CN107862586A - A kind of energy hinge competitive tender method and system towards the transaction of multipotency stream - Google Patents

A kind of energy hinge competitive tender method and system towards the transaction of multipotency stream Download PDF

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CN107862586A
CN107862586A CN201711260158.2A CN201711260158A CN107862586A CN 107862586 A CN107862586 A CN 107862586A CN 201711260158 A CN201711260158 A CN 201711260158A CN 107862586 A CN107862586 A CN 107862586A
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梅生伟
魏韡
李�瑞
刘锋
陈来军
方宇娟
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Tsinghua University
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Abstract

The invention discloses a kind of energy hinge competitive tender method and system towards the transaction of multipotency stream, this method initially sets up the energy hinge leader-followers games Bidding Strategiess model towards the transaction of multipotency stream；Then energy hinge leader-followers games Bidding Strategiess model is handled, obtains MILP model；And according to MILP model, calculate the Bidding Strategiess of energy hinge and maximum running income；The electricity sales amounts of Bidding Strategiess including energy hinge, sale of electricity price, purchase of electricity, power purchase price, sell heat and sell level Waste Heat Price.Using method provided by the invention or system, take into full account possess thermoelectricity Federal Reserve, cogeneration of heat and power function energy hinge multipotency stream trading capacity, the Bidding Strategiess of the energy hinge subject of operation towards the transaction of multipotency stream are given, improve income of the energy hinge subject of operation in multipotency stream trade market.

Description

Energy hub bidding method and system for multi-energy flow transaction
Technical Field
The invention relates to the field of multi-energy flow bidding, in particular to an energy hub bidding method and system for multi-energy flow transaction.
Background
The interconnection of gas, heat, electricity and other multi-energy networks to construct an integrated energy system (or energy internet) is recognized as an effective means for enhancing the flexibility and reliability of the energy network. The key equipment for realizing network interconnection of gas, electricity, heat and the like in the comprehensive energy system is an energy hub. The energy hub has the functions of transferring, converting and storing multiple energy flow carriers, and is an important embodiment of the flexibility of the comprehensive energy system. From the energy consumption perspective, electric energy and heat energy are the most important terminal energy demands, and are mainly satisfied by a regional power distribution network and a regional centralized heating network. Under the background of energy Internet, with the common adoption of the electrified heating technology, a regional power distribution network and a regional centralized heating network are closely coupled through an energy hub with the capacity of thermoelectric coupling and thermoelectric coupling storage, so that the complementary synergistic effect of electric energy (easy to transport and difficult to store) and heat energy (easy to store and difficult to transport) is realized, and the flexibility of the system is improved. The energy hub is expected to be used as an independent market operation main body to participate in the trade of two energy flows of a regional power distribution network and a regional centralized heating network, and the bidding strategy of the energy hub in the multi-energy flow trading market is the key for improving the operation economy.
At present, a great deal of research results are available for bidding strategies of power supplies, loads and energy storage equipment in the power market. Such research generally studies bidding strategies of power sources, loads and energy storage devices in electric energy single-energy-flow trading from the perspective of price acceptors or price influencers. The energy hub relates to the trading of multiple energy flows such as electricity and thermoelectricity, and the competitive bidding strategy based on the trading of single energy flow of electricity is researched at present, so that the energy hub is difficult to be directly popularized to the scene of the trading of the multi-energy flow market. By integrating practical engineering problems such as energy system planning and scheduling, competitive bidding strategy research facing to an energy hub of multi-energy flow transaction is urgently needed.
Disclosure of Invention
The invention aims to provide a competitive bidding method and a competitive bidding system for an energy hub of multi-energy flow trading, which expand the competitive bidding method in the traditional electric power single-energy flow market, fully consider the combined heat and power storage and combined heat and power generation functions of the energy hub of multi-energy flow trading, improve the bidding strategy in the electric heat double-energy flow market and the interactive characteristics of the electric power and heat power market, and improve the operation income of an energy hub operation main body in the multi-energy flow trading market.
In order to achieve the purpose, the invention provides the following scheme:
an energy hub bidding method facing multi-energy flow transaction, the energy hub bidding method comprising:
establishing an energy hub master-slave game competitive bidding strategy model facing multi-energy flow transaction; the energy hub is a hub with combined heat and power supply and combined heat and power storage capacity; the energy hub master-slave game bidding strategy model comprises a master model and a slave model; the main model comprises an energy hub income objective function and energy hub operation constraint conditions; the slave model comprises an electric power market clearing objective function, an electric power market clearing constraint condition, a thermal power market clearing objective function and a thermal power market clearing constraint condition;
processing the energy hub profit objective function, the energy hub operation constraint condition, the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function and the thermal power market clearing constraint condition to obtain a mixed integer linear programming model;
calculating a bidding strategy and a maximum operation income of the energy hub according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
Optionally, the processing the energy hub revenue objective function, the energy hub operation constraint condition, the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function, and the thermal power market clearing constraint condition to obtain a mixed integer linear programming model specifically includes:
processing the power market clearing objective function, the power market clearing constraint condition, the thermal market clearing objective function and the thermal market clearing constraint condition by adopting an optimality condition to obtain a power clearing KKT system equality constraint condition, a power clearing KKT system nonlinear complementary relaxation constraint condition, a thermal clearing KKT system equality constraint condition and a thermal clearing KKT system nonlinear complementary relaxation constraint condition;
performing linear processing on the nonlinear complementary relaxation constraint condition of the power clear KKT system and the nonlinear complementary relaxation constraint condition of the heat clear KKT system by using a large M method to obtain the linear complementary relaxation constraint condition of the power clear KKT system and the linear complementary relaxation constraint condition of the heat clear KKT system;
performing linearization processing on the energy hub gain objective function by adopting a Boolean expansion method to obtain an energy hub linear gain objective function;
and obtaining a mixed integer linear programming model according to the energy hub linear gain objective function, the energy hub operation constraint condition, the power clear KKT system equality constraint condition, the power clear KKT system linear complementary relaxation constraint condition, the heating power clear KKT system equality constraint condition and the heating power clear KKT system linear complementary relaxation constraint condition.
Optionally, calculating a bidding strategy and a maximum operation revenue of the energy hub according to the mixed integer linear programming model specifically includes:
calculating a bidding strategy and a maximum operation income of the energy hub by adopting a commercial solver according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
Optionally, the energy hub gain objective function is:
wherein, T is a transposition symbol, and 1 represents a column vector with elements all being 1; the price of the electricity sold is shown,in order to indicate the time of the scheduling,is the total scheduling time;is a collection of nodes of the power grid,the nodes are represented as a list of nodes,representation collectionThe number of the elements in the (A) is, representing the electricity purchase price; representing the amount of electricity sold; representing the electricity purchasing quantity; represents a heat sales price;γj,trepresenting a natural gas purchase price; the representative of the amount of heat sold, representing the gas purchase amount;participating in the revenue of the electricity market trade for the energy hub;a benefit to participate in a thermodynamic market transaction for an energy hub;the cost of purchasing natural gas for an energy hub;
the energy hub operation constraint conditions comprise bidding upper and lower limit constraint conditions, electricity and heat storage unit operation constraint conditions and energy hub internal power balance constraint conditions;
the competitive bidding upper and lower limit constraint conditions are as follows:
wherein,respectively representing electricity purchasing quantity bidding, electricity selling quantity bidding and heat selling quantity bidding of the energy hub;respectively representing the upper limits of the electricity purchasing quantity bidding, the electricity selling quantity bidding and the heat selling quantity bidding of the energy hub;respectively representing the lower limits of the electricity purchasing price label, the electricity selling price label and the heat selling price label of the energy hub;respectively representing the upper limits of the electricity purchasing price label, the electricity selling price label and the heat selling price label of the energy hub;
the operation constraint conditions of the electricity and heat storage unit are as follows:
wherein,0-1 variables respectively representing the charging and discharging states of the power storage units in the energy hub;respectively representing 0-1 variable of the heat charging and heat discharging states of the heat storage unit in the energy hub;respectively representing the electricity storage level of an electricity storage unit and the heat storage level of a heat storage unit in the energy hub;respectively representing the lower limit and the upper limit of the power storage level of the power storage unit;respectively representing the lower limit and the upper limit of the heat storage level of the heat storage unit; respectively representing the charging power and the discharging power of the electricity storage unit;respectively representing the upper limit of the charging power and the upper limit of the discharging power of the power storage unit;respectively showing the heat charging power and the heat discharging power of the heat storage unit,respectively representing the upper limits of the charging power and the heat release power; respectively representing the cycle efficiency of the electricity storage unit and the cycle efficiency of the heat storage unit;
the internal power balance constraint conditions of the energy hub are as follows:
wherein,representing the electrical efficiency and the thermal efficiency of the cogeneration unit,representing the heat pump efficiency.
Optionally, the power market clearing objective function is as follows:
wherein, the clear electricity output quantity of the conventional unit in the power distribution network;representing the marginal cost coefficient of the conventional unit;represents the price of electricity sold in the superior electric power market,representing the purchased electric quantity of the power distribution network from a superior electric power market;represents the conventional unit operating cost, thetaTpuRepresenting the cost of electricity purchase from an upper-level grid;represents the energy hub operating cost;
the electric power market clearing constraint conditions comprise power balance constraint conditions, voltage limits and unit operation constraint conditions of a power distribution network;
the power balance constraint conditions of the power distribution network are as follows:
wherein,respectively representing the equivalent active power and reactive power of a line injected by a node;representing the reactive power injected by the reactive power compensation device to the node; l (f), l (t) respectively represent a head end node and a tail end node of the line;respectively representing active power and reactive power transmitted on a line l;respectively representing the active power demand and the reactive power demand of the node;
the voltage limitation and unit operation constraint conditions are as follows:
wherein,uj,tthe square of the node voltage amplitude is shown, and r and x represent the resistance and the reactance of the line;respectively corresponding to the upper and lower bounds of the physical quantity.
Optionally, the thermal market clearing objective function is:
wherein,is a set of nodes of a heat supply network,representing the number of the heat supply network nodes; the heat removal amount is discharged for the conventional thermodynamic unit; is the marginal cost coefficient of the conventional thermodynamic unit;represents the running cost of the conventional heat machine set,represents the heating cost of the energy hub;
the heat market clearing constraint conditions comprise regional centralized heat supply pipe network tide constraint conditions and upper and lower limit constraint conditions;
the regional centralized heat supply pipe network tide constraint conditions are as follows:
wherein c is the specific heat capacity of the heat-carrying fluid, and S and R are respectively a water supply pipe network and a water return pipe network; respectively representing the node temperature of a water supply pipe network and the node temperature of a water return pipe network;respectively representing the mass flow of the heat-carrying fluid of a conventional thermodynamic unit, an energy hub and a heat load node;is a thermal load demand; b (f), b (t) represents the first node and the last node of the pipeline,the temperature of an outlet and an inlet of a water supply and return pipe network is represented;represents the ambient temperature;which is indicative of the mass flow rate of the pipeline,respectively representing the heat loss coefficients of the pipelines of the water supply pipeline network and the water return pipeline network, wherein the heat loss coefficients are constants when the mass flow of the pipelines is fixed;
the upper and lower limiting constraint conditions are as follows:
wherein,respectively, represent a lower bound or an upper bound of the respective physical quantity.
Optionally, the mixed integer linear programming model includes the energy junction linear gain objective function and a linear constraint condition;
the energy hub linear gain objective function is:
wherein,for the step size of the electricity selling price label,for the step amount of the price label for electricity purchase,step size for heat of sale price label;auxiliary continuous quantities corresponding to the electricity selling quantity standard, the electricity purchasing quantity standard and the heat selling quantity standard respectively; k is a constant and satisfies the condition that G is 2KG represents the total number of sections of the price label;
the constraint conditions comprise the energy hub operation constraint condition, the power clear KKT system equality constraint condition, the power clear KKT system linear complementary relaxation constraint condition, the heating power clear KKT system equality constraint condition and the heating power clear KKT system linear complementary relaxation constraint condition.
The invention also provides an energy hub bidding system for multi-energy flow transaction, which comprises:
the establishment module is used for establishing an energy hub master-slave game bidding strategy model facing multi-energy flow transaction; the energy hub is a hub with combined heat and power supply and combined heat and power storage capacity; the energy hub master-slave game bidding strategy model comprises a master model and a slave model; the main model comprises an energy hub income objective function and energy hub operation constraint conditions; the slave model comprises an electric power market clearing objective function, an electric power market clearing constraint condition, a thermal power market clearing objective function and a thermal power market clearing constraint condition;
an obtaining module, configured to process the energy hub revenue objective function, the energy hub operation constraint condition, the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function, and the thermal power market clearing constraint condition to obtain a mixed integer linear programming model;
the calculation module is used for calculating the bidding strategy and the maximum operation income of the energy hub according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
Optionally, the obtaining module specifically includes:
a first obtaining unit, configured to process the power market clearing objective function, the power market clearing constraint condition, the thermal market clearing objective function, and the thermal market clearing constraint condition by using an optimality condition, so as to obtain a power clearing KKT system equality constraint condition, a power clearing KKT system nonlinear complementary relaxation constraint condition, a thermal clearing KKT system equality constraint condition, and a thermal clearing KKT system nonlinear complementary relaxation constraint condition;
a second obtaining unit, configured to perform linearization processing on the nonlinear complementary relaxation constraint condition of the power clear KKT system and the nonlinear complementary relaxation constraint condition of the thermal clear KKT system by using a large M method to obtain a linear complementary relaxation constraint condition of the power clear KKT system and a linear complementary relaxation constraint condition of the thermal clear KKT system;
a third obtaining unit, configured to perform linear processing on the energy hub gain objective function by using a boolean expansion method to obtain an energy hub linear gain objective function;
and the mixed integer linear programming model obtaining unit is used for obtaining a mixed integer linear programming model according to the energy hub linear gain objective function, the energy hub operation constraint condition, the power clear KKT system equality constraint condition, the power clear KKT system linear complementary relaxation constraint condition, the heat clear KKT system equality constraint condition and the heat clear KKT system linear complementary relaxation constraint condition.
Optionally, the calculation module specifically includes:
the calculating unit is used for calculating the bidding strategy and the maximum operation income of the energy hub by adopting a commercial solver according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention aims to provide a competitive bidding method and a competitive bidding system of an energy hub facing to multi-energy flow transaction, wherein the method comprises the steps of firstly establishing an energy hub master-slave game competitive bidding strategy model facing to the multi-energy flow transaction; the energy hub is a hub with combined heat and power supply and combined heat and power storage capacity; the energy hub master-slave game bidding strategy model for the multi-energy flow transaction comprises a master model and a slave model; the main model comprises an energy hub income objective function and energy hub operation constraint conditions; the slave model comprises an electric power market clearing objective function, an electric power market clearing constraint condition, a thermal power market clearing objective function and a thermal power market clearing constraint condition; then, processing the established energy hub master-slave game bidding strategy model facing the multi-energy flow transaction to obtain a mixed integer linear programming model; calculating a bidding strategy and a maximum operation income of the energy hub according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub. By adopting the method or the system provided by the invention, the bidding method in the traditional power single-energy-flow market is expanded, the combined heat and power storage and combined heat and power generation functions of the energy hub facing the multi-energy-flow trading are fully considered, the bidding strategy in the electric-heat double-energy-flow market and the interaction characteristic of the power and heat market are taken into consideration, and the operation income of the energy hub operation main body in the multi-energy-flow trading market is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an energy hub bidding method according to an embodiment of the present invention;
FIG. 2 is a block diagram of an exemplary energy hub in accordance with an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating an implementation of a master-slave game bidding strategy in an energy hub multi-energy flow trading market according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an energy hub bidding system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a competitive bidding method and a competitive bidding system for an energy hub of multi-energy flow trading, which expand the competitive bidding method in the traditional electric power single-energy flow market, fully consider the combined heat and power storage and combined heat and power generation functions of the energy hub of multi-energy flow trading, improve the bidding strategy in the electric heat double-energy flow market and the interactive characteristics of the electric power and heat power market, and improve the operation income of an energy hub operation main body in the multi-energy flow trading market.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow diagram of an energy hub bidding method according to an embodiment of the present invention, and as shown in fig. 1, the energy hub bidding method specifically includes the following steps:
step 101: establishing an energy hub master-slave game competitive bidding strategy model facing multi-energy flow transaction; the energy hub is a hub with combined heat and power supply and combined heat and power storage capacity; the energy hub master-slave game bidding strategy model comprises a master model and a slave model; the main model comprises an energy hub income objective function and energy hub operation constraint conditions; the slave model comprises an electric power market clearing objective function, an electric power market clearing constraint condition, a thermal power market clearing objective function and a thermal power market clearing constraint condition.
Step 102: and processing the energy hub income objective function, the energy hub operation constraint condition, the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function and the thermal power market clearing constraint condition to obtain a mixed integer linear programming model.
Step 103: calculating a bidding strategy and a maximum operation income of the energy hub according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
The energy hub is a hub with combined heat and power supply and combined heat and power storage capacity; the energy hub serves as an independent market operation subject to participate in the trading of the electric power and heat power multi-energy flow market. The operation subject of the energy hub participates in bidding of the electric power market and the heating power market by taking the income of the maximized electric power and heating power multi-energy flow transaction as a target, reports selling and purchasing electric quantity and selling and purchasing price to the electric power market, and reports selling quantity and selling heat price to the heating power market. The energy hub can also provide heat purchasing quantity and heat purchasing price for the heat market, the invention assumes that the energy hub does not purchase heat from the heat market, but the method in the invention can also be directly popularized to the scene that the energy hub purchases heat from the heat market.
Fig. 2 is a block diagram of a typical energy hub according to an embodiment of the present invention. The energy hub shown in fig. 2 has cogeneration and cogeneration storage functions, and is equivalent to a multi-energy (electric energy and thermal energy) combined storage device. In theory, electric energy can be purchased for storage when the electricity price of a power grid is low, a heat supply network is purchased for storage when the heat supply network has a low heat price, and electric energy and heat energy are sold at high price when the actual demand of electric power and heat is high, so that profit is realized through price difference. In particular, compared to the capacity of the distribution network and the capacity of the district central heating network. Fig. 3 is a schematic diagram of implementing a principal and subordinate game bidding strategy in an energy hub multi-energy flow trading market according to an embodiment of the present invention, and the energy hub shown in fig. 3 has a large capacity and can be used as a price influencer to influence the clear price of the regional power grid and the clear price of the regional heat grid.
As an operation subject of the market, namely a master-slave game leader, the energy hub shown in FIG. 2 participates in competitive bidding (energy and price) of multi-energy flow trading of the thermoelectric two markets, and maximum operation income is obtained. Taking the energy hub shown in fig. 2 as an example, the energy hub profit objective function is:
wherein, T is a transposition symbol, and 1 represents a column vector with elements all being 1; the price of the electricity sold is shown,in order to indicate the time of the scheduling,is the total scheduling time;is a collection of nodes of the power grid,the nodes are represented as a list of nodes,representation collectionThe number of the elements in the (A) is, representing the electricity purchase price; representing the amount of electricity sold; representing the electricity purchasing quantity; represents a heat sales price;γj,trepresenting a natural gas purchase price; the representative of the amount of heat sold, representing the gas purchase amount;participating in the revenue of the electricity market trade for the energy hub;a benefit to participate in a thermodynamic market transaction for an energy hub;the cost of purchasing natural gas for an energy hub.
The operation constraint conditions of the energy hub comprise bidding upper and lower limit constraint conditions, electricity storage and heat storage unit operation constraint conditions and internal power balance constraint conditions of the energy hub.
The competitive bidding upper and lower limit constraint conditions are as follows:
wherein,respectively representing electricity purchasing quantity bidding, electricity selling quantity bidding and heat selling quantity bidding of the energy hub;respectively representing the upper limits of the electricity purchasing quantity bidding, the electricity selling quantity bidding and the heat selling quantity bidding of the energy hub;respectively representing the lower limits of the electricity purchasing price label, the electricity selling price label and the heat selling price label of the energy hub;respectively represents the upper limit of the electricity purchase price label, the electricity sale price label and the heat sale price label of the energy hub.
The operation constraint conditions of the electricity and heat storage unit are as follows:
wherein,0-1 variables respectively representing the charging and discharging states of the power storage units in the energy hub;respectively representing 0-1 variable of the heat charging and heat discharging states of the heat storage unit in the energy hub;respectively representing the electricity storage level of an electricity storage unit and the heat storage level of a heat storage unit in the energy hub;respectively representing the lower limit and the upper limit of the power storage level of the power storage unit;respectively representing the lower limit and the upper limit of the heat storage level of the heat storage unit; respectively representing the charging power and the discharging power of the electricity storage unit;respectively representing the upper limit of the charging power and the upper limit of the discharging power of the power storage unit;respectively showing the heat charging power and the heat discharging power of the heat storage unit,respectively representing the upper limits of the charging power and the heat release power; respectively representing the cycle efficiency of the electricity storage unit and the cycle efficiency of the heat storage unit.
The internal power balance constraint conditions of the energy hub are as follows:
wherein,represents the electrical efficiency and thermal efficiency of a combined heat and power cogeneration unit (CHP),representing the Heat Pump (HP) efficiency. It should be noted that the internal power balance constraint of the energy hub corresponds to fig. 3, but an internal power balance constraint like equation (4) can be written for any structure.
As shown in fig. 3, as a follower of a master-slave game of an operation subject of an energy hub, the electric power market scheduling mechanism finds an electric power market according to bidding provided by the energy hub, other power sources and loads, with the aim of maximizing the social welfare of the electric power market, thereby providing a purchase (sale) electric quantity and a purchase (sale) price signal to the electric power market participating subject including the energy hub.
The power market clearing objective function (power market clearing aims at maximizing social welfare) is as follows:
wherein, the clear electricity output quantity of the conventional unit in the power distribution network;representing the marginal cost coefficient of the conventional unit;represents the price of electricity sold in the superior electric power market,representing the purchased electric quantity of the power distribution network from a superior electric power market;represents the conventional unit operating cost, thetaTpuRepresenting the cost of electricity purchase from an upper-level grid;representing energy hub operating costs.
The electric power market clearing constraint conditions comprise power balance constraint conditions, voltage limits and unit operation constraint conditions of the power distribution network.
The power balance constraint conditions of the power distribution network are as follows:
wherein,respectively representing the equivalent active power and reactive power of a line injected by a node;representing the reactive power injected by the reactive power compensation device to the node; l (f), l (t) respectively represent a head end node and a tail end node of the line;respectively representing active power and reactive power transmitted on a line l;and respectively representing the active power demand and the reactive power demand of the node.
The voltage limitation and unit operation constraint conditions are as follows:
wherein,uj,tthe square of the node voltage amplitude is shown, and r and x represent the resistance and the reactance of the line;respectively corresponding to the upper and lower bounds of the physical quantity.
As a follower of a master-slave game of an operation main body of an energy hub, a thermal market scheduling mechanism bids according to heat sources and heat load requirements such as the energy hub and heat price and heat, and aims to maximize the social welfare of the thermal market, clears the thermal market and provides heat selling (purchasing) and heat price selling (purchasing) signals for a participating main body of the thermal market including the energy hub.
The thermal market clearing objective function (thermal market clearing aims at maximizing social welfare) is as follows:
wherein,is a set of nodes of a heat supply network,representing the number of the heat supply network nodes; the heat removal amount is discharged for the conventional thermodynamic unit;is the marginal cost coefficient of the conventional thermodynamic unit;represents the running cost of the conventional heat machine set,represents the heating cost of the energy hub;
the constraint conditions of the heat market clearing comprise regional centralized heat supply pipe network tide constraint conditions and upper and lower limit constraint conditions.
The regional centralized heat supply pipe network tide constraint conditions are as follows:
wherein c is the specific heat capacity of the heat-carrying fluid, and S and R are respectively a water supply pipe network and a water return pipe network; respectively representing the node temperature of a water supply pipe network and the node temperature of a water return pipe network;respectively representing the mass flow of the heat-carrying fluid of a conventional thermodynamic unit, an energy hub and a heat load node;is a thermal load demand; b(f) B (t) represents the first and last nodes of the pipeline,the temperature of an outlet and an inlet of a water supply and return pipe network is represented;represents the ambient temperature;which is indicative of the mass flow rate of the pipeline,respectively representing the heat loss coefficients of the pipelines of the water supply pipeline network and the water return pipeline network, wherein the heat loss coefficients are constants when the mass flow of the pipelines is fixed;
the upper and lower limiting constraint conditions are as follows:
wherein,respectively, represent a lower bound or an upper bound of the respective physical quantity.
Establishing an energy hub master-slave game bidding strategy model facing multi-energy flow transaction according to the energy hub income objective function, the energy hub operation constraint condition, the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function and the thermal power market clearing constraint condition; the energy hub is a hub with combined heat and power supply and combined heat and power storage capacity. However, the established energy hub master-slave game bidding strategy model facing the multi-energy flow transaction is a nonlinear model, and the energy hub master-slave game bidding strategy model is divided into a master model (an upper layer) and a slave model (a lower layer). The upper layer is the energy hub income objective function and the energy hub operation constraint condition, and the lower layer is the electric power market clearing objective function, the electric power market clearing constraint condition, the heating power market clearing objective function and the heating power market clearing constraint condition.
The lower layer is linear constrained quadratic programming, and the double-layer model (the energy hub master-slave game competitive bidding strategy model facing the multi-energy flow transaction) can be converted into a single-layer model by using an optimality condition algorithm. However, the converted single-layer model is also a nonlinear model, and the nonlinear single-layer model mainly comprises two types, namely a nonlinear complementary relaxation constraint introduced through an optimality condition, and a price and energy bilinear product term in an upper-layer objective function (a profit objective function). Therefore, the nonlinear complementary relaxation constraints are linearized by a large M method, a Boolean expansion method is adopted to approximate bilinear product terms, finally, an energy hub master-slave game bidding strategy model is converted into a mixed integer linear programming model, a commercial solver is adopted to efficiently solve, and the bidding strategy and the maximum operation income of the energy hub are obtained; the bidding strategy comprises electricity selling quantity, electricity selling price, electricity purchasing price, heat selling quantity and heat selling price of the energy hub.
The lower-layer power market clearance (objective function and constraint condition) and the thermal market clearance (objective function and constraint condition) of the energy hub master-slave game competitive bidding strategy model are convex quadratic programming and have the following abstract forms:
wherein, x represents unknown quantity of electric power (heating power) market clearing, and W and f are respectively constant matrix and vector, which can be obtained by objective function of electric power (heating power) market clearing problem; a. theεx＝bε,AIx≤bIRespectively representing equality constraint conditions and inequality constraint conditions of the electric power (heating power) market clearing problems; λ, μ are dual variables corresponding to equality constraints and inequality constraints, respectively. The power (thermal) market clearing problem (including objective function and constraint condition) canThe strain is converted into a KKT (Karush-Kuhn-Tucker, hereinafter referred to as KKT) system as follows by optimality conditions:
where the first two terms are linear equality constraints, μT(-AIλ+bI) When the linear coefficient is equal to 0, mu is more than or equal to 0, the nonlinear complementary relaxation constraint can be represented by a large M normal linearization:
where M is a sufficiently large positive number and v is an auxiliary vector consisting of variables 0-1.
An upper-layer objective function (income objective function) of the energy hub master-slave game bidding strategy comprises bilinear product terms of price and quantityThe bilinear product term can be linearized using a Boolean expansion method toBilinear product term involved inFor example, a Boolean expansion method can be used to linearize as:and the following constraints are introduced:
wherein,for the introduction of the auxiliary 0-1 variable,in order to assist in the continuous quantity,the step amount of the electricity selling price is G, and the number of the sections of the electricity selling price is G. In a similar manner to that described above,the bilinear product terms involved in (1) can also be respectively linearizedAndand a constraint condition similar to (15) is attached.
Through the transformation, an energy hub thermoelectric multi-energy flow transaction double-layer planning bidding model (an energy hub master-slave game bidding strategy model facing multi-energy flow transaction) is converted into a mixed integer linear planning model with linear constraint, and a commercial solver is adopted for efficient solution. The mixed integer linear programming model comprises an energy pivot linear gain objective function and linear constraint conditions. The energy pivot linear gain objective function is:
wherein,for the step size of the electricity selling price label,for the step amount of the price label for electricity purchase,step size for heat of sale price label;auxiliary continuous quantities corresponding to the electricity selling quantity standard, the electricity purchasing quantity standard and the heat selling quantity standard respectively; k is a constant and satisfies the condition that G is 2KAnd G represents the total number of stages of the price label.
The constraint conditions comprise the energy hub operation constraint condition, the power clear KKT system equality constraint condition, the power clear KKT system linear complementary relaxation constraint condition, the heat clear KKT system equality constraint condition and the heat clear KKT system linear complementary relaxation constraint condition.
The embodiment of the invention provides an energy hub bidding method facing multi-energy flow trading, and provides a master-slave game bidding strategy for an energy hub thermoelectric multi-energy flow trading market, which is characterized in that: the energy hub has the functions of combined heat and power supply and combined heat and power storage, participates in electric power market transaction and thermal power market transaction, and realizes arbitrage operation. The energy hub operation main body participates in bidding in an electric power market and a heat power market, provides electricity selling (purchasing) quantity and electricity selling (purchasing) price for the electric power market, and provides heat selling quantity and heat selling price for the heat power market so as to maximize operation income of electric power and heat power multi-energy flow transaction. The electric power market scheduling mechanism is used for offering a clear electric power market according to bidding of the energy hub, other power supplies and loads, and further providing electric quantity purchasing (selling) and electricity price purchasing (selling) signals for the energy hub. The heat market scheduling mechanism bids according to the heat source and heat load requirements and the heat price and the heat, so that the heat market society benefits the heat market and provides heat purchasing and heat price purchasing signals for the energy hub. And converting the double-layer principal and subordinate game bidding model into a single-layer model through optimality conditions, adopting a large M method for linearization and complementary relaxation constraint, simultaneously adopting a Boolean expansion method for approximating a bilinear term of price and energy in a linear single-layer model objective function, further converting the single-layer bidding model into a mixed integer linear programming, and finally adopting a commercial solver for efficient solution. The bidding method for the multi-energy flow trading can be used for guiding an energy hub in an energy internet to participate in the operation of a thermoelectric multi-energy flow trading market.
To achieve the above object, the present invention further provides a bidding system, fig. 4 is a schematic structural diagram of an energy hub bidding system according to an embodiment of the present invention, and as shown in fig. 4, the bidding system includes:
the establishing module 401 is used for establishing an energy hub master-slave game bidding strategy model facing to multi-energy flow transaction; the energy hub is a hub with combined heat and power supply and combined heat and power storage capacity; the energy hub master-slave game bidding strategy model comprises a master model and a slave model; the main model comprises an energy hub income objective function and energy hub operation constraint conditions; the slave model comprises an electric power market clearing objective function, an electric power market clearing constraint condition, a thermal power market clearing objective function and a thermal power market clearing constraint condition.
An obtaining module 402, configured to process the energy hub revenue objective function, the energy hub operation constraint condition, the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function, and the thermal power market clearing constraint condition, so as to obtain a mixed integer linear programming model.
A calculating module 403, configured to calculate a bidding strategy and a maximum operation revenue of the energy hub according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
Wherein the obtaining module 402 specifically includes:
the first obtaining unit is used for processing the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function and the thermal power market clearing constraint condition by adopting an optimality condition to obtain an electric power clearing KKT system equality constraint condition, an electric power clearing KKT system nonlinear complementary relaxation constraint condition, a thermal power clearing KKT system equality constraint condition and a thermal power clearing KKT system nonlinear complementary relaxation constraint condition.
And the second obtaining unit is used for performing linear processing on the nonlinear complementary relaxation constraint conditions of the power clear KKT system and the nonlinear complementary relaxation constraint conditions of the heat clear KKT system by using a large M method to obtain the linear complementary relaxation constraint conditions of the power clear KKT system and the linear complementary relaxation constraint conditions of the heat clear KKT system.
And the third obtaining unit is used for performing linear processing on the energy hub gain objective function by adopting a Boolean expansion method to obtain the energy hub linear gain objective function.
And the mixed integer linear programming model obtaining unit is used for obtaining a mixed integer linear programming model according to the energy hub linear gain objective function, the energy hub operation constraint condition, the power clear KKT system equality constraint condition, the power clear KKT system linear complementary relaxation constraint condition, the heat clear KKT system equality constraint condition and the heat clear KKT system linear complementary relaxation constraint condition.
The calculation module 403 specifically includes:
the calculating unit is used for calculating the bidding strategy and the maximum operation income of the energy hub by adopting a commercial solver according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
The embodiment of the invention protects a bidding system facing to multi-energy flow transaction, which comprises an establishing module 401, an obtaining module 402 and a calculating module 403. According to the modules, a bidding strategy and the maximum operation income of the energy hub are obtained; the bidding strategy comprises electricity selling quantity, electricity selling price, electricity purchasing quantity, electricity purchasing price, heat selling quantity and heat selling price of the energy hub. Therefore, by adopting the system provided by the invention, the multi-energy flow trading capacity of the energy hub with the combined heat and power storage and combined heat and power generation functions is fully considered, the bidding strategy of the energy hub operation main body facing the multi-energy flow trading is given, and the income of the energy hub operation main body in the multi-energy flow trading market is improved.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. An energy hub bidding method facing multi-energy flow transaction is characterized in that the energy hub bidding method comprises the following steps:
establishing an energy hub master-slave game competitive bidding strategy model facing multi-energy flow transaction; the energy hub is a hub with combined heat and power supply and combined heat and power storage capacity; the energy hub master-slave game bidding strategy model comprises a master model and a slave model; the main model comprises an energy hub income objective function and energy hub operation constraint conditions; the slave model comprises an electric power market clearing objective function, an electric power market clearing constraint condition, a thermal power market clearing objective function and a thermal power market clearing constraint condition;
processing the energy hub profit objective function, the energy hub operation constraint condition, the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function and the thermal power market clearing constraint condition to obtain a mixed integer linear programming model;
calculating a bidding strategy and a maximum operation income of the energy hub according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
2. The energy hub bidding method according to claim 1, wherein the processing of the energy hub revenue objective function, the energy hub operation constraint, the electric power market clearing objective function, the electric power market clearing constraint, the thermal power market clearing objective function, and the thermal power market clearing constraint to obtain a mixed integer linear programming model specifically comprises:
processing the power market clearing objective function, the power market clearing constraint condition, the thermal market clearing objective function and the thermal market clearing constraint condition by adopting an optimality condition to obtain a power clearing KKT system equality constraint condition, a power clearing KKT system nonlinear complementary relaxation constraint condition, a thermal clearing KKT system equality constraint condition and a thermal clearing KKT system nonlinear complementary relaxation constraint condition;
performing linear processing on the nonlinear complementary relaxation constraint condition of the power clear KKT system and the nonlinear complementary relaxation constraint condition of the heat clear KKT system by using a large M method to obtain the linear complementary relaxation constraint condition of the power clear KKT system and the linear complementary relaxation constraint condition of the heat clear KKT system;
performing linearization processing on the energy hub gain objective function by adopting a Boolean expansion method to obtain an energy hub linear gain objective function;
and obtaining a mixed integer linear programming model according to the energy hub linear gain objective function, the energy hub operation constraint condition, the power clear KKT system equality constraint condition, the power clear KKT system linear complementary relaxation constraint condition, the heating power clear KKT system equality constraint condition and the heating power clear KKT system linear complementary relaxation constraint condition.
3. The energy hub bidding method according to claim 1, wherein calculating the bidding strategy and the maximum operation revenue of the energy hub according to the mixed integer linear programming model specifically comprises:
calculating a bidding strategy and a maximum operation income of the energy hub by adopting a commercial solver according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
4. The energy hub bidding method according to claim 1, wherein the energy hub profit objective function is:
<mrow> <msub> <mi>Q</mi> <mn>1</mn> </msub> <mo>=</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <msup> <mn>1</mn> <mi>T</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>&amp;Lambda;</mi> <mi>o</mi> </msub> <msubsup> <mi>P</mi> <mi>o</mi> <mi>T</mi> </msubsup> <mo>-</mo> <msub> <mi>&amp;Lambda;</mi> <mi>i</mi> </msub> <msubsup> <mi>P</mi> <mi>i</mi> <mi>T</mi> </msubsup> <mo>)</mo> </mrow> <mo>+</mo> <msup> <mn>1</mn> <mi>T</mi> </msup> <msubsup> <mi>&amp;Omega;H</mi> <mi>o</mi> <mi>T</mi> </msubsup> <mo>-</mo> <msup> <mn>1</mn> <mi>T</mi> </msup> <msubsup> <mi>&amp;Gamma;P</mi> <mi>a</mi> <mi>T</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
wherein, T is a transposition symbol, and 1 represents a column vector with elements all being 1; the price of the electricity sold is shown,in order to indicate the time of the scheduling,is the total scheduling time;is a collection of nodes of the power grid,the nodes are represented as a list of nodes,representation collectionThe number of the elements in the (A) is,representing the electricity purchase price; representing the amount of electricity sold;representing the electricity purchasing quantity;represents a heat sales price;γj,trepresenting a natural gas purchase price; the representative of the amount of heat sold,representing the gas purchase amount;participating in the revenue of the electricity market trade for the energy hub;a benefit to participate in a thermodynamic market transaction for an energy hub;the cost of purchasing natural gas for an energy hub;
the energy hub operation constraint conditions comprise bidding upper and lower limit constraint conditions, electricity and heat storage unit operation constraint conditions and energy hub internal power balance constraint conditions;
the competitive bidding upper and lower limit constraint conditions are as follows:
wherein,respectively representing electricity purchasing quantity bidding, electricity selling quantity bidding and heat selling quantity bidding of the energy hub;respectively representing the upper limits of the electricity purchasing quantity bidding, the electricity selling quantity bidding and the heat selling quantity bidding of the energy hub;respectively representing the lower limits of the electricity purchasing price label, the electricity selling price label and the heat selling price label of the energy hub;respectively representing the upper limits of the electricity purchasing price label, the electricity selling price label and the heat selling price label of the energy hub;
the operation constraint conditions of the electricity and heat storage unit are as follows:
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>u</mi> <mi>c</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>u</mi> <mi>d</mi> <mi>j</mi> </msubsup> <mo>&amp;le;</mo> <mn>1</mn> <mo>,</mo> <msubsup> <mi>v</mi> <mi>c</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>v</mi> <mi>d</mi> <mi>j</mi> </msubsup> <mo>&amp;le;</mo> <mn>1</mn> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <munder> <mi>E</mi> <mo>&amp;OverBar;</mo> </munder> <mi>j</mi> </msup> <mo>&amp;le;</mo> <msup> <mi>E</mi> <mi>j</mi> </msup> <mo>&amp;le;</mo> <msup> <mover> <mi>E</mi> <mo>&amp;OverBar;</mo> </mover> <mi>j</mi> </msup> <mo>,</mo> <msup> <munder> <mi>H</mi> <mo>&amp;OverBar;</mo> </munder> <mi>j</mi> </msup> <mo>&amp;le;</mo> <msup> <mi>H</mi> <mi>j</mi> </msup> <mo>&amp;le;</mo> <msup> <mover> <mi>H</mi> <mo>&amp;OverBar;</mo> </mover> <mi>j</mi> </msup> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>E</mi> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>=</mo> <msubsup> <mi>E</mi> <mi>t</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>p</mi> <mrow> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>p</mi> <mrow> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> <mi>j</mi> </msubsup> <mo>/</mo> <msubsup> <mi>&amp;eta;</mi> <mrow> <mi>e</mi> <mi>e</mi> <mi>s</mi> </mrow> <mi>j</mi> </msubsup> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>t</mi> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>H</mi> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>=</mo> <msubsup> <mi>H</mi> <mi>t</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>h</mi> <mrow> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>h</mi> <mrow> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> <mi>j</mi> </msubsup> <mo>/</mo> <msubsup> <mi>&amp;eta;</mi> <mrow> <mi>t</mi> <mi>e</mi> <mi>s</mi> </mrow> <mi>j</mi> </msubsup> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>t</mi> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>p</mi> <mi>c</mi> <mi>j</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mover> <mi>p</mi> <mo>&amp;OverBar;</mo> </mover> <mi>c</mi> <mi>j</mi> </msubsup> <msubsup> <mi>u</mi> <mi>c</mi> <mi>j</mi> </msubsup> <mo>,</mo> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>p</mi> <mi>d</mi> <mi>j</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mover> <mi>p</mi> <mo>&amp;OverBar;</mo> </mover> <mi>d</mi> <mi>j</mi> </msubsup> <msubsup> <mi>u</mi> <mi>d</mi> <mi>j</mi> </msubsup> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>h</mi> <mi>c</mi> <mi>j</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mover> <mi>h</mi> <mo>&amp;OverBar;</mo> </mover> <mi>c</mi> <mi>j</mi> </msubsup> <msubsup> <mi>v</mi> <mi>c</mi> <mi>j</mi> </msubsup> <mo>,</mo> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>h</mi> <mi>d</mi> <mi>j</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mover> <mi>h</mi> <mo>&amp;OverBar;</mo> </mover> <mi>d</mi> <mi>j</mi> </msubsup> <msubsup> <mi>v</mi> <mi>d</mi> <mi>j</mi> </msubsup> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
wherein,0-1 variables respectively representing the charging and discharging states of the power storage units in the energy hub;respectively representing 0-1 variable of the heat charging and heat discharging states of the heat storage unit in the energy hub;respectively representing the electricity storage level of an electricity storage unit and the heat storage level of a heat storage unit in the energy hub;E j,respectively representing the lower limit and the upper limit of the power storage level of the power storage unit;H j,respectively representing the lower limit and the upper limit of the heat storage level of the heat storage unit; respectively representing the charging power and the discharging power of the electricity storage unit;respectively representing the upper limit of the charging power and the upper limit of the discharging power of the power storage unit;respectively showing the heat charging power and the heat discharging power of the heat storage unit,respectively representing the upper limits of the charging power and the heat release power; respectively representing the cycle efficiency of the electricity storage unit and the cycle efficiency of the heat storage unit;
the internal power balance constraint conditions of the energy hub are as follows:
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>p</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>&amp;eta;</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>p</mi> </mrow> <mrow> <mi>e</mi> <mo>,</mo> <mi>j</mi> </mrow> </msubsup> <msubsup> <mi>p</mi> <mi>a</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>p</mi> <mi>d</mi> <mi>j</mi> </msubsup> <mo>=</mo> <msubsup> <mi>p</mi> <mi>o</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>p</mi> <mi>c</mi> <mi>j</mi> </msubsup> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>&amp;eta;</mi> <mrow> <mi>h</mi> <mi>p</mi> </mrow> <mi>j</mi> </msubsup> <msubsup> <mi>p</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>&amp;eta;</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>p</mi> </mrow> <mrow> <mi>h</mi> <mo>,</mo> <mi>j</mi> </mrow> </msubsup> <msub> <mi>p</mi> <mi>a</mi> </msub> <mo>+</mo> <msubsup> <mi>h</mi> <mi>d</mi> <mi>j</mi> </msubsup> <mo>=</mo> <msubsup> <mi>h</mi> <mi>o</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>h</mi> <mi>c</mi> <mi>j</mi> </msubsup> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>p</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>=</mo> <msubsup> <mi>p</mi> <mrow> <mi>i</mi> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>p</mi> <mrow> <mi>i</mi> <mn>2</mn> </mrow> <mi>j</mi> </msubsup> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>j</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
wherein,representing the electrical efficiency and the thermal efficiency of the cogeneration unit,representing the heat pump efficiency.
5. The energy hub bidding method of claim 1, wherein the electricity market clearing objective function is:
<mrow> <msub> <mi>Q</mi> <mn>2</mn> </msub> <mo>=</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <msup> <mn>1</mn> <mi>T</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>g</mi> </msub> <msubsup> <mi>&amp;Theta;P</mi> <mi>g</mi> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>P</mi> <mi>g</mi> </msub> <mi>c</mi> <mo>)</mo> </mrow> <mo>+</mo> <msup> <mi>&amp;theta;</mi> <mi>T</mi> </msup> <msub> <mi>p</mi> <mi>u</mi> </msub> <mo>+</mo> <msup> <mn>1</mn> <mi>T</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>&amp;Lambda;</mi> <mi>o</mi> </msub> <msubsup> <mi>P</mi> <mi>o</mi> <mi>T</mi> </msubsup> <mo>-</mo> <msub> <mi>&amp;Lambda;</mi> <mi>i</mi> </msub> <msubsup> <mi>P</mi> <mi>i</mi> <mi>T</mi> </msubsup> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
wherein,the clear electricity output quantity of the conventional unit in the power distribution network;representing the marginal cost coefficient of the conventional unit;represents the price of electricity sold in the superior electric power market,representing the purchased electric quantity of the power distribution network from a superior electric power market;represents the conventional unit operating cost, thetaTpuRepresenting the cost of electricity purchase from an upper-level grid;represents the energy hub operating cost;
the electric power market clearing constraint conditions comprise power balance constraint conditions, voltage limits and unit operation constraint conditions of a power distribution network;
the power balance constraint conditions of the power distribution network are as follows:
wherein,respectively representing the equivalent active power and reactive power of a line injected by a node;representing the reactive power injected by the reactive power compensation device to the node; l (f), l (t) respectively represent a head end node and a tail end node of the line;respectively representing active power and reactive power transmitted on a line l;respectively representing the active power demand and the reactive power demand of the node;
the voltage limitation and unit operation constraint conditions are as follows:
wherein,uj,tthe square of the node voltage amplitude is shown, and r and x represent the resistance and the reactance of the line; u jrespectively corresponding to the upper and lower bounds of the physical quantity.
6. The energy hub bidding method of claim 1, wherein the thermal market clearing objective function is:
<mrow> <msub> <mi>Q</mi> <mn>3</mn> </msub> <mo>=</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <msup> <mn>1</mn> <mi>T</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>H</mi> <mi>g</mi> </msub> <msubsup> <mi>&amp;Phi;H</mi> <mi>g</mi> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>H</mi> <mi>g</mi> </msub> <mi>f</mi> <mo>)</mo> </mrow> <mo>+</mo> <msup> <mn>1</mn> <mi>T</mi> </msup> <msubsup> <mi>&amp;Omega;H</mi> <mi>o</mi> <mi>T</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
wherein,is a set of nodes of a heat supply network,representing the number of the heat supply network nodes; the heat removal amount is discharged for the conventional thermodynamic unit; is the marginal cost coefficient of the conventional thermodynamic unit;represents the running cost of the conventional heat machine set,represents the heating cost of the energy hub;
the heat market clearing constraint conditions comprise regional centralized heat supply pipe network tide constraint conditions and upper and lower limit constraint conditions;
the regional centralized heat supply pipe network tide constraint conditions are as follows:
wherein c is the specific heat capacity of the heat-carrying fluid, and S and R are respectively a water supply pipe network and a water return pipe network; respectively representing the node temperature of a water supply pipe network and the node temperature of a water return pipe network;respectively representing the mass flow of the heat-carrying fluid of a conventional thermodynamic unit, an energy hub and a heat load node;is a thermal load demand; b (f), b (t) represents the first node and the last node of the pipeline,the temperature of an outlet and an inlet of a water supply and return pipe network is represented;represents the ambient temperature;which is indicative of the mass flow rate of the pipeline,respectively representing the heat loss coefficients of the pipelines of the water supply pipeline network and the water return pipeline network, wherein the heat loss coefficients are constants when the mass flow of the pipelines is fixed;
the upper and lower limiting constraint conditions are as follows:
wherein,respectively, represent a lower bound or an upper bound of the respective physical quantity.
7. The energy hub bidding method according to claim 2, wherein the mixed integer linear programming model comprises the energy hub linear revenue objective function and linear constraints;
the energy hub linear gain objective function is:
wherein,for the step size of the electricity selling price label,for the step amount of the price label for electricity purchase,step size for heat of sale price label;auxiliary continuous quantities corresponding to the electricity selling quantity standard, the electricity purchasing quantity standard and the heat selling quantity standard respectively; k is a constant and satisfies the condition that G is 2KG represents the total number of sections of the price label;
the constraint conditions comprise the energy hub operation constraint condition, the power clear KKT system equality constraint condition, the power clear KKT system linear complementary relaxation constraint condition, the heating power clear KKT system equality constraint condition and the heating power clear KKT system linear complementary relaxation constraint condition.
8. An energy hub bidding system oriented to multi-energy flow transaction, wherein the energy hub bidding system comprises:
the establishment module is used for establishing an energy hub master-slave game bidding strategy model facing multi-energy flow transaction; the energy hub is a hub with combined heat and power supply and combined heat and power storage capacity; the energy hub master-slave game bidding strategy model comprises a master model and a slave model; the main model comprises an energy hub income objective function and energy hub operation constraint conditions; the slave model comprises an electric power market clearing objective function, an electric power market clearing constraint condition, a thermal power market clearing objective function and a thermal power market clearing constraint condition;
an obtaining module, configured to process the energy hub revenue objective function, the energy hub operation constraint condition, the electric power market clearing objective function, the electric power market clearing constraint condition, the thermal power market clearing objective function, and the thermal power market clearing constraint condition to obtain a mixed integer linear programming model;
the calculation module is used for calculating the bidding strategy and the maximum operation income of the energy hub according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
9. The energy hub bidding system of claim 8, wherein the obtaining module specifically comprises:
a first obtaining unit, configured to process the power market clearing objective function, the power market clearing constraint condition, the thermal market clearing objective function, and the thermal market clearing constraint condition by using an optimality condition, so as to obtain a power clearing KKT system equality constraint condition, a power clearing KKT system nonlinear complementary relaxation constraint condition, a thermal clearing KKT system equality constraint condition, and a thermal clearing KKT system nonlinear complementary relaxation constraint condition;
a second obtaining unit, configured to perform linearization processing on the nonlinear complementary relaxation constraint condition of the power clear KKT system and the nonlinear complementary relaxation constraint condition of the thermal clear KKT system by using a large M method to obtain a linear complementary relaxation constraint condition of the power clear KKT system and a linear complementary relaxation constraint condition of the thermal clear KKT system;
a third obtaining unit, configured to perform linear processing on the energy hub gain objective function by using a boolean expansion method to obtain an energy hub linear gain objective function;
and the mixed integer linear programming model obtaining unit is used for obtaining a mixed integer linear programming model according to the energy hub linear gain objective function, the energy hub operation constraint condition, the power clear KKT system equality constraint condition, the power clear KKT system linear complementary relaxation constraint condition, the heat clear KKT system equality constraint condition and the heat clear KKT system linear complementary relaxation constraint condition.
10. The energy hub bidding system of claim 8, wherein the computing module specifically comprises:
the calculating unit is used for calculating the bidding strategy and the maximum operation income of the energy hub by adopting a commercial solver according to the mixed integer linear programming model; the bidding strategy comprises electricity selling amount, electricity selling price, electricity purchasing price, heat selling amount and heat selling price of the energy hub.
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