CN116433046A - Multi-main-game-considered network storage collaborative planning method for multi-energy power distribution system - Google Patents
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Abstract
The invention discloses a network storage collaborative planning method of a multi-energy power distribution system considering multi-main game. The main content comprises analysis of differentiated interest subjects, and determination of interest right attribution and differentiated appeal of each subject in the game planning process; aiming at a multi-energy distribution system, respectively establishing an investment operation decision-making layer model, a distributed energy station operation decision-making layer model and a power load energy utilization decision-making layer model of a power grid company; and converting the double-layer multi-main-body game planning model into a mixed integer linear programming problem to solve the problem by using a KKT condition, a Big-M method and a bilinear substitution method. By the method, the multi-type resource adjustment capability of the user side can be fully utilized, the respective benefit balance among the power users, the distributed energy stations and the power grid company is realized, the economy of a network storage collaborative planning scheme of the power distribution system is effectively improved, and the planning level of the power distribution system under the background of multi-type source load access is improved.
Description
Technical Field
The invention relates to a network storage collaborative planning method of a multi-energy power distribution system considering multi-main game, which is suitable for planning and constructing work of various novel controllable elements in power distribution system planning, and belongs to the field of power distribution networks.
Background
Under the large background of 'double carbon' promise in China, the traditional power distribution network gradually develops to a novel power distribution system with the synergistic complementation of multiple energy types. The coordination and rapid development of the source network charge storage aspects of the novel power distribution system enables the number of various elements of the power distribution system to be increased, operation and maintenance main bodies to be increased, source charge interaction to be deeper and source charge interaction to be more complex and operation characteristics to be more complex, and each main body has respective interest requirements and needs to bear important tasks for efficient operation of the maintenance system. The practical requirements of specific interest requirements of different main bodies are considered to bring new problems to the refined planning of the power distribution system, the problems are embodied that the construction of distributed energy stations easily causes peak load increase, the line load rate exceeds the safety standard, the distributed photovoltaic access causes line tide peak Gu Chalv increase, the energy storage device needs to be accessed to improve the system operation level, and the problems cause the increase of the investment cost of power grid planning.
Aiming at the problems, domestic and foreign scholars develop researches on the collaborative planning strategy of the source network and the charge storage of different game subjects. The power distribution network investment operators and the comprehensive energy system investment operators are respectively used as the upper and lower layers of the game by partial scholars, the economic benefit of each main body is improved through a master-slave game planning method, and the overall energy supply cost is reduced; meanwhile, partial researchers pay attention to differentiated interest demands among power grid companies, natural gas companies and energy stations, and the maximization of the interests of each of three main market bodies is realized through a double-layer Nash-Stackelberg game planning method; and the upper layer model considers the decisions of the source, the network and the load respectively from the planning layer, aims at balancing the benefit of each benefit main body in the game mode, and the lower layer considers the operation strategy from the active management layer to couple the planning and the operation so as to improve the economy of the planning result.
Through analysis of the above research, it can be found that the current collaborative planning strategy for the multi-energy distribution system mainly takes a distribution network operator and a comprehensive energy system as game main bodies, does not consider the participation of power loads in games, and does not consider the influence of the construction of distributed energy stations on the line capacity and the influence of an energy storage device on the system operation level. Therefore, the invention fully compensates the problems on the basis of the existing researches and has innovation.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention aims to provide a network storage collaborative planning method of a multi-energy power distribution system considering multi-main game. By the method, the line capacity and the adjusting capacity of the energy storage device can be fully utilized to realize optimal generation of planning strategies considering benefits of multiple main bodies, thereby meeting benefit requirements of all game main bodies and optimizing the system operation level of the novel power distribution system.
The invention provides a network storage collaborative planning method of a multi-energy distribution system considering multi-main game, which comprises the following specific steps:
(1) Determining that a main layer of the game is an investment operation decision layer of the power grid company, a distributed energy station and an energy utilization decision layer of the power consumer, and a guest layer is the investment operation decision layer of the distributed energy station and the energy utilization decision layer of the power consumer by considering the decision-making resources of the power grid company, the distributed energy station and the power consumer and the operation hierarchical relation among the power grid company, the distributed energy station and the energy utilization decision layer of the power consumer, and establishing a network storage collaborative game planning model;
(2) Taking the minimum total cost of investment operation of a power grid company as an objective function and the multi-element operation condition of a power distribution system as constraint, and establishing a decision layer model of investment operation of the power grid company as a main layer of a game planning model;
(3) The method comprises the steps of taking the minimum investment operation cost of a distributed energy station as an objective function, taking the operation requirement of each device as a constraint, establishing a distributed energy station investment operation decision-making layer model, then taking the minimum electricity purchasing cost of a power consumer as an objective function, establishing a power load energy consumption decision-making layer model, and taking the distributed energy station and the power load model as game planning model object layers;
(4) And (3) respectively writing KKT conditions of the distributed energy stations and the power load layer model built in the step (3) because nonlinear items are contained in the investment operation decision layer of the power grid company built in the step (2), and then converting a network storage collaborative game planning model formed by the power grid company built in the step (2) and the power load three-way model into a single-layer mixed integer linear planning model by using a Big-M method and a bilinear substitution method, solving the single-layer mixed integer linear planning model, and obtaining an optimal network storage collaborative game planning scheme taking into consideration of multiple benefits. The specific content of the game hierarchical relationship and the interest right in the step (1) is as follows:
the game level is divided into a game host layer and a game object layer, the game host is a power grid company, the objects are distributed energy stations and power users, and the relation among the three is as follows: the power grid company reflects the real-time electricity price to the distributed energy stations and the power consumers through the electric energy information center, so that the electricity consumption cost of the distributed energy stations and the power consumers is influenced, the operation of the power distribution network is influenced through the net power demand and the load power, and the income of the power grid company and the operation condition of the power distribution system are further influenced.
The ownership of the benefit is as follows:
(1) The power load adjusts the energy consumption characteristics of the power load according to the real-time electricity price formulated by the power grid company, so that the energy consumption cost of the power load is reduced;
(2) The distributed power supply and the network side energy storage device are built by an electric company, and the construction cost of the electric company comprises: line expansion cost, energy storage construction cost and electricity selling income;
(3) Distributed energy stations can sell electricity to grid companies through gas turbines to improve self profit, and investment operation costs include: the construction cost of the cold and heat storage device, the electricity selling benefit of the gas turbine unit, and the cooling and heating benefits.
The step (2) establishes a power grid company investment operation decision layer model based on the interest attribute proposed in the step (1), and specifically comprises the following steps:
1) Objective function:
min C DRO ＝C line +C ess +C ele,IES +C buy,ups -C sell,load
the specific calculation formula is as follows:
wherein C is DRO Investment of running total cost for electric network company, C line Equal annual cost for line expansion, C ess For equal annual construction costs of energy storage devices, C ele,IES C, the expense for the power grid company to purchase electricity from the distributed energy station buy,ups C, purchasing electricity to the upper power grid for power grid company sell,load The income of selling electricity to the power consumers for the power grid company; η (eta) line 、d line And m line Respectively representing the operation and maintenance cost coefficient, discount rate and service life of the circuit, l line The length of the line is K, which is the set of all lines; η (eta) ess 、d ess And m ess Respectively representing the operation and maintenance cost coefficient, the discount rate and the service life of the energy storage equipment, N node Is a set of all nodes; c line,k 、c s,ess 、c p,ess 、c t 、c buy The method comprises the steps of respectively representing the cost of a line k expansion unit length, the cost of an energy storage device unit capacity, the cost of an energy storage device unit active power, the real-time electricity price and the unit cost of electricity purchasing from a superior power grid by a power grid company; a, a line,k And a ess,i The variables are 0-1, and respectively represent whether the k line expands or not and whether the i node is provided with an energy storage device or not; s is S i,ess And P i,ess Respectively representing the rated capacity and the active power of the energy storage device at the i node; p (P) GT,t The power generation amount of the gas turbine at the time t is represented; p (P) EC,t And P HP,t Respectively representing the electricity consumption of a conventional chiller and an electric boiler at the time t; p (P) load,i,t And P ess,i,t Respectively representing the load electricity consumption and the energy storage charge of the inode at the moment t; p (P) DG,i,t Representing the active power output of the distributed power supply;
2) Constraint conditions:
constraint conditions of the multi-main game multi-energy distribution system network storage collaborative planning on an investment operation decision layer of a power grid company comprise linear tide constraint, operation safety constraint and electric energy storage operation constraint, and the constraint conditions are as follows:
(1) linear power flow constraint:
U j,t ＝U i,t -(R ij P ij,t +X ij Q ij,t )
wherein P is ij,t 、Q ij,t 、I ij,t Active power, reactive power and current flowing through the starting end of a line between ij nodes at the moment t are respectively shown; r is R ij 、X ij Representing the resistance and reactance of the line between ij nodes; p (P) jk,t 、Q jk,t Active power and reactive power of a line between jk nodes (i.e., a line ij adjacent to the line) are respectively represented; p (P) j 、Q j Respectively representing active power and reactive power flowing out of the node j; u (U) j,t Representing the voltage at node j; a (j) and b (j) respectively represent an associated node set before the j node and an associated node set after the j node;
(2) operational safety constraints:
wherein S is ij For the rated capacity of the line eta N-1 The line load rate set under the N-1 safety constraint is met;
(3) electric energy storage operation constraint:
the energy storage device is limited by energy storage charging and discharging power, energy storage capacity time sequence, scheduling period charging and discharging balance and charge state in operation constraint, and the specific expression is as follows:
-P ess,i,max ≤P ess,i,t ≤P ess,i,max
S ess,i,t +P ess,i,t ＝S ess.i.t+1
S ess,i,0 ＝S ess,i,T
SOC min ≤SOC t ≤SOC max
wherein P is ess,i,t The charging and discharging power is stored at the time t, and the charging is represented when the charging power is positive, and the charging power is equivalent to a load for a power grid; p (P) ess,i,max Is the maximum charge and discharge power; s is S ess,i,t For the electric quantity of the energy storage equipment at the moment t, S ess,i,0 And S is ess,i,T Respectively representing the energy storage electric quantity at the starting time and the ending time; s is S ess,i,max Rated capacity of the energy storage device at the i node; SOC (State of Charge) t 、SOC max And SOC (System on chip) min Respectively representing the energy storage charge state and the upper limit and the lower limit thereof;
(4) real-time electricity price constraint:
c buy ≤c t ≤c max
wherein, c max Representing the highest real-time electricity price constraint.
The step (3) establishes a distributed energy station operation decision layer model based on the interest attribute proposed in the step (1), and specifically comprises the following steps:
1) Objective function:
min C IES ＝C ele,DRO +C buy,gas +C tax -C sell,c -C sell,h
the specific calculation formula is as follows:
wherein C is IES Investment of running total cost for distributed energy resource stations, C ele,DRO C for selling negative value of electricity income to electric network company buy,gas Purchase expense for consuming natural gas for gas turbine, C tax For carbon tax of gas turbine, C sell,c And C sell,h The benefits of cooling and heating to the cold and hot loads are respectively obtained for the distributed energy stations; c t 、c gas 、c carbon 、c energy Real-time electricity price, natural gas unit gas purchase cost, unit carbon tax and cold and hot energy unit selling price are respectively adopted; p (P) GT,t Representing the power generation amount of the gas turbine at time t, P EC,t And P HP,t Respectively representing the electricity consumption of a conventional chiller and an electric boiler at the time t; f (F) GT,t Represents the consumption of natural gas at the time t, L c,t The cold energy produced by a conventional cold machine at the time t is represented by L HP,h,t And L GT,h,T Respectively representing heat energy generated by an electric boiler and recycled by a gas turbine at the time t;
2) Constraint conditions:
constraint conditions of the multi-main-body game multi-energy distribution system network storage collaborative planning on a distributed energy station investment operation decision layer are considered to comprise energy consumption models of gas turbines, conventional cold machines and electric boilers, operation constraints of cold storage and heat storage devices, energy balance constraints and equipment operation constraints, and the constraint conditions are as follows:
(5) energy model for equipment:
consider here three types of equipment, a gas turbine that combusts natural gas to produce electrical energy and recyclable thermal energy, a conventional chiller and an electric boiler that consume electrical energy to produce cold and heat energy to supply a cold and heat load.
<1> gas turbine:
1) The gas turbine generates electricity:
P GT,t ＝η GT F GT,t
2) The gas turbine can utilize recovered heat energy:
L GT,h,t ＝η GT,h F GT,t
<2> conventional chiller:
L c,t ＝α EC P EC,t
<3> electric boiler:
L HP,h,t ＝α HP P HP,t
wherein eta GT 、η GT,h 、α EC 、α HP The conversion coefficient of the chemical energy converted into electric energy by the gas turbine, the conversion coefficient of the chemical energy converted into heat energy by the gas turbine, the conversion coefficient of the electric energy refrigeration by the conventional cold machine and the conversion coefficient of the heat energy converted into electric energy by the electric boiler are respectively;
(6) cold and heat storage operation constraint:
the cold and heat accumulation device is limited and restrained by cold and heat accumulation and cold and heat supply power in operation, time sequence constraint of cold and heat accumulation capacity, energy accumulation and energy supply balance constraint of scheduling period and rated capacity constraint, and the method is as follows:
-P ist,i,max ≤P ist,i,t ≤P ist,i,max
η i,loss S ist,i,t +P ist,i,t ＝S ist.i.t+1
0≤S ist,i,t ≤S ist,i,max
-P hst,i,max ≤P hst,i,t ≤P hst,i,max
η h,loss S hst,i,t +P hst,i,t ＝S hst.i.t+1
0≤S hst,i,t ≤S hst,i,max
wherein S is hst,i,t 、P hst,i,t 、S ist,i,t 、P ist,i,t Respectively representing the power and the capacity of the heat and cold accumulation device with the number i at the time t; p (P) ist,i,max 、P hst,i,max Respectively represents the maximum cold storage (heat release) power and the maximum heat storage (heat release) power; s is S ist,i,max And S is hst,i,max Respectively representing the maximum capacity of the cold and heat storage device;
(7) energy balance constraint:
in the running process, balance relation exists between the energy converted mutually, part of the cold generated by the conventional cold machine is supplied to the cold load, and the other part of the cold is stored by the cold storage device; the heat energy generated by the electric boiler and the heat energy recycled by the gas turbine are partially supplied to the heat load, and a part of the heat energy is stored by the heat storage device;
L c,t ＝P ist,t +L load,ist,t
L GT,h,t +L HP,h,t ＝P hst,t +L load,hst,t
L load,ist,t 、L load,hst,t respectively representing the demand of the cold and hot loads at the time t;
(8) device operation constraints:
gas turbines, conventional chillers, electric boiler plants are subject to capacity and climbing constraints during operation, as follows:
<1> Capacity constraint
<2> climbing constraint
Wherein,,
and->
Representing the minimum and maximum operating power of the gas turbine, the conventional chiller and the electric boiler respectively; />
And->
The minimum and maximum power variation per time period of the gas turbine, the conventional chiller, and the electric boiler are respectively represented.
The step (3) establishes an energy consumption decision layer model for the power load based on the interest attribute proposed in the step (1), and specifically comprises the following steps:
1) Objective function:
wherein C is load The total cost of energy for the power consumer, c t For the real-time electricity price at the time t, P load,i,t Active power required by i node load at t moment;
2) Constraint conditions:
P load,pu,t is the active power after load transfer, P dr,t Is the active power of the transferred partial load, and the power conservation of the transferred load in the period, eta dr The demand response signing proportion between the power grid company and the power consumer is;
the game object layer model established based on the step (3) is converted and solved by using the model conversion method based on KKT conditions, big-M method and bilinear substitution, and the method comprises the following steps:
(1) Writing out a KKT condition of an energy consumption decision layer model of the electric load and a distributed energy station investment operation decision layer model by utilizing the basic principle of the KKT condition, and then introducing a Boolean variable by utilizing a Big-M method to convert nonlinear constraint in the KKT condition into a linear inequality;
(2) On the basis of performing linear transformation by using a KKT condition and a Big-M method, replacing relevant parts in a game main layer objective function by using a bilinear replacement method, so that the original game problem is converted into a single-layer mixed integer linear programming model;
(3) And solving the single-layer mixed integer linear programming model.
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In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a network storage collaborative planning method of a multi-energy distribution system in consideration of multi-main game according to the embodiment;
fig. 2 is a diagram of a power distribution system to be planned according to the network storage collaborative planning method of the multi-main game considered in this embodiment.
Fig. 3 is a system payload timing diagram of a multi-energy distribution system network storage collaborative planning method considering multi-body gaming according to this embodiment.
Detailed Description
In order to make the structure and advantages of the present invention more apparent, the structure of the present invention will be further described with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a network storage collaborative planning method for a multi-energy power distribution system considering multi-main game includes the following steps in combination with a specific embodiment:
(1) Example overview:
taking an improved IEEE-33 node power distribution network example as a typical embodiment, the voltage class of the power distribution network is 12.66kV, the peak active power of an access power load is 3715kW, the power load type comprises four types of residential load, industrial load, commercial load and administrative load, and the maximum load rate of a feeder line is 50%. The regional planning construction distributed energy station 1 comprises five types of equipment, namely a gas turbine, an electric boiler, a traditional chiller, a heat storage tank and a cold storage tank, and the power consumption requirement can be regulated through the gas turbine and the energy storage device. The load change ratio of the power load per moment is not more than 20%. The distributed photovoltaic of this district access is scattered and inserted, and the installed capacity adds up to 1200kW. The area topology and the pre-construction position of the distributed energy stations are shown in fig. 2.
(2) According to the access source load condition and the differentiated energy consumption condition of the regional power distribution system, the main body can be divided into three types of power grid companies, distributed energy station operators and power users. Each subject obeys a master-slave game level, the game subjects are power grid companies, the objects are distributed energy station operators and power users, and the relationship among the three is as follows: the power grid company reflects the real-time electricity price to the distributed energy stations and the power consumers through the electric energy information center, so that the electricity consumption cost of the distributed energy stations and the power consumers is influenced, the operation of the power distribution network is influenced through the net power demand and the load power, and the income of the power grid company and the operation condition of the power distribution system are further influenced.
The ownership of the benefit is as follows:
1) The power load adjusts the energy consumption characteristics of the power load according to the real-time electricity price formulated by the power grid company, so that the energy consumption cost of the power load is reduced;
2) The distributed power supply and the network side energy storage device are built by an electric company, and the construction cost of the electric company comprises: line expansion cost, energy storage construction cost and electricity selling income;
3) Distributed energy stations can sell electricity to grid companies through gas turbines to improve self profit, and investment operation costs include: the construction cost of the cold and heat storage device, the electricity selling benefit of the gas turbine, the cooling and heating benefit.
(3) Establishing a power grid company investment operation decision layer model according to the aim of carrying out line capacity expansion of a power distribution system and network side energy storage planning of the power grid company, wherein the method specifically comprises the following steps of:
1) Objective function:
min C DRO ＝C line +C ess +C ele,IES +C buy,ups -C sell,load
the specific calculation formula is as follows:
wherein C is DRO Investment of running total cost for electric network company, C line Equal annual cost for line expansion, C ess For equal annual construction costs of energy storage devices, C ele,IES C, the expense for the power grid company to purchase electricity from the distributed energy station buy,ups C, purchasing electricity to the upper power grid for power grid company sell,load The income of selling electricity to the power consumers for the power grid company; η (eta) line 、d line And m line Respectively representing the operation and maintenance cost coefficient, discount rate and service life of the circuit, l line The length of the line is K, which is the set of all lines; η (eta) ess 、d ess And m ess Respectively representing the operation and maintenance cost coefficient, the discount rate and the service life of the energy storage equipment, N node Is a set of all nodes; c line,k 、c s,ess 、c p,ess 、c t 、c buy The method comprises the steps of respectively representing the cost of a line k expansion unit length, the cost of an energy storage device unit capacity, the cost of an energy storage device unit active power, the real-time electricity price and the unit cost of electricity purchasing from a superior power grid by a power grid company; a, a line,k And a ess,i The variables are 0-1, and respectively represent whether the k line expands or not and whether the i node is provided with an energy storage device or not; s is S i,ess And P i,ess Respectively representing the rated capacity and the active power of the energy storage device at the i node; p (P) GT,t The power generation amount of the gas turbine at the time t is represented; p (P) EC,t And P HP,t Respectively representing the electricity consumption of a conventional chiller and an electric boiler at the time t; p (P) load,i,t And P ess,i,t Respectively representing the load electricity consumption and the energy storage charge of the inode at the moment t; p (P) DG,i,t Representing the active power output of the distributed power supply;
2) Constraint conditions:
constraint conditions of the multi-main game multi-energy distribution system network storage collaborative planning on an investment operation decision layer of a power grid company comprise linear tide constraint, operation safety constraint and electric energy storage operation constraint, and the constraint conditions are as follows:
(1) linear power flow constraint:
U j,t ＝U i,t -(R ij P ij,t +X ij Q ij,t )
wherein P is ij,t 、Q ij,t 、I ij,t Active power, reactive power and current flowing through the starting end of a line between ij nodes at the moment t are respectively shown; r is R ij 、X ij Representing the resistance and reactance of the line between ij nodes; p (P) jk,t 、Q jk,t Active power and reactive power of a line between jk nodes (i.e., a line ij adjacent to the line) are respectively represented; p (P) j 、Q j Respectively representing active power and reactive power flowing out of the node j; u (U) j,t Representing the voltage at node j; a (j) and b (j) respectively represent an associated node set before the j node and an associated node set after the j node;
(2) operational safety constraints:
wherein S is ij For the rated capacity of the line eta N-1 The line load rate set under the N-1 safety constraint is met;
(3) electric energy storage operation constraint:
the energy storage device is limited by energy storage charging and discharging power, energy storage capacity time sequence, scheduling period charging and discharging balance and charge state in operation constraint, and the specific expression is as follows:
-P ess,i,max ≤P ess,i,t ≤P ess,i,max
S ess,i,t +P ess,i,t ＝S ess.i.t+1
S ess,i,0 ＝S ess,i,T
SOC min ≤SOC t ≤SOC max
wherein P is ess,i,t The charging and discharging power of the stored energy at the time t is positive, and the charging is represented when the charging power is positive, and the charging power is equivalent to the load for the power grid；P ess,i,max Is the maximum charge and discharge power; s is S ess,i,t For the electric quantity of the energy storage equipment at the moment t, S ess,i,0 And S is ess,i,T Respectively representing the energy storage electric quantity at the starting time and the ending time; s is S ess,i,max Rated capacity of the energy storage device at the i node; SOC (State of Charge) t 、SOC max And SOC (System on chip) min Respectively representing the energy storage charge state and the upper limit and the lower limit thereof;
(4) real-time electricity price constraint:
c buy ≤c t ≤c max
wherein, c max Representing the highest real-time electricity price constraint.
(4) The method for building the distributed energy station operation decision layer model according to the optimized operation requirements of the distributed energy station operators comprises the following steps:
1) Objective function:
min C IES ＝C ele,DRO +C buy,gas +C tax -C sell,c -C sell,h
the specific calculation formula is as follows:
wherein C is IES Investment of running total cost for distributed energy resource stations, C ele,DRO To sell electricity to grid companiesNegative value of benefit, C buy,gas Purchase expense for consuming natural gas for gas turbine, C tax For carbon tax of gas turbine, C sell,c And C sell,h The benefits of cooling and heating to the cold and hot loads are respectively obtained for the distributed energy stations; c t 、c gas 、c carbon 、c energy Real-time electricity price, natural gas unit gas purchase cost, unit carbon tax and cold and hot energy unit selling price are respectively adopted; p (P) GT,t Representing the power generation amount of the gas turbine at time t, P EC,t And P HP,t Respectively representing the electricity consumption of a conventional chiller and an electric boiler at the time t; f (F) GT,t Represents the consumption of natural gas at the time t, L c,t The cold energy produced by a conventional cold machine at the time t is represented by L HP,h,t And L GT,h,T Respectively representing heat energy generated by an electric boiler and recycled by a gas turbine at the time t;
2) Constraint conditions:
constraint conditions of the multi-main-body game multi-energy distribution system network storage collaborative planning on a distributed energy station investment operation decision layer are considered to comprise energy consumption models of gas turbines, conventional cold machines and electric boilers, operation constraints of cold storage and heat storage devices, energy balance constraints and equipment operation constraints, and the constraint conditions are as follows:
(1) energy model for equipment:
consider here three types of equipment, a gas turbine, a conventional chiller and an electric boiler, the gas turbine combusting natural gas to produce electricity and recyclable heat energy, the conventional chiller and the electric boiler consuming the electricity to produce cold and heat energy to supply cold and heat loads;
<1> gas turbine:
1) The gas turbine generates electricity:
P GT,t ＝η GT F GT,t
2) The gas turbine can utilize recovered heat energy:
L GT,h,t ＝η GT,h F GT,t
<2> conventional chiller:
L c,t ＝α EC P EC,t
<3> electric boiler:
L HP,h,t ＝α HP P HP,t
wherein eta GT 、η GT,h 、α EC 、α HP The conversion coefficient of the chemical energy converted into electric energy by the gas turbine, the conversion coefficient of the chemical energy converted into heat energy by the gas turbine, the conversion coefficient of the electric energy refrigeration by the conventional cold machine and the conversion coefficient of the heat energy converted into electric energy by the electric boiler are respectively;
(2) cold and heat storage operation constraint:
the cold and heat accumulation device is limited and restrained by cold and heat accumulation and cold and heat supply power in operation, time sequence constraint of cold and heat accumulation capacity, energy accumulation and energy supply balance constraint of scheduling period and rated capacity constraint, and the method is as follows:
-P ist,i,max ≤P ist,i,t ≤P ist,i,max
η i,loss S ist,i,t +P ist,i,t ＝S ist.i.t+1
0≤S ist,i,t ≤S ist,i,max
-P hst,i,max ≤P hst,i,t ≤P hst,i,max
η h,loss S hst,i,t +P hst,i,t ＝S hst.i.t+1
0≤S hst,i,t ≤S hst,i,max
(3) wherein S is hst,i,t 、P hst,i,t 、S ist,i,t 、P ist,i,t Respectively representing the power and the capacity of the heat and cold accumulation device with the number i at the time t; p (P) ist,i,max 、P hst,i,max Respectively represents the maximum cold storage (heat release) power and the maximum heat storage (heat release) power; s is S ist,i,max And S is hst,i,max Respectively representing the maximum capacity of the cold and heat storage device; can be used forQuantity balance constraint:
in the running process, balance relation exists between the energy converted mutually, part of the cold generated by the conventional cold machine is supplied to the cold load, and the other part of the cold is stored by the cold storage device; the heat energy generated by the electric boiler and the heat energy recycled by the gas turbine are partially supplied to the heat load, and a part of the heat energy is stored by the heat storage device;
L c,t ＝P ist,t +L load,ist,t
L GT,h,t +L HP,h,t ＝P hst,t +L load,hst,t
L load,ist,t 、L load,hst,t respectively representing the demand of the cold and hot loads at the time t;
(4) device operation constraints:
gas turbines, conventional chillers, electric boiler plants are subject to capacity and climbing constraints during operation, as follows:
<1> Capacity constraint
<2> climbing constraint
Wherein,,
and->
Representing the minimum and maximum operating power of the gas turbine, the conventional chiller and the electric boiler respectively; />
And->
The minimum and maximum power variation per time period of the gas turbine, the conventional chiller, and the electric boiler are respectively represented.
(5) According to the power load obeying transferable load characteristic, establishing a power load energy utilization decision layer model, which specifically comprises the following steps:
1) Objective function:
wherein C is load The total cost of energy for the power consumer, c t For the real-time electricity price at the time t, P load,i,t Active power required by i node load at t moment;
2) Constraint conditions:
P load,pu,t is the active power after load transfer, P dr,t Is the active power of the transferred partial load, and the power conservation of the transferred load in the period, eta dr The demand response signing proportion between the power grid company and the power consumer is;
(6) The built distributed energy operation decision layer model and the energy decision layer model for the power consumer are processed into a single-layer mixed integer linear programming model by using a KKT condition, a Big-M method and a bilinear substitution method, and the specific steps comprise:
1) The transformation of the distributed energy station operational decision layer model is as follows:
(1) when KKT conversion is carried out, lagrange function L under inequality constraint is written out, and then KKT conditions of the model are written out according to constraint conditions of the model and the Lagrange function.
The Lagrangian function L is:
wherein,,
alpha and alpha j (j=1, 2..6) are the dual variables of the corresponding constraint, respectively.
The constraints under the KKT condition are:
S hst,i,t :
P hst,i,t :
S ist,i,t :
P ist,i,t :
P GT,t :
P EC,t :
P HP,t :
P GT,t ＝η GT F GT,t 
L GT,h,t ＝η GT,h F GT,t 
L c,t ＝α EC P EC,t 
L HP,h,t ＝α HP P HP,t 
η i,loss S ist,i,t +P ist,i,t ＝S ist.i.t+1 
η h,loss S hst,i,t +P hst,i,t ＝S hst.i.t+1 
(2) the nonlinear equation is linearized by using a Big-M method based on KKT conditions, and the specific method is as follows: introducing 20 Boolean variables θ - And theta + And a sufficiently large number M. After the transformation is completed, the constraint conditions are as follows:
2) The transformation of the power load energy decision layer model is as follows:
(1) and the KKT condition transformation is firstly carried out with the operation decision layer model of the distributed energy station, and the steps are as follows: the Lagrangian function L is:
the KKT conditions derived from the lagrangian function and the original constraint are:
wherein,,
and θ is the dual variable of the corresponding constraint.
(2) Performing Big-M transformation on a nonlinear equation in the KKT condition, changing the nonlinear equation into a linear equation, and obtaining a transformation result:
wherein,,
and->
For the introduced boolean variable, M is a sufficiently large number, and the analysis section of the decision layer model is operated with the distributed energy station.
Based on the linear constraint type obtained by KKT condition transformation and Big-M transformation in the step (4), carrying out bilinear replacement on an objective function of a game main layer model, namely a power grid company investment operation decision layer model, and changing an original master-slave game planning model into a mixed integer linear planning model to solve, wherein the specific steps are as follows:
1) C in objective function of electric network company model ele,IES Item comprises c t And (P) GT,t -P EC,t -P HP,t ) The two variables are multiplied, double linear replacement is needed by utilizing the KKT condition of the distributed energy station model and the linear type after Big-M conversion, and the expression after replacement is as follows:
2) C in objective function of electric network company model sell,load Item comprises c t And P load,i,t The two variables are multiplied, double linear replacement is needed by using the KKT condition of the electric power user model and the linear expression after Big-M conversion, and the expression after replacement is as follows:
3) After bilinear replacement, the original master is changed into a mixed integer linear programming model from the game programming model, and the optimal programming scheme considering multiparty games can be obtained by solving by using commercial solvers such as CPLEX, MOSEK and the like.
By using the program system of the network storage collaborative planning method of the multi-energy power distribution system considering multi-main game, the construction scheme solving of the target area can be realized.
And (3) combining the solving method to solve the solving model established in the step (3) and the step (4), thereby obtaining the power distribution system planning scheme. Based on the embodiment, the network side energy storage and power user optimization operation strategy can be obtained by combining the solving method, and is shown in fig. 3.
Advantageous effects
According to the drawings and the operation strategies, the method can fully mobilize each type of adjustable resource of the power distribution system, and effectively reduce the peak-valley difference of the net load curve of the operation of the power distribution system on the basis of considering benefit requirements of each operation and maintenance subject. Specifically, in the embodiment, the power grid company effectively guides demand response adjustment by configuring and actively controlling the energy storage device and utilizing the time-of-use electricity price strategy, so that the peak value of the net load of the system is reduced by more than 10%, the average utilization rate of the line is obviously improved, the demand of capacity expansion and upgrading of the line of the power distribution system is delayed, and the planning level and the planning economy of the system are effectively improved.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather, the present invention is to be construed as limited to the appended claims.
Claims (9)
1. A network storage collaborative planning method of a multi-energy distribution system considering multi-main game is characterized by comprising the following specific steps:
(1) Determining that a main layer of the game is an investment operation decision layer of the power grid company, and a guest layer is an investment operation decision layer of the distributed energy stations and an energy utilization decision layer of the power consumers by considering the decision-making resources of the power grid company, the distributed energy stations and the power consumers and the operation hierarchical relation among the power grid company, the distributed energy stations and the power consumers so as to establish a network storage collaborative game planning model;
(2) Taking the minimum total cost of investment operation of a power grid company as an objective function and the multi-element operation condition of a power distribution system as constraint, and establishing a decision layer model of investment operation of the power grid company as a main layer of a game planning model;
(3) The method comprises the steps of taking the minimum investment operation cost of a distributed energy station as an objective function, taking the operation requirement of each device as a constraint, establishing a distributed energy station investment operation decision layer model, then taking the minimum electricity purchasing cost of a power consumer as an objective function, establishing a power load energy decision layer model, and taking the established distributed energy station and power load model as game planning model object layers;
(4) Firstly, respectively writing KKT conditions of the distributed energy stations and the power load layer model established in the step (3), then using a Big-M method and a bilinear replacement method, converting a network storage collaborative game planning model formed by the power grid company, the distributed energy stations and the power load three-party model established in the step (2) and the step (3) into a single-layer mixed integer linear planning model, solving the single-layer mixed integer linear planning model, and obtaining an optimal network storage collaborative game planning scheme taking into consideration of multiparty benefits.
2. The method for collaborative planning of network storage of a multi-energy distribution system that allows for multi-body gaming according to claim 1, wherein the grid company investment operational decision layer model includes:
(1) Objective function:
min C DRO ＝C line +C ess +C ele,IES +C buy,ups -C sell,load
the specific calculation formula is as follows:
wherein C is DRO Investment of running total cost for electric network company, C line Equal annual cost for line expansion, C ess For equal annual construction costs of energy storage devices, C ele,IES C, the expense for the power grid company to purchase electricity from the distributed energy station buy,ups C, purchasing electricity to the upper power grid for power grid company sell,load The income of selling electricity to the power consumers for the power grid company; η (eta) line 、d line And m line Respectively representing the operation and maintenance cost coefficient, discount rate and service life of the circuit, l line The length of the line is K, which is the set of all lines; η (eta) ess 、d ess And m ess Respectively representing the operation and maintenance cost coefficient, the discount rate and the service life of the energy storage equipment, N node Is a set of all nodes; c line,k 、c s,ess 、c p,ess 、c t 、c buy The method comprises the steps of respectively representing the cost of a line k expansion unit length, the cost of an energy storage device unit capacity, the cost of an energy storage device unit active power, the real-time electricity price and the unit cost of electricity purchasing from a superior power grid by a power grid company; a, a line,k And a ess,i The variables are 0-1, and respectively represent whether the k line expands or not and whether the i node is provided with an energy storage device or not; s is S i,ess And P i,ess Respectively representing the rated capacity and the active power of the energy storage device at the i node; p (P) GT,t Representing the power generation amount of the gas turbine at time t, P EC,t And P HP,t Respectively representing the electricity consumption of a conventional chiller and an electric boiler at the time t; p (P) load,i,t And P ess,i,t Respectively representing the load electricity consumption and the energy storage charge of the inode at the moment t; p (P) DG,i,t Representing the active power output of the distributed power supply;
(2) Constraint conditions:
constraint conditions of the multi-main game multi-energy distribution system network storage collaborative planning on an investment operation decision layer of a power grid company are considered to comprise linear tide constraint, operation safety constraint, electric energy storage operation constraint and real-time electricity price constraint;
(1) linear power flow constraint:
U j,t ＝U i,t -(R ij P ij,t +X ij Q ij,t )
wherein P is ij,t 、Q ij,t 、I ij,t Active power, reactive power and current flowing through the starting end of a line between ij nodes at the moment t are respectively shown; r is R ij 、X ij Representing the resistance and reactance of the line between ij nodes; p (P) jk,t 、Q jk,t Respectively representing the active power and the reactive power of a line between jk nodes; p (P) j 、Q j Respectively representing active power and reactive power flowing out of the node j; u (U) j,t Representing the voltage at node j; a (j) and b (j) respectively represent an associated node set before the j node and an associated node set after the j node;
(2) operational safety constraints:
wherein S is ij For the rated capacity of the line eta N-1 The line load rate set under the N-1 safety constraint is met;
(3) electric energy storage operation constraint:
the energy storage device is limited by energy storage charging and discharging power, energy storage capacity time sequence, scheduling period charging and discharging balance and charge state in operation constraint, and the energy storage device is concretely as follows:
-P ess,i,max ≤P ess,i,t ≤P ess,i,max
S ess,i,t +P ess,i,t ＝S ess.i.t+1
S ess,i,0 ＝S ess,i,T
SOC min ≤SOC t ≤SOC max
wherein P is ess,i,t The charging and discharging power is stored at the time t, and the charging is represented when the charging power is positive, and the charging power is equivalent to a load for a power grid; p (P) ess,i,max Is the maximum charge and discharge power; s is S ess,i,t For the electric quantity of the energy storage equipment at the moment t, S ess,i,0 And S is ess,i,T Respectively representing the energy storage electric quantity at the starting time and the ending time; s is S ess,i,max Rated capacity of the energy storage device at the i node; SOC (State of Charge) t 、SOC max And SOC (System on chip) min Respectively representing the energy storage charge state and the upper limit and the lower limit thereof;
(4) real-time electricity price constraint:
c buy ≤c t ≤c max
wherein, c max Representing the highest real-time electricity price constraint.
3. The method for collaborative planning of network storage of a multi-energy distribution system that allows for multi-body gaming according to claim 1, wherein the distributed energy station investment operational decision layer model comprises:
(1) Objective function:
min C IES ＝C ele,DRO +C buy,gas +C tax -C sell,c -C sell,h
the specific calculation formula is as follows:
wherein C is IES Investment of running total cost for distributed energy resource stations, C ele,DRO C for selling negative value of electricity income to electric network company buy,gas Purchase expense for consuming natural gas for gas turbine, C tax For carbon tax of gas turbine, C sell,c And C sell,h The benefits of cooling and heating to the cold and hot loads are respectively obtained for the distributed energy stations; c t 、c gas 、c carbon 、c energy Real-time electricity price, natural gas unit gas purchase cost, unit carbon tax and cold and hot energy unit selling price are respectively adopted; p (P) GT,t Representing the power generation amount of the gas turbine at time t, P EC,t And P HP,t Respectively representing the electricity consumption of a conventional chiller and an electric boiler at the time t; f (F) GT,t Represents the consumption of natural gas at the time t, L c,t The cold energy produced by a conventional cold machine at the time t is represented by L HP,h,t And L GT,h,T Respectively representing heat energy generated by an electric boiler and recycled by a gas turbine at the time t;
(2) Constraint conditions:
constraint conditions of the multi-main game multi-energy distribution system network storage collaborative planning on a distributed energy station investment operation decision layer are considered, wherein the constraint conditions comprise energy consumption models of gas turbines, conventional cold machines and electric boilers, cold storage and heat storage device operation constraint, energy balance constraint and equipment operation constraint;
(1) energy model for each device:
consider three types of equipment, gas turbines, conventional chillers, and electric boilers:
<1> gas turbine:
1) The gas turbine generates electricity:
P GT,t ＝η GT F GT,t
2) The gas turbine can utilize recovered heat energy:
L GT,h,t ＝η GT,h F GT,t
<2> conventional chiller:
L c,t ＝α EC P EC,t
<3> electric boiler:
L HP,h,t ＝α HP P HP,t
wherein eta GT 、η GT,h 、α EC 、α HP The conversion coefficient of the chemical energy converted into electric energy by the gas turbine, the conversion coefficient of the chemical energy converted into heat energy by the gas turbine, the conversion coefficient of the electric energy refrigeration by the conventional cold machine and the conversion coefficient of the heat energy converted into electric energy by the electric boiler are respectively;
(2) and (3) the operation constraint of the cold and heat storage device:
the cold and heat accumulation device is limited and restrained by cold and heat accumulation and cold and heat supply power in operation, time sequence constraint of cold and heat accumulation capacity, energy accumulation and energy supply balance constraint of scheduling period and rated capacity constraint, and the method is as follows:
-P ist,i,max ≤P ist,i,t ≤P ist,i,max
η i,loss S ist,i,t +P ist,i,t ＝S ist.i.t+1
0≤S ist,i,t ≤S ist,i,max
-P hst,i,max ≤P hst,i,t ≤P hst,i,max
η h,loss S hst,i,t +P hst,i,t ＝S hst.i.t+1
0≤S hst,i,t ≤S hst,i,max
wherein S is hst,i,t 、P hst,i,t 、S ist,i,t 、P ist,i,t Respectively representing the power and the capacity of the heat and cold accumulation device with the number i at the time t; p (P) ist,i,max 、P hst,i,max Respectively represents the maximum cold storage (heat release) power and the maximum heat storage (heat release) power; s is S ist,i,max And S is hst,i,max Respectively representing the maximum capacity of the cold and heat storage device;
(3) energy balance constraint:
L c,t ＝P ist,t +L load,ist,t
L GT,h,t +L HP,h,t ＝P hst,t +L load,hst,t
L load,ist,t 、L load,hst,t respectively representing the demand of the cold and hot loads at the time t;
(4) device operation constraints:
gas turbines, conventional chillers, electric boiler plants are subject to capacity and climbing constraints during operation, as follows:
<1> Capacity constraint
<2> climbing constraint
Wherein,,
and->
Representing the minimum and maximum operating power of the gas turbine, the conventional chiller and the electric boiler respectively; />
And->
The minimum and maximum power variation per time period of the gas turbine, the conventional chiller, and the electric boiler are respectively represented.
4. The method for collaborative planning of network storage of a multi-energy distribution system that allows for multi-body gaming according to claim 1, wherein the power load energy decision layer model comprises:
(1) Objective function:
wherein C is load The total cost of energy for the power consumer, c t For the real-time electricity price at the time t, P load,i,t Active power required by i node load at t moment;
(2) Constraint conditions:
the constraint condition of the network storage collaborative planning of the multi-main-body game multi-energy distribution system in the power load energy utilization decision layer is considered to be that all power loads obey transferable loads, and the method specifically comprises the following steps:
wherein P is load,pu,t Is the active power after load transfer, P dr,t Is the active power of the transferred partial load, and the power conservation of the transferred load in the period, eta dr Is the demand response subscription proportion between the power grid company and the power consumer.
5. The network storage collaborative planning method for a multi-energy distribution system considering multi-body gaming according to claim 1, wherein the model conversion method based on KKT conditions, big-M method and bilinear substitution comprises:
(1) Writing out a KKT condition of an energy consumption decision layer model of the electric load and a distributed energy station investment operation decision layer model by utilizing the basic principle of the KKT condition, and then introducing a Boolean variable by utilizing a Big-M method to convert nonlinear constraint in the KKT condition into a linear inequality;
(2) On the basis of performing linear transformation by using a KKT condition and a Big-M method, replacing relevant parts in a game main layer objective function by using a bilinear replacement method, so that the original game problem is converted into a single-layer mixed integer linear programming model;
(3) And solving the single-layer mixed integer linear programming model.
6. A program system for a network storage collaborative planning method of a multi-energy distribution system considering multi-body gaming, comprising:
the acquisition module is used for: the power distribution system is used for collecting and acquiring the active demand of the power load of each node, the construction condition of each device of the distributed energy station and the output condition of each distributed power supply in the power distribution system;
the processing module is used for: the method is used for acquiring the network storage collaborative planning result of the power distribution system based on the network storage collaborative game planning model and specifically comprises the steps of firstly, establishing an investment operation decision model and an electric load energy utilization decision model of a power grid company and a distributed energy station according to specified interest rights; secondly, changing constraint conditions in the distributed energy stations and the power load model into KKT conditions, and linearizing by a Big-M method; finally, based on KKT conditions and linear constraints obtained by transformation, bilinear replacement is carried out on an objective function of an investment operation decision layer model of a power grid company, an original master-slave game problem is converted into a single-layer mixed integer linear programming model, and the single-layer mixed integer linear programming model is solved, so that an optimal network storage planning scheme considering multiparty games is obtained;
and a sending module: the method is used for outputting information such as an optimal network storage planning scheme considering multi-party games, line tide and real-time electricity price.
7. The program system of the network storage collaborative planning method for a multi-energy distribution system considering multi-body gaming according to claim 6, wherein the specific flow of the processing module comprises:
(1) Determining a main layer of game as an investment operation decision layer of the power grid company, and a guest layer as an investment operation decision layer of the distributed energy station and an energy utilization decision layer of the power consumer by considering the decision-making resources of the power grid company, the distributed energy station and the power consumer and the operation hierarchical relation among the power grid company, the distributed energy station and the power consumer;
establishing a network storage cooperative game planning model, wherein the network storage cooperative game planning model comprises a power grid company investment operation decision layer model, a distributed energy station investment operation decision layer model and a power load energy utilization decision layer model, and specifically comprises the following steps:
(1) power grid company investment operation decision layer model
<1> objective function:
min C DRO ＝C line +C ess +C ele,IES +C buy,ups -C sell,load
the specific calculation formula is as follows:
<2> constraint:
1) Linear power flow constraint:
U j,t ＝U i,t -(R ij P ij,t +X ij Q ij,t )
2) Operational safety constraints:
3) Electric energy storage operation constraint:
-P ess,i,max ≤P ess,i,t ≤P ess,i,max
S ess,i,t +P ess,i,t ＝S ess.i.t+1
S ess,i,0 ＝S ess,i,T
SOC min ≤SOC t ≤SOC max
4) Real-time electricity price constraint:
c buy ≤c t ≤c max
(2) decision layer model for investment operation of distributed energy resource station
<1> objective function:
min C IES ＝C ele,DRO +C buy,gas +C tax -C sell,c -C sell,h
the specific calculation formula is as follows:
<2> constraint:
1) Energy model for each device:
gas turbine:
P GT,t ＝η GT F GT,t
L GT,h,t ＝η GT,h F GT,t
conventional chiller:
L c,t ＝α EC P EC,t
electric boiler:
L HP,h,t ＝α HP P HP,t
2) And (3) the operation constraint of the cold and heat storage device:
-P ist,i,max ≤P ist,i,t ≤P ist,i,max
η i,loss S ist,i,t +P ist,i,t ＝S ist.i.t+1
0≤S ist,i,t ≤S ist,i,max
-P hst,i,max ≤P hst,i,t ≤P hst,i,max
η h,loss S hst,i,t +P hst,i,t ＝S hst.i.t+1
0≤S hst,i,t ≤S hst,i,max
3) Energy balance constraint:
L c,t ＝P ist,t +L load,ist,t
L GT,h,t +L HP,h,t ＝P hst,t +L load,hst,t
4) Device operation constraints:
capacity constraint:
climbing constraint:
(3) power consumer energy utilization decision layer
<1> objective function:
<2> constraint:
(2) Writing out a KKT condition of an energy consumption decision layer model of the electric load and a distributed energy station investment operation decision layer model by utilizing the basic principle of the KKT condition, and then introducing a Boolean variable by utilizing a Big-M method to convert nonlinear constraint in the KKT condition into a linear inequality;
(3) On the basis of performing linear transformation by using a KKT condition and a Big-M method, replacing relevant parts in a game main layer objective function by using a bilinear replacement method, so that the original game problem is converted into a single-layer mixed integer linear programming model;
(4) The above model is solved using a commercial solver such as CPLEX, MOSEK, etc.
8. An apparatus for a multi-energy distribution system network storage collaborative planning method considering multi-body gaming, comprising a memory and a processor, wherein the memory stores a program running on the processor, and the processor executes the steps of the multi-energy distribution system network storage collaborative planning method considering multi-body gaming according to any one of claims 1-5 when running the program.
9. A computer readable storage medium having stored thereon computer instructions which, when executed, perform the steps of a multi-energy distribution system network storage collaborative planning method in view of multi-body gaming according to any of claims 1-5.
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| CN117035202B (en) * | 2023-10-10 | 2024-01-23 | 国网山西省电力公司电力科学研究院 | Double-layer collaborative expansion planning method for electric heating comprehensive energy system considering demand response |
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