CN112884191A - Thermoelectric day-ahead scheduling model based on network source coordination and calculation method - Google Patents
Thermoelectric day-ahead scheduling model based on network source coordination and calculation method Download PDFInfo
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Abstract
The invention relates to a thermoelectric day-ahead scheduling model based on network source coordination, which takes the minimum system cost on the premise of meeting thermoelectric supply as an objective function, and is subject to the operation constraints of thermoelectric supply and demand balance, line safety and various devices. The invention solves a complex optimization decision problem into a plurality of different sub-problems which are small and easy to solve by constructing a day-ahead scheduling model applied to urban power grid source coordination, combining a brand-new longitudinal interaction mode of a power transmission network and a power distribution network, a thermoelectric day-ahead integrated coordination scheduling model containing large-scale intermittent energy and solving through an alternating direction multiplier algorithm, and solves to obtain the global optimal solution of the original problem through continuous iteration solution among the sub-problems until global convergence.
Description
Technical Field
The invention belongs to the technical field of electrical information, and particularly relates to a thermoelectric day-ahead scheduling model and a calculating method based on network source coordination.
Background
With the development of society, the consumption of energy is continuously increasing. The current energy structure mainly comprises primary energy, which brings great challenges to the sustainability of world energy supply and also brings huge environmental problems. Renewable energy is vigorously developed, and a distributed terminal comprehensive energy unit and a centralized energy supply network coupled with the distributed terminal comprehensive energy unit are constructed through reasonable planning and operation optimization control of an electricity/gas/heat comprehensive energy system, so that the renewable energy becomes an important form of future energy development. Coupling complementation and cascade utilization of various energy forms are beneficial to reducing impact of distributed energy fluctuation on a power grid and promoting development and application of renewable energy. From the perspective of energy utilization, various energy systems have correlation and complementarity on different time scales, and can store and supply energy on multiple time scales.
The comprehensive energy system is an important physical carrier of an energy internet and undertakes the tasks of energy conversion, distribution, storage and the like of electricity, heat, cold and the like. The integrated energy system may be divided into a trans-regional level, an area level and a user level according to geographical factors and energy transmission/distribution/use characteristics. The trans-regional comprehensive energy system takes a centralized power supply of a large wind farm, a hydraulic power plant and the like as a main energy source, takes a large power transmission and gas transmission network as a backbone network frame, and mainly plays a role in remote energy transmission. The urban comprehensive energy has the characteristics of cleanness, distribution, interconnectivity, intellectualization, flexibility and openness:
the aim of the operation scheduling of the energy internet is to reduce the total power generation cost of the distributed power supply while ensuring the whole real-time power balance of the energy internet, which is equivalent to converting the economic scheduling problem into the problem of increment cost consistency in the power distribution process. Therefore, the real-time power distribution problem in the energy Internet operation scheduling is of great significance. Modeling and solving of the optimization scheduling problem of the integrated energy system are two closely related processes, and the solution method of the optimization problem is generally divided into two main types: namely Heuristic Methods (Heuristic Methods) and Mathematical Optimization Methods (Mathematical Optimization Methods). The heuristic method is typically represented as an intelligent algorithm, and the mathematical optimization method is most widely applied as a mathematical programming method.
In recent years, with the development of distributed power generation technology, most scholars focus on the research on the comprehensive energy system at the regional level, but the research on the multi-energy coordination comprehensive management of the urban power grid under the application of large-scale clean energy is lack of targeted research. On the other hand, the traditional dispatching mode is mainly used for meeting the power balance of a power grid to the maximum extent by dispatching a power supply at a power generation side. When the proportion of the grid-connected capacity of the intermittent energy is large, the full consumption of the intermittent energy cannot be realized only by adjusting the output of the unit; in addition, in order to reserve a reserve for intermittent energy power generation, a conventional energy unit has to operate for a long time at low efficiency, energy waste is caused indirectly, and the energy-saving and emission-reducing value of new energy power generation cannot be fully exerted. Therefore, the operation requirement of the new energy power system cannot be met only by the vertical dispatching mode from top to bottom on the power generation side, and a new dispatching mode must be researched. The invention provides a thermoelectric day-ahead scheduling model considering network source coordination and a solving method, and aims to solve the problem of multi-energy coordination control of an urban power grid under the application of large-scale clean energy.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides the day-ahead scheduling applied to urban power grid source coordination, and combines a brand-new longitudinal interaction mode of a power transmission network and a power distribution network, a thermoelectric day-ahead integrated coordination scheduling model containing large-scale intermittent energy and a thermoelectric day-ahead scheduling model based on the grid source coordination, which is solved by an alternating direction multiplier algorithm.
The invention adopts the following specific technical scheme:
a thermoelectric day-ahead scheduling model based on network source coordination is characterized in that: the model is used as an objective function for the minimum system cost on the premise of satisfying the thermoelectric supply, and is subject to the thermoelectric supply and demand balance, the line safety and the operation constraint of various devices.
Furthermore, the objective function is:
wherein the content of the first and second substances,
and
respectively buying the quantity of electricity and heat from the system in the t period;
and
the quantities of electricity and heat produced by the equipment in the system in the t-th time period respectively; cPBThe electricity purchasing cost for the power grid; cPGThe cost of generating electricity for the own equipment; cHBCost of purchasing heat for the system; cHGThe heating cost of the own equipment is high.
Furthermore, the first and second electrodes are provided with,
the method for calculating the electricity and heat purchasing cost of the system comprises the following steps:
wherein:
and
the prices of electricity and heat purchased from the system in the t period are respectively; t is the time period number of the scheduling period;
the system power generation cost calculation method comprises the following steps:
wherein: m is the number of the power generation equipment;
the power generation amount of the ith power generation device in the t period; a isi，biAnd ciThe power generation cost coefficients of the ith power generation equipment are respectively; cPss,iThe start-stop cost of the ith power generation equipment;
is the starting and stopping state of the power generation equipment i in a time period t;
the method for calculating the heat production cost of the system comprises the following steps:
wherein: n is the number of the heating equipment;
the heat generation amount of the ith heat generation device in the t period; di，eiAnd fiRespectively are the heating cost coefficients of the ith heating equipment; cHss,iThe start-up and shutdown cost of the ith heating equipment;
is the start-stop state of the heat-generating device i in the time period t.
And the constraints comprise thermoelectric balance constraint, line safety constraint, unit output constraint, unit climbing constraint, rotary standby constraint, electric boiler output constraint and energy storage equipment constraint.
Furthermore, the first and second electrodes are provided with,
the thermoelectric balance constraint calculation method comprises the following steps:
wherein:
load power predicted value of the system in the t period;
predicting the heat load of the system in the t period; the output of the pumped storage unit in the pumped state is counted as a negative value, and the heat pump and the heat storage boiler are the loads of the system in the heat storage state;
the line safety constraint calculation method comprises the following steps:
wherein: gallFor all units, γgjIs the power distribution factor, L, of the unit g on the line jjIs a line flow limit;
the method for calculating the output constraint and the climbing constraint of the unit comprises the following steps:
wherein: qgmin、QgmaxRespectively the minimum output and the maximum output of the thermal power generating unit g; r isg,upThe upward slope rate r of the thermal power generating unit ng,downThe downward climbing rate of the thermal power generating unit g;
the rotating standby constraint calculation method comprises the following steps:
wherein: gconFor all units except renewable energy and virtual motors, GrenFor fans and photovoltaic generators, RtThe rotating standby requirement of the system time t is met;
the constraint calculation method of the electric boiler comprises the following steps:
wherein:
the heat storage amount of the heat storage tank in the period t; qHS,maxAnd QHS,minThe maximum heat storage quantity and the minimum heat storage quantity of the heat storage tank are respectively;
the output constraint calculation method of the electric boiler comprises the following steps:
wherein:
the power consumption of the heat storage boiler is t time period; pHS,maxThe upper limit of the power of the sectional heat accumulating type electric boiler is set;
the energy storage equipment constraint calculation method comprises the following steps:
SOCmin≤SOC≤SOCmax
wherein: SOC is the state of charge of the energy storage system; SOCmaxAnd SOCminUpper and lower limits of the state of charge, respectively.
Another object of the present invention is to provide a method for calculating a thermoelectric day-ahead scheduling model based on network source coordination, which is characterized in that: including the grid source coordination based thermoelectric day-ahead scheduling model of claim 5, the computational method comprising the steps of:
s21: quantifying a thermoelectric day-ahead scheduling target considering network source coordination;
s22: quantifying thermoelectric day-ahead scheduling constraints that account for network source coordination;
s23: initializing parameters, and setting an original residual error, a dual factor and a penalty factor;
s24: setting the iteration number k to be 1;
s25: calculating the optimal subproblem of power supply scheduling of the system and the optimal subproblem of heat supply scheduling of the system;
s26: judging whether the original residual error and the dual residual error are converged;
if the convergence criterion is not satisfied, adding 1 to k, updating the multiplier, and returning to the step S25;
if the convergence criterion is satisfied, proceeding to step S27;
s27: solving a pair energy coordination optimization result;
s28: and outputting the scheduling cost and the operation curve.
Furthermore, the optimal sub-problem of step S25 uses the ADMM algorithm, which is in the form of:
min f(x)+g(y)
s.t.Ax+By＝C
wherein: for any period of time, the time period is,
and
to optimize the variables;
the solving method comprises the following steps:
in each iterative calculation, solving a minimization problem related to x and updating a variable x; then solving the minimization problem related to y and updating the variable y; finally, the multiplier z is updated.
The convergence criterion in step S26 is:
wherein:
and
sum of original residuals after k +1 iterationDual residual errors; epsilonpriAnd εdualThe tolerance upper limit of the original residual and the dual residual are respectively.
The invention has the advantages and beneficial effects that:
in the invention, a network source coordinated thermoelectric day-ahead scheduling model takes the minimum system cost on the premise of meeting thermoelectric supply as an objective function, and the model is subject to thermoelectric supply and demand balance, line safety and operation constraints of various devices. The invention solves a complex optimization decision problem into a plurality of different sub-problems which are small and easy to solve by constructing a day-ahead scheduling model applied to urban power grid source coordination, combining a brand-new longitudinal interaction mode of a power transmission network and a power distribution network, a thermoelectric day-ahead integrated coordination scheduling model containing large-scale intermittent energy and solving through an alternating direction multiplier algorithm, and solves to obtain the global optimal solution of the original problem through continuous iteration solution among the sub-problems until global convergence.
Drawings
FIG. 1 is a flow chart for solving a multi-energy coordination problem based on an alternating direction multiplier algorithm;
FIG. 2 is a thermoelectric day-ahead integrated coordinated scheduling model structure with large-scale intermittent energy sources;
FIG. 3 is a graph of urban grid load and new energy forecast;
fig. 4 is a force diagram of typical day-wide energy sources.
Fig. 5 is a diagram of the individual units after consideration of the energy storage system.
Detailed Description
The present invention is further described in the following examples, but the technical content described in the examples is illustrative and not restrictive, and the scope of the present invention should not be limited thereby.
A thermoelectric day-ahead scheduling model based on network source coordination is disclosed, as shown in figures 1-4, the innovation of the invention is as follows: the model is used as an objective function for the minimum system cost on the premise of satisfying the thermoelectric supply, and is subject to the thermoelectric supply and demand balance, the line safety and the operation constraint of various devices.
A brand-new longitudinal interaction mode of a transmission network and a distribution network, a thermoelectric day-ahead integrated coordination scheduling model containing large-scale intermittent energy sources and solving by an Alternating Direction multiplier (ADMM) algorithm are combined by constructing a day-ahead scheduling model applied to network source coordination of an urban power grid.
Furthermore, the objective function is:
wherein the content of the first and second substances,
and
respectively buying the quantity of electricity and heat from the system in the t period;
and
the quantities of electricity and heat produced by the equipment in the system in the t-th time period respectively; cPBThe electricity purchasing cost for the power grid; cPGThe cost of generating electricity for the own equipment; cHBCost of purchasing heat for the system; cHGThe heating cost of the own equipment is high. The cost is the function of the electricity purchasing heat, the power generation and the heat productivity. In the implementation process, the urban power grid multi-energy collaborative scheduling is subject to the operation constraints of thermoelectric power supply and demand balance, line safety and various devices.
Furthermore, the first and second electrodes are provided with,
the method for calculating the electricity and heat purchasing cost of the system comprises the following steps:
wherein:
and
the prices for electricity and heat, respectively, purchased from outside the system for the t-th period, typically vary due to imbalances in the supply and demand of renewable energy sources and loads. T is the number of periods of the scheduling cycle, for day-ahead scheduling, typically 60 minutes is 1 period, T-24.
The system power generation cost calculation method comprises the following steps:
wherein: m is the number of power generation equipment, including thermal power, gas power, renewable energy power generation and other types of equipment;
the power generation amount of the ith power generation device in the t period; a isi，biAnd ciThe power generation cost coefficients of the ith power generation equipment are respectively expressed by a quadratic function; cPss,iThe start-stop cost of the ith power generation device is 0 for power generation devices such as renewable energy sources.
The power generation equipment i is in a start-stop state in a time period t, the value of 0 represents a stop state, and the value of 1 represents a start-up state.
The method for calculating the heat production cost of the system comprises the following steps:
wherein: n is the serial number of the heating equipment, and comprises a micro gas turbine, a heat pump and a heat storage potFurnaces, gas-fired boilers, and the like;
the heat generation amount of the ith heat generation device in the t period; di，eiAnd fiRespectively are the heating cost coefficients of the ith heating equipment; cHss,iThe start-up and shutdown cost of the ith heating equipment;
the heating device i is in a start-stop state in a time period t, the value of 0 represents a stop state, and the value of 1 represents a start-up state.
The constraints comprise thermoelectric balance constraint, line safety constraint, unit output constraint, unit climbing constraint, rotary standby constraint, electric boiler output constraint and energy storage equipment constraint.
Moreover, in order to ensure the stable operation of the system, the system should ensure the supply requirements of the whole electric energy and heat energy of the load at any time, namely the constraint of the thermal-electric balance, specifically:
wherein:
load power predicted value of the system in the t period;
predicting the heat load of the system in the t period; the output of the pumped storage unit in the pumped state is counted as a negative value, and the heat pump and the heat storage boiler are the loads of the system in the heat storage state.
For a power supply line in the system, the power transmission amount of the power supply line cannot exceed the maximum power transmission amount under the condition of ensuring the safe operation of the line, namely the line safety constraint, which is specifically expressed as follows:
wherein: gallFor all units, γgjIs the power distribution factor, L, of the unit g on the line jjIs a line flow limit;
for a generator set in a system, the primary consideration is the output constraint and the climbing constraint of the generator set, and the method specifically comprises the following steps:
wherein: qgmin、QgmaxRespectively the minimum output and the maximum output of the thermal power generating unit g; r isg,upThe upward slope rate r of the thermal power generating unit ng,downThe downward climbing rate of the thermal power generating unit g;
because in order to guarantee the full use of renewable energy, a certain rotation reserve capacity must be reserved for the generating set, namely the rotation reserve constraint, which is specifically expressed as:
wherein: gconFor all units except renewable energy and virtual motors, GrenFor fans and photovoltaic generators, RtThe rotating standby requirement of the system time t is met;
the large-capacity heat accumulating electric boiler is mainly composed of an electrode type boiler and a water tank heat accumulating device, one part of heat generated by the electrode type boiler is directly used for supplying heat, and the other part of the heat can be used as a movable load to be stored in the heat accumulating device. The heat storage amount of the heat storage tank is within the limit value, namely the electric boiler is restricted, and the specific steps are as follows:
wherein:
the heat storage amount of the heat storage tank in the period t; qHS,maxAnd QHS,minThe maximum heat storage quantity and the minimum heat storage quantity of the heat storage tank are respectively;
the output constraint calculation method of the electric boiler comprises the following steps:
wherein:
the power consumption of the heat storage boiler is t time period; pHS,maxThe upper limit of the power of the sectional heat accumulating type electric boiler is set;
the operation constraint of the energy storage device requires that the state of charge of the energy storage device should be within a proper range, namely the energy storage device constraint, specifically:
SOCmin≤SOC≤SOCmax
wherein: SOC is the state of charge of the energy storage system; SOCmaxAnd SOCminThe upper and lower limits of the state of charge are, respectively, typically 0.8 for the upper limit and 0.2 for the lower limit.
Renewable new energy fluctuation is absorbed by fully utilizing the adjusting capacity of the adjustable generator set and the heat storage equipment through the thermoelectric day-ahead integrated coordinated scheduling model, and then the optimal economic operation of the system is realized. And adopting an alternative multiplier algorithm to solve a thermoelectric day-ahead scheduling model considering network source coordination, decomposing the model into two subproblems of power network optimization and heating system optimization, and forming cooperative optimization of power supply flow and heating flow through limited-time communication.
The method for calculating the thermoelectric day-ahead scheduling model based on network source coordination comprises the thermoelectric day-ahead scheduling model based on network source coordination, and comprises the following steps:
s21: quantifying a thermoelectric day-ahead scheduling target considering network source coordination;
s22: quantifying thermoelectric day-ahead scheduling constraints that account for network source coordination;
s23: initializing parameters, and setting an original residual error, a dual factor and a penalty factor;
s24: setting the iteration number k to be 1;
s25: calculating the optimal subproblem of power supply scheduling of the system and the optimal subproblem of heat supply scheduling of the system;
s26: judging whether the original residual error and the dual residual error are converged;
if the convergence criterion is not satisfied, adding 1 to k, updating the multiplier, and returning to the step S25;
if the convergence criterion is satisfied, proceeding to step S27;
s27: solving a pair energy coordination optimization result;
s28: and outputting the scheduling cost and the operation curve.
S21 is the establishment of the objective function, and S22 is the establishment of various constraints.
The alternative direction multiplier algorithm decomposes a more complex optimization decision problem into a plurality of smaller and easily solved different sub-problems through a Decomposition-Coordination (Decomposition-Coordination) process, and solves the global optimal solution of the original problem through continuous iteration among the sub-problems until global convergence.
The optimization of the electricity-heat interconnection comprehensive energy system based on the ADMM decomposes the urban multi-energy coordination scheduling problem into two sub-problems of power network optimization and heating system optimization. After decomposition, the total operation cost of the interconnected network system is also taken as the lowest optimization target, the electric-thermal interconnected comprehensive energy system can be regarded as operating based on the limited number of information interaction, the power grid dispatching mechanism and the heat supply network dispatching mechanism can be regarded as two separated decision-making main bodies, and the cooperative optimization of the power supply energy flow and the heat supply energy flow is formed through the limited number of communication.
In the power network optimization sub-problem, the coupling constraints of the electric heating will be considered in its objective function. At this time, the selected gas turbine-based electric-gas coupling relation is substituted into the objective function. The objective function of the grid optimization sub-problem may be expressed as:
in the natural gas network optimization subproblem, the selected gas turbine-based electrical-gas coupling relation is also substituted into the objective function. The objective function of the natural gas optimization sub-problem can be expressed as:
by forming an optimization objective by using the two formulas above, and considering the constraint conditions in the foregoing model, the optimal sub-problem of S25 can be converted into an ADMM optimization problem in the form:
min f(x)+g(y)
s.t.Ax+By＝C
wherein, for any period of time,
and
to optimize the variables.
The ADMM algorithm may be solved using a gaussian-seidel iteration method. In the solving process, only one of the two subproblems is in an operation state, and when the value of the coupling variable is obtained, the coupling variable is replaced into the other subproblem to be solved. And when another subproblem is waited for updating the value of the coupling variable, the coupling variable is replaced back to the previous subproblem to carry out the next round of iterative operation. The multipliers must be updated at the end of each iteration and upon entering the next iteration. The solving method can be expressed as follows:
in each iterative calculation, there are three steps in total: solving the minimization problem related to x and updating the variable x; solving a minimization problem related to y and updating a variable y; the multiplier z is updated.
The convergence criterion of step S26 is:
wherein:
and
original residual errors and dual residual errors after the (k + 1) th iteration calculation are obtained; epsilonpriAnd εdualThe tolerance upper limit of the original residual and the dual residual are respectively.
And for the steps S27 and S28, the subproblem results obtained in the step S25 and judged to be converged in the step S26 are mainly integrated to obtain the optimal day-ahead scheduling result of the thermoelectric integrated system, the scheduling cost is calculated, and the cost and the scheduling result represented in a curve form are output.
Examples
By example analysis of a power grid of a certain city in south China, the power grid power supply capacity ratio is shown in the following table:
the actual output and load curves of the renewable energy source are shown in fig. 3.
The output analysis of each type of unit is shown in fig. 4: the thermal power generating unit is a main generating unit of a power grid, and the output of the thermal power generating unit accounts for more than half of the total load. The load changes regularly and mainly borne by the thermal power generating unit. After eight points, the load gradually climbs to a peak and remains at a higher level. At the moment, the eight thermal power generating units are all started, and rated output is achieved at 9 am. In consideration of the flexibility of hydroelectric power generation, a hydroelectric generating set cannot always keep a higher output level; after 10 am, the hydroelectric power generation closely follows the load change, and mainly plays a role in peak regulation.
The output of each unit after considering the energy storage system is shown in fig. 5, and it can be seen that the energy storage system mainly generates power in the daytime during the peak period of power, and mainly consumes the redundant electric quantity generated by the wind turbine units at night, so that the occurrence of wind and light abandoning is reduced.
The comprehensive operation cost of the system is 10,268,850.3 in the traditional scheduling method, and 10,252,905 in the method of the invention, so that the method of the invention has higher economic benefit.
In the invention, a network source coordinated thermoelectric day-ahead scheduling model takes the minimum system cost on the premise of meeting thermoelectric supply as an objective function, and the model is subject to thermoelectric supply and demand balance, line safety and operation constraints of various devices. The method combines a brand-new longitudinal interaction mode of the transmission network and the distribution network, a thermoelectric day-ahead integrated coordination scheduling model containing large-scale intermittent energy and solving through an alternating direction multiplier algorithm by constructing a day-ahead scheduling model applied to urban power grid source coordination.
Claims (8)
1. A thermoelectric day-ahead scheduling model based on network source coordination is characterized in that: the model is used as an objective function for the minimum system cost on the premise of satisfying the thermoelectric supply, and is subject to the thermoelectric supply and demand balance, the line safety and the operation constraint of various devices.
2. The grid source coordination based thermoelectric day-ahead scheduling model of claim 1, wherein: the objective function is:
wherein the content of the first and second substances,
and
respectively buying the quantity of electricity and heat from the system in the t period;
and
the quantities of electricity and heat produced by the equipment in the system in the t-th time period respectively; cPBThe electricity purchasing cost for the power grid; cPGThe cost of generating electricity for the own equipment; cHBCost of purchasing heat for the system; cHGThe heating cost of the own equipment is high.
3. The grid source coordination based thermoelectric day-ahead scheduling model of claim 2, wherein:
the method for calculating the electricity and heat purchasing cost of the system comprises the following steps:
wherein:
and
the prices of electricity and heat purchased from the system in the t period are respectively; t is the period of the scheduling cycleCounting;
the system power generation cost calculation method comprises the following steps:
wherein: m is the number of the power generation equipment;
the power generation amount of the ith power generation device in the t period; a isi，biAnd ciThe power generation cost coefficients of the ith power generation equipment are respectively; cPss,iThe start-stop cost of the ith power generation equipment;
is the starting and stopping state of the power generation equipment i in a time period t;
the method for calculating the heat production cost of the system comprises the following steps:
wherein: n is the number of the heating equipment;
the heat generation amount of the ith heat generation device in the t period; di，eiAnd fiRespectively are the heating cost coefficients of the ith heating equipment; cHss,iThe start-up and shutdown cost of the ith heating equipment;
is the start-stop state of the heat-generating device i in the time period t.
4. The grid source coordination based thermoelectric day-ahead scheduling model of claim 3, wherein: the constraints comprise thermoelectric balance constraints, line safety constraints, unit output constraints, unit climbing constraints, rotary standby constraints, electric boiler output constraints and energy storage equipment constraints.
5. The grid source coordination based thermoelectric day-ahead scheduling model of claim 4, wherein:
the thermoelectric balance constraint calculation method comprises the following steps:
wherein:
load power predicted value of the system in the t period;
predicting the heat load of the system in the t period; the output of the pumped storage unit in the pumped state is counted as a negative value, and the heat pump and the heat storage boiler are the loads of the system in the heat storage state;
the line safety constraint calculation method comprises the following steps:
wherein: gallFor all units, γgjIs the power distribution factor, L, of the unit g on the line jjIs a line flow limit;
the method for calculating the output constraint and the climbing constraint of the unit comprises the following steps:
wherein: qgmin、QgmaxRespectively the minimum output and the maximum output of the thermal power generating unit g; r isg,upThe upward slope rate r of the thermal power generating unit ng,downThe downward climbing rate of the thermal power generating unit g;
the rotating standby constraint calculation method comprises the following steps:
wherein: gconFor all units except renewable energy and virtual motors, GrenFor fans and photovoltaic generators, RtThe rotating standby requirement of the system time t is met;
the constraint calculation method of the electric boiler comprises the following steps:
wherein:
the heat storage amount of the heat storage tank in the period t; qHS,maxAnd QHS,minThe maximum heat storage quantity and the minimum heat storage quantity of the heat storage tank are respectively;
the output constraint calculation method of the electric boiler comprises the following steps:
wherein:
the power consumption of the heat storage boiler is t time period; pHS,maxThe upper limit of the power of the sectional heat accumulating type electric boiler is set;
the energy storage equipment constraint calculation method comprises the following steps:
SOCmin≤SOC≤SOCmax
wherein: SOC is the state of charge of the energy storage system; SOCmaxAnd SOCminUpper and lower limits of the state of charge, respectively.
6. A method for calculating a thermoelectric day-ahead scheduling model based on network source coordination is characterized by comprising the following steps: including the grid source coordination based thermoelectric day-ahead scheduling model of claim 5, the computational method comprising the steps of:
s21: quantifying a thermoelectric day-ahead scheduling target considering network source coordination;
s22: quantifying thermoelectric day-ahead scheduling constraints that account for network source coordination;
s23: initializing parameters, and setting an original residual error, a dual factor and a penalty factor;
s24: setting the iteration number k to be 1;
s25: calculating the optimal subproblem of power supply scheduling of the system and the optimal subproblem of heat supply scheduling of the system;
s26: judging whether the original residual error and the dual residual error are converged;
if the convergence criterion is not satisfied, adding 1 to k, updating the multiplier, and returning to the step S25;
if the convergence criterion is satisfied, proceeding to step S27;
s27: solving a pair energy coordination optimization result;
s28: and outputting the scheduling cost and the operation curve.
7. The method of claim 6, wherein the method comprises: the optimal subproblem of step S25 uses the ADMM algorithm in the form of:
min f(x)+g(y)
s.t.Ax+By＝C
wherein: for any period of time, the time period is,
and
to optimize the variables;
the solving method comprises the following steps:
in each iterative calculation, solving a minimization problem related to x and updating a variable x; then solving the minimization problem related to y and updating the variable y; finally, the multiplier z is updated.
8. The method of claim 6, wherein the method comprises: the convergence criterion of step S26 is:
wherein:
and
original residual errors and dual residual errors after the (k + 1) th iteration calculation are obtained; epsilonpriAnd εdualThe tolerance upper limit of the original residual and the dual residual are respectively.
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