem of identifying the worst scene, the scene causing the maximum violation of the safety specified value of the system is found through the double-layer max-min problem shown in the step 4, namely the specific value of the uncertain quantity in the worst scene is determined, and the double-layer max-min problem is converted into a single-layer bilinear maximum optimization sub-problem shown below through a dual theory.
And 7, solving a two-stage adjustable robust model considering the joint thermoelectric demand response by utilizing a CCG (column and constraint generation) method.
(7.1) Main problem
Ax+By≤b
(7.2) sub-problem
(7.3) CCG solving step
Step (7.3.1): let the iteration counter s =0 set the maximum value epsilon allowed by the system for violating the safety regulations RO 。
Step (7.3.2): solving the main problem, if the main problem is solved, obtaining a system unit starting and stopping state x and a unit output arrangement y, and performing the step (7.3.3); otherwise, stopping iteration and outputting no solution.
Step (7.3.3): and (4) solving the maximum and minimum subproblems according to the x and y obtained by the solution in the step (7.3.2), and finding out the wind power and photovoltaic output under the worst scene which causes the maximum possibility of violating the safety specified value.
Step (7.3.4): if the maximum possible violation safety provision resolved in step (7.3.3) is less than ε RO Then x and y are the final optimization solution and the iteration is stopped; conversely, let s = s +1, according to the worst scene solved in step (7.3.3)Lower wind and photovoltaic output value
The CCG constraint shown below is added to the main question, returning to step (7.3.2).
f T v s ≤ε RO
And 8, inputting the data of the park comprehensive energy system, and further comprising the specific composition, energy price, equipment parameters and values of each energy conversion equipment, demand response proportion, new energy output fluctuation conditions under a basic scene and a worst scene, maximum violation safety specified values, predicted values of new energy output and load and the like of the park comprehensive energy system, and solving the two-stage adjustable robust optimization operation model of the park comprehensive energy system by adopting a commercial solver Gurobi to obtain a robust optimization scheduling result.
The effects of the present invention will be described in detail below with reference to specific examples.
(1) Introduction to the examples.
Fig. 2 shows the specific components of the park energy system. And (4) selecting a multi-energy complementary park system model containing wind, light, gas, storage and consideration of electricity-to-gas and electricity-to-heat technologies in a simulation mode. The system comprises a gas turbine, a wind driven generator, an electric boiler, P2G equipment, heat storage equipment, gas storage equipment, two CHPs and a group of photovoltaic cells. Heating coefficient of bromine refrigerator
And flue gas recovery ratio>
Respectively 0.9 and 1.2, and the one-time startup and shutdown costs of the gas turbine, the CHP and the electric boiler are respectively as follows: 3.5, 1.94, 2.74 yuan. Assuming that the initial state of the CHP and the gas turbine is the off-stream state, the initial gas storage capacity of the gas storage equipment is 10m 3 The initial heat storage capacity of the heat storage equipment is 100 kW.h, and the self-consumption rate of the gas storage and heat storage equipment is 0.01. Multiple energy sources for parkThe predicted values of the fan, photovoltaic output, electrical load and thermal load of the integrated system on a typical winter day are shown in fig. 4.
(2) Description of embodiment scenarios.
In order to verify the effectiveness of the proposed two-stage robust adjustable optimization model considering the response of the combined heat and power demand, 7 scheduling operation modes shown in table 1 are set.
TABLE 1 7 scheduling operation modes
The wind power and photovoltaic predicted output is adjusted to be 2 times of the original output, and meanwhile uncertainty of wind and photovoltaic output is considered, and a new operation scheme 1-7 is obtained. The threshold value of the safety regulation violation is set to be 0, namely the system does not allow to lose load and overload under any scene, and the uncertain budget of the wind power output and the photovoltaic output is assumed to be 24.
And increasing the electrical load to 2.4 times of the original electrical load, and simultaneously considering the uncertainty of wind-solar output to obtain a new operation scheme 8-14, wherein the violation safety specified value is set to be 0.
(3) Examples analysis of results.
Table 2 gives the operating costs and violation of safety regulations for operating scenarios 1-7 at different uncertainty ratios, from which can be derived: along with the increase of the uncertain proportion of the wind power output and the photovoltaic output, the operation cost of the park is increased, and the maximum value of violating the safety regulations possibly appearing in the park is larger. However, with the continuous addition of energy conversion equipment such as heat storage equipment, P2G and gas storage equipment thereof, the park can continuously adjust the energy storage state in real time to cope with the real-time change of wind power photovoltaic output, the running risk of the system is reduced, the uncertainty of the system can be flexibly coped with by considering demand response, the combined thermoelectric demand response is considered, and the running cost under the basic scene of the park is further reduced.
TABLE 2 running costs and violations of safety regulations for running schemes 1-7 at different uncertainty ratios
Table 3 gives the number of iterations and violation of the safety specified values for the operating scenario 1 under different wind power and photovoltaic uncertainty budgets, from which can be derived: the larger the uncertainty budget value of the renewable energy source is, the fewer the number of iterations required for obtaining an optimization scheme meeting the system requirements is, and the larger the violation of the safety regulation value which may occur in the system is. The robustness of the system can be adjusted by adjusting the uncertainty budget value.
TABLE 3 iteration number and violation of safety regulation value of operation scheme 1 under different wind power and photovoltaic uncertainty budgets
Table 4 gives the operating costs and violation of safety regulations for operating scenarios 8-14 at different uncertainty ratios, from which one can derive: when the system load demand is obviously greater than the renewable energy output, the renewable energy output can be completely absorbed by the system, the gas-to-electricity conversion is superior to the electricity-to-gas conversion, the P2G equipment does not work, and the robustness of the system cannot be improved due to the introduction of the energy storage equipment. When the uncertain ratio of the output of the renewable energy is 0.2, compared with the schemes 8-10, the more comprehensive the equipment contained in the system is, the lower the running cost of the system in the basic scene is, and compared with the schemes 10-14, the introduction of the response of the demand side can further reduce the running cost of the system. Given the uncertain proportion of operating costs and violations of safety regulations for the different renewable energy sources of the comparison schemes 11-14, the combined heat and power demand response can reduce power load losses by reducing heat loads, greatly enhancing the robustness, flexibility and economy of the campus integrated energy system.
TABLE 4 running cost and violation of safety regulations for running schemes 8-14 at different uncertainty ratios
The above description is only an embodiment of the present invention, but not intended to limit the scope of the present invention, and all equivalent changes or substitutions made by using the contents of the present specification and the drawings, which are directly or indirectly applied to other related arts, should be included within the scope of the present invention.
Claims (1)
1. A park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty is characterized by comprising the following steps:
step 1: determining the specific composition of the comprehensive energy system of the park, including the introduced new energy form and the specific equipment composition;
step 2: establishing models of various energy conversion equipment in the park;
and 3, step 3: establishing a demand response model;
and 4, step 4: on the premise of meeting system safety constraints, establishing a two-stage adjustable robust model considering combined heat and power demand response by taking the minimum running cost of a basic scene as a target function;
step 5, obtaining an abstract expression of a two-stage adjustable robust optimization model of the park comprehensive energy system day-ahead economic dispatching;
step 6, establishing the maximum and minimum subproblems of the worst scene identification;
step 7, solving a two-stage adjustable robust model considering combined heat and power demand response by using a CCG method;
and step 8: inputting energy access, new energy output data, equipment parameters and operating parameters of the park integrated energy system, and solving a two-stage robust optimization model of the park integrated energy system day-ahead economic dispatching considering wind-solar uncertainty by adopting a commercial solver to obtain a dispatching strategy of the park integrated energy system day-ahead economic dispatching model;
the park comprehensive energy system in the step 1 specifically comprises the following components:
(1) The new energy form of the integrated energy system accessed to the park is as follows: wind power and photovoltaic power generation;
(2) The energy conversion equipment for introducing the park comprehensive energy system comprises: electricity-to-gas equipment, an electric boiler, a gas turbine, a cogeneration unit, and gas/heat storage equipment;
step 2, each energy conversion equipment model is as follows;
(1) Electric gas conversion equipment model
In the formula: t is scheduling time; m is an index of the electric-to-gas equipment;
respectively gas making power, consumed power and electricity-to-gas efficiency, L, of an electricity-to-gas (P2G) facility HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
The minimum and maximum breathing power of the mth P2G;
(2) Electric boiler model
In the formula: t is scheduling time; n is an electric boiler index;
and &>
Respectively the power consumption and the heat production of the nth electric boiler in the t period;
Is the electric heat conversion efficiency of the nth electric boiler>
Dividing into the minimum and maximum heating power of the nth electric boiler;
Starting and stopping the nth electric boiler at a time period t;
(3) Gas turbine model
In the formula: t is scheduling time; q is a gas turbine index;
and &>
Respectively representing the power generation power and the gas consumption power of the gas turbine; f (-) represents the gas turbine energy consumption curve;
And &>
Respectively representing the consumption of natural gas required by the startup and shutdown of the gas turbine; a is q 、b q And c q A gas coefficient representing F ('); l is HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
Dividing the power into the minimum and maximum power generation powers of the q gas turbines;
Representing the generated power of the qth gas turbine in the t-1 period;
For the start-stop state of the qth gas turbine in the time period t, ->
Starting and stopping states of a qth gas turbine in a t-1 time period;
Is the climbing rate and the descending rate of the qth gas turbine>
For the continuous start-up and shutdown time of the qth gas turbine in the t-1 time period, based on the preset time interval>
The minimum startup and shutdown time of the qth gas turbine in the time period t is defined;
(4) Combined heat and power generation unit model
In the formula: t is scheduling time; p is an index of the cogeneration unit;
and &>
Respectively representing heat production power and gas consumption power of a combined heat and power generation unit (CHP);
The generating power and the generating efficiency of the micro-combustion engine in the t period are based on the pressure value>
For the heat dissipation loss rate, the device can be used for>
And &>
The heating coefficient and the flue gas recovery rate of the bromine refrigerator are respectively; l is a radical of an alcohol HANG Taking 9.7kWh/m as low heating value of natural gas 3 ；
Dividing into the p (th) CHP unit minimum and maximum generating power;
Representing the generated power of the p-th combined heat and power generation unit in a t-1 period;
For the start-stop state of the pth CHP unit in the t-1 time period, the system is turned on or off>
The starting and stopping state of the P-th cogeneration unit in the t-1 time period is shown;
The climbing rate and the descending rate of the pth CHP unit;
for the continuous startup and shutdown time of the pth CHP unit in the t-1 period>
The minimum startup and shutdown time of the pth CHP unit in the time period t is obtained;
(5) Energy storage equipment model
ESS(t)＝(1-σ S )·ESS(t-1)+ES in (t)·η in -ES out (t)/η out
In the formula: t is scheduling time; ESS (t), ES in (t)、ES out (t) storing energy, stored energy power and released energy power of the energy storage device at a time period t, respectively; ESS (t-1) represents the stored energy of the energy storage device during the t-1 period; sigma S Is the self-consumption rate of the energy storage system; eta in 、η out Respectively storing energy and releasing energy efficiency for the energy storage equipment;
gas storage, gas discharge power, GS, for a period of t in,max 、GS out,max The maximum gas storage and gas release power of the gas storage device are respectively;
The air storage quantity of the air storage device is greater or less for a time period t>
The gas storage capacity of the gas storage equipment is t-1 time; c GS,min 、C GS,max Respectively, the minimum and maximum gas storage capacities, eta, of the gas storage equipment CGS 、η GS,in 、η GS,out The self-consumption rate, the gas storage efficiency and the gas release efficiency of the gas storage equipment are improved;
For the heat-storage and heat-release power of t time period, HS in ,max 、HS out,max The maximum heat storage power and the maximum heat release power of the heat storage equipment are respectively;
For a gas reserve of the heat storage device for a period t>
The gas storage capacity of the heat storage equipment is t-1 time period; c HS,min 、C HS,max Minimum and maximum heat storage capacity, eta, of the heat storage apparatus CHS 、η HS,in 、η HS,out The self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment are obtained;
step 3, the demand response model is as follows:
P t DR ＝P t DR,inte +P t DR,shif
in the formula: t is scheduling time;
P t DR 、P t DR,inte 、P t DR,shif 、P t LD,max predicted value of electric load, demand side response electric load, demand side interruptible electric load, and demand side transfer for t periodElectric load, maximum electric load allowed by the system, P inte,max The maximum interruptible electrical load within the scheduling time;
A maximum interruptible and exportable electrical load ratio for the t period; p is t DR,shif Positive represents the load that can be shifted out, and vice versa represents the load that can be shifted in;
in the formula: t is scheduling time;
and &>
Respectively a predicted value of the heat load, a response heat load of a demand side, a response heat load proportion of the demand side, the maximum heat load allowed by a system and the heat load after considering the demand response in a time period t; h DR,max The maximum interruptible thermal load within the scheduling time; n is a radical of t Scheduling the time period for the whole;
step 4 the two-stage tunable robust model considering the combined heat and power demand response is as follows:
(1) Objective function
In the formula: t is scheduling time;
the electricity and gas purchase costs are respectively;
The starting-up and shutdown costs of the pth CHP are respectively;
Penalty fee for wind abandon, based on the sum of the wind and the wind>
Punishment of cost for light abandonment;
To earn electricity sales, C E,cc A compensation cost to interrupt the electrical load for demand side response; p t in For purchasing power, is turned on or off>
For purchasing air power, is selected>
Wind abandoning power and light abandoning power of the ith fan and the jth group of photovoltaic cells in the t period are respectively set; p is t out To sell electric power, P t DR,inte For interrupting the electrical load on the demand side, Δ t is the scheduling time interval, N t For scheduling period number, ->
The unit purchase price of electricity, gas and electricity, phi t WT 、φ t PV A penalty price for wind abandon and light abandon of the unit, respectively>
For the compensation price of the unit demand side in response to an interrupted electrical load, based on the value of the compensation>
The starting and stopping states of the pth CHP in the period t, 1 represents starting, 0 represents stopping,
the cost of starting up and stopping the p-th CHP once respectively, N WT 、N PV 、N CHP The number of the fan, the photovoltaic cell, the electric boiler and the CHP are respectively;
(2) Constraint conditions
P t u,DR ＝P t u,DR,inte +P t u,DR,shif :(λ 10_6,t )
In the formula: t is scheduling time; (. Cndot.) u Is a corresponding adjusted variable under the real-time change of wind and light output; v. of 1,t And v 2,t A power balance constraint relaxation variable; v. of 3,t And v 4,t Constraint relaxation variables for thermal balance; omega WT 、Ω PV Respectively representing uncertain collections of wind power output and photovoltaic output;
deviation of wind power, photovoltaic output and predicted value respectively>
The ratio of the wind power and photovoltaic output deviation value to the predicted value is obtained;
Is a variable of 0 to 1 in the uncertain convergence set; delta i 、Δ j Respectively obtaining wind power and photovoltaic output uncertainty precalculated values;
Correcting the climbing and descending of the pth CHP unit;
Correcting the climbing and descending of the qth gas turbine unit; lambda (-) is a dual variable corresponding to a constraint condition>
And &>
Respectively taking the average values of the predicted values of the wind power output and the photovoltaic output;
And &>
Respectively the abandoned wind power and the abandoned light power after the uncertainty parameters are considered;
Representing the wind abandoning proportion;
and 5, an abstract expression of the two-stage adjustable robust optimization model of the park comprehensive energy system economic dispatch in the day ahead is as follows:
s.t.Ax+By≤b
in the formula: x represents the starting and stopping states of units related to the CHP and the gas turbine, y and z represent the basic scene and the scheduling output of other units of the system adjusted according to the wind-light output transformation, u is an uncertain variable related to the uncertainty of the wind power output and the photovoltaic output, and c b 、c g A, B, B, F, h, C, D, E, F, G can be derived from the objective function and constraint conditions, C b And c g The method comprises the following steps that A, B, C, D, E, F and G are abstract coefficient vector matrixes of variables in inequality constraints respectively; b. h represents an abstract matrix of constants in the inequality constraint; f is an abstract coefficient vector corresponding to a max-min double-layer problem objective function;
step 6, the maximum and minimum subproblems of the worst scene recognition are as follows:
ΔD＝ΔD 1 +ΔD 2
s.t.λ 9_1,t ≤0,λ 9_2,t ≤0,λ 9_3,t ≤0,λ 9_4,t ≤0,λ 9_5,t ≤0,λ 9_6,t ≤0,t∈N T
λ 6_2,q,t ≤0,λ 6_3,q,t ≤0,λ 6_4,q,t ≤0,λ 6_6,q,t ≤0,λ 6_7,q,t ≤0,q∈N GT ,t∈N T
λ 7_2,p,t ≤0,λ 7_3,p,t ≤0,λ 7_4,p,t ≤0,λ 7_6,p,t ≤0,λ 7_7,p,t ≤0,p∈N CHP ,t∈N T
λ 8_1,t ≤0,λ 8_2,t ≤0,λ 8_3,t ≤0,λ 8_4,t ≤0,t∈N T
λ 13_1,t ≤0,λ 13_2,t ≤0,λ 13_3,t ≤0,λ 13_4,t ≤0,t∈N T
λ 4_2,m,t ≤0,λ 4_3,m,t ≤0,t∈N T ,m∈N P2G ,t∈N T
λ 5_2,n,t ≤0,λ 5_3,n,t ≤0,t∈N T ,n∈N EB ,t∈N T
λ 10_1,t ≤0,λ 10_2,t ≤0,λ 10_3,t ≤0,λ 10_4,t ≤0,t∈N T
λ 11_3,t ≤0,λ 11_3,t ≤0,λ 12_1,t ≤0,λ 12_2,t ≤0,t∈N T
λ 10_7 ≤0,λ 10_8 ≤0,λ 11_4 ≤0,λ 11_5 ≤0,t∈N T
s.t.-λ 1,t -λ 6_1,q,t ·b q -λ 6_2,q,t +λ 6_3,q,t -λ 6_4,q,t+1 +
λ 6_4,q,t +λ 6_5,p,t+1 -λ 6_5,p,t -λ 6_6,q,t +λ 6_7,q,t ≤0
-λ 1,t -λ 9_1,t +λ 9_2,t ≤0,t∈N T
λ 1,t -λ 9_3,t +λ 9_4,t ≤0,t∈N T
λ 1,t +λ 10_5,t ≤0,t∈N T
-λ 10_1,t -λ 10_2,t +λ 10_3,t +λ 10_5,t -λ 10_7 +λ 10_8 ＝0,t∈N T
-λ 10_1,t +λ 10_4,t +λ 10_5,t +λ 10_5,t +λ 10_6 ＝0,t∈N T
λ 1,t +λ 12_1,i,t ≤0,t∈N T ,i∈N WT
λ 1,t +λ 12_2,j,t ≤0,t∈N T ,j∈N PV
-λ 2,t +λ 7_1,p,t ≤0,t∈N T ,p∈N CHP
-λ 2,t +λ 6_1,q,t ≤0,t∈N T ,q∈N GT
-λ 2,t +λ 4_1,m,t -λ 4_2,m,t +λ 4_3,m,t ≤0,t∈N T ,m∈N P2G
λ 2,t -λ 9_5,t +λ 9_6,t ≤0,t∈N T
λ 2,t +λ 8_2,t +λ 8_5,t /η GS,out ≤0,t∈N T
-λ 2,t +λ 8_1,t -λ 8_5,t ·η GS,in ≤0,t∈N T
-λ 8_3,t +λ 8_4,t -λ 8_5,t+1 ·η GS,in +λ 8_5,t ≤0,t∈N T -λ 3,t ·η h +λ 13_2,t +λ 13_5,t /η HS,out ≤0,t∈N T
λ 3,t ·η h +λ 13_1,t -λ 13_5,t ·η HS,in ≤0,t∈N T -λ 13_3,t +λ 13_4,t -λ 13_5,t+1 ·η HS,in +λ 13_5,t ≤0,t∈N T
-λ 3,t ·η h +λ 5_1,n,t -λ 5_2,n,t +λ 5_3,n,t ≤0,t∈N T ,n∈N EB -λ 3,t ·η h +λ 7_8,n,t ≤0,t∈N T ,n∈N EB
λ 3,t +λ 11_1,t ≤0,t∈N T λ 11_1,t -λ 11_2,t +λ 11_3,t -λ 11_4 +λ 11_5,t ≤0,t∈N T
-1≤λ 1,t ≤1,-1≤λ 3,t ≤1,t∈N T ；
and 7, solving a two-stage adjustable robust model considering combined heat and power demand response by using a CCG method as follows:
(1) Major problems
Ax+By≤b
(2) Sub-problems
(3) CCG solving step
Step 1: let the iteration counter s =0 set the maximum value epsilon allowed by the system for violating the safety regulations RO ；
Step 2: solving the main problem, if the main problem is solved, obtaining a system unit starting and stopping state x and a unit output arrangement y, and performing the step 3; otherwise, stopping iteration and outputting no solution;
and 3, step 3: solving the maximum and minimum subproblems according to the x and y obtained by the solving in the step 2, and finding out the wind power and photovoltaic output under the worst scene which causes the maximum possibility of violating the safety specified value;
and 4, step 4: if solved in step 3Maximum possible violation of the safety prescribed value less than epsilon RO Then x and y are the final optimization solution and the iteration is stopped; otherwise, let s = s +1, according to the wind power and photovoltaic output value under the worst scene solved in step 3
Adding CCG constraint shown below into the main problem, and returning to the step 2;
and 8, the park comprehensive energy system data further comprises the specific composition of the park comprehensive energy system, the energy price, the equipment parameters and values of each energy conversion equipment, the demand response proportion, the new energy output fluctuation situation under the basic scene and the worst scene, the maximum violation safety specified value and the predicted value of the new energy output and load.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108832665A (en) * | 2018-07-04 | 2018-11-16 | 四川大学 | A kind of probabilistic electric heating integrated system Robust distributed coordination optimization scheduling model of consideration wind-powered electricity generation |
| CN110009152A (en) * | 2019-04-03 | 2019-07-12 | 东南大学 | A kind of consideration electricity turns gas and probabilistic regional complex energy system operation robust Optimal methods |
| CN110555595A (en) * | 2019-08-09 | 2019-12-10 | 四川大学 | biogas-wind-light all-renewable energy system based on energy hub and method thereof |
| CN111355265A (en) * | 2020-04-10 | 2020-06-30 | 中国人民解放军国防科技大学 | Micro-grid energy two-stage robust optimization method and system |
-
2020
- 2020-08-04 CN CN202010773898.1A patent/CN113256045B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108832665A (en) * | 2018-07-04 | 2018-11-16 | 四川大学 | A kind of probabilistic electric heating integrated system Robust distributed coordination optimization scheduling model of consideration wind-powered electricity generation |
| CN110009152A (en) * | 2019-04-03 | 2019-07-12 | 东南大学 | A kind of consideration electricity turns gas and probabilistic regional complex energy system operation robust Optimal methods |
| CN110555595A (en) * | 2019-08-09 | 2019-12-10 | 四川大学 | biogas-wind-light all-renewable energy system based on energy hub and method thereof |
| CN111355265A (en) * | 2020-04-10 | 2020-06-30 | 中国人民解放军国防科技大学 | Micro-grid energy two-stage robust optimization method and system |
Non-Patent Citations (4)
| Title |
|---|
| Chuan He 等.Co-optimization scheduling of interdependent power and gas systems with electricity and gas uncertainties.Energy.2018,第159卷1003-1015. * |
| Jun Lu 等.Robust day-ahead coordinated scheduling of multi-energy systems with integrated heat-electricity demand response and high penetration of renewable energy.Renewable Energy.2021,第178卷466-482. * |
| 单福州 等.基于改进两阶段鲁棒优化的区域综合能源系统经济调度.电测与仪表.2018,第55卷(第23期),103-108. * |
| 税月 等.考虑风电不确定性的电气能源系统两阶段分布鲁棒协同调度.电力系统自动化.2018,第42卷(第13期),43-50,75. * |
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