CN113344259A - Multifunctional coupling optimization method, system and device for park comprehensive energy - Google Patents
Multifunctional coupling optimization method, system and device for park comprehensive energy Download PDFInfo
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Abstract
The invention relates to the technical field of park comprehensive energy optimization, and particularly provides a park comprehensive energy multi-energy coupling optimization method, system and device, aiming at solving the technical problem of poor operation efficiency of a comprehensive energy system operation mechanism. Specifically, a pre-established upper layer optimization model and a pre-established lower layer optimization model are utilized to solve the optimal scheduling instructions of unit equipment and an energy storage system in a region, and finally the output of the unit equipment and the energy storage system in the region is adjusted by utilizing the optimal scheduling instructions of the unit equipment and the energy storage system in the region; the scheme improves the running mechanism of the existing comprehensive energy system, improves the running efficiency of the system, perfects an energy optimization scheduling model of the system and improves the utilization efficiency of energy.
Description
Technical Field
The invention relates to the field of optimization of comprehensive energy of a park, in particular to a multifunctional coupling optimization method, system and device for the comprehensive energy of the park.
Background
With the continuous development and progress of human society, the demand of people on energy sources is increasing day by day, and the traditional fossil energy sources such as petroleum and coal are gradually exhausted, and meanwhile, the environmental problem is also getting more serious, and the problems of developing environment-friendly renewable energy sources and improving the utilization efficiency of the energy sources are needed to be solved at present. The comprehensive energy system can integrate various energy sources, realize cascade utilization of the energy sources and coordination and complementation among different energy sources, is an important choice for improving the energy utilization efficiency and relieving the environmental pressure, and is generally concerned by energy industries of various countries.
Most of the existing integrated energy systems are centralized operation mechanisms, all information data are collected to the cloud center and are processed by the cloud center, and the operation mechanisms cause huge computing pressure on the cloud center and delay the response speed of the information. In addition, the existing comprehensive energy system is only optimized and configured for a single energy system in the optimization process, the influence of the coupling effect among different energy systems is ignored, and the obtained configuration effect is not ideal.
Disclosure of Invention
In order to overcome the above drawbacks, the present invention is proposed to provide a method, a system and a device for optimizing the multifunctional coupling of the park integrated energy, which solve or at least partially solve the technical problem of poor operation efficiency of the operation mechanism of the integrated energy system.
In a first aspect, a method for optimizing the multi-energy coupling of the comprehensive energy of a park is provided, and the method for optimizing the multi-energy coupling of the comprehensive energy of the park comprises the following steps:
collecting the output power of each energy subsystem in the region and the load of the park comprehensive energy system;
substituting the output power of each energy subsystem in the region and the load of the park comprehensive energy system into a pre-established upper-layer optimization model, and solving the interactive power instruction value of each energy subsystem in the region;
substituting the load of each energy subsystem, the transmission power of each branch, the output of a wind turbine set, the output of a photovoltaic set and the interactive power instruction value of each energy subsystem in the region into a pre-established lower-layer optimization model, and solving the optimal scheduling instruction of unit equipment and an energy storage system in the region;
and adjusting the output of the unit equipment and the energy storage system in the region by using the optimal scheduling instruction of the unit equipment and the energy storage system in the region.
Preferably, the optimal scheduling instruction includes: the power generation power of the power of a fan, a photovoltaic unit, a CCHP unit and a CCS unit of each energy subsystem in the region, the heating power of an electric heating unit and a CCHP unit, the cooling power of an electric cooling and heating unit and a CCHP unit, the gas making power of a P2G unit, the charging power and the discharging power of an electric energy storage device, the charging power and the discharging power of a hot energy storage device, the charging power and the discharging power of a cold energy storage device, and the charging power and the discharging power of the gas energy storage device.
Preferably, the objective function in the pre-established upper layer optimization model is calculated as follows:
in the above formula, F is the total operating loss of the park energy system, Le,i(t) output loss, L, of regional energy subsystem ip,i(T) is the power exchange loss of the regional energy subsystem i, n is the total number of the regional energy subsystems, T is the scheduling period, alpha1、α2、α3、α4Output power loss coefficients, beta, of electricity, heat, cold and natural gas, respectively1、β2、β3、β4The interactive power loss coefficients of electricity, heat, cold and natural gas, mu1、μ2、μ3、μ4Are all balance factors, Pi(t)、Hi(t)、Ci(t)、Qi(t) the output powers of electricity, heat, cold and natural gas of the regional energy subsystem i at the moment t, Pi,H(t)、Hi,H(t)、Ci,H(t)、Qi,HAnd (t) the interactive power of electricity, heat, cold and natural gas of the regional energy subsystem i on the tie line at the moment t respectively.
Further, the system power balance constraint calculation formula in the pre-established upper layer optimization model is as follows:
in the above formula, Pload(t)、Hload(t)、Cload(t)、Qload(t) the electric, heat, cold and natural gas loads of the park comprehensive energy system at the moment t respectively;
the output constraint calculation formula of the regional energy subsystem in the pre-established upper-layer optimization model is as follows:
Pi min<Pi(t)<Pi max
in the above formula, Pi min、
Minimum output constraints of electricity, heat, cold and natural gas, P, of the regional energy subsystem i, respectivelyi max、
Respectively limiting the maximum output of electricity, heat, cold and natural gas of the regional energy subsystem i;
the power interaction constraint calculation formula among subsystems in the pre-established upper-layer optimization model is as follows:
in the above formula, the first and second carbon atoms are,
the maximum values of power interaction of electricity, heat, cold and natural gas among subsystems are respectively.
Preferably, the objective function calculation formula in the pre-established lower layer optimization model is as follows:
minf3＝xPCCHP(t)-yPCCS(t)
in the above formula, f1For the operating losses of the regional energy subsystem i,/e,i(t) is the equipment operating losses,/l,i(t) Branch Power loss, PCCS(t) is the output of CCS unit, PPV(t)、PWT(t)、PCCHP(t) the generated power of the fan, the photovoltaic and the CCHP units, HEB(t)、HCCHP(t) heating powers of the electric heating unit and the CCHP unit, respectively, CEC(t)、CCCHP(t) refrigeration powers of the electric cooling and heating unit and the CCHP unit, QP2G(t) is the gas making power of P2G unit, epsilon1To epsilon7Respectively being a CCHP unit, a photovoltaic unit, a fan, an electric heating unit, a CCHP unit and an electric heating and cooling unitAnd the output loss factor, delta, of the P2G unit1、δ2、δ3、δ4Power loss coefficient of branch circuit, mu, of electricity, heat, cold and natural gas respectively1、μ2、μ3、μ4Are all balance factors, NbrIs the total number of branches, T is the scheduling period, f2For total power fluctuation, Δ P, of regional energy subsystemsi,H(t)、ΔHi,H(t)、ΔCi,H(t)、ΔQi,H(t) power fluctuations, P, of the regional energy subsystem, i electricity, heat, cold, and natural gas, respectivelyCCHP(t)、PCCS(t) power of CCHP unit and CCS unit, respectively, f3For the total carbon emission, x is the carbon emission coefficient of the CCHP unit, y is the carbon capture coefficient of the CCS unit, Pi,l(t)、Hi,l(t)、Ci,l(t)、Qi,lAnd (t) the power transmission of electricity, heat, cold and natural gas of the ith branch circuit is respectively carried out.
Further, the calculation formula of the regional energy power balance constraint in the pre-established lower layer optimization model is as follows:
PPV(t)+PWT(t)+PCCHP(t)+Pesd,d(t)＝Pesd，c(t)+Pi，H(t)+Pi，load(t)
HEB(t)+HCCHP(t)+Hesd，d(t)＝Hesd，c(t)+Hi，H(t)+Hi，load(t)
CEC(t)+CCCHP(t)+Cesd,d(t)＝Cesd,c(t)+Ci,H(t)+Ci,load(t)
QP2G(t)+Qesd,d(t)＝Qesd,c(t)+Qi,H(t)+Qi,load(t)
the branch power constraint calculation formula in the pre-established lower layer optimization model is as follows:
the calculation formula of the upper and lower limit constraints of the output of the equipment units in the pre-established lower-layer optimization model is as follows:
the maximum and minimum value constraint calculation formula of the state of charge in the pre-established lower-layer optimization model is as follows:
the thermal energy storage constraint calculation formula in the pre-established lower-layer optimization model is as follows:
the cold energy storage constraint calculation formula in the pre-established lower-layer optimization model is as follows:
the gas energy storage constraint calculation formula in the pre-established lower layer optimization model is as follows:
in the above formula, Pi,load(t)、Hi，load(t)、Ci，load(t)、Qi,load(t) load requirements for electricity, heat, cold and natural gas of the regional energy subsystem i, respectively; pesd，d(t)、Pesd，c(t) the charge and discharge power of the electric energy storage device at the moment t respectively; hesd,d(t)、Hesd,c(t) the heat charging and discharging powers of the thermal energy storage device at the moment t respectively; cesd,d(t)、Cesd,c(t) the cold charging and discharging power of the cold energy storage device at the moment t respectively; qesd，d(t)、Qesd，c(t) the charging and discharging power, P, of the gas energy storage device at the time tesd，cFor discharge power of electric energy-storage devices, Pi,H(t)、Hi，H(t)、Ci，H(t)、Qi,H(t) the interactive power of electricity, heat, cold and natural gas on the tie line of the regional energy subsystem i at the moment t respectively,
the maximum transmission power of electricity, heat, cold and natural gas of the ith branch circuit respectively,
respectively are the upper and lower limits of output of the CCHP unit for generating power,
respectively are the upper and lower limit constraints of the output of the CCS unit,
respectively are the upper and lower limit constraints of the output of the wind turbine,
respectively are the output upper and lower limit constraints of the photovoltaic unit,
respectively are the upper and lower limit constraints of the output of the electric heating unit,
respectively are the upper and lower limit constraints of the output of the CCHP unit for heating,
respectively are the upper and lower limit constraints of the output of the electric refrigerating unit,
respectively are the upper and lower limit constraints of the output of CCHP unit refrigeration,
respectively the upper and lower output limits of the P2G machine set,
the upper and lower limits of the charging capacity of the energy storage system are set,
is the maximum charge-discharge power of the electrical energy storage system,
the upper and lower limits of the charging heat quantity of the heat energy storage system,
is the maximum heat charging and discharging power of the heat energy storage system,
the upper limit and the lower limit of the charging cold quantity of the heat energy storage system,
is the maximum charging and discharging cold power of the cold energy storage system,
for the upper and lower limits of the charge capacity of the heat energy storage system,
maximum charging and discharging power, SOC, of gas energy storage systemPt、SOCHt、SOCCt、SOCQtThe charging capacity of the electric energy storage system, the charging heat of the heat energy storage system, the charging cold of the cold energy storage system and the charging capacity of the gas energy storage system are respectively.
In a second aspect, a park energy-integrated multi-energy coupling optimization system is provided, which includes:
the intelligent acquisition module is used for acquiring the output power of each energy subsystem in the region and the load of the park comprehensive energy system;
the edge side is used for preprocessing the acquired load data and uploading the preprocessed data to the cloud center;
the cloud center is used for substituting the output power of each energy subsystem in the region and the load of the park comprehensive energy system into a pre-established upper-layer optimization model, and solving the interactive power instruction value of each energy subsystem in the region;
and the edge side is also used for substituting the load of each energy subsystem in the region, the transmission power of each branch, the output of the wind turbine set, the output of the photovoltaic set and the interactive power instruction value of each energy subsystem into a pre-established lower-layer optimization model, solving the optimal scheduling instruction of the unit equipment and the energy storage system in the region, and sending the optimal scheduling instruction of the unit equipment and the energy storage system in the region to each energy subsystem in the region.
In a third aspect, a storage device is provided, wherein a plurality of program codes are stored in the storage device, and the program codes are suitable for being loaded and executed by a processor to execute the method for optimizing the comprehensive energy and energy coupling of the park according to any one of the above technical solutions.
In a fourth aspect, there is provided a control apparatus comprising a processor and a storage device, the storage device adapted to store a plurality of program codes, the program codes adapted to be loaded and run by the processor to perform the method of optimizing a campus renewable energy multi-energy coupling according to any of the preceding claims.
One or more technical schemes of the invention at least have one or more of the following beneficial effects:
the invention provides a multifunctional coupling optimization method, a system and a device for park comprehensive energy, which specifically comprise the following steps: collecting the output power of each energy subsystem in the region and the load of the park comprehensive energy system; substituting the output power of each energy subsystem in the region and the load of the park comprehensive energy system into a pre-established upper-layer optimization model, and solving the interactive power instruction value of each energy subsystem in the region; substituting the load of each energy subsystem, the transmission power of each branch, the output of a wind turbine set, the output of a photovoltaic set and the interactive power instruction value of each energy subsystem in the region into a pre-established lower-layer optimization model, and solving the optimal scheduling instruction of unit equipment and an energy storage system in the region; and adjusting the output of the unit equipment and the energy storage system in the region by using the optimal scheduling instruction of the unit equipment and the energy storage system in the region. The scheme greatly relieves the calculation pressure of a calculation center, improves the operation efficiency of the system, simultaneously considers the influence of the multi-energy coupling phenomenon existing in the park on the optimized scheduling, and effectively improves the energy utilization rate.
Drawings
FIG. 1 is a flow chart illustrating the main steps of a park energy integration multi-energy coupling optimization method according to an embodiment of the present invention;
FIG. 2 is a block diagram of the main structure of a park renewable energy multi-energy coupling optimization system according to one embodiment of the present invention;
FIG. 3 is a flow chart of the operation mechanism of the park energy-integrated multi-energy coupling optimization system in the embodiment of the invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating the main steps of a park renewable energy multi-energy coupling optimization method according to an embodiment of the present invention. As shown in fig. 1, the method for optimizing the multi-energy coupling of the park comprehensive energy resource in the embodiment of the present invention mainly includes the following steps:
s101, collecting output power of each energy subsystem in an area and load of a park comprehensive energy system;
step S102, substituting the output power of each energy subsystem in the region and the load of the park comprehensive energy system into a pre-established upper-layer optimization model, and solving the interactive power instruction value of each energy subsystem in the region;
step S103, substituting the load of each energy subsystem, the transmission power of each branch, the output of a wind turbine set, the output of a photovoltaic set and the interactive power instruction value of each energy subsystem in the region into a pre-established lower-layer optimization model, and solving the optimal scheduling instruction of unit equipment and an energy storage system in the region;
and step S104, adjusting the output of the unit devices and the energy storage system in the region by using the optimal scheduling instructions of the unit devices and the energy storage system in the region.
Wherein the optimal scheduling instruction comprises: the power generation power of the power of a fan, a photovoltaic unit, a CCHP unit and a CCS unit of each energy subsystem in the region, the heating power of an electric heating unit and a CCHP unit, the cooling power of an electric cooling and heating unit and a CCHP unit, the gas making power of a P2G unit, the charging power and the discharging power of an electric energy storage device, the charging power and the discharging power of a hot energy storage device, the charging power and the discharging power of a cold energy storage device, and the charging power and the discharging power of the gas energy storage device.
In this embodiment, the objective function calculation formula in the pre-established upper layer optimization model is as follows:
in the above formula, F is the total operating loss of the park energy system, Le,i(t) output loss, L, of regional energy subsystem ip,i(t)Is the power exchange loss of the regional energy subsystem i, n is the total number of the regional energy subsystems, T is the scheduling period, alpha1、α2、α3、α4Output power loss coefficients, beta, of electricity, heat, cold and natural gas, respectively1、β2、β3、β4The interactive power loss coefficients of electricity, heat, cold and natural gas, mu1、μ2、μ3、μ4Are all balance factors, Pi(t)、Hi(t)、Ci(t)、Qi(t) the output powers of electricity, heat, cold and natural gas of the regional energy subsystem i at the moment t, Pi，H(t)、Hi,H(t)、Ci，H(t)、Qi,HAnd (t) the interactive power of electricity, heat, cold and natural gas of the regional energy subsystem i on the tie line at the moment t respectively.
In this embodiment, the system power balance constraint calculation formula in the pre-established upper layer optimization model is as follows:
in the above formula, Pload(t)、Hload(t)、Cload(t)、Qload(t) the electric, heat, cold and natural gas loads of the park comprehensive energy system at the moment t respectively;
the output constraint calculation formula of the regional energy subsystem in the pre-established upper-layer optimization model is as follows:
Pi min<Pi(t)<Pi max
in the above formula, Pi min、
Minimum output constraints of electricity, heat, cold and natural gas, P, of the regional energy subsystem i, respectivelyi max、
Respectively limiting the maximum output of electricity, heat, cold and natural gas of the regional energy subsystem i;
the power interaction constraint calculation formula among subsystems in the pre-established upper-layer optimization model is as follows:
in the above formula, the first and second carbon atoms are,
the maximum values of power interaction of electricity, heat, cold and natural gas among subsystems are respectively.
In this embodiment, the objective function calculation formula in the pre-established lower layer optimization model is as follows:
minf3＝xPCCHP(t)-yPCCS(t)
in the above formula, f1For the operating losses of the regional energy subsystem i,/e,i(t) is the equipment operating losses,/l，i(t) Branch Power loss, PCCS(t) is the output of CCS unit, PPV(t)、PWT(t)、PCCHP(t) the generated power of the fan, the photovoltaic and the CCHP units, HEB(t)、HCCHP(t) heating powers of the electric heating unit and the CCHP unit, respectively, CEC(t)、CCCHP(t) refrigeration powers of the electric cooling and heating unit and the CCHP unit, QP2G(t) is the gas making power of P2G unit, epsilon1To epsilon7The output loss coefficients of the CCHP unit, the photovoltaic unit, the fan, the electric heating unit, the CCHP unit, the electric heating unit and the P2G unit are delta1、δ2、δ3、δ4Power loss coefficient of branch circuit, mu, of electricity, heat, cold and natural gas respectively1、μ2、μ3、μ4Are all balance factors, NbrIs the total number of branches, T is the scheduling period, f2For total power fluctuation, Δ P, of regional energy subsystemsi，H(t)、ΔHi，H(t)、ΔCi，H(t)、ΔQi，H(t) power fluctuations, P, of the regional energy subsystem, i electricity, heat, cold, and natural gas, respectivelyCCHP(t)、PCCS(t) power of CCHP unit and CCS unit, respectively, f3For the total carbon emission, x is the carbon emission coefficient of the CCHP unit, y is the carbon capture coefficient of the CCS unit, Pi,l(t)、Hi,l(t)、Ci,l(t)、Qi,lAnd (t) the power transmission of electricity, heat, cold and natural gas of the ith branch circuit is respectively carried out.
In this embodiment, the calculation formula of the regional energy power balance constraint in the pre-established lower layer optimization model is as follows:
PPV(t)+PWT(t)+PCCHP(t)+Pesd,d(t)＝Pesd,c(t)+Pi,H(t)+Pi,load(t)
HEB(t)+HCCHP(t)+Hesd,d(t)＝Hesd,c(t)+Hi,H(t)+Hi,load(t)
CEC(t)+CCCHP(t)+Cesd，d(t)＝Cesd，c(t)+Ci,H(t)+Ci,load(t)
QP2G(t)+Qesd,d(t)＝Qesd，c(t)+Qi,H(t)+Qi,load(t)
the branch power constraint calculation formula in the pre-established lower layer optimization model is as follows:
the calculation formula of the upper and lower limit constraints of the output of the equipment units in the pre-established lower-layer optimization model is as follows:
the maximum and minimum value constraint calculation formula of the state of charge in the pre-established lower-layer optimization model is as follows:
the thermal energy storage constraint calculation formula in the pre-established lower-layer optimization model is as follows:
the cold energy storage constraint calculation formula in the pre-established lower-layer optimization model is as follows:
the gas energy storage constraint calculation formula in the pre-established lower layer optimization model is as follows:
in the above formula, Pi,load(t)、Hi,load(t)、Ci,load(t)、Qi,load(t) load requirements for electricity, heat, cold and natural gas of the regional energy subsystem i, respectively; pesd,d(t)、Pesd,c(t) the charge and discharge power of the electric energy storage device at the moment t respectively; hesd，d(t)、Hesd，c(t) the heat charging and discharging powers of the thermal energy storage device at the moment t respectively; cesd,d(t)、Cesd，c(t) the cold charging and discharging power of the cold energy storage device at the moment t respectively; qesd，d(t)、Qesd,c(t) the charging and discharging power, P, of the gas energy storage device at the time tesd，cFor discharge power of electric energy-storage devices, Pi,H(t)、Hi，H(t)、Ci，H(t)、Qi,H(t) are each a regionThe domain energy subsystem i is connected with the power, heat, cold and natural gas interactive power at the time t,
the maximum transmission power of electricity, heat, cold and natural gas of the ith branch circuit respectively,
respectively are the upper and lower limits of output of the CCHP unit for generating power,
respectively are the upper and lower limit constraints of the output of the CCS unit,
respectively are the upper and lower limit constraints of the output of the wind turbine,
respectively are the output upper and lower limit constraints of the photovoltaic unit,
respectively are the upper and lower limit constraints of the output of the electric heating unit,
respectively are the upper and lower limit constraints of the output of the CCHP unit for heating,
respectively are the upper and lower limit constraints of the output of the electric refrigerating unit,
respectively are the upper and lower limit constraints of the output of CCHP unit refrigeration,
respectively the upper and lower output limits of the P2G machine set,
the upper and lower limits of the charging capacity of the energy storage system are set,
is the maximum charge-discharge power of the electrical energy storage system,
the upper and lower limits of the charging heat quantity of the heat energy storage system,
is the maximum heat charging and discharging power of the heat energy storage system,
the upper limit and the lower limit of the charging cold quantity of the heat energy storage system,
is the maximum charging and discharging cold power of the cold energy storage system,
for the upper and lower limits of the charge capacity of the heat energy storage system,
maximum charging and discharging power, SOC, of gas energy storage systemPt、SOCHt、SOCCt、SOCQtThe charging capacity of the electric energy storage system, the charging heat of the heat energy storage system, the charging cold of the cold energy storage system and the charging capacity of the gas energy storage system are respectively.
It should be noted that, although the foregoing embodiments describe each step in a specific sequence, those skilled in the art will understand that, in order to achieve the effect of the present invention, different steps do not necessarily need to be executed in such a sequence, and they may be executed simultaneously (in parallel) or in other sequences, and these changes are all within the protection scope of the present invention.
Based on the same invention concept, the invention also provides a park comprehensive energy multi-energy coupling optimization system, which comprises:
the intelligent acquisition module is used for acquiring the output power of each energy subsystem in the region and the load of the park comprehensive energy system;
the edge side is used for preprocessing the acquired load data and uploading the preprocessed data to the cloud center;
the cloud center is used for substituting the output power of each energy subsystem in the region and the load of the park comprehensive energy system into a pre-established upper-layer optimization model, and solving the interactive power instruction value of each energy subsystem in the region;
and the edge side is also used for substituting the load of each energy subsystem in the region, the transmission power of each branch, the output of the wind turbine set, the output of the photovoltaic set and the interactive power instruction value of each energy subsystem into a pre-established lower-layer optimization model, solving the optimal scheduling instruction of the unit equipment and the energy storage system in the region, and sending the optimal scheduling instruction of the unit equipment and the energy storage system in the region to each energy subsystem in the region.
Wherein the optimal scheduling instruction comprises: the power generation power of the power of a fan, a photovoltaic unit, a CCHP unit and a CCS unit of each energy subsystem in the region, the heating power of an electric heating unit and a CCHP unit, the cooling power of an electric cooling and heating unit and a CCHP unit, the gas making power of a P2G unit, the charging power and the discharging power of an electric energy storage device, the charging power and the discharging power of a hot energy storage device, the charging power and the discharging power of a cold energy storage device, and the charging power and the discharging power of the gas energy storage device.
In this embodiment, the objective function calculation formula in the pre-established upper layer optimization model is as follows:
in the above formula, F is the total operating loss of the park energy system, Le,i(t) output loss, L, of regional energy subsystem ip,i(T) is the power exchange loss of the regional energy subsystem i, n is the total number of the regional energy subsystems, T is the scheduling period, alpha1、α2、α3、α4Output power loss coefficients, beta, of electricity, heat, cold and natural gas, respectively1、β2、β3、β4The interactive power loss coefficients of electricity, heat, cold and natural gas, mu1、μ2、μ3、μ4Are all balance factors, Pi(t)、Hi(t)、Ci(t)、Qi(t) the output powers of electricity, heat, cold and natural gas of the regional energy subsystem i at the moment t, Pi,H(t)、Hi，H(t)、Ci,H(t)、Qi,HAnd (t) the interactive power of electricity, heat, cold and natural gas of the regional energy subsystem i on the tie line at the moment t respectively.
In this embodiment, the system power balance constraint calculation formula in the pre-established upper layer optimization model is as follows:
in the above formula, Pload(t)、Hload(t)、Cload(t)、Qload(t) the electric, heat, cold and natural gas loads of the park comprehensive energy system at the moment t respectively;
the output constraint calculation formula of the regional energy subsystem in the pre-established upper-layer optimization model is as follows:
Pi min<Pi(t)<Pi max
in the above formula, Pi min、
Minimum output constraints of electricity, heat, cold and natural gas, P, of the regional energy subsystem i, respectivelyi max、
Respectively limiting the maximum output of electricity, heat, cold and natural gas of the regional energy subsystem i;
the power interaction constraint calculation formula among subsystems in the pre-established upper-layer optimization model is as follows:
in the above formula, the first and second carbon atoms are,
the maximum values of power interaction of electricity, heat, cold and natural gas among subsystems are respectively.
In this embodiment, the objective function calculation formula in the pre-established lower layer optimization model is as follows:
minf3＝xPCCHP(t)-yPCCS(t)
in the above formula, f1For the operating losses of the regional energy subsystem i,/e,i(t) is the equipment operating losses,/l,i(t) Branch Power loss, PCCS(t) is the output of CCS unit, PPV(t)、PWT(t)、PCCHP(t) the generated power of the fan, the photovoltaic and the CCHP units, HEB(t)、HCCHP(t) heating powers of the electric heating unit and the CCHP unit, respectively, CEC(t)、CCCHP(t) refrigeration powers of the electric cooling and heating unit and the CCHP unit, QP2G(t) is the gas making power of P2G unit, epsilon1To epsilon7Respectively CCHP unit, photovoltaic, fan and electricityOutput loss coefficient, delta, of the heating unit, the CCHP unit, the electric heating and cooling unit and the P2G unit1、δ2、δ3、δ4Power loss coefficient of branch circuit, mu, of electricity, heat, cold and natural gas respectively1、μ2、μ3、μ4Are all balance factors, NbrIs the total number of branches, T is the scheduling period, f2For total power fluctuation, Δ P, of regional energy subsystemsi,H(t)、ΔHi,H(t)、ΔCi,H(t)、ΔQi,H(t) power fluctuations, P, of the regional energy subsystem, i electricity, heat, cold, and natural gas, respectivelyCCHP(t)、PCCS(t) power of CCHP unit and CCS unit, respectively, f3For the total carbon emission, x is the carbon emission coefficient of the CCHP unit, y is the carbon capture coefficient of the CCS unit, Pi,l(t)、Hi,l(t)、Ci,l(t)、Qi,lAnd (t) the power transmission of electricity, heat, cold and natural gas of the ith branch circuit is respectively carried out.
In this embodiment, the calculation formula of the regional energy power balance constraint in the pre-established lower layer optimization model is as follows:
PPV(t)+PWT(t)+PCCHP(t)+Pesd,d(t)＝Pesd，c(t)+Pi，H(t)+Pi，load(t)
HEB(t)+HCCHP(t)+Hesd，d(t)＝Hesd，c(t)+Hi，H(t)+Hi，load(t)
CEC(t)+CCCHP(t)+Cesd,d(t)＝Cesd,c(t)+Ci,H(t)+Ci,load(t)
QP2G(t)+Qesd,d(t)＝Qesd,c(t)+Qi,H(t)+Qi,load(t)
the branch power constraint calculation formula in the pre-established lower layer optimization model is as follows:
the calculation formula of the upper and lower limit constraints of the output of the equipment units in the pre-established lower-layer optimization model is as follows:
the maximum and minimum value constraint calculation formula of the state of charge in the pre-established lower-layer optimization model is as follows:
the thermal energy storage constraint calculation formula in the pre-established lower-layer optimization model is as follows:
the cold energy storage constraint calculation formula in the pre-established lower-layer optimization model is as follows:
the gas energy storage constraint calculation formula in the pre-established lower layer optimization model is as follows:
in the above formula, Pi,load(t)、Hi，load(t)、Ci，load(t)、Qi,load(t) load requirements for electricity, heat, cold and natural gas of the regional energy subsystem i, respectively; pesd，d(t)、Pesd，c(t) the charge and discharge power of the electric energy storage device at the moment t respectively; hesd,d(t)、Hesd,c(t) the heat charging and discharging powers of the thermal energy storage device at the moment t respectively; cesd,d(t)、Cesd,c(t) Cold energy storage devices at respective times tThe charging and discharging cold power of (2); qesd，d(t)、Qesd，c(t) the charging and discharging power, P, of the gas energy storage device at the time tesd，cFor discharge power of electric energy-storage devices, Pi,H(t)、Hi，H(t)、Ci，H(t)、Qi,H(t) the interactive power of electricity, heat, cold and natural gas on the tie line of the regional energy subsystem i at the moment t respectively,
the maximum transmission power of electricity, heat, cold and natural gas of the ith branch circuit respectively,
respectively are the upper and lower limits of output of the CCHP unit for generating power,
respectively are the upper and lower limit constraints of the output of the CCS unit,
respectively are the upper and lower limit constraints of the output of the wind turbine,
respectively are the output upper and lower limit constraints of the photovoltaic unit,
respectively are the upper and lower limit constraints of the output of the electric heating unit,
respectively are the upper and lower limit constraints of the output of the CCHP unit for heating,
respectively are the upper and lower limit constraints of the output of the electric refrigerating unit,
respectively are the upper and lower limit constraints of the output of CCHP unit refrigeration,
respectively the upper and lower output limits of the P2G machine set,
the upper and lower limits of the charging capacity of the energy storage system are set,
is the maximum charge-discharge power of the electrical energy storage system,
the upper and lower limits of the charging heat quantity of the heat energy storage system,
is the maximum heat charging and discharging power of the heat energy storage system,
the upper limit and the lower limit of the charging cold quantity of the heat energy storage system,
is the maximum charging and discharging cold power of the cold energy storage system,
for the upper and lower limits of the charge capacity of the heat energy storage system,
maximum charging and discharging power, SOC, of gas energy storage systemPt、SOCHt、SOCCt、SOCQtThe charging capacity of the electric energy storage system, the charging heat of the heat energy storage system, the charging cold of the cold energy storage system and the charging capacity of the gas energy storage system are respectively.
In an application scenario, as shown in fig. 2, the system includes a cloud center, an edge side, and an intelligent terminal; the cloud center comprises a cloud computing center and a data exchange center, the edge side comprises an edge computing module and an information transmission module, and the intelligent terminal comprises an information acquisition unit, an information transmission unit and an energy equipment control unit.
The hardware equipment of the cloud center comprises a large cloud server and a data center switch; the software comprises a cloud center data platform; some large intelligent models and top-level management policies are also included. Collecting data information of each edge side, and generating a park comprehensive energy synergistic optimization scheduling instruction by combining a large intelligent model from the perspective of the whole park;
the edge side hardware equipment comprises an intelligent chip and edge data storage equipment; the software comprises an edge computing system operating platform; also comprises some intelligent models and optimization algorithms; the information of the energy source layer equipment in the convergence region is preprocessed (including links such as data filtering, cleaning and fusion); dynamic equivalence of comprehensive energy sources; generating a regional energy optimization scheduling instruction by using an intelligent model and an optimization algorithm;
the intelligent terminal comprises various intelligent sensors for collecting data, a camera, an electronic tag in the equipment, a wireless communication module for data transmission and a control unit for controlling the energy equipment.
The park comprehensive energy system consists of a plurality of regional energy subsystems and a park comprehensive energy regulation cloud center, wherein the energy layer of each regional energy subsystem respectively comprises an input side energy source, an output side energy source, a capacity device, an energy supply network, an energy utilization load and the like; wherein the general input side energy of the park comprises wind energy, light energy, electric energy, natural gas and the like; the output side energy sources comprise cold energy, heat energy, electricity energy and gas energy; the energy generating equipment comprises a wind driven generator, a photovoltaic battery pack, a CCHP unit, a CCS unit, a P2G unit, an electric heating unit, an electric refrigerating unit and the like; the energy supply network comprises a power supply network, an air supply network, a cooling network and a heating network; the load comprises an electric load, a gas load, a thermal load and a cold air load; aiming at different regional load energy utilization scenes, the energy structure proportion of the energy source layer of each regional energy subsystem is different;
the multifunctional coupling refers to the interactive coupling of electricity, heat, cold and natural gas among different energy subsystems, a plurality of regional energy subsystems are arranged in the comprehensive energy system, the interaction of electricity, heat, cold and natural gas among the regional energy subsystems can be realized through energy connecting lines, and a park comprehensive energy optimization scheduling scheme considering the multifunctional coupling among the different energy subsystems is designed by analyzing the coupling relation among the different energy subsystems.
The optimal scheduling scheme of the park comprehensive energy comprises a double-layer optimal scheduling model, wherein an upper-layer optimal model exists in a cloud center, and the total operation loss of a park comprehensive energy system is taken as an optimization target; the lower-layer optimization models are dispersed on each edge side, and the operation loss, power fluctuation and carbon emission of the regional energy subsystems are taken as optimization targets; in the mathematical model, an upper-layer optimization result, the exchange capacity and the output among the regional energy subsystems are used as scheduling instructions to be sent to a lower-layer optimization model; and if the lower-layer optimization model cannot obtain an optimization solution under the constraint condition, feeding the result back to the upper-layer model, adjusting the scheduling instruction and recalculating until a feasible scheduling scheme is obtained.
The specific implementation process is shown in fig. 3, and includes:
the method comprises the following steps: the intelligent terminal utilizes an intelligent sensor, a camera and an electronic tag in the equipment to acquire various information from the comprehensive energy system in real time; transmitting the data information acquired by the terminal to the edge side through a wireless network communication technology;
step two: the edge side is responsible for preprocessing the original data (including links such as data filtering, cleaning and fusion) and dynamic equivalence of comprehensive energy; the edge data is transmitted to the cloud center through the optical fiber;
step three: the cloud center analyzes and processes the collected edge data through a built-in park comprehensive energy optimization scheduling model (an upper optimization model), generates a park comprehensive energy optimization scheduling instruction and sends the instruction to an edge side;
the park comprehensive energy scheduling instruction is obtained by solving a park comprehensive energy optimization scheduling model from the overall view of the park by the cloud center under the influence of considering the multi-energy coupling of the park.
The input of the upper optimization model is the load P of the comprehensive energy systemload(t)、Hload(t)、Cload(t)、Qload(t) the output P of each energy subsystemi(t)、Hi(t)、Ci(t)、Qi(t) subsystem force constraints Pi min、
Pi max、
Power interaction constraints
Model parameter alpha1To alpha4、β1To beta4、μ1To mu4(ii) a Outputting the interactive power P of each energy subsystemi,H(t)、Hi,H(t)、Ci,H(t)、Qi,H(t)。
Step four: the edge side integrates regional energy information according to the park comprehensive energy optimization scheduling instruction, solves a regional energy optimization scheduling model (a lower layer optimization model) and generates a regional energy optimization scheduling instruction; and if the optimized dispatching instruction cannot be generated, uploading the result to the cloud center, and repeating the third step and the fourth step until the regional energy optimized dispatching instruction is generated.
The input of the lower optimization model is the interactive power P of the regional energy subsystemi,H(t)、Hi，H(t)、Ci,H(t)、Qi,H(t) renewable energy contribution PPV、PWTLoad of energy subsystem Pi,load(t)、Hi,load(t)、Ci，load(t)、Qi,load(t) branch power Pi,l、Hi，l、Ci，l、Qi,lBranch power constraint
Upper and lower limit constraints of equipment output
Model parameter ε1To epsilon7、δ1To delta4、μ1To mu4(ii) a The output is the unit equipment output force PPV(t)、PWT(t)、PCCHP(t)、PCCS(t)、HEB(t)、HCCHP(t)、CEC(t)、CCCHP(t)、QP2G(t) output of the energy storage system Pesd，d(t)、Pesd，c(t)、Hesd,d(t)、Hesd,c(t)、Cesd,d(t)、Cesd,c(t)、Qesd,d(t)、Qesd,c(t)。
Step five: and the edge side sends the final regional energy optimization scheduling instruction to each intelligent terminal through the optical fiber, and the intelligent terminals regulate and control corresponding energy equipment according to the instruction.
Furthermore, the invention also provides a storage device. In one embodiment of the storage device according to the present invention, the storage device may be configured to store a program for executing the method for optimizing the campus renewable energy coupling of the above method embodiment, and the program may be loaded and executed by the processor to implement the method for optimizing the campus renewable energy coupling. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The storage device may be a storage device apparatus formed by including various electronic devices, and optionally, a non-transitory computer-readable storage medium is stored in the embodiment of the present invention.
Furthermore, the invention also provides a control device. In an embodiment of the control apparatus according to the present invention, the control apparatus comprises a processor and a storage device, the storage device may be configured to store a program for executing the method for optimizing the campus renewable energy coupling of the above method embodiment, and the processor may be configured to execute the program in the storage device, the program including but not limited to the program for executing the method for optimizing the campus renewable energy coupling of the above method embodiment. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The control device may be a control device apparatus formed including various electronic apparatuses.
All or part of the flow of the method according to the above embodiment of the present invention may also be implemented by a computer program instructing related hardware, where the computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. A park comprehensive energy multi-energy coupling optimization method is characterized by comprising the following steps:
collecting the output power of each energy subsystem in the region and the load of the park comprehensive energy system;
substituting the output power of each energy subsystem in the region and the load of the park comprehensive energy system into a pre-established upper-layer optimization model, and solving the interactive power instruction value of each energy subsystem in the region;
substituting the load of each energy subsystem, the transmission power of each branch, the output of a wind turbine set, the output of a photovoltaic set and the interactive power instruction value of each energy subsystem in the region into a pre-established lower-layer optimization model, and solving the optimal scheduling instruction of unit equipment and an energy storage system in the region;
and adjusting the output of the unit equipment and the energy storage system in the region by using the optimal scheduling instruction of the unit equipment and the energy storage system in the region.
2. The method of claim 1, wherein the optimal scheduling instruction comprises: the power generation power of the power of a fan, a photovoltaic unit, a CCHP unit and a CCS unit of each energy subsystem in the region, the heating power of an electric heating unit and a CCHP unit, the cooling power of an electric cooling and heating unit and a CCHP unit, the gas making power of a P2G unit, the charging power and the discharging power of an electric energy storage device, the charging power and the discharging power of a hot energy storage device, the charging power and the discharging power of a cold energy storage device, and the charging power and the discharging power of the gas energy storage device.
3. The method of claim 1, wherein the objective function in the pre-established upper-layer optimization model is calculated as follows:
in the above formula, F is the total operating loss of the park energy system, Le，i(t) output loss, L, of regional energy subsystem ip,i(T) is the power exchange loss of the regional energy subsystem i, n is the total number of the regional energy subsystems, T is the scheduling period, alpha1、α2、α3、α4Output power loss coefficients, beta, of electricity, heat, cold and natural gas, respectively1、β2、β3、β4The interactive power loss coefficients of electricity, heat, cold and natural gas, mu1、μ2、μ3、μ4Are all balance factors, Pi(t)、Hi(t)、Ci(t)、Qi(t) the output powers of electricity, heat, cold and natural gas of the regional energy subsystem i at the moment t, Pi,H(t)、Hi,H(t)、Ci,H(t)、Qi,HAnd (t) the interactive power of electricity, heat, cold and natural gas of the regional energy subsystem i on the tie line at the moment t respectively.
4. The method of claim 3, wherein the system power balance constraint in the pre-established upper-layer optimization model is calculated as follows:
in the above formula, Pload(t)、Hload(t)、Cload(t)、Qload(t) the electric, heat, cold and natural gas loads of the park comprehensive energy system at the moment t respectively;
the output constraint calculation formula of the regional energy subsystem in the pre-established upper-layer optimization model is as follows:
in the above formula, the first and second carbon atoms are,
respectively the minimum output constraints of electricity, heat, cold and natural gas of the regional energy subsystem i,
respectively limiting the maximum output of electricity, heat, cold and natural gas of the regional energy subsystem i;
the power interaction constraint calculation formula among subsystems in the pre-established upper-layer optimization model is as follows:
in the above formula, the first and second carbon atoms are,
the maximum values of power interaction of electricity, heat, cold and natural gas among subsystems are respectively.
5. The method of claim 1, wherein the objective function in the pre-established underlying optimization model is calculated as follows:
minf3＝xPCCHP(t)-yPCCS(t)
in the above formula, f1For the operating losses of the regional energy subsystem i,/e,i(t) is the equipment operating losses,/l,i(t) Branch Power loss, PCCS(t) is the output of CCS unit, PPV(t)、PWT(t)、PCCHP(t) the generated power of the fan, the photovoltaic and the CCHP units, HEB(t)、HCCHP(t) heating powers of the electric heating unit and the CCHP unit, respectively, CEC(t)、CCCHP(t) refrigeration powers of the electric cooling and heating unit and the CCHP unit, QP2G(t) is the gas making power of P2G unit, epsilon1To epsilon7The output loss coefficients of the CCHP unit, the photovoltaic unit, the fan, the electric heating unit, the CCHP unit, the electric heating unit and the P2G unit are delta1、δ2、δ3、δ4Power loss coefficient of branch circuit, mu, of electricity, heat, cold and natural gas respectively1、μ2、μ3、μ4Are all balance factors, NbrIs the total number of branches, T is the scheduling period, f2For total power fluctuation, Δ P, of regional energy subsystemsi,H(t)、ΔHi，H(t)、ΔCi,H(t)、ΔQi,H(t) power fluctuations, P, of the regional energy subsystem, i electricity, heat, cold, and natural gas, respectivelyCCHP(t)、PCCS(t) power of CCHP unit and CCS unit, respectively, f3For the total carbon emission, x is the carbon emission coefficient of the CCHP unit, y is the carbon capture coefficient of the CCS unit, Pi，l(t)、Hi,l(t)、Ci，l(t)、Qi，lAnd (t) the power transmission of electricity, heat, cold and natural gas of the ith branch circuit is respectively carried out.
6. The method of claim 5, wherein the regional energy power balance constraint in the pre-established underlying optimization model is calculated as follows:
PPV(t)+PWT(t)+PCCHP(t)+Pesd,d(t)＝Pesd,c(t)+Pi,H(t)+Pi,load(t)
HEB(t)+HCCHP(t)+Hesd,d(t)＝Hesd,c(t)+Hi,H(t)+Hi,load(t)
CEC(t)+CCCHP(t)+Cesd,d(t)＝Cesd,c(t)+Ci,H(t)+Ci,load(t)
QP2G(t)+Qesd,d(t)＝Qesd,c(t)+Qi,H(t)+Qi,load(t)
the branch power constraint calculation formula in the pre-established lower layer optimization model is as follows:
the calculation formula of the upper and lower limit constraints of the output of the equipment units in the pre-established lower-layer optimization model is as follows:
the maximum and minimum value constraint calculation formula of the state of charge in the pre-established lower-layer optimization model is as follows:
the thermal energy storage constraint calculation formula in the pre-established lower-layer optimization model is as follows:
the cold energy storage constraint calculation formula in the pre-established lower-layer optimization model is as follows:
the gas energy storage constraint calculation formula in the pre-established lower layer optimization model is as follows:
in the above formula, Pi,load(t)、Hi,load(t)、Ci,load(t)、Qi,load(t) load requirements for electricity, heat, cold and natural gas of the regional energy subsystem i, respectively; pesd,d(t)、Pesd,c(t) the charge and discharge power of the electric energy storage device at the moment t respectively; hesd,d(t)、Hesd,c(t) the heat charging and discharging powers of the thermal energy storage device at the moment t respectively; cesd,d(t)、Cesd,c(t) the cold charging and discharging power of the cold energy storage device at the moment t respectively; qesd,d(t)、Qesd,c(t) the charging and discharging power, P, of the gas energy storage device at the time tesd,cFor discharge power of electric energy-storage devices, Pi,H(t)、Hi,H(t)、Ci,H(t)、Qi,H(t) the interactive power of electricity, heat, cold and natural gas on the tie line of the regional energy subsystem i at the moment t respectively
The maximum transmission power of electricity, heat, cold and natural gas of the ith branch circuit respectively,
respectively are the upper and lower limits of output of the CCHP unit for generating power,
respectively are the upper and lower limit constraints of the output of the CCS unit,
respectively are the upper and lower limit constraints of the output of the wind turbine,
are respectively a photovoltaic unitThe upper and lower limits of the output force are restricted,
respectively are the upper and lower limit constraints of the output of the electric heating unit,
respectively are the upper and lower limit constraints of the output of the CCHP unit for heating,
respectively are the upper and lower limit constraints of the output of the electric refrigerating unit,
respectively are the upper and lower limit constraints of the output of CCHP unit refrigeration,
respectively the upper and lower output limits of the P2G machine set,
the upper and lower limits of the charging capacity of the energy storage system are set,
is the maximum charge-discharge power of the electrical energy storage system,
the upper and lower limits of the charging heat quantity of the heat energy storage system,
is the maximum heat charging and discharging power of the heat energy storage system,
the upper limit and the lower limit of the charging cold quantity of the heat energy storage system,
is the maximum charging and discharging cold power of the cold energy storage system,
for the upper and lower limits of the charge capacity of the heat energy storage system,
maximum charging and discharging power, SOC, of gas energy storage systemPt、SOCHt、SOCCt、SOCQtThe charging capacity of the electric energy storage system, the charging heat of the heat energy storage system, the charging cold of the cold energy storage system and the charging capacity of the gas energy storage system are respectively.
7. A campus complex energy multipotency coupling optimization system, the system comprising:
the intelligent acquisition module is used for acquiring the output power of each energy subsystem in the region and the load of the park comprehensive energy system;
the edge side is used for preprocessing the acquired load data and uploading the preprocessed data to the cloud center;
the cloud center is used for substituting the output power of each energy subsystem in the region and the load of the park comprehensive energy system into a pre-established upper-layer optimization model, and solving the interactive power instruction value of each energy subsystem in the region;
and the edge side is also used for substituting the load of each energy subsystem in the region, the transmission power of each branch, the output of the wind turbine set, the output of the photovoltaic set and the interactive power instruction value of each energy subsystem into a pre-established lower-layer optimization model, solving the optimal scheduling instruction of the unit equipment and the energy storage system in the region, and sending the optimal scheduling instruction of the unit equipment and the energy storage system in the region to each energy subsystem in the region.
8. A storage means having a plurality of program codes stored therein, wherein said program codes are adapted to be loaded and run by a processor to perform the method of integrated energy multi-energy coupling optimization of a campus as claimed in any one of claims 1 to 6.
9. A control apparatus comprising a processor and a memory device, said memory device adapted to store a plurality of program codes, wherein said program codes are adapted to be loaded and run by said processor to perform the method of campus renewable energy multi-energy coupling optimization of any one of claims 1 to 6.
Priority Applications (1)
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114142460A (en) * | 2021-11-17 | 2022-03-04 | 浙江华云电力工程设计咨询有限公司 | Energy storage double-layer target optimization configuration method and terminal in comprehensive energy system |
| CN114357743A (en) * | 2021-12-22 | 2022-04-15 | 天津大学 | Edge cloud collaborative optimization method and device for regional energy Internet |
| CN117788207A (en) * | 2023-11-29 | 2024-03-29 | 山东正晨科技股份有限公司 | Optimization method of highway comprehensive energy system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114142460A (en) * | 2021-11-17 | 2022-03-04 | 浙江华云电力工程设计咨询有限公司 | Energy storage double-layer target optimization configuration method and terminal in comprehensive energy system |
| CN114142460B (en) * | 2021-11-17 | 2024-03-15 | 浙江华云电力工程设计咨询有限公司 | Energy storage double-layer target optimal configuration method and terminal in comprehensive energy system |
| CN114357743A (en) * | 2021-12-22 | 2022-04-15 | 天津大学 | Edge cloud collaborative optimization method and device for regional energy Internet |
| CN117788207A (en) * | 2023-11-29 | 2024-03-29 | 山东正晨科技股份有限公司 | Optimization method of highway comprehensive energy system |
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