WO2020237700A1

WO2020237700A1 - Operation scheduling method for multiple energy systems

Info

Publication number: WO2020237700A1
Application number: PCT/CN2019/089897
Authority: WO
Inventors: 滕贤亮; 杜刚; 吴仕强; 陈�胜; 卫志农; 孙国强; 臧海祥; 王文学
Original assignee: 国电南瑞科技股份有限公司; 河海大学
Priority date: 2019-05-28
Filing date: 2019-06-04
Publication date: 2020-12-03
Also published as: CN110210104B; ZA202110354B; CN110210104A

Abstract

Disclosed is an operation scheduling method for multiple energy systems, comprising: separately constructing a power system operation scheduling model, a natural gas system operation scheduling model, and a thermodynamic system operation scheduling model in the multiple energy systems; separately solving the optimality conditions of a power system scheduling model, a natural gas system scheduling model, and a thermodynamic system scheduling model; constructing an operation scheduling objective optimization function of the multiple energy systems, and using the solved optimality conditions as constraints; solving the objective optimization function, and obtaining the Nash equilibrium point of a non-cooperative game of the multiple energy systems; and separately performing operation scheduling on a power system, a natural gas system, and a thermodynamic system in the multiple energy systems according to a scheduling policy corresponding to the Nash equilibrium point. The present invention can realize optimal operation scheduling.

Description

Operation scheduling method for multi-energy system

Technical field

The invention relates to a multi-energy system technology, in particular to a multi-energy system operation scheduling method.

Background technique

Combined heat and power (CHP) has high energy conversion efficiency and has been widely used in power systems (especially power distribution systems) in recent years. CHP uses natural gas as fuel and simultaneously produces electrical power and thermal power. Therefore, CHP acts as a power source and a heat source respectively in the power system and the thermal system, and is equivalent to the gas load in the natural gas system, and gradually deepens the coupling between multi-energy systems.

The traditional planning and operation of electric power, natural gas, and thermal systems are independent of each other. With the gradual deepening of coupling between sub-energy systems, independent operation scheduling is not economically strict and optimal, and may even cause hidden dangers in safety and reliability. The most direct way to solve this problem is to build a unified integrated energy operation scheduling framework, that is, a single dispatcher obtains the operating parameters of the multi-energy system at the same time and executes unified and synchronized scheduling decisions. This method guarantees the optimality of scheduling decisions from a global perspective. However, there are still many difficulties in actual operation, such as failure to protect the information privacy of multi-energy systems, inconsistent scheduling goals among sub-energy systems, and unsynchronized decision-making. Therefore, a more practical solution is to maintain the framework of independent operation of the existing energy systems, and realize the coordinated optimal scheduling of multi-energy systems through sufficient information interaction between the systems.

Summary of the invention

Objective of the invention: In view of the problems existing in the prior art, the present invention provides a method for scheduling operation of a multi-energy system.

Technical solution: The multi-energy system operation scheduling method of the present invention includes:

(1) Construct a power system operation dispatch model in a multi-energy system;

(2) Construct a natural gas system operation scheduling model in a multi-energy system;

(3) Establish a thermal system operation scheduling model in a multi-energy system;

(4) Solve the optimality conditions of the power system dispatch model, natural gas system dispatch model and thermal system dispatch model respectively;

(5) Construct the operation scheduling objective optimization function of the multi-energy system, and take the optimality conditions solved in step (4) as constraints;

(6) Solve the objective optimization function in step (5) to obtain the Nash equilibrium point of the non-cooperative game of the multi-energy system;

(7) The power system, natural gas system, and thermal system in the multi-energy system are respectively dispatched according to the dispatch strategy corresponding to the Nash equilibrium point.

Further, the power system operation dispatch model constructed in step (1) is specifically:

In the formula: subscripts i, j and l represent power system nodes, subscript c represents CHP system, superscripts max and min respectively refer to the upper limit and lower limit of the variable, and P _G and Q _G refer to the active power injected by the power supply. And reactive power, P _L and Q _L respectively refer to the active power and reactive power of the load, C _G refers to the cost of power supply, F _{CHP, c} refers to the amount of natural gas consumed by the CHP unit in the power system dispatching, and u _c refers to the node of the natural gas system Marginal gas price,

Refers to the electrical output efficiency of the CHP unit,

Refers to the nodal marginal heat price HLMP of the thermal system, P _jl and P _ij represent the active power of line jl and line ij respectively, and Q _jl and Q _ij represent the reactive power of line jl and line ij respectively,

Refers to the square of the current amplitude of the line ij, U _i and U _j refer to the square of the voltage amplitude of the nodes i and j, t represents the corresponding value at time t, and r _ij and x _ij refer to the resistance and reactance of the line ij respectively.

Further, the natural gas system operation scheduling model constructed in step (2) is specifically:

τ _k =θ _k F _C,k (A13)

In the formula: subscripts m and n represent natural gas nodes, subscript k represents pressurizing stations, superscripts max and min respectively refer to the upper limit and lower limit of the variable, F _S is the gas source point output, and C _S is the gas source Cost coefficient, F _D is the natural gas load, τ _k is the gas load of the pressurized station, F _{CHP,c is} the natural gas consumed by the CHP unit in the power system dispatching, F _mn is the natural gas flow of the pipeline mn, and F _C is the flow of the pressurized station , Π is the node pressure variable, C _mn is the Weymouth constant of pipeline mn, θ _k is the energy consumption coefficient of natural gas-driven pressurizing station k,

versus

Are the maximum pressure ratio and minimum pressure ratio of pressure station k,

versus

These are the inlet and outlet pressures of pressurizing station k.

Furthermore, the operation scheduling model of the thermal system constructed in step (3) is specifically:

(∑m _out )T _out =∑(m _in T _in ) (A21)

Where: the subscript w refers to the heat source, the superscript max and min refer to the upper limit and the lower limit of the variable respectively, Φ _w refers to the heat power output of the heat source, C _w is the cost coefficient of the heat source supply, A _w , A _c and A _d are the heat source-node, CHP-node and load-node correlation matrix respectively, F _CHP,c refer to the amount of natural gas consumed by CHP units in power system scheduling,

Is the heat conversion efficiency of CHP, Φ _d is the heat load power, C _{P is} the specific heat capacity of water, m _q is the fixed flow rate of the pipeline, T _{s is the} temperature of the hot water injection node, T _{o is the} temperature of the hot water outflow node, T _start and T _end are temperature pipeline start and end, T _a is the ambient temperature, L is the pipe length, λ represents a pipe thermal conductivity, m represents a pipe flow, m _in the m _out denote flow injection and the outflow conduit, T _in and T _Out refers to the temperature of hot water at the injection node and the outlet node respectively.

Further, the objective optimization function constructed in step (5) is specifically:

st J ₁ r+E ₁ sh ₁ = 0: μ (A26)

J ₂ r+E ₂ sh ₂ ≤0:v (A27)

s∈κ:w (A28)

A ₁ x+B ₁ yg ₁ =0:λ (A32)

A ₂ x+B ₂ yg ₂ ≤0: γ (A33)

s.t.A(26)-A(36)

In the formula, the superscript T represents matrix/vector transposition, r and s are decision variables, J ₁ , J ₂ , E ₁ and E ₂ are constant matrices, e, f, h ₁ and h ₂ are constant vectors, μ, v and w are the dual variables of A(26)-A(28) respectively, s∈κ refers to the SOC constraint, x and y are decision variables, A ₁ , A ₂ , B ₁ and B ₂ are constant matrices, and c ₁ , c ₂ , g ₁ and g ₂ are constant vectors, λ and γ are the dual variables of A(32) and A(33) respectively, P _G,i refers to the active power injected by the node i power supply and C _G,i refers to the power supply cost , F _S,m is the gas source point output of natural gas node m, C _S,m is the gas source cost coefficient, Φ _{w is} the heat power output of the heat source, C _w is the cost coefficient of heat source supply, Υ _i is the electricity price of node i, u _m represents the gas price at node m,

Represents the heat price of node w, Υ _c represents the electricity price of node c, F _{CHP, c} refers to the amount of natural gas consumed by CHP units in the power system scheduling,

Refers to the electrical output efficiency of the CHP unit,

Refers to the nodal marginal heat price HLMP of the thermal system,

It represents the heat output efficiency of the CHP unit, and u _c refers to the node marginal gas price of the natural gas system.

Beneficial effects: Compared with the prior art, the present invention has significant advantages: the present invention proposes an operation scheduling method for a multi-energy system, which independently executes operation scheduling for electric power, natural gas and thermal systems, and interacts with sub-energy systems Complete energy transaction volume and transaction price information. The power system transmits the gas purchase price quote to the natural gas system, and the natural gas system feeds back the gas price information; on the other hand, the power system transmits the heating quote to the thermal system, and the thermal system feeds back the heat price information. In other words, the power, natural gas, and thermal systems aim to maximize their respective interests (optimal operating costs) and make independent decisions. However, their respective interests are related to each other, forming a non-cooperative game relationship; at the equilibrium point of the game (ie Nash equilibrium) At this point, either party cannot achieve the purpose of improving its own interests by changing its decision-making. In summary, the equilibrium point of the game is strictly optimal for the individual (each sub-energy system), and the conditions for complete information interaction are also optimal for the overall situation (the entire integrated energy system).

Description of the drawings

Fig. 1 is a schematic flowchart of an embodiment of the present invention.

Detailed ways

This embodiment provides an operation scheduling method for a multi-energy system, as shown in Fig. 1, including the following steps:

Step 1: Build a power system operation dispatch model in a multi-energy system.

Among them, the power system operation scheduling model adopts the second-order cone (SOC) form, specifically:

In the formula: subscripts i, j and l represent power system nodes, subscript c represents CHP system, superscripts max and min respectively refer to the upper limit and lower limit of the variable, and P _G and Q _G refer to the active power injected by the power supply. And reactive power, P _L and Q _L respectively refer to the active power and reactive power of the load, C _G refers to the cost of power supply, F _{CHP, c} refers to the amount of natural gas consumed by the CHP unit in the power system dispatching, and u _c refers to the node of the natural gas system Gas locational marginal price (GLMP),

Refers to the electrical output efficiency of the CHP unit,

Refers to the heat locational marginal price (HLMP) of the thermal system. P _jl and P _ij represent the active power of line jl and line ij, respectively, and Q _jl and Q _ij represent the reactive power of line jl and line ij, respectively.

The power system dispatch objective function (1) includes the power cost, the fuel cost of the CHP unit and the negative value of the profit of the CHP unit supplying heat to the heating network. Equations (2)-(9) represent power system operation constraints. Equations (2) and (3) respectively refer to the active power and reactive power balance constraints of node j. Equation (4) represents the linear relationship between the square of the voltage amplitude at the head and end nodes of the line i-j, the line power and the square of the current amplitude. Equation (5) refers to the SOC constraint related to line i-j and apparent power. Equations (6) and (7) are power system node voltage amplitude constraints and line transmission current constraints, respectively. Equations (8) and (9) are the constraints on the active output and reactive output of the power supply, respectively.

It should be noted that the natural gas system u (GLMP) and thermal system

Is a fixed constant, so the power system scheduling model (1)-(9) is a second-order cone optimization (SOC programming, SOCP) problem, and the dual variable of the active power balance equation (2) is the nodal marginal price Υ (electricity locational marginal price, ELMP).

Step 2: Construct a natural gas system operation scheduling model in a multi-energy system.

Among them, the natural gas system operation scheduling model in the form of SOC is specifically:

τ _k =θ _k F _C,k (13)

In the formula: subscripts m and n represent natural gas nodes, and subscript k represents pressurizing stations. F _S is the gas source point output, F _D is the natural gas load, τ _k is the gas load of the pressurized station, F _mn is the natural gas flow rate of the pipeline mn, F _C is the pressurized station flow, and π is the node pressure variable,

versus

These are the inlet and outlet pressures of pressurizing station k. C _S is the gas source cost coefficient, C _mn is the Weymouth constant of pipeline mn, θ _k is the energy consumption coefficient of natural gas-driven pressurizing station k,

versus

They are the maximum compression ratio and the minimum compression ratio of the compression station k.

The natural gas system scheduling objective function (10) is the minimum gas supply cost. Equation (11) is the natural gas flow balance equation, which takes into account the gas source input, the natural gas load, and the gas load of the pressurized station. Equation (12) is the SOC constraint between the m-n flow rate of the pipeline and the pressure drop at the first and last sections. Equation (13) indicates that the natural gas consumed by the pressurizing station is proportional to the flow rate (generally between 1% and 3%). Equation (14) is the constraint of the boost ratio of the pressurizing station. Equation (15) is the transmission flow restriction of the pressurizing station. Equations (16) and (17) are gas supply constraints and nodal pressure constraints, respectively.

In a nutshell, the natural gas system operation constraint equation (12) is the SOC constraint, and the rest are linear constraints, so the natural gas system scheduling model is a SOCP problem, and the dual variable of the flow balance equation (11) is GLMP.

Step 3: Build a thermal system operation scheduling model in a multi-energy system.

Among them, the operation scheduling model of the thermal system is specifically:

(∑m _out )T _out =∑(m _in T _in ) (21)

Where: subscript w refers to the heat source. Φ _w power output refers to a heat source, Φ _d heat load power, m _q is the fixed flow conduit, m _in m _out with flow injection and refer to the outflow conduit, T _s, hot water injection junction temperature, T _o refers to the heat The temperature of the water outflow node, T _in and T _out refer to the temperature of the hot water at the injection node and the outflow node respectively, and T _start and T _end are the temperatures at the beginning and end of the pipeline, respectively. C _w is the cost coefficient of heat supply,

Heat conversion efficiency of CHP, A _w, A _c and A _d are heat - node, CHP- and the load node - node correlation matrix, T _a is the ambient temperature, L is the pipe length, λ represents the thermal conductivity of the pipe, C _P Refers to the specific heat capacity of water, and m represents the pipe flow.

The optimization goal (18) of the thermal system scheduling is the optimal heat supply cost. Equation (19) represents the heat supply balance equation of each node. Equation (20) describes the functional relationship between the temperature difference between the head and the end of the pipe and the pipe flow. Equation (21) represents the node temperature mixing equation. Equations (22) and (23) are the upper and lower limits of the hot water temperature at the injection node and the outflow node, and the equation (24) is the upper and lower limits of the heat source supply.

Assuming that the pipe flow of the thermal system is fixed, the thermal system scheduling model (18)-(24) is a linear programming (LP) problem, and the dual variable of the thermal power balance equation (19) is HLMP.

Step 4: Solve the optimality conditions of power system dispatching model, natural gas system dispatching model and thermal system dispatching model respectively.

Step 5: Construct the operation scheduling objective optimization function of the multi-energy system, and take the optimality condition solved in step 4 as the constraint.

Among them, the power, natural gas, and thermal systems each aim at the optimal system operation cost, and independently perform system operation scheduling. However, the operation scheduling results of multi-energy systems are related to each other; in other words, the decision-making of the sub-energy system is independent. Self-interest is affected by the decision of the coupled system. Therefore, the dispatching of the electricity-natural gas-thermal system constitutes a non-cooperative game relationship, and the optimal solution of the game is the Nash equilibrium point.

In the present invention, power system and natural gas system scheduling are SOCP problems; for ease of description, the general form of SOCP problems is:

J ₁ r+E ₁ sh ₁ =0: μ (26)

J ₂ r+E ₂ sh ₂ ≤0:v (27)

s∈κ:w (28)

In the formula: superscript T represents matrix/vector transpose. r and s are decision variables. J ₁ , J ₂ , E ₁ and E ₂ are constant matrices. e, f, h ₁ and h ₂ are constant vectors. μ, v and w are the dual variables of equations (26)-(28) respectively. s∈κ refers to the SOC constraint, which is equivalent to

(o is the dimension of variable s).

The optimality conditions of SOCP problems (25)-(28) include:

1) Original problem constraints: (26)-(28);

2) Dual problem constraints:

3) Strong dual equation:

Equations (26)-(31) constitute the optimality conditions of the original SOCP problems (25)-(28). Since SOCP problems (25)-(28) are convex optimization problems, the solutions satisfying formulas (26)-(31) are strictly equivalent to the optimal solutions of the original problems (25)-(28).

Similarly, the thermal system is optimized as an LP problem, and its general form is:

A ₁ x+B ₁ yg ₁ =0:λ (33)

A ₂ x+B ₂ yg ₂ ≤0: γ (34)

Where: x and y are decision variables. A ₁ , A ₂ , B ₁ and B ₂ are constant matrices, and c ₁ , c ₂ , g ₁ and g ₂ are constant vectors. λ and γ are the dual variables of equations (33) and (34), respectively.

The optimality conditions of LP problems (32)-(34) include:

1) The original problem constraints (33) and (34);

2) Dual problem constraints:

3) Strong dual equation:

Equations (33)-(37) constitute the optimality conditions of convex optimization LP problems (32)-(34); in other words, the global optimal solutions of LP problems (32)-(34) and equations ( The solution sets of 33)-(37) are strictly equivalent.

The Nash equilibrium point of the multi-energy system game is the optimal solution for the power, natural gas and thermal systems, that is, the SOCP problem (25)-(28) and the LP problem (32)-(34) simultaneously reach the optimal solution. According to the above optimality conditions, the optimal solution of SOCP problem (25)-(28) is equivalent to formula (26)-(31), and the optimal solution of LP problem (32)-(34) is equivalent to formula ( 33)-(37). Therefore, the necessary and sufficient condition for the Nash equilibrium solution of the multi-energy system game is to satisfy the solution set (single or multiple solutions) of equations (26)-(31) and equations (33)-(37).

Equations (26)-(31) and (33)-(37) contain multiple equality and inequality constraints, and it is difficult to directly obtain analytical solutions. To this end, the present invention proposes the following optimization model:

The optimal solution of the optimization model (38) must be the Nash equilibrium point. This method is essentially a direct method, without iteration and setting initial values.

Furthermore, for multiple Nash equilibrium point problems, dispatchers generally seek the Nash equilibrium point corresponding to the maximization of social benefits (39) or the maximization of profitability of energy producers (40):

In the formula, Υ _i represents the electricity price of node i, _um represents the gas price of node m,

Represents the heat price of node w, Υ _c represents the electricity price of node c,

Indicates the heat output efficiency of the CHP unit.

Generally speaking, maximizing social benefits corresponds to low energy prices (including ELMP, GLMP, and HLMP), while maximizing profits for energy producers corresponds to high energy prices.

Step 6: Solve the objective optimization function in Step 5 to obtain the Nash equilibrium point of the non-cooperative game of the multi-energy system.

At the non-cooperative equilibrium point, that is, the optimal solution of the optimization model (38), (39) or (40), either party (electricity, natural gas, or thermal system dispatcher) cannot change its own decisions to reduce their respective operations Cost, that is, the multi-energy system has reached a global optimal balance.

Step 7: The power system, the natural gas system, and the thermal system in the multi-energy system are respectively dispatched according to the dispatch strategy corresponding to the Nash equilibrium point.

What is disclosed above is only a preferred embodiment of the present invention, and cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope of the present invention.

Claims

A multi-energy system operation scheduling method, characterized in that the method includes:

(1) Construct a power system operation dispatch model in a multi-energy system;

(2) Construct a natural gas system operation scheduling model in a multi-energy system;

(3) Establish a thermal system operation scheduling model in a multi-energy system;

(4) Solve the optimality conditions of the power system dispatch model, natural gas system dispatch model and thermal system dispatch model respectively;

(5) Construct the operation scheduling objective optimization function of the multi-energy system, and take the optimality conditions solved in step (4) as constraints;

(6) Solve the objective optimization function in step (5) to obtain the Nash equilibrium point of the non-cooperative game of the multi-energy system;

(7) The power system, natural gas system, and thermal system in the multi-energy system are respectively dispatched according to the dispatch strategy corresponding to the Nash equilibrium point.
The multi-energy system operation scheduling method according to claim 1, wherein the power system operation scheduling model constructed in step (1) is specifically:

In the formula: subscripts i, j and l represent power system nodes, subscript c represents CHP system, superscripts max and min respectively refer to the upper limit and lower limit of the variable, and P G and Q G refer to the active power injected by the power supply. And reactive power, P L and Q L respectively refer to the active power and reactive power of the load, C G refers to the cost of power supply, F CHP, c refers to the amount of natural gas consumed by the CHP unit in the power system dispatching, and u c refers to the node of the natural gas system Marginal gas price,
Refers to the electrical output efficiency of the CHP unit,
Refers to the nodal marginal heat price HLMP of the thermal system, P jl and P ij represent the active power of line jl and line ij respectively, and Q jl and Q ij represent the reactive power of line jl and line ij respectively,
Refers to the square of the current amplitude of the line ij, U i and U j refer to the square of the voltage amplitude of the nodes i and j, t represents the corresponding value at time t, and r ij and x ij refer to the resistance and reactance of the line ij respectively.
The multi-energy system operation scheduling method according to claim 1, wherein the natural gas system operation scheduling model constructed in step (2) is specifically:

τ k =θ k F C,k (A13)

In the formula: subscripts m and n represent natural gas nodes, subscript k represents pressurizing stations, superscripts max and min respectively refer to the upper limit and lower limit of the variable, F S is the gas source point output, and C S is the gas source Cost coefficient, F D is the natural gas load, τ k is the gas load of the pressurized station, F CHP,c is the natural gas consumed by the CHP unit in the power system dispatching, F mn is the natural gas flow of the pipeline mn, and F C is the flow of the pressurized station , Π is the node pressure variable, C mn is the Weymouth constant of pipeline mn, θ k is the energy consumption coefficient of natural gas-driven pressurizing station k,
versus
Are the maximum pressure ratio and minimum pressure ratio of pressure station k,
versus
These are the inlet and outlet pressures of pressurizing station k.
The operation scheduling method of a multi-energy system according to claim 1, wherein the operation scheduling model of the thermal system constructed in step (3) is specifically:

(∑m out )T out =∑(m in T in ) (A21)

Where: the subscript w refers to the heat source, the superscript max and min refer to the upper limit and the lower limit of the variable respectively, Φ w refers to the heat power output of the heat source, C w is the cost coefficient of the heat source supply, A w , A c and A d are the heat source-node, CHP-node and load-node correlation matrix respectively, F CHP,c refer to the amount of natural gas consumed by CHP units in power system scheduling,
Is the heat conversion efficiency of CHP, Φ d is the heat load power, C P is the specific heat capacity of water, m q is the fixed flow rate of the pipeline, T s is the temperature of the hot water injection node, T o is the temperature of the hot water outflow node, T start and T end are temperature pipeline start and end, T a is the ambient temperature, L is the pipe length, λ represents a pipe thermal conductivity, m represents a pipe flow, m in the m out denote flow injection and the outflow conduit, T in and T Out refers to the temperature of hot water at the injection node and the outlet node respectively.
The operation scheduling method of a multi-energy system according to claim 1, wherein the objective optimization function constructed in step (5) is specifically:

st J 1 r+E 1 sh 1 = 0: μ (A26)

J 2 r+E 2 sh 2 ≤0:v (A27)

s∈κ:w (A28)

A 1 x+B 1 yg 1 =0:λ (A32)

A 2 x+B 2 yg 2 ≤0: γ (A33)

In the formula, the superscript T represents matrix/vector transposition, r and s are decision variables, J 1 , J 2 , E 1 and E 2 are constant matrices, e, f, h 1 and h 2 are constant vectors, μ, v and w are the dual variables of A(26)-A(28) respectively, s∈κ refers to the SOC constraint, x and y are decision variables, A 1 , A 2 , B 1 and B 2 are constant matrices, and c 1 , c 2 , g 1 and g 2 are constant vectors, λ and γ are the dual variables of A(32) and A(33) respectively, P G,i refers to the active power injected by the node i power supply and C G,i refers to the power supply cost , F S,m is the gas source point output of natural gas node m, C S,m is the gas source cost coefficient, Φ w is the heat power output of the heat source, C w is the cost coefficient of heat source supply, Υ i is the electricity price of node i, u m represents the gas price at node m,
Represents the heat price of node w, Υ c represents the electricity price of node c, F CHP, c refers to the amount of natural gas consumed by CHP units in the power system scheduling,
Refers to the electrical output efficiency of the CHP unit,
Refers to the nodal marginal heat price HLMP of the thermal system,
It represents the heat output efficiency of CHP, and u c refers to the node marginal gas price of the natural gas system.