Optimization method for energy hub with uncertainty participating in energy and reserve market bidding

Abstract

The application discloses a kind of uncertain energy hub participation energy and standby market's bidding optimization method, it is related to electric power market technical field, including input market basic information and energy hub's basic information and new energy parameter information, establish the uncertainty model of new energy output outside energy hub;Based on comprehensive energy information, the energy relationship inside comprehensive energy and the safe operation condition, establish the self-scheduling model of comprehensive energy;According to market information, new energy information and network information, establish the comprehensive clearing model of day-ahead energy market and standby market;Using Matlab describes double-layer optimization problem, and using gurobi commercial solver solves, obtains the optimization bidding method of energy hub in considering uncertainty.The application considers the influence of the uncertainty factor of new energy unit in market on energy hub bidding, fully considers market factor outside energy hub, and the bidding method after optimization has significant accuracy improvement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power market, in particular to a bidding optimization method for energy hub participating in energy and reserve market under uncertainty. BACKGROUND

[0002] Under the background of gradual marketization of power system, various components of power system participate in market transactions. However, there are many new energy participants in the power market, and the output of the new energy participants has uncertainty and volatility, which will have a significant impact on the market price. The energy hub contains various energy devices, and can meet its multi-energy demand from various markets. Due to the multi-energy complementarity, the energy hub also has sufficient adjustable and reserve potential. However, the uncertainty and volatility in the market will bring huge transaction risks to the energy hub. Therefore, it has full research value for the energy hub to develop a reasonable bidding strategy to try to avoid the uncertainty in the market. SUMMARY

[0003] In view of the above problems, the present application is proposed.

[0004] Therefore, the technical problem solved by the present application is that the existing traditional power energy market method has the defect of not considering the unit capacity value, and the energy hub as a market participant also does not consider the interaction with the market and is exposed to market risks.

[0005] To solve the above technical problems, the present application provides the following technical scheme: a bidding optimization method for an energy hub participating in an energy and reserve market under uncertainty, comprising inputting market basic information and basic information of the energy hub and parameter information of new energy, and establishing an uncertainty model of new energy output outside the energy hub. Based on the comprehensive energy information, the energy relationship inside the comprehensive energy and the safe operation condition, a self-scheduling model of the comprehensive energy is established. According to the market information, the new energy information and the network information, a comprehensive clearing model of the day-ahead energy market and the reserve market is established. A double-layer optimization problem is described using Matlab, and a gurobi commercial solver is used to solve, so as to obtain an optimization bidding method of the energy hub considering uncertainty.

[0006] As a preferred scheme of the bidding optimization method for an energy hub participating in an energy and reserve market under uncertainty, wherein: the input market basic information and basic information of the energy hub include network information, basic information of other participants of the energy market and the reserve market, and information of each device inside the comprehensive energy.

[0007] The network information includes the upper and lower limits F l max of the transmission capacity of the transmission line l l min , and the upper and lower limits of the output of the other units g upper and lower limits of load d at time t reserve R demanded by the market at time t t , topology of the network, node admittance matrix X.

[0008] Market information includes cost coefficient Ceg of other units g g , reserve compensation cost coefficient Crg of other units g g Cost coefficient Ced of other loads d d .

[0009] The multi-energy equipment inside the comprehensive energy commercial complex is small gas units, small combined heat and power units, electric heating boilers, gas heating boilers, electric energy storage, thermal energy storage, and small photovoltaic devices. The energy equipment plus the transformer constitutes the energy hub of the comprehensive energy commercial complex.

[0010] Upper and lower limits of electric output of small gas units gi Gas-to-electricity efficiency Upper and lower limits of electric output ramping Operating cost coefficient CeGI gi .

[0011] Upper and lower limits of electric output of small combined heat and power units c Upper and lower limits of thermal output Gas-to-heat efficiency ηCHP c G2P , electricity-to-heat efficiency Upper and lower limits of electric output ramping Upper and lower limits of thermal output ramping Operating cost coefficient CeCHP c .

[0012] Upper and lower limits of thermal output of electric boilers eb Electricity-to-heat efficiency Upper and lower limits of thermal output ramping Operating cost coefficient CeEB eb .

[0013] Upper and lower limits of thermal output of gas boilers gb Electricity-to-heat efficiency Upper and lower limits of thermal output ramping Operating cost coefficient CeGB gb .

[0014] Upper and lower limits of charging power of electric energy storage e Upper and lower limits of discharging power Upper and lower limits of state of charge SOC Initial value of state of charge SOC0, charging and discharging efficiency of energy storage ηec e , ηed e, the operation cost coefficient CeES e .

[0015] Upper and lower limits of the charging power of the thermal energy storage h Upper limit of discharging power Upper and lower limits of the storage thermal energy capacity Initial value EHS0 of the state of energy, charging and discharging efficiency of the energy storage ηhc h , ηhd h , the operation cost coefficient CeHS h .

[0016] Upper limit of small photovoltaic v, t time electric output Abandoned light cost coefficient CePVcur v , the operation cost coefficient CePV v .

[0017] As a preferred scheme of the bidding optimization method of the uncertain energy hub participating in the energy and standby market described in the present application, wherein: the parameter information of the new energy includes external wind power w information, the predicted wind power output average value at t time obtained according to historical data Standard deviation of wind power output According to two historical parameters, the S uncertain scenarios are obtained by using the Monte Carlo sampling method and the scenario compression method based on probability distance, and the probability of each scenario is

[0018] In a certain s scenario, at t time, the maximum output of wind power w is

[0019] The operation cost coefficient CeWT of wind power w , the abandoned wind cost coefficient CeWTcur w .

[0020] As a preferred scheme of the bidding optimization method of the uncertain energy hub participating in the energy and standby market described in the present application, wherein: the self-scheduling model of the comprehensive energy is established based on the comprehensive energy information, the energy relationship inside the comprehensive energy and the safe operation condition, which includes determining the constraint condition of the energy hub operation and bidding and determining the objective function.

[0021] According to the energy relationship inside the comprehensive energy and the safe operation condition, the safe operation and the establishment of energy coupling constraint.

[0022] The safe operation of the internal device is at t time, under s scenario.

[0023] The safe constraint of the small gas turbine unit is represented as:

[0024] GiGI gi,t,s = K gas• ViGI gi,t,s

[0025]

[0026] 0 < PoGI gi,t,s + RGI gi,t,s ≤ PoGI g m i ax

[0027]

[0028] 0 < RGI gi,t,s

[0029]

[0030] where GiGI gi,t,s is the power of gas combustion input to the i-th gas unit, K gas is the low heating value of natural gas, ViGI gi,t,s is the volume of natural gas input to the i-th gas unit, PoGI gi,t,s is the power output of the i-th small gas unit, RGI gi,t,s is the reserve capacity provided by the i-th small gas unit.

[0031] The safety constraint of small cogeneration units is expressed as:

[0032] GiCHP c,t,s = K gas • ViCHP c,t,s

[0033]

[0034] 0 < RCHP c,t,s

[0035]

[0036] where GiCHP c,t,s is the power of gas combustion input to the i-th CHP unit, ViCHP c,t,s is the volume of natural gas input to the i-th CHP unit, PoCHP c,t,s , HoCHP c,t,s are the power output and heat output of the i-th small cogeneration unit, respectively, RCHP c,t,s is the reserve capacity provided by the i-th small cogeneration unit.

[0037] The safety constraint of electric boilers is expressed as:

[0038]

[0039] Among them, HoEB eb,t,s It is the heat output of the electric boiler eb, PiEB eb,t,s This is the electrical power input to the electric hot pot.

[0040] Safety constraints for gas-fired boilers are expressed as follows:

[0041] GiGB gb,t,s =K gas ViGB gb,t,s

[0042]

[0043] Among them, GiGB gb,t,s This is the power output of the gas combustion in the GB boiler, ViGB. gb,t,s This is the input GB of natural gas volume. HoGB gb,t,s It is the heat output of the gas-fired boiler (GB).

[0044] Battery safety constraints are expressed as follows:

[0045]

[0046] 0≤RES e,t,s

[0047]

[0048] SOC e,t,s =SOC e,t-1,s +(ηec e ·Pc e,t,s -Pd e,t,s / ηed e )·Δt

[0049] SOC e,T,s =SOC0

[0050] Among them, Pc e,t,s Pd e,t,s These are the charging and discharging power of the energy storage device e, and the SOC. e,t,s Let e ​​be the state of charge of the energy storage device, Δt be the time interval, T be the total time, and SOC be the state of charge. e,T,s It is the final energy state of energy storage, RES e,t,s It is the backup capacity provided by battery safety energy storage.

[0051] The safety constraints for thermal energy storage are expressed as follows:

[0052]

[0053] EHS h,t,s =EHS h,t-1,s +(ηhc h· Hc h,t,s · Hd h,t,s / ηhd h ) · Δt

[0054] EHS e,T,s = EHS0

[0055] where Hc h,t,s , Hd h,t,s are the charging and discharging power of the thermal storage h, EHS e,t,s is the state of charge of the thermal storage h, EHS e,T,s is the final energy state of the thermal storage.

[0056] The small photovoltaic safety constraint is expressed as:

[0057]

[0058] where PoPV v,t,s is the output of the photovoltaic v.

[0059] The energy hub energy coupling relationship includes the electrical load balance expressed as:

[0060]

[0061] where Po t,s , Pi t,s are the selling and buying power of the energy hub, D t is the electrical load inside the integrated energy.

[0062] The thermal load balance is expressed as:

[0063]

[0064] where H t is the thermal load inside the integrated energy.

[0065] The gas volume composition balance is expressed as:

[0066]

[0067] where Vi t,s is the total gas purchase volume of the integrated energy.

[0068] The capacity composition balance is expressed as:

[0069]

[0070] where REH t,s is the total amount of standby provided by the integrated energy.

[0071] The safety operation constraint and the energy coupling relationship constitute the operation constraint of the integrated energy.

[0072] As a preferred scheme of the bidding optimization method of the uncertain energy hub participating in the energy and reserve market, wherein: the self-scheduling model of the integrated energy based on the integrated energy information, the energy relationship in the integrated energy and the safe operation condition further comprises constructing a target function of the integrated energy scheduling with the minimum operation cost as the target, and the target function is expressed as:

[0073]

[0074]

[0075] Among them, respectively represent the gas purchase cost, the electricity purchase cost, the operation cost, the light cutting cost and the income of the reserve market under the s scene, respectively represent the natural gas price at t time, the out-of-clear electric energy price of node N, the out-of-clear capacity price, F s is the total cost of the energy hub.

[0076] As a preferred scheme of the bidding optimization method of the uncertain energy hub participating in the energy and reserve market, wherein: the comprehensive out-of-clear model of the day-ahead energy market and the reserve market according to the market information, the new energy information and the network information comprises determining the market rule condition and the network safe operation condition, and constructing a target function.

[0077] According to the new energy information, the network information and the safe operation condition, the market and network constraints comprising the ordinary unit constraints are expressed as:

[0078]

[0079] 0≤RG g,t,s

[0080] Among them, PG g,t,s , RG g,t,s are the output and the reserve amount of the unit g at t time and under the s scene.

[0081] The adjustable load is expressed as:

[0082]

[0083] Among them, PD d,t,s is the load amount of the load d at t time and under the s scene.

[0084] The wind turbine is expressed as:

[0085]

[0086] Among them, PW w,t,sis the output of the wind turbine w at time t and in scenario s.

[0087] The line network constraints are expressed as:

[0088] F l min ≤ PF l,t,s ≤ F l max

[0089]

[0090] - π ≤ θ n,t,s ≤ π

[0091] θ ref,t,s = 0

[0092] where PF l,t,s is the over flow of line l at time t and in scenario s, θ n,t,s is the phase angle of node n at time t and in scenario s, θ ref,t,s is the phase angle at reference node ref, X nm is the admittance from node n to node m in the node admittance matrix X.

[0093] The energy hub bid winning constraints are expressed as:

[0094] 0 ≤ Po t,s ≤ Po bid t

[0095] 0 ≤ Pi t,s ≤ Pi bid t

[0096] 0 ≤ REH t,s ≤ REH bid t

[0097] 0 ≤ a0 t ≤ a0 max

[0098] 0 ≤ ai t ≤ ai max

[0099] 0 ≤ ar t ≤ ar max

[0100]

[0101] 0 ≤ Po bid t

[0102] 0 ≤ REH t,s

[0103]

[0104] Pobid t Pibid t REHbid t respectively are the selling, buying and capacity bid of the integrated energy at time t, αo t αi t αr t αo max αi max αr max respectively are the selling, buying and capacity price and the upper limit of the three.

[0105] The node power balance constraint is expressed as:

[0106]

[0107] PD d(n),t,s PG g(n),t,s PW w(n),t,s Po (n),t,s Pi (n),t,s denotes the node form converted by PD d,t,s PG g,t,s PW w,t,s Po t,s Pi t,s . is the dual variable of the constraint, and is the node electricity price of market clearing at time t, s scenario, is the energy price of the node N where the energy hub is located.

[0108] The capacity balance is expressed as:

[0109]

[0110] wherein, is the dual variable of the constraint, and is the capacity price of market clearing at time t, s scenario.

[0111] The objective function is constructed with the goal of maximizing social welfare, which is expressed as:

[0112]

[0113] As a preferred scheme of the bidding optimization method of the uncertain energy hub participating in the energy and reserve market, the double-layer optimization problem is described using Matlab, and a gurobi commercial solver is used for solving, including programming the optimization problem using Matlab, solving using Gurobi, obtaining the bidding method of the energy hub under the condition of considering new energy fluctuation.

[0114] Another object of the present application is to provide a bidding optimization system for uncertain energy hubs participating in energy and reserve markets, which can solve the problems contained in the current single energy market technology by adding a capacity market and considering the interaction between energy hubs and the market: the defect of not considering the unit capacity value, and the exposure of energy hubs to market risks as market participants without considering the interaction with the market.

[0115] As a preferred solution of the bidding optimization system for uncertain energy hubs participating in energy and reserve markets, the system comprises a data input module, an energy scheduling module, a clearing module, and a bidding solution module.

[0116] The data input module is used to input market basic information, energy hub basic information, and new energy parameter information, and to establish an uncertainty model of new energy output outside the energy hub.

[0117] The energy scheduling module is used to establish a self-scheduling model of the comprehensive energy based on comprehensive energy information, energy relationship inside the comprehensive energy, and safe operation conditions.

[0118] The clearing module is used to establish a comprehensive clearing model of the day-ahead energy market and the reserve market according to market information, new energy information, and network information.

[0119] The bidding solution module uses Matlab to describe a double-layer optimization problem, and uses a gurobi commercial solver to solve, so as to obtain an optimized bidding method of the energy hub considering uncertainty.

[0120] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the bidding optimization method for uncertain energy hubs participating in energy and reserve markets.

[0121] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the bidding optimization method for uncertain energy hubs participating in energy and reserve markets.

[0122] The bidding optimization method for uncertain energy hubs participating in energy and reserve markets provided by the present application considers the influence of the uncertainty factors of new energy units in the market on the bidding of energy hubs, fully considers the market factors outside the energy hub, and the optimized bidding method has significantly improved accuracy. The energy market and the capacity market are considered, which reflects the flexibility of the energy hub participating in the market. The present application achieves better results in accuracy and flexibility. BRIEF DESCRIPTION OF DRAWINGS

[0123] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only show some of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0124] Figure 1 The overall flowchart of the bidding optimization method of the uncertain energy hub participating in the energy and reserve market provided by the first embodiment of the present application.

[0125] Figure 2 The overall flowchart of the bidding optimization system of the uncertain energy hub participating in the energy and reserve market provided by the third embodiment of the present application. DETAILED DESCRIPTION

[0126] In order to make the above objectives, features and advantages of the present application more apparent and understandable, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should fall within the protection scope of the present application.

[0127] Embodiment 1

[0128] Reference Figure 1 For an embodiment of the present application, a bidding optimization method of an uncertain energy hub participating in an energy and reserve market is provided, comprising:

[0129] S1: inputting market basic information, basic information of the energy hub and parameter information of new energy, and establishing an uncertainty model of new energy output outside the energy hub.

[0130] Further, the input market basic information and the basic information of the energy hub include network information, basic information of other participants in the energy market and the reserve market, and information of each device inside the comprehensive energy.

[0131] The network information includes the upper and lower limits F l max ,F l min of the transmission capacity of the transmission line l, the upper and lower limits of the output of other units g the upper and lower limits of the load d at time t the reserve R required by the market at time t t the topology of the network, and the node admittance matrix X.

[0132] The market information includes the cost coefficient Ceg of other units gg , the standby compensation cost coefficient Crg of other units g g The cost coefficient Ced of other loads d d .

[0133] The multi-energy equipment inside the comprehensive energy commercial complex is a small gas unit, a small combined heat and power unit, an electric heating kettle, a gas heating kettle, an electric energy storage, a thermal energy storage, and a small photovoltaic. The energy equipment plus the transformer constitutes the energy hub of the comprehensive energy commercial complex.

[0134] The upper and lower limits of the electric output of the small gas unit gi The efficiency of gas to electricity The upper and lower limits of the electric output: The operation cost coefficient CeGI gi .

[0135] The upper and lower limits of the electric output of the small combined heat and power unit c The upper and lower limits of the thermal output The efficiency of gas to electricity The efficiency of gas to heat The upper and lower limits of the electric output The upper and lower limits of the thermal output: The operation cost coefficient CeCHP c .

[0136] The upper and lower limits of the thermal output of the electric boiler eb The efficiency of electric to heat The upper and lower limits of the thermal output The operation cost coefficient CeEB eb .

[0137] The upper and lower limits of the thermal output of the gas boiler gb The efficiency of electric to heat The upper and lower limits of the thermal output The operation cost coefficient CeGB gb .

[0138] The upper and lower limits of the charging power of the electric energy storage e The upper and lower limits of the discharging power The upper and lower limits of the energy state SOC The initial value of the energy state SOC0, the charging and discharging efficiency of the energy storage ηec e , ηed e The operation cost coefficient CeES e .

[0139] The upper and lower limits of the charging power of the thermal energy storage h The upper and lower limits of the discharging power The upper and lower limits of the stored thermal energy capacity Initial value of energy state EHS0, energy storage charging and discharging efficiency ηhc h , ηhd h , operation cost coefficient CeHS h .

[0140] Upper limit of small photovoltaic v, t time electric output Curtailed photovoltaic cost coefficient CePVcur v , operation cost coefficient CePV v .

[0141] It should be noted that the parameter information of new energy includes external wind power w information, and the average value of predicted wind power output at t time obtained according to historical data Standard deviation of wind power output According to two historical parameters, a Monte Carlo sampling method and a scene compression method based on probability distance are used to obtain S uncertain scenes, and the probability of each scene is

[0142] In a scene s, at t time, the maximum output of wind power w is

[0143] Operation cost coefficient of wind power CeWT w , curtailed wind power cost coefficient CeWTcur w .

[0144] S2: Based on the comprehensive energy information, the energy relationship inside the comprehensive energy and the safe operation condition, a self-scheduling model of the comprehensive energy is established.

[0145] Further, based on the comprehensive energy information, the energy relationship inside the comprehensive energy and the safe operation condition, a self-scheduling model of the comprehensive energy is established, including determining the constraint condition of energy hub operation and bidding and determining the objective function.

[0146] According to the energy relationship inside the comprehensive energy and the safe operation condition, the safe operation and the establishment of energy coupling constraint.

[0147] The safe operation of internal equipment is at t time, in scene s.

[0148] The safe constraint of small gas turbine unit is represented as:

[0149] GiGI gi,t,s = K gas ·ViGI gi,t,s

[0150]

[0151] 0≤RGI gi,t,s

[0152]

[0153] where GiGI gi,t,s is the power of the gas combustion input to the gi unit, K gas is the low heating value of natural gas, ViGI gi,t,s is the volume of natural gas input to the gi unit, PoGI gi,t,s is the electrical power output of the small gas unit, RGI gi,t,s is the reserve capacity provided by the small gas unit. Reserve capacity refers to the power reserve, that is, the capacity of the unit that is used for power generation in part of its large power generation capacity and is used for reserve, which is used for emergency power generation in emergency situations (faults, etc.).

[0154] The safety constraints of the small cogeneration unit are expressed as:

[0155] GiCHP c,t,s = K gas · ViCHP c,t,s

[0156]

[0157] 0 < RCHP c,t,s

[0158]

[0159] where GiCHP c,t,s is the power of the gas combustion input to the c unit, ViCHP c,t,s is the volume of natural gas input to the chp, PoCHP c,t,s , HoCHP c,t,s are the electrical and thermal power outputs of the small cogeneration unit, respectively, RCHP c,t,s is the reserve capacity provided by the small cogeneration unit.

[0160] The safety constraints of the electric boiler are expressed as:

[0161]

[0162] where HoEB eb,t,s is the thermal power output of the electric boiler eb, PiEB eb,t,s is the electrical power input to the electric boiler.

[0163] The safety constraints of the gas boiler are expressed as:

[0164] GiGB gb,t,s = K gas · ViGB gb,t,s

[0165]

[0166] where GiGB gb,t,s is the power of gas combustion input to the boiler gb, ViGB gb,t,s is the volume of natural gas input to the boiler gb. HoGB gb,t,s is the thermal power output of the gas boiler gb.

[0167] The battery safety constraint is expressed as:

[0168]

[0169] 0 < RES e,t,s

[0170]

[0171] SOC e,t,s = SOC e,t-1,s + (ηec e · Pc e,t,s - Pd e,t,s / ηed e ) · Δt

[0172] SOC e,T,s = SOC0

[0173] where Pc e,t,s , Pd e,t,s are the charging and discharging power of the energy storage e, respectively, SOC e,t,s is the state of charge of the energy storage e, Δt is the time interval, T is the total time, SOC e,T,s is the final state of energy of the energy storage, RES e,t,s is the reserve capacity provided by the battery safety energy storage.

[0174] The thermal energy storage safety constraint is expressed as:

[0175]

[0176] EHS h,t,s = EHS h,t-1,s + (ηhc h · Hc h,t,s - Hd h,t,s / ηhd h ) · Δt

[0177] EHS e,T,s = EHS0

[0178] where Hc h,t,s , Hd h,t,s are the charging and discharging power of the thermal energy storage h, respectively, EHS e,t,s is the state of charge of the thermal energy storage h, EHS e,T,sis the final energy state of the thermal storage energy.

[0179] The small photovoltaic safety constraint is expressed as:

[0180]

[0181] wherein PoPV v,t,s is the output of the photovoltaic v.

[0182] The energy coupling relationship of the energy hub includes the electrical load balance expressed as:

[0183]

[0184] wherein Po t,s , Pi t,s are the selling power and buying power of the energy hub respectively, D t is the electrical load inside the integrated energy.

[0185] The thermal load balance is expressed as:

[0186]

[0187] wherein H t is the thermal load inside the integrated energy.

[0188] The gas quantity composition balance is expressed as:

[0189]

[0190] wherein Vi t,s is the total gas purchase quantity of the integrated energy.

[0191] The capacity composition balance is expressed as:

[0192]

[0193] wherein REH t,s is the total quantity of the integrated energy provided for backup.

[0194] The safety operation constraint and the energy coupling relationship constitute the operation constraint of the integrated energy.

[0195] It should be noted that, based on the integrated energy information, the energy relationship inside the integrated energy and the safety operation condition, the self-scheduling model of the integrated energy is also established to include the target function of the integrated energy scheduling constructed with the minimum operation cost as the target expressed as:

[0196]

[0197]

[0198] wherein, respectively represent the gas purchase cost, electricity purchase cost, operation cost, light cutting cost, standby market income under s scenario, respectively represent the natural gas price at t time, the clearing electricity energy price at node N, the clearing capacity price, F s is the total cost of the energy hub.

[0199] S3: according to market information, new energy information and network information, establish a comprehensive clearing model of day-ahead energy market and standby market.

[0200] Further, according to market information, new energy information and network information, the comprehensive clearing model of day-ahead energy market and standby market includes determining market rule conditions and network safe operation conditions, and constructing an objective function.

[0201] According to new energy information, network information and safe operation conditions, the market and network constraints include ordinary unit constraints, which are represented as:

[0202]

[0203] 0≤RG g,t,s

[0204] Wherein, PG g,t,s , RG g,t,s are the output and standby capacity of unit g at t time and s scenario.

[0205] The adjustable load is represented as:

[0206]

[0207] Wherein, PD d,t,s is the load of load d at t time and s scenario.

[0208] The wind turbine is represented as:

[0209]

[0210] Wherein, PW w,t,s is the output of wind turbine w at t time and s scenario.

[0211] The line network constraint is represented as:

[0212] F l min ≤PF l,t,s ≤F l max

[0213]

[0214] -π≤θ n,t,s≤ π

[0215] θ ref,t,s = 0

[0216] where, PF l,t,s is the super flow of line l at time t, scene s, θ n,t,s is the phase angle of node n at time t, scene s, θ ref,t,s is the phase angle at the reference node ref, X nm is the admittance from node n to node m in the node admittance matrix X.

[0217] The winning constraint in the energy hub bidding is expressed as:

[0218] 0 ≤ Po t,s ≤ Pobid t

[0219] 0 ≤ Pi t,s ≤ Pibid t

[0220] 0 ≤ REH t,s ≤ REHbid t

[0221] 0 ≤ αo t ≤ αo max

[0222] 0 ≤ αi t ≤ αi max

[0223] 0 ≤ αr t ≤ αr max

[0224]

[0225] 0 ≤ Pobid t

[0226] 0 ≤ REH t,s

[0227]

[0228] where, Pobid t , Pibid t , REHbid t are the electricity selling, electricity buying bidding quantity, capacity bidding quantity of the comprehensive energy at time t, respectively, αo t , αi t , αr t , αo max , αi max , αr maxrespectively are the electricity selling, electricity buying, capacity offer and the upper limit of the three.

[0229] The node power balance constraint is expressed as:

[0230]

[0231] Where PD d(n),t,s , PG g(n),t,s , PW w(n),t,s , Po (n),t,s , Pi (n),t,s represents the node form converted by PD d,t,s , PG g,t,s , PW w,t,s , Po t,s , Pi t,s . is the dual variable of the constraint, which is the node electricity price of market clearing at time t, scene s, is the energy price of the node N where the energy hub is located.

[0232] The capacity balance is expressed as:

[0233]

[0234] Where, is the dual variable of the constraint, which is the capacity electricity price of market clearing at time t, scene s.

[0235] The objective function is constructed with the goal of maximizing social welfare, which is expressed as:

[0236]

[0237] S4: the double-layer optimization problem is described by Matlab, and the gurobi commercial solver is used to solve, and the optimization bidding method of the energy hub considering uncertainty is obtained.

[0238] Further, the double-layer optimization problem is described by Matlab, and the gurobi commercial solver is used to solve, including programming the optimization problem by Matlab, and solving by Gurobi, to obtain the bidding method of the energy hub under the condition of considering new energy fluctuation.

[0239] Embodiment 2

[0240] In an embodiment of the present application, an optimization method for bidding of an energy hub participating in the energy and reserve market under uncertainty is provided. In order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiments are carried out for scientific demonstration. Here, the specific process of the experiment is mainly introduced, and the specific setting of the parameters is too much, which will not be described in detail.

[0241] Firstly, compared with the ordinary bidding mode without considering market fluctuation, the method proposed by the application improves the income of the energy hub and effectively avoids part of the market risk.

[0242] In the 24-hour trading range, and in the market environment, other market participants and the parameters of new energy remain unchanged, the bidding mode of the energy hub is changed. One uses the method proposed by the application, considering the market clearing and new energy fluctuation; the other does not consider the above factors, and is only a passive participant who simply accepts the market price. The experiment is designed to compare the size of the final income of the energy hub. The results are shown in the following table:

[0243]

[0244]

[0245] It can be seen that the method proposed by the application improves the income of the energy hub, reduces the additional cost caused by market risk, improves the enthusiasm of the energy hub in participating in market transactions, and taps the potential of the energy hub in participating in market regulation.

[0246] Embodiment 3

[0247] Reference Figure 2 For an embodiment of the application, an uncertain energy hub participating in the bidding optimization system of energy and reserve market is provided, including a data input module, an energy scheduling module, a clearing module and a bidding solution module.

[0248] The data input module is used to input market basic information, energy hub basic information and new energy parameter information, and establish an uncertainty model of new energy output outside the energy hub; the energy scheduling module is used to establish a self-scheduling model of the comprehensive energy based on comprehensive energy information, energy relationship inside the comprehensive energy and safe operation conditions; the clearing module is used to establish a comprehensive clearing model of the day-ahead energy market and the reserve market according to market information, new energy information and network information; the bidding solution module uses Matlab to describe a double-layer optimization problem and uses a gurobi commercial solver to solve, so as to obtain an optimal bidding method of the energy hub considering uncertainty.

[0249] If the functions are implemented in software, the functions can be stored in or implemented as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium can be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, or twisted pair, then the coaxial cable, fiber optic cable, or twisted pair are included in the definition of medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), and Blu-Ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0250] In other words, like a human driver of a vehicle, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on. In some embodiments, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment using a machine learning algorithm. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on using a machine learning algorithm.

[0251] In other words, like a human driver of a vehicle, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on. In some embodiments, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment using a machine learning algorithm. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on using a machine learning algorithm.

[0252] It should be understood that portions of the present application can be implemented with hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be understood that the foregoing embodiments are merely illustrative of the present application and are not to be used to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, it will be apparent to those skilled in the art that various changes and modifications can be contributed to the present application without departing from the spirit and scope of the present application, and such changes and modifications should be encompassed within the scope of the appended claims.

[0253] It should be understood that the foregoing embodiments are merely illustrative of the present application and are not to be used to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, it will be apparent to those skilled in the art that various changes and modifications can be contributed to the present application without departing from the spirit and scope of the present application, and such changes and modifications should be encompassed within the scope of the appended claims.

Claims

1. A method for optimization of bidding of an energy hub with uncertainty participation in energy and reserve markets, characterized by, The method comprises the following steps: inputting market basic information and basic information of the energy hub and parameter information of new energy, establishing an uncertainty model of new energy output outside the energy hub, and obtaining maximum output values of wind turbines under multiple uncertainty scenarios; taking the maximum output values of the wind turbines as upper limits of output constraints, establishing a self-scheduling model of the comprehensive energy based on comprehensive energy information, energy relationships inside the comprehensive energy, and safe operation conditions, and the self-scheduling model outputs power purchase bid amounts, power sale bid amounts, and standby bid amounts of the energy hub; taking the power purchase bid amounts, the power sale bid amounts, and the standby bid amounts as upper limit parameters of energy hub bid winning constraints, establishing a comprehensive clearing model of the day-ahead energy market and the standby market according to cost information, new energy information, and network information, and the comprehensive clearing model outputs node prices and capacity prices under each scenario at each time; calculating power sale revenues of the energy hub based on the node prices, calculating standby capacity revenues of the energy hub based on the capacity prices, and constructing a bi-level optimization problem with the maximum expected profit of the energy hub as a target, using Matlab to describe the bi-level optimization problem, and using a gurobi commercial solver to solve the bi-level optimization problem, thereby obtaining an optimal bidding method of the energy hub considering uncertainty.

2. The method of claim 1, wherein: The input market basic information and the basic information of the energy hub comprise network information, cost information, and device information inside the comprehensive energy; Network information includes transmission line l transmission capacity upper and lower limits F l max l min , other units g output upper and lower limits t time, other load d upper and lower limits Market demand for standby R at t time t , network topology, node admittance matrix X;​ The cost information includes a cost coefficient Ceg of the other generating unit g g , a reserve compensation cost coefficient Crg of the other generating unit g g , and a cost coefficient Ced of the other load d d ; the multi-energy devices inside the comprehensive energy commercial complex are small gas turbine units, small combined heat and power units, electric boilers, gas heat boilers, electric energy storage, thermal energy storage, and small photovoltaic units, and the above multi-energy devices plus transformers constitute the energy hub of the comprehensive energy commercial complex; Upper and lower limits of the electrical power output of the small gas engine unit gi Gas-to-electricity efficiency Upper and lower limits of the electrical power output Operating cost coefficient CeGI gi ; Upper and lower limits of the electric power output of the small cogeneration unit c PoCHP c max , PoCHP c min Upper and lower limits of the heat output HoCHP c max , HoCHP c min Efficiency of gas to electricity conversion ηCHP c G2P Efficiency of gas to heat conversion ηCHP c G2H Upper and lower limits of the electric power output ramp PoCHP c ramp+ , PoCHP c ramp- Upper and lower limits of the heat output ramp HoCHP c ramp+ , HoCHP c ramp- Operating cost coefficient CeCHP c ; Upper and lower limits of thermal output of electric boiler eb Efficiency of electric heat conversion Upper and lower limits of thermal output ramping Operating cost coefficient CeEB eb ; Upper and lower limits of thermal power of gas boiler gb Efficiency of electric-to-thermal conversion Upper and lower limits of thermal power ramping Operating cost coefficient CeGB gb ; Upper and lower limits of charging power of electrical energy storage e Upper and lower limits of discharging power Upper and lower limits of state of energy SOC Initial value of state of energy SOC0, charging and discharging efficiency of electrical energy storage ηec e , ηed e , operating cost coefficient CeES e ; upper and lower limits of the charging power of the thermal storage energy h upper and lower limits of the discharging power upper and lower limits of the storage thermal energy capacity initial value of the state of energy EHS0, charging and discharging efficiency of the thermal storage ηhc h , ηhd h , operating cost coefficient CeHS h ; Upper limit of small photovoltaic v, t time electric power output Stranded light cost coefficient CePVcur v Operation cost coefficient CePV v .

3. The method of claim 2, wherein: The parameter information of the new energy includes external wind turbine w information, and a predicted wind turbine output average value at t moment obtained according to historical data A standard deviation of the wind turbine output According to two historical parameters, a Monte Carlo sampling method and a scenario compression method based on a probability distance are used to obtain S uncertainty scenarios, and the probability of each scenario is In the scenario of certain s, at time t, the maximum output of the wind turbine w is The operating cost coefficient of the wind turbine is CeWT w , and the curtailment cost coefficient is CeWTcur w .

4. The method of claim 3, wherein: the self-scheduling model of the comprehensive energy is established based on comprehensive energy information, energy relationships inside the comprehensive energy, and safe operation conditions, which comprises determining constraint conditions of energy hub operation and bidding and determining a target function; operation constraints of the energy hub comprise operation constraints of the internal multi-energy devices and energy coupling relationship constraints of the energy hub; according to the energy relationships inside the comprehensive energy and the safe operation conditions, the safe operation and the establishment of the energy coupling constraints are used to ensure the safe operation of the internal multi-energy devices of the energy hub; for the s-th uncertainty scenario, at the t-th time, the multi-energy devices need to satisfy the following operation constraints; the small gas turbine unit safety constraint is expressed as: GiGI gi,t,s = K gas · ViGI gi,t,s 0 < RGI gi,t,s where GiGI gi,t,s is the power of gas combustion of the input gi unit, K gas is the low calorific value of natural gas, ViGI gi,t,s is the volume of natural gas input into the gas unit, PoGI gi,t,s is the electric output of the small gas unit, RGI gi,t,s is the standby capacity provided by the small gas unit; the small combined heat and power unit operation constraint is expressed as: GiCHP c,t,s = K gas · ViCHP c,t,s PoCHP c,t,s = ηCHP c G2P · GiCHP c,t,s HoCHP c,t,s = ηCHP c G2H · GiCHP c,t,s HoCHP c min ≤ HoCHP c,t,s ≤ HoCHP c max 0 < PoCHP c,t,s + RCHP c,t,s ≤ PoCHP c max PoCHP c min ≤PoCHP c,t,s 0 < RCHP c,t,s HoCHP c ramp- ≤ HoCHP c,t,s - HoCHP c,t-1,s ≤ HoCHP c ramp+ PoCHP c ramp- ≤PoCHP c,t,s -PoCHP c,t-1,s ≤PoCHP c ramp+ wherein, G1CHP c,t,s is the power of gas combustion of the input c unit, V1CHP c,t,s is the natural gas volume of the input small-scale cogeneration unit, P0CHP c,t,s , H0CHP c,t,s are the electric output and heat output of the small-scale cogeneration unit, respectively, RCHP c,t,s is the standby capacity provided by the small-scale cogeneration unit; the electric boiler operation constraint is expressed as: where HoEB eb,t,s is the thermal output of the electric boiler eb, PiEB eb,t,s is the electric power input to the electric boiler; the gas boiler operation constraint is expressed as: GiGB gb,t,s = K gas · ViGB gb,t,s wherein G1GB gb,t,s is the power of the gas combustion input to the boiler gb, V1GB gb,t,s is the volume of natural gas input to the boiler gb, H0GB gb,t,s is the thermal output of the gas boiler gb; the electric energy storage operation constraint is expressed as: 0 < RES e,t,s SOC e,t,s = SOC e,t-1,s + (ηec e · Pc e,t,s - Pd e,t,s / ηed e ) · Δt SOC e,T,s = SOC0 where Pc e,t,s and Pd e,t,s are the charge and discharge power of the energy storage e, respectively, SOC e,t,s is the state of charge of the energy storage e, Δt is the time interval, T is the total time, SOC e,T,s is the final state of charge of the energy storage, and RES e,t,s is the reserve capacity provided by the battery safety energy storage. the thermal energy storage operation constraint is expressed as: EHS h,t,s = EHS h,t-1,s + (ηhc h · Hc h,t,s - Hd h,t,s / ηhd h ) · Δt EHS e,T,s = EHS0 where Hc h,t,s , Hd h,t,s are the charging and discharging power of the thermal storage h, respectively, EHS e,t,s is the state of charge of the thermal storage h, EHS e,T,s is the end energy state of the thermal storage. the small photovoltaic operation constraint is expressed as: wherein PoPV v,t,s is the power output of the photovoltaic v; the energy coupling relationship of the energy hub comprises electric load balance, heat load balance, gas quantity composition balance, and capacity composition balance; the electric load balance of the energy coupling relationship is expressed as: wherein, Po t,s , Pi t,s are the selling and buying power of the energy hub, respectively, and D t is the electrical load within the integrated energy system. the heat load balance is expressed as: where H t is the heat load inside the integrated energy source; the gas quantity composition balance is expressed as: wherein Vi t,s is the total gas purchase amount of comprehensive energy; the capacity composition balance is expressed as: where REH t,s is the total amount of comprehensive energy to provide backup.

5. The method of claim 4, wherein: the self-scheduling model of the comprehensive energy is also established based on the comprehensive energy information, the energy relationships inside the comprehensive energy, and the safe operation conditions, which comprises constructing a target function of the comprehensive energy scheduling with the minimum operation cost as a target and expressed as: where COST s gas , COST s ele , COST s op , COST s cur , respectively represent the gas purchase cost, electricity purchase cost, operation cost, light curtailment cost, standby market income, λ t gas , respectively represent the natural gas price at time t, the out-of-clearing electricity energy price at node N, the out-of-clearing capacity price, F s is the total cost of the energy hub.

6. The method of claim 5, wherein: The comprehensive clearing model of the day-ahead energy market and the reserve market is established according to the cost information, the new energy information and the network information. According to the cost information, the new energy information and the network information, the market and network constraints are established, including the general unit constraint, which is expressed as: 0 < RG g,t,s where PG g,t,s , RG g,t,s are the output and reserve of other units g at time t and scenario s, respectively. The adjustable load is expressed as: where PD d,t,s is the load amount of other load d at time t in scenario s; The wind turbine is expressed as: wherein, PW w,t,s is the output of the wind turbine w at time t in scenario s; The line network constraint is expressed as: F l min ≤PF l,t,s ≤F l max - π < θ < π n,t,s ≤ π θ ref,t,s = 0 where PF l,t,s is the super-flow of line l at time t, in scenario s, θ n,t,s is the phase angle of node n at time t, in scenario s, θ ref,t,s is the phase angle at the reference node ref, X nm is the admittance from node n to node m in the admittance matrix X. The energy hub bidding winning constraint is expressed as: 0 < Po t,s ≤ Pobid t 0 < Pi t,s ≤Pibid t 0 < REH t,s ≤ REHbid t 0 < a0 t 0 < a0 max 0 < αi t 0 < αi max 0≤αr t ≤αr max 0 < Pobid t 0 < REH t,s wherein, Po t,s , Pi t,s are the winning bid quantity of electricity selling and buying of the energy hub at time t; Pobid t , Pibid t , REHbid t are the bid quantity of electricity selling, buying and capacity of the integrated energy at time t; a0 t , ai t , ar t , a0 max , ai max , ar max are the price of electricity selling, buying and capacity and the upper limit of the three prices, respectively. The node power balance constraint is expressed as: where PD d(n),t,s , PG g(n),t,s , PW w(n),t,s , Po (n),t,s , Pi (n),t,s denote the form of the node converted by PD d,t,s , PG g,t,s , PW w,t,s , Po t,s , Pi t,s , that is, for each node n, there is such a power balance constraint; is the dual Lagrange multiplier of the constraint, and is the nodal price of the market clearing node n at time t, scene s, is the energy price of the node N where the energy hub is located, wherein n represents any node number contained in the energy and backup market, N represents the node number of the node where the energy hub is located, N∈n. The capacity balance is expressed as: wherein, is the dual Lagrange multiplier for the constraint, is the capacity price at market clearing at time t, for scenario s. The objective function is constructed with the maximum social welfare as the target, which is expressed as:

7. The method of claim 6, wherein: The double-layer optimization problem is described by using Matlab, and a gurobi commercial solver is used for solving, including programming the optimization problem by using Matlab, solving by using Gurobi, and obtaining the bidding method of the energy hub under the condition of considering new energy fluctuation.

8. A system for a method of bidding optimization for an energy hub with uncertainty participation in energy and reserve markets according to any of claims 1 to 7, characterized in that: The data input module, the energy dispatching module, the clearing module and the bidding solving module are included. The data input module is used for inputting market basic information, energy hub basic information and new energy parameter information, and establishing an uncertainty model of new energy output outside the energy hub. The energy dispatching module is used for establishing a self-dispatching model of the comprehensive energy based on comprehensive energy information, energy relationship inside the comprehensive energy and safe operation conditions. The clearing module is used for establishing a comprehensive clearing model of the day-ahead energy market and the reserve market according to the cost information, the new energy information and the network information. The bidding solving module uses Matlab to describe a double-layer optimization problem, and uses a gurobi commercial solver for solving, so as to obtain an optimal bidding method of the energy hub considering uncertainty. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-7. The processor executes the computer program to realize the steps of the bidding optimization method of the energy hub participating in the energy and reserve market under uncertainty according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the bidding optimization method of the energy hub participating in the energy and reserve market under uncertainty according to any one of claims 1 to 7.

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