CN114139837A

CN114139837A - Regional multi-system double-layer distributed optimization scheduling method considering double-layer carbon emission optimization distribution model

Info

Publication number: CN114139837A
Application number: CN202210115359.8A
Authority: CN
Inventors: 顾海飞; 刘福建; 孙晓蕾; 车昌盛; 黄庆
Original assignee: China Construction Industrial and Energy Engineering Group Co Ltd
Current assignee: China Construction Industrial and Energy Engineering Group Co Ltd
Priority date: 2022-02-07
Filing date: 2022-02-07
Publication date: 2022-03-04
Anticipated expiration: 2042-02-07
Also published as: CN114139837B

Abstract

The invention provides a regional multi-system double-layer dispersion optimization scheduling method considering a double-layer carbon emission optimization distribution model, which constructs the regional multi-energy system double-layer carbon emission optimization distribution model, wherein an upper-layer multi-energy main system mainly considers a power distribution network, a gas distribution network and a heat distribution network system, real-time carbon emission constraint is formulated based on real-time environment monitoring, historical carbon emission of each multi-energy subsystem in a lower-layer region is decomposed to each multi-energy subsystem in the lower-layer region in real time, and each multi-energy subsystem in the lower-layer region needs to meet the real-time carbon emission constraint while being optimized; and finally, solving a double-layer dispersion optimization scheduling model of the multi-energy system in the region between the upper layer and the lower layer by adopting an improved target cascade analysis method. The method can reduce the total network loss of the upper distribution network system of the regional multi-energy system, reduce the operation cost of each energy subsystem in the lower region, and determine the optimal distribution scheme of the whole carbon emission of the regional multi-energy system.

Description

Regional multi-system double-layer distributed optimization scheduling method considering double-layer carbon emission optimization distribution model

Technical Field

The invention belongs to the technical field of operation management of power systems, and particularly relates to a regional multi-system double-layer distributed optimization scheduling method considering a double-layer carbon emission optimization distribution model.

Background

At present, China is still in the middle and later stages of rapid urbanization development, and the energy consumption and carbon emission of various domestic areas continue to increase every year. However, with the increasing energy demand and the worsening of ecological environment problems, regional multi-energy systems are showing new trends. The traditional multi-energy system planning and operation are limited to single energy forms of electricity, natural gas, cold, heat and the like, and complementary advantages and synergistic benefits of the multi-energy systems in regions cannot be fully exerted.

Compared with a single energy system, the regional multi-energy system mainly comprises a comprehensive energy supply and terminal use system, such as a regional power, natural gas and heat distribution system, and coordinated optimization operation of multiple types of energy can be realized by interconnecting terminals of various multi-energy subsystems. In addition, the regional multi-energy system can effectively promote the coordination and complementary operation of various urban energy sources, and fully exert the potential advantages of different types of energy subsystems. Meanwhile, various energy types are comprehensively considered, and the comprehensive energy consumption of the regional multi-energy system can be greatly reduced. Therefore, research on regional multi-energy systems is of great significance to energy conservation and emission reduction in various regions in China.

Multi-energy systems that allow for different subjects exhibit a diverse distribution across various levels and regions. Currently, many researches focus on the influence of models, strategies, demand response or comprehensive demand response mechanism designs, methods and the like on the optimized operation of regional multi-energy systems. In addition, researchers have also studied operational factors that affect carbon emissions during the optimized operation of regional multi-energy systems.

However, in recent years, each region in china has put forward a series of strict requirements for the double-carbon emission reduction policy. Although some scholars consider carbon transaction schemes or real-time energy efficiency indexes in an objective function of an economic dispatching model, low-carbon equipment such as electricity-to-gas equipment and ground source heat pumps is introduced, and low-carbon emission reduction technologies such as a given carbon emission total quota index are provided. However, in the past research, the design of a double-carbon optimal distribution mechanism of a regional multi-energy system comprising a plurality of energy subsystems and a specific distribution network model is less considered, and the influence of the double-carbon emission optimal distribution model of the specific analysis regional multi-energy system on the specific distribution network loss and the economic operation cost of each energy subsystem in the double-layer dispersion coordination optimal scheduling process is also ignored.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a regional multi-system double-layer dispersion optimization scheduling method considering a double-layer carbon emission optimization distribution model, which effectively reduces the total network loss of an upper distribution network system of a regional multi-energy system, reduces the operation cost of each energy subsystem in a lower region and can determine the optimal distribution scheme of the whole carbon emission of the regional multi-energy system.

The present invention achieves the above-described object by the following technical means.

A regional multi-system double-layer dispersion optimization scheduling method considering a double-layer carbon emission optimization distribution model comprises the following steps:

step 1: establishing an upper-layer multi-energy main system optimization model comprising an upper-layer optimization objective function and upper-layer optimization constraint conditions;

step 2: establishing an optimization model of each multi-energy subsystem in the lower layer area, wherein the optimization model comprises a lower layer optimization objective function and a lower layer optimization constraint condition;

and step 3: establishing a double-layer carbon emission optimization distribution model between the multi-energy subsystems in the upper-layer multi-energy main system and the lower-layer area, and decomposing a total carbon emission constraint value of the area multi-energy system to the multi-energy main system of the upper-layer power distribution network, the distribution network and the heat distribution network and the multi-energy subsystems in the lower-layer area on the basis of real-time environment detection;

and 4, step 4: inputting a total carbon emission constraint value distribution parameter of the regional multi-energy system, an upper region multi-energy main system and a lower region equipment output parameter of each multi-energy subsystem; setting initial values of coupling variables of a regional multi-energy system, and parameter values and iteration initial values of various multipliers of an improved target cascade analysis method;

and 5: respectively solving the optimized scheduling problem of each multi-energy subsystem in the lower layer area, transmitting the solved coupling variable to the multi-energy main system of the power distribution network, the gas distribution network and the heat distribution network in the upper layer area for optimization, and transmitting the optimized real-time carbon emission of each multi-energy subsystem in the lower layer area to the multi-energy main system in the upper layer area so as to update and decompose the carbon emission constraint value of each multi-energy subsystem in the lower layer area;

step 6: after receiving all variables transmitted by each multi-energy subsystem in the lower layer area, the multi-energy main system of the upper layer area comprises a power distribution network, a gas distribution network and a heat distribution network, respectively solving the optimization problem of the multi-energy main system of the upper layer area, and transmitting the optimized real-time carbon emission amount of the multi-energy main system of the power distribution network, the gas distribution network and the heat distribution network to the multi-energy main system of the upper layer area so as to update the carbon emission constraint value decomposed to the multi-energy main system of the upper layer area;

and 7: checking the convergence condition of the distributed coordination optimization algorithm between the multi-energy subsystems in the upper-layer multi-energy main system and the lower-layer area, stopping iteration when the convergence criterion is met, respectively outputting the optimal scheduling results of the multi-energy subsystems in the upper-layer multi-energy main system and the lower-layer area, updating the multiplier parameter values of the improved target cascade analysis method when the convergence criterion is not met, and returning to the step 5 to continue iterative solution.

Further, in the step 1, an upper layer optimization objective function of the upper layer multi-energy-source main system is established with the goal of minimizing the network loss of the power distribution network, the gas distribution network and the heat distribution network:

wherein the content of the first and second substances,

an optimization objective function for the distribution network;

for the distribution network

Time interval circuit

The current of (a);

for branch of distribution network

The resistance of (1);

scheduling the period of the cycle for the day ahead;

the number of nodes of the power distribution network;

the number of branches of the power distribution network;

an optimization objective function for the gas distribution network;

for distributing gas in

Time interval pressurizer consumptionThe natural gas flow rate of (c);

is a natural gas pipeline

Average flow over a period of time;

branch of pressurizer for gas distribution network

The natural gas pipeline damping coefficient of (1);

the number of the nodes for installing the pressurizers on the branch of the gas distribution pipe network;

the number of the distribution pipe network branches is;

an optimized objective function for the heat distribution network;

for heat distribution net at

Time interval pipeline

The thermal power of (3);

for heat distribution net at

Time interval pipeline

Heat power transmission ofA loss coefficient of transmission;

the number of nodes of the heat distribution pipe network;

the number of branches of the heat distribution pipe network.

Further, the upper layer optimization constraints in step 1 include:

node power and voltage equality/inequality constraints of the power distribution network:

wherein the content of the first and second substances,

for the distribution network

Time interval node

The active power injected;

for the distribution network

Time interval circuit

Active power of (d);

for the distribution network

Time interval node

The reactive power injected;

for the distribution network

Time interval circuit

The reactive power of (c);

for the distribution network

The time interval can be scheduled to output power of a coal-fired or diesel generator set;

for the distribution network

Time interval nodes

Active power load of (2);

for the electrical energy storage of the distribution network side

A charging power of a period;

for the electrical energy storage of the distribution network side

Discharge power of a time period;

for the distribution network

Time period slave node

The active power transmitted to each multi-energy subsystem of the lower layer area;

representing a multi-energy subsystem in an underlying region;

the number of each multi-energy subsystem in the lower layer area is collected;

for the distribution network

Segment line

The current of (a);

for branch of distribution network

The resistance of (1);

for branch of distribution network

A reactance of (d);

the voltage amplitude lower limit value of each node of the power distribution network is set;

the voltage amplitude upper limit value of each node of the power distribution network is obtained;

for branch of distribution network

An apparent capacity upper limit value of (d);

the apparent capacity of the distribution network is restricted in a variation range;

is a node

The square of the voltage of (c);

is a node

The square of the voltage of (c);

the capacity equality/inequality constraint of the electric energy storage equipment on the side of the power distribution network is as follows:

wherein the content of the first and second substances,

for the storage battery on the distribution network side

A storage capacity of the time period;

self-discharge efficiency of the storage battery device on the distribution network side;

the charging efficiency of the storage battery device on the power distribution network side;

the discharge efficiency of the storage battery device on the distribution network side is obtained;

、

are binary 0-1 variables;

the maximum charge rate of the storage battery device is set;

the maximum discharge rate of the storage battery device;

storing energy for the accumulator means;

and

respectively the minimum rated storage capacity and the maximum rated storage capacity allowed by the storage battery device;

the power distribution network can schedule the climbing output inequality constraint of the coal/diesel engine set:

wherein the content of the first and second substances,

、

upper limit climbing force restraint system for coal-fired and diesel generator set respectivelyCounting and limiting the lower limit climbing force constraint coefficient; subscript

Subscript of 1 or 3 for coal-fired unit

When the number is 2, the diesel engine group is represented;

the transmission power inequality constraints of each multi-energy subsystem in the power distribution network and the park are as follows:

wherein the content of the first and second substances,

and

are respectively a distribution network

The maximum value and the minimum value of the transmission power of the tie lines between the time interval and each multi-energy subsystem in the park;

based on real-time environment detection, the carbon emission constraint of a coal-fired/diesel generator set can be scheduled by a power distribution network in the upper-layer multi-energy main system:

wherein the content of the first and second substances,

、

the carbon emission intensity coefficients of the generated energy per unit time of the coal-fired generator set and the diesel generator set are respectively;

、

the upper limit value of the carbon emission constraint which can be relaxed is respectively the coal-fired generator set and the diesel generator set;

、

respectively optimizing the carbon emission of a coal-fired generator set and a diesel generator set in real time;

scheduling the period of the cycle for the day ahead; all superscripts

The numbers of the recorded data of the multi-energy main system in the upper layer area and the multi-energy subsystems in the lower layer area in the iterative optimization process are shown.

Further, the upper layer optimization constraints in step 1 include:

the power balance equality and inequality constraint of the distribution network node are as follows:

wherein the content of the first and second substances,

for distributing gas in

The gas flow output by the natural gas source point of the time period;

and

respectively being natural gas pipelines

In that

Inlet gas flow and outlet gas flow for a time period;

representing a multi-energy subsystem in an underlying region;

the number of each multi-energy subsystem in the lower layer area is collected;

for distributing gas in

Time period slave node

Transmitting the natural gas pipeline transmission flow to each multi-energy subsystem in the lower layer region;

for natural gas pipeline joint

In that

Natural gas load flow over a period of time;

for distributing gas in

Natural gas flow consumed by the pressurizer over time;

and

are respectively a gas distribution net

The upper limit value and the lower limit value of the gas flow output by the gas source point in the time interval;

the natural gas pressurizer device is constrained in an inequality way:

wherein the content of the first and second substances,

branch of pressurizer for gas distribution network

The natural gas pipeline damping coefficient of (1);

is a natural gas pipeline

Average flow over a period of time;

is a natural gas pipeline

Time interval node

The pressure of (a);

is the compression factor of the pressurizer;

is a natural gas pipeline

Time interval node

The pressure of (a);

natural gas pipeline flow equality and inequality constraints:

wherein the content of the first and second substances,

is a natural gas pipe section

In that

Gas flow direction of the time period;

representing a pipe section

In that

Pipe constant of time interval, its value and length of pipe section

And diameter

(ii) related;

and

respectively natural gas pipe section

In that

A flow upper limit constraint value and a flow lower limit constraint value of a time period;

and

are respectively natural gas nodes

Upper and lower pressure limits of (a);

is the natural gas flow index;

the gas network dynamic characteristic equality and inequality constraint:

wherein the content of the first and second substances,

is a natural gas pipeline

In that

Managing and storing time intervals;

and

are respectively pipelines

In that

Managing an upper limit constraint value and a lower limit constraint value of the time period;

is a pipe

In that

The initial value of the time interval is managed;

is a pipe

Natural gas pipe inventory after a cycle of operation;

is a natural gas pipeline

A set of (a);

scheduling the period of the cycle for the day ahead;

the transmission power inequality constraint of the connecting lines between the gas distribution network and each multi-energy subsystem in the lower layer area is as follows:

wherein the content of the first and second substances,

and

are respectively a gas distribution net

And the upper limit flow value and the lower limit flow value of the transmission flow of the communication pipeline between the time interval and each multi-energy subsystem in the lower layer area.

Further, the upper layer optimization constraints in step 1 include:

and (3) carrying out constraint of a power balance equation of the heat distribution network nodes:

wherein the content of the first and second substances,

for heat distribution net at

Time interval node

The thermal power of the injection;

for heat distribution net at

Time interval pipeline

The thermal power of (3);

for heat distribution net at

Time interval nodes

Thermal power load of (2);

for heat distribution net at

Time interval node

The heating power of (a);

for heat distribution net at

Transmitting power to hot water pipelines of each multi-energy subsystem in the lower layer region in a time interval;

for heat distribution net at

Time interval pipeline

Thermal power transmission loss coefficient of (1);

the heat source point output power is constrained by an inequality:

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

Outputting an upper limit value and a lower limit value of thermal power by a heat source point in a time interval;

the transmission power inequality constraint of the heat distribution network pipeline is as follows:

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

Time interval circuit

The upper limit value and the lower limit value of the thermal power flow;

the inequality constraint of the transmission power of the communication pipeline between the heat distribution network and each multi-energy subsystem in the lower layer area is as follows:

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

The time interval is respectively transmitted with the hot water pipeline between the multi-energy subsystems in the lower layer areaAn upper thermal power limit and a lower thermal power limit.

Further, in the step 2, a lower-layer optimization objective function is established for the optimization objective with the minimum economic operation cost of each multi-energy subsystem in the lower-layer region

Comprises the following steps:

wherein the content of the first and second substances,

is the first in the lower layer region

The economic operating costs of the plurality of multi-energy subsystems;

is the first in the lower layer region

Gas acquisition costs for micro gas turbines within the multiple energy subsystems;

is the first in the lower layer region

Photovoltaic, fan, micro gas turbine, accumulator, gas storage tank, cold storage tank and heat storage tank in multiple multi-energy subsystems

Total operating cost for a time period;

the number of each multi-energy subsystem in the lower layer area is collected;

wherein the content of the first and second substances,

is the price of natural gas;

is the low calorific value of natural gas;

electricity production efficiency of consuming natural gas for the micro gas turbine;

the heat production efficiency of natural gas consumed by the boiler;

the number of devices for micro gas turbines;

is as follows

Multiple multi-energy subsystem micro gas turbines in

An output power of the time period;

for each multi-energy subsystem in the lower layer region, the gas boiler is arranged

Thermal power input in a time period;

wherein the content of the first and second substances,

is the first in the lower layer region

The total operating costs of the photovoltaic, the fan, the micro gas turbine and the energy storage equipment in the multiple energy subsystems;

、

、

、

the running costs of the photovoltaic power generation system, the fan, the micro gas turbine and the energy storage equipment are respectively calculated;

、

、

、

、

、

、

respectively photovoltaic, fan, micro gas turbine, storage battery, gas storage tank, cold storage tank and heat storage tankAn operating cost parameter;

、

respectively a photovoltaic power and a fan

An output power of the time period;

、

、

、

respectively comprises a storage battery, a gas storage tank, a cold accumulation tank and a heat accumulation tank

The power of the absorbed energy of the time period;

、

、

、

The power of the released energy of the time period.

Further, in the step 2, the lower layer optimization constraint conditions include power balance constraints of power, natural gas, cold energy, heat energy and waste heat flue gas buses of each multi-energy subsystem in the lower layer region:

wherein the content of the first and second substances,

、

respectively a photovoltaic power and a fan

An output power of the time period;

is as follows

Multiple multi-energy subsystem micro gas turbines in

An output power of the time period;

the number of devices for micro gas turbines;

is a storage battery device

Power of the released energy over a period of time;

is a storage battery device

The power of the absorbed energy of the time period;

、

respectively comprises an electric heating device and an electric refrigerating device in each multi-energy subsystem in the lower layer area

An electrical power load consumed over a period of time;

、

、

、

respectively distribute power, gas, heat and cold in the upper region

Transmitting the power to each multi-energy subsystem in the lower layer region in a time interval;

、

、

、

respectively, the multi-energy subsystems in the lower layer area

Electrical, gas, hot, cold power loads for a period of time;

、

、

respectively comprises an air storage tank, a cold accumulation tank and a heat accumulation tank

Power of the released energy over a period of time;

for each multi-energy subsystem in the lower layer region, the heat converter is arranged

Thermal power load consumed over a period of time;

for the waste heat boiler in each multi-energy subsystem in the lower layer area

The waste heat power output in time intervals;

、

、

、

the devices of the electric refrigeration, the electric heating, the absorption refrigeration and the heat converter in each multi-energy subsystem in the lower layer area are respectively arranged

An output power of the time period;

Thermal power output in time intervals;

and

respectively providing a conventional electric load and an adjustable electric load for each multi-energy subsystem terminal in a lower layer area;

、

、

adjustable power, gas and cold loads for the virtual energy output power increase participating in the comprehensive demand response plan respectively;

、

、

respectively outputting adjustable electricity, gas and cold loads of the power reduction for the virtual energy participating in the comprehensive demand response plan;

and

respectively a conventional gas load and an adjustable gas load;

and

respectively a conventional cold load and an adjustable cold load;

、

respectively a temperature control type hot air load and a flexible hot water supply type load;

converting factors for waste heat power generation;

for each multi-energy subsystem terminal in the lower layer area

Electrical loading of the time period.

Further, in step 2, the lower optimization constraint condition includes an energy balance constraint between energy supply devices in each multi-energy subsystem in the lower zone:

wherein the content of the first and second substances,

、

、

、

An output power of the time period;

、

An electrical power load consumed over a period of time;

is an absorption type refrigerating device in each multi-energy subsystem in the lower layer area

An input power of the time period;

、

、

、

the conversion efficiency of the electric refrigeration, electric heating, absorption refrigeration and heat converter devices respectively;

is left forThermal power generation conversion factor.

Further, in step 2, the lower optimization constraints include energy coupling constraints between energy supply devices in the multi-energy subsystems in the lower region:

wherein the content of the first and second substances,

for the micro gas turbine in each multi-energy subsystem in the lower layer area

An output power of the time period;

the thermoelectric ratio of the micro gas turbine in each multi-energy subsystem in the lower layer area is obtained;

The waste heat power input in time intervals;

An output power of the time period;

Thermal power input in a time period;

the efficiency of converting natural gas consumed by the micro gas turbine in each multi-energy subsystem in the lower layer area into electricity;

the heat conversion efficiency of the gas boilers in each multi-energy subsystem in the lower layer area is obtained;

the heat conversion efficiency of the waste heat boiler in each multi-energy subsystem in the lower layer area is obtained;

Thermal power output in time intervals;

The waste heat power output in time intervals.

Further, in the step 2, the lower layer optimization constraint condition includes the equation and inequality constraints of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank device in each multi-energy subsystem in the lower layer region:

wherein the content of the first and second substances,

、

、

、

the self-discharge efficiency coefficients of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank device are respectively;

、

、

、

the charging efficiency coefficients of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank device are respectively;

、

、

、

energy release efficiency coefficients of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank device are respectively set;

、

、

、

respectively arranged on a storage battery, an air storage tank, a cold storage tank and a heat storage tank

A storage capacity of the time period;

、

、

、

respectively comprises a storage battery, a gas storage tank, a cold accumulation tank and a hot accumulation tank

(ii) a charging power over a period of time;

、

、

、

Discharge power of a time period;

the maximum charge multiplying power of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank is obtained;

the maximum discharge multiplying power of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank is obtained;

、

are binary 0-1 variables;

is arranged in a storage battery, a gas storage tank, a cold storage tank and a heat storage tank

A minimum stored energy for a time period;

Maximum stored energy for a time period;

is rated capacity.

Further, in step 2, the lower optimization constraint condition includes inequality constraints of each energy supply device in each multi-energy subsystem in the lower zone:

wherein the content of the first and second substances,

and

respectively a micro gas turbine in each multi-energy subsystem in the lower layer area

An upper limit value and a lower limit value of output power of a time period;

and

respectively an upper limit climbing force constraint coefficient and a lower limit climbing force constraint coefficient of the micro gas turbine;

、

、

、

respectively comprises a waste heat boiler, a gas boiler, a photovoltaic device and a wind power device in each multi-energy subsystem in the lower layer area

An upper limit value of output power of the time period;

、

、

、

A lower limit value of the output power of the period.

Further, in step 2, the lower layer optimization constraint condition includes inequality constraints of energy conversion devices in the multi-energy subsystems in the lower layer region:

wherein the content of the first and second substances,

、

、

、

An upper limit value of the period output power;

、

、

、

A lower limit value of the period output power.

Further, in the step 2, the lower-layer optimization constraint condition includes inequality constraint of tie line transmission power between each multi-energy subsystem in the lower-layer region and each distribution network in the upper-layer region:

wherein the content of the first and second substances,

、

、

、

respectively, the multi-energy subsystems in the lower layer area

And the upper limit of the transmission power of the connecting line between the time interval and the distribution network, the heat distribution network and the cold distribution network in the upper layer area.

Further, in step 2, the lower layer optimization constraint condition includes a carbon emission constraint of each multi-energy subsystem in the lower layer region, which is obtained based on real-time environment detection:

wherein the content of the first and second substances,

the comprehensive energy supply carbon emission intensity coefficient of the micro gas turbine, the waste heat boiler and the gas boiler device in each multi-energy subsystem in the lower layer area in unit time is obtained;

is as follows

Multiple multi-energy subsystem micro gas turbines in

An output power of the time period;

the number of devices for micro gas turbines;

scheduling the period of the cycle for the day ahead;

The waste heat power input in time intervals;

Thermal power output in time intervals;

Thermal power input in a time period; all superscripts

Both show the iterative optimization of the multi-energy main system in the upper layer area and each multi-energy subsystem in the lower layer areaThe reference numbers of the data recorded during the process,

the upper limit value of the total carbon emission quota of each multi-energy subsystem in the lower layer area is restricted,

optimizing real-time carbon emissions for operation of the various multi-energy subsystems in the lower region, wherein the subscripts

Indicating that the lower zone region has 3 multi-energy subsystems; and the third line and the fourth line respectively represent that the real-time carbon emission of each multi-energy subsystem in the lower layer area is calculated uniformly after optimization, and the total carbon emission quota constraint upper limit value of each multi-energy subsystem in the lower layer area is transmitted to the upper layer area.

Further, in step 2, the lower-layer optimization constraint conditions include constraints of movable electrical, gas and cold loads of each multi-energy subsystem terminal in the lower-layer region:

wherein the content of the first and second substances,

scheduling the period of the cycle for the day ahead;

/

、

/

、

/

the maximum change power allowed by movable electric, gas and cold loads of each multi-energy subsystem terminal in the lower layer area is respectively;

、

、

、

、

、

are binary variables from 0 to 1.

Further, in step 2, the lower layer optimization constraint condition includes constraint of a flexible indoor air conditioning power equivalent model of each multi-energy subsystem terminal user in the lower layer region:

wherein the content of the first and second substances,

is composed of

Predicted cold/heat load demand power at a time;

for each multiple energy source in the lower zoneIs sub-system in

Cold or hot power supplied to the end user at any time;

is composed of

The comfortable temperature of the indoor of each multi-energy subsystem terminal user in the lower layer area is measured at the moment;

and

the indoor and outdoor air temperature of each multi-energy subsystem terminal user in the lower layer area is measured;

equivalent thermal resistance of a building where each multi-energy subsystem terminal user is located in a lower layer area;

and

respectively obtaining the minimum value and the maximum value allowed by the indoor temperature fluctuation change of the building where each multi-energy subsystem terminal user is located in the lower layer area;

、

respectively are the fluctuation parameters of the cold load and the heat load;

the period of the cycle is scheduled for the day ahead.

Further, in the step 2, the lower-layer optimization constraint condition includes constraint of an indoor flexible hot water supply type load power equivalent model of the building where the terminal users of each multi-energy subsystem in the lower-layer region are located:

wherein the content of the first and second substances,

demand power for the predicted hot water load;

is a hot water parameter;

is composed of

The cold water storage capacity at the moment;

is composed of

The storage temperature of the hot water at any moment;

is composed of

The temperature of cold water replacing hot water at any moment;

is composed of

The hot water quantity required to be provided by each multi-energy subsystem terminal user in the lower layer area at any time;

and

all are hot water load fluctuation coefficients;

is composed of

The hot water comfort temperature value of the indoor of the building where each multi-energy subsystem terminal user is located in the lower layer area at the moment;

and

are respectively as

Minimum and maximum allowable hot water storage temperatures;

the period of the cycle is scheduled for the day ahead.

Further, the specific process of step 3 is as follows:

wherein the content of the first and second substances,

the constraint value is a total carbon emission constraint value of the regional multi-energy system, and the constraint value is iteratively updated according to the actual carbon emission optimized by each multi-energy subsystem in the lower region and the power distribution network system in the upper region multi-energy main system in the double-layer optimization process;

for each multi-energy subsystem in the lower region

（

) A total carbon emission allowance constraint upper limit value;

、

respectively determining the upper limit value of carbon emission constraint of a power distribution network system in the upper-layer multi-energy main system and each multi-energy subsystem in the lower-layer area for the whole area;

the number of each multi-energy subsystem in the lower layer area is collected.

Further, a specific method for solving the complex regional multi-energy system double-layer dispersion coordination optimization scheduling model by adopting the improved target cascade analysis method is as follows:

respectively converting the objective functions of the upper-layer power distribution network, the gas distribution network and the heat distribution network system into the following forms:

wherein the content of the first and second substances,

、

、

respectively taking the transformed target functions of the distribution network, the distribution network and the heat distribution network system in the upper layer area;

、

、

respectively as target functions of the upper power distribution network, the gas distribution network and the heat distribution network system;

、

、

respectively, the multi-energy subsystems in the lower layer area

Connecting lines among a power distribution network, a gas distribution network and a heat distribution network system in the multi-energy main system in the time interval and the upper region transmit power auxiliary coupling variables;

、

、

the system of a power distribution network, a gas distribution network and a heat distribution network in the multi-energy main system of the upper layer respectively is arranged

Connecting lines between the time interval and each multi-energy subsystem in the lower layer region transmit power coupling variables;

、

、

the target functions of the power distribution network, the gas distribution network and the heat distribution network are respectively

A first order multiplier of a period lagrange penalty function;

、

、

A quadratic multiplier of a period lagrange penalty function;

converting the objective function of each multi-energy subsystem in the lower zone area into the following form:

wherein the content of the first and second substances,

representing a multi-energy subsystem in an underlying region;

is the lower layer regionThe number of each multi-energy subsystem is integrated;

the period of the cycle is scheduled for the day ahead.

Further, in step 7, as a convergence condition, whether the difference between the coupling variable optimized by the upper/lower layer system and the objective function in the iterative optimization process meets the precision requirement is specifically as follows:

wherein the content of the first and second substances,

、

、

respectively representing the convergence precision of the coupling variable difference and the target function difference between the multi-energy main system in the upper layer area and each multi-energy subsystem in the lower layer area; all superscripts

The marks of the recorded data of the multi-energy main system in the upper layer area and the multi-energy subsystems in the lower layer area in the iterative optimization process are both represented;

representing a multi-energy subsystem in an underlying region;

when the convergence criterion is not met, updating each multiplier parameter value of the improved target cascade analysis method according to the following formula, namely updating the quadratic term multiplier parameter value of the Lagrange penalty function:

wherein the content of the first and second substances,

the value is 2.5; the value of the lagrange penalty function multiplier is uniformly set to 1.5.

The invention has the following beneficial effects:

the invention constructs a regional multi-energy system double-layer carbon emission optimization distribution model for the first time, an upper region multi-energy main system mainly considers a power distribution network, a gas distribution network and a heat distribution network system, real-time carbon emission constraints are formulated based on real-time environment monitoring, historical carbon emission of each multi-energy subsystem in a lower region is decomposed in real time to each multi-energy subsystem in the lower region, and each multi-energy subsystem in the lower region needs to meet the real-time carbon emission constraints while being optimized; and finally, solving a double-layer dispersion optimization scheduling model of the multi-energy system in the region between the upper layer and the lower layer by adopting an improved target cascade analysis method. The method can reduce the total network loss of the upper distribution network system of the regional multi-energy system, reduce the operation cost of each energy subsystem in the lower region, and determine the optimal distribution scheme of the whole carbon emission of the regional multi-energy system.

Drawings

Fig. 1 is a flowchart of a regional multi-system double-layer decentralized optimization scheduling method considering a double-layer carbon emission optimization distribution model.

Detailed Description

The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.

The regional multi-system double-layer distributed optimal scheduling method considering the double-layer carbon emission optimal allocation model is shown in fig. 1 and specifically comprises the following steps:

step 1: establishing an upper-layer multi-energy main system optimization model, wherein the upper-layer multi-energy main system optimization model comprises an upper-layer optimization objective function and an upper-layer optimization constraint condition;

the minimum network loss of a power distribution network, a gas distribution network and a heat distribution network is considered as a target, so that three upper-layer optimization objective functions of the upper-layer multi-energy-source main system are respectively as follows:

the power distribution network optimization objective function is as follows:

wherein the content of the first and second substances,

an optimization objective function for the distribution network;

for the distribution network

Time interval circuit

Current in units of a;

for branch of distribution network

Resistance in units ofohms；

The period of the scheduling cycle is day ahead, and the unit ish；

The number of nodes of the power distribution network;

the number of branches of the power distribution network;

the optimization objective function of the gas distribution network is as follows:

wherein the content of the first and second substances,

an optimization objective function for the gas distribution network;

for distributing gas in

Natural gas flow rate in m consumed by the time interval pressurizer³/h；

Is a natural gas pipeline

Average flow rate in m of time interval³/h；

Branch of pressurizer for gas distribution network

The damping coefficient of the natural gas pipeline is usually 0.06-0.10;

the number of the distribution pipe network branches is;

the optimization objective function of the heat distribution network is as follows:

wherein the content of the first and second substances,

an optimized objective function for the heat distribution network;

for heat distribution net at

Time interval pipeline

The unit of the thermal power of (A) is kW;

for heat distribution net at

Time interval pipeline

The thermal power transmission loss coefficient is 95% in unit;

the number of nodes of the heat distribution pipe network;

the number of branches of the heat distribution pipe network.

The upper-layer optimization constraint condition comprises equality and inequality constraints of a power distribution network system in the upper-layer multi-energy main system, and the equality and inequality constraints of the power distribution network system in the upper-layer multi-energy main system comprise: node power and voltage equality/inequality constraint of a power distribution network, capacity equality/inequality constraint of electric energy storage equipment at the side of the power distribution network, gradable power-saving/power-saving unit climbing output inequality constraint of the power distribution network, junctor transmission power inequality constraint between the power distribution network and each multi-energy subsystem in a park and scalable power-saving/power-saving unit carbon emission constraint of the power distribution network;

the node power and voltage equality/inequality constraints of the power distribution network are as follows:

wherein the content of the first and second substances,

for the distribution network

Time interval node

The unit of the active power is kW;

for the distribution network

Time interval circuit

Active power of (a) in kW;

for the distribution network

Time interval node

The unit of the injected reactive power is kW;

for the distribution network

Time interval circuit

The unit of the reactive power of (a) is kW;

for the distribution network

The output power of the coal-fired or diesel generating set which can be dispatched in time intervals is kW;

for the distribution network

Time interval nodes

The unit of active power load of (a) is kW;

for the electrical energy storage of the distribution network side

The unit of charging power of a time interval is kW;

for the electrical energy storage of the distribution network side

The discharge power of the time period is kW;

for the distribution network

Time period slave node

The unit of active power transmitted to each multi-energy subsystem of the lower layer area is kW;

the number of each multi-energy subsystem in the lower layer area is collected;

for branch of distribution network

Reactance in ohms;

the unit is the lower limit value of the voltage amplitude of each node of the power distribution network and is kV;

the unit is the upper limit value of the voltage amplitude of each node of the power distribution network and is kV;

for branch of distribution network

The apparent capacity upper limit value of (1) in kVA;

is a node

The square of the voltage of (c);

is a node

Square of the voltage of (c).

The electrical energy storage equipment capacity equality/inequality constraint on the distribution network side is as follows:

wherein the content of the first and second substances,

for the storage battery on the distribution network side

Storage capacity of a time period in kWh;

the unit of the energy stored by the accumulator device is kWh;

and

respectively the minimum rated storage capacity and the maximum rated storage capacity allowed by the storage battery device, wherein the unit is kWh;

、

both binary 0-1 variables are used for representing the charging and discharging states of the storage battery device;

the maximum charge rate of the storage battery device is set;

the maximum discharge rate of the storage battery device.

The power distribution network dispatchable coal/diesel engine unit climbing output inequality constraint is as follows:

wherein the content of the first and second substances,

、

the upper limit climbing output constraint coefficient and the lower limit climbing output constraint coefficient of the coal-fired and diesel generator sets are respectively, and the unit is kW/min; when subscript

When 1 or 3, represents a coal-fired unit, subscript

When the number is 2, the diesel engine group is represented.

The inequality constraint of the transmission power of the tie line between the power distribution network and each multi-energy subsystem in the park is as follows:

wherein the content of the first and second substances,

and

are respectively prepared asIn the electric network

The maximum value and the minimum value of the transmission power of the tie lines between the time interval and each multi-energy subsystem in the park are kW.

Based on real-time environment detection, the carbon emission constraint of the power distribution network in the upper-layer multi-energy main system capable of scheduling coal-fired/diesel generator sets is as follows:

wherein the content of the first and second substances,

、

the carbon emission intensity coefficients of the generated energy per unit time of the coal-fired generator set and the diesel generator set are respectively, and the unit is t/kWh;

、

the upper limit values of the carbon emission constraint which can be relaxed of the coal-fired generator set and the diesel generator set are respectively, and the unit is t;

、

the carbon emission is respectively the real-time optimized carbon emission of a coal-fired power generator set and a diesel power generator set, and the unit is t/h. The third line and the fourth line of the constraint formula are respectively a coal-fired unit and a diesel unit which can be dispatched by a power distribution network in the upper-layer multi-energy main system, and the real-time carbon emission amount of the coal-fired unit and the diesel unit is calculated and transmitted to the upper-layer multi-energy main system after optimization. All superscripts in this example

The upper-layer optimization constraint condition further comprises equality and inequality constraints of the gas distribution network system in the upper-layer multi-energy-source main system, and the equality and inequality constraints of the gas distribution network system in the upper-layer multi-energy-source main system comprise: the system comprises a gas distribution network node power balance equality and inequality constraint, a natural gas pressurizer device inequality constraint, a natural gas pipeline flow equality and inequality constraint, a gas network dynamic characteristic equality and inequality constraint, and a connecting line transmission power inequality constraint between a gas distribution network and each multi-energy subsystem in a lower layer region;

the power balance equation and inequality constraint of the distribution network node are as follows:

wherein the content of the first and second substances,

for distributing gas in

The unit of the gas flow output by the natural gas source point of the time interval is m³/h；

For natural gas pipeline joint

In that

Natural gas load flow rate in m³/h；

And

respectively being natural gas pipelines

In that

The gas flow at the inlet and the gas flow at the outlet of the time interval are both m³/h；

For distributing gas in

Time period slave node

The unit of the transmission flow of the natural gas pipeline transmitted to each multi-energy subsystem in the lower layer area is m³/h；

And

are respectively a gas distribution net

The upper limit value and the lower limit value of the output gas flow of the gas source point in the time interval are m³/h。

The natural gas pressurizer device has inequality constraints as follows:

wherein the content of the first and second substances,

is a natural gas pipeline

Time interval node

Pressure of (d) in bar;

the compression coefficient of the pressurizer, namely the boosting proportion of the pressurizer, is 1.2;

is a natural gas pipeline

Time interval node

The pressure of (2) is in bar.

The natural gas pipeline flow equality and inequality constraints are as follows:

wherein the content of the first and second substances,

representing a pipe section

In that

The pipe constant of the time interval, the value of the parameter and the length of the pipe section

(in m) and diameter

(in mm) are related;

and

respectively natural gas pipe section

In that

The unit of the upper limit of flow and the lower limit of flow of the time interval is m³/h；

Is a natural gas pipe section

In that

The gas flow direction of the time interval, the invention fixes the flow direction of the gas in the pipeline by fixing the pressure difference of the nodes in the optimization process;

the natural gas flow index is approximately 2 in a low-pressure pipe network;

and

are respectively natural gas nodes

The upper and lower pressure limits of (2) are in bar.

The gas network dynamic characteristic equality and inequality constraint is as follows:

wherein the content of the first and second substances,

is a natural gas pipeline

In that

Time period in m³/h；

Is a pipe

In that

The time interval has an upper limit constraint value in m³/h；

Is a pipe

In that

A lower bound value in m for the period of time³/h；

Is a pipe

In that

The initial value of the time interval is stored in m³/h；

Is a pipe

Natural gas tube inventory after a cycle of operation in m³/h；

Is a natural gas pipeline

A collection of (a).

The inequality constraint of the junctor transmission power between the gas distribution network and each multi-energy subsystem in the lower layer area is as follows:

wherein the content of the first and second substances,

and

are respectively a gas distribution net

The upper limit flow value and the lower limit flow value of the transmission flow of the communication pipeline between the multi-energy subsystems in the time interval and the lower layer area are m³/h。

The upper-layer optimization constraint condition further includes the equation and inequality constraint of the heat distribution network in the upper-layer multi-energy-source main system, and the equation and inequality constraint of the heat distribution network in the upper-layer multi-energy-source main system further includes: the heat distribution network node power balance equality constraint, the heat source point output power inequality constraint, the heat distribution network pipeline transmission power inequality constraint and the connection pipeline transmission power inequality constraint between the heat distribution network and each multi-energy subsystem in the lower layer area;

the power balance equality constraint of the heat distribution network node is as follows:

wherein the content of the first and second substances,

for heat distribution net at

Time interval node

The unit of the injected thermal power is kW;

for heat distribution net at

Time interval nodes

The unit of thermal power load of (a) is kW;

for heat distribution net at

Time interval node

If the heating power is higher than

Is the upper level heatPower injected into the distribution network by the network;

for heat distribution net at

The hot water pipeline transmission power of each multi-energy subsystem in the lower layer region is transmitted in a time period, and the unit is kW.

The inequality constraint of the heat source point output power is as follows:

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

The upper limit value and the lower limit value of the thermal power output by the heat source point in the time period are both kW.

The inequality constraint of the transmission power of the heat distribution network pipeline is as follows:

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

Time interval circuit

The unit of the upper limit value and the lower limit value of the thermal power flow is kW.

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

The time interval is respectively connected with the upper limit value and the lower limit value of the thermal power transmitted by the hot water pipeline between the multi-energy subsystems in the lower layer area, and the unit is kW.

Step 2: establishing optimization models of all multi-energy subsystems in the lower layer area, wherein the optimization models of all multi-energy subsystems in the lower layer area comprise a lower layer optimization objective function and a lower layer optimization constraint condition;

minimizing the economic operating cost of each multi-energy subsystem in the lower layer area is an optimization objective, and therefore, the lower layer optimization objective function

Comprises the following steps:

wherein the content of the first and second substances,

is the first in the lower layer region

The economic operating costs of the plurality of multi-energy subsystems;

is the first in the lower layer region

is the first in the lower layer region

Total operating cost for a time period;

wherein the content of the first and second substances,

is the price of natural gas in units of [/m ]³；

Is the low calorific value of natural gas and has the unit of kWh/m³；

the heat production efficiency of natural gas consumed by the boiler;

the number of devices for micro gas turbines;

is as follows

Multiple multi-energy subsystem micro gas turbines in

The output power of the time period is in kW.

Wherein the content of the first and second substances,

is the first in the lower layer region

、

、

、

、

、

、

、

、

、

the running cost parameters of photovoltaic equipment, a fan equipment, a micro gas turbine equipment, a storage battery equipment, a gas storage tank equipment, a cold storage tank equipment and a heat storage tank equipment are respectively expressed in units of WrH;

、

respectively a photovoltaic power and a fan

The output power of the time interval is kW.

、

、

、

The unit of the power of the absorbed energy in the time period is kW;

、

、

、

The power of the released energy of the time period is in kW.

The lower layer optimization constraint conditions in the optimization model of each multi-energy subsystem in the lower layer area comprise power balance constraints of electric power, natural gas, cold energy, heat energy and waste heat flue gas buses of each multi-energy subsystem in the lower layer area, and the method specifically comprises the following steps:

the first row to the fifth row are respectively constrained by an active power balance equation of the power bus, the natural gas bus, the cold energy bus, the heat energy bus and the waste heat flue gas bus.

、

、

、

Respectively in the upper regionDistribution, gas, heat and cold net

The power transmitted to each multi-energy subsystem in the lower layer region in time intervals is kW;

、

、

、

respectively, the multi-energy subsystems in the lower layer area

The unit of electric, gas, hot and cold power load of the time period is kW;

、

The unit of electric power load consumed in time intervals is kW;

The thermal power load consumed in a time period is in kW.

And

the conventional electric load and the adjustable electric load of each multi-energy subsystem terminal in the lower layer area are respectively the kW;

and

respectively a conventional gas load and an adjustable gas load;

and

the unit is kW respectively for conventional cooling load and adjustable cooling load.

Converting factors for waste heat power generation;

for each multi-energy subsystem terminal in the lower layer area

Electrical loading of the time period.

、

、

Respectively outputting adjustable electricity, gas and cold loads for increasing power for virtual energy participating in a comprehensive demand response plan, wherein the unit is kW;

、

、

the unit is kW for adjustable electricity, gas and cold loads of virtual energy output power reduction participating in the comprehensive demand response plan.

、

Respectively are temperature control type hot air load and flexible hot water supply type load, and the unit is kW.

The lower layer optimization constraint conditions in the optimization model of each multi-energy subsystem in the lower layer region further include energy coupling constraints among energy supply devices in each multi-energy subsystem in the lower layer region, and the specific conditions are as follows:

wherein the content of the first and second substances,

The output power of the time period is kW;

The output power of the time period is in kW.

Is as followsThe waste heat boilers in the multi-energy subsystems in the layer area are

The unit of waste heat power input in a time period is kW;

The unit of the waste heat power output in a time period is kW;

The unit of thermal power input in a time period is kW;

The unit of thermal power output in a time period is kW;

the thermoelectric ratio of the micro gas turbine in each multi-energy subsystem in the lower layer area.

The lower layer optimization constraint conditions in the optimization model of each multi-energy subsystem in the lower layer region further include energy balance constraints among energy supply equipment in each multi-energy subsystem in the lower layer region, and the specific conditions are as follows:

wherein the content of the first and second substances,

、

、

、

The unit of input power of the time interval is kW;

、

、

、

The unit of the output power of the time interval is kW;

、

、

、

the conversion efficiency of the electric refrigeration, electric heating, absorption refrigeration and heat converter device is respectively.

The lower layer optimization constraint conditions in the optimization model of each multi-energy subsystem in the lower layer region further comprise equality and inequality constraints of storage batteries, gas storage tanks, cold storage tanks and heat storage tank equipment in each multi-energy subsystem in the lower layer region, and the lower layer optimization constraint conditions are as follows:

the first to fourth rows are respectively the change relations of the stored energy change of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank with the stored energy charging/discharging efficiency, the charging/discharging power, the self-discharging efficiency and the duration.

、

、

、

、

、

、

the energy release efficiency coefficients of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank device are respectively.

、

、

、

The self-discharge efficiency coefficients of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank device are respectively.

、

、

、

The storage capacity of the time interval is kWh;

、

、

、

The unit of the charging power of the time interval is kW;

、

、

、

The energy discharge power of the time period is kW.

the maximum discharge rate of the storage battery, the gas storage tank, the cold storage tank and the heat storage tank.

、

Are all binary 0-1 variables；

Minimum energy storage in time interval, rated capacity

0.2 times of (A), in kWh;

Maximum energy storage in time interval, taking rated capacity

0.92 times of (a) in kWh.

The lower layer optimization constraint conditions in the optimization model of each multi-energy subsystem in the lower layer region further include inequality constraints of each energy supply device in each multi-energy subsystem in the lower layer region, and the method specifically comprises the following steps:

wherein the content of the first and second substances,

and

Upper and lower limits of output power for a time periodThe unit is kW.

And

the upper limit climbing force constraint coefficient and the lower limit climbing force constraint coefficient of the micro gas turbine are respectively.

、

、

、

The unit of the upper limit value of the output power of the time period is kW;

、

、

、

The lower limit value of the output power of the time interval is kW.

The lower layer optimization constraint conditions in the optimization model of each multi-energy subsystem in the lower layer region further include inequality constraints of each energy conversion device in each multi-energy subsystem in the lower layer region, and the method specifically comprises the following steps:

wherein the content of the first and second substances,

、

、

、

The unit of the upper limit value of the time interval output power is kW.

、

、

、

The lower limit value of the time interval output power is kW.

The lower layer optimization constraint conditions in the optimization model of each multi-energy subsystem in the lower layer region further comprise inequality constraints of junctor transmission power between each multi-energy subsystem in the lower layer region and each distribution network in the upper layer region, and the inequality constraints are as follows:

wherein the content of the first and second substances,

、

、

、

respectively, the multi-energy subsystems in the lower layer area

And the upper limit of the transmission power of the connecting lines among the distribution network, the heat distribution network and the cold distribution network in the time interval and the upper layer area is kW.

The lower layer optimization constraint conditions in the optimization models of the multiple energy subsystems in the lower layer region further include carbon emission constraints of the multiple energy subsystems in the lower layer region obtained based on real-time environment detection, and the carbon emission constraints are as follows:

wherein the content of the first and second substances,

the comprehensive energy supply carbon emission intensity coefficient of the micro gas turbine, the waste heat boiler and the gas boiler device in each multi-energy subsystem in the lower layer area in unit time is t/kWh;

the total carbon emission quota constraint upper limit value of each multi-energy subsystem in the lower layer area is t;

optimizing the real-time carbon emission of operation of each multi-energy subsystem in the lower layer area, wherein the unit is t/h; wherein the subscript

Showing that there are 3 multi-energy subsystems in the lower zone. The third line and the fourth line respectively represent the real-time carbon emission of each multi-energy subsystem in the lower layer area after optimization (

、

、

Respectively, the real-time carbon emission amount of the 3 multi-energy subsystems in the lower layer region), and transmitting the total carbon emission quota constraint upper limit value of each multi-energy subsystem in the lower layer region to the upper layer region.

The lower layer optimization constraint conditions in the optimization models of the multiple energy subsystems in the lower layer region further include constraints of movable electricity, gas and cold loads of the multiple energy subsystems in the lower layer region, and the constraints are as follows:

wherein the content of the first and second substances,

/

、

/

、

/

the maximum variable power allowed by the movable electric, gas and cold loads of each multi-energy subsystem terminal in the lower layer area is respectively.

、

、

、

、

、

All are binary variables of 0-1.

The lower layer optimization constraint conditions in the optimization models of the multiple energy subsystems in the lower layer region further comprise constraint of the flexible indoor air conditioning power equivalent models of terminal users of the multiple energy subsystems in the lower layer region, and the constraint conditions are as follows:

wherein the content of the first and second substances,

is composed of

The predicted cold/heat load demand power at that moment is in kW.

For each multi-energy subsystem in the lower layer region

The cold or hot power, in kW, is supplied to the end user at the moment.

Is composed of

And (3) the indoor comfortable temperature of each multi-energy subsystem terminal user in the lower layer area at the time is in the unit of o C.

And

the indoor and outdoor air temperature of each multi-energy subsystem terminal user in the lower layer area is represented by the unit of o C.

The equivalent thermal resistance of the building where each multi-energy subsystem terminal user is located in the lower layer area is represented by the unit of degree C/kW.

And

the minimum value and the maximum value allowed by the indoor temperature fluctuation change of the building where each multi-energy subsystem terminal user is located in the lower layer area are respectively in the unit of C. The third line and the fourth line in the formula represent that the indoor cold power and the indoor heat power of the building where each multi-energy subsystem terminal user in the lower layer area is located can be flexibly adjusted according to the monitored outdoor air temperature of the building.

、

The fluctuation parameters of the cold load and the heat load are respectively.

The lower layer optimization constraint conditions in the optimization models of the multiple energy subsystems in the lower layer region further comprise indoor flexible hot water supply type load power equivalent model constraints of buildings where terminal users of the multiple energy subsystems in the lower layer region are located, and the lower layer optimization constraint conditions are as follows:

the constraint condition of the first row is that the indoor flexible hot water supply requirement of the building where the terminal users of each multi-energy subsystem in the lower layer area are located can be predicted to keep the optimal water storage temperature for the users.

The unit is kWh/(L.degree C) as a hot water parameter;

is composed of

The cold water storage capacity at that time is in units of L.

Is composed of

The temperature of cold water replacing hot water at the moment is in the unit of DEG C.

Is composed of

The storage temperature of the hot water at the moment is in the unit of C;

is composed of

And the unit of the hot water comfortable temperature value in the indoor of the building where each multi-energy subsystem terminal user is located in the lower layer area at one time is C.

The predicted hot water load demand power is in kW.

Is composed of

The unit of the hot water quantity required to be provided by each multi-energy subsystem terminal user in the lower layer area at any time is kW.

And

all are hot water load fluctuation coefficients.

And

is composed of

The minimum and maximum hot water storage temperatures allowed at the moment are in the unit of ℃ C.

And step 3: establishing a double-layer carbon emission optimization distribution model between the multi-energy subsystems in the upper-layer multi-energy main system and the lower-layer area, and decomposing the total carbon emission constraint value of the regional multi-energy system to the multi-energy main system of the upper-layer power distribution network, the gas distribution network and the heat distribution network and the multi-energy subsystems in the lower-layer area on the basis of real-time environment detection, wherein the method specifically comprises the following steps:

wherein the content of the first and second substances,

the constraint value is a total carbon emission constraint value of the regional multi-energy system, the unit is t, and the constraint value is iteratively updated according to the actual carbon emission optimized by each multi-energy subsystem in the lower region and the power distribution network system in the upper region multi-energy subsystem in the double-layer optimization process;

、

、

respectively responsible for decomposing the region into 3 multi-energy subsystems in the lower region

（

) The carbon emission constraint upper limit value of (a), in units of t;

、

and respectively determining the upper limit value of the carbon emission constraint of each multi-energy subsystem in the upper-layer multi-energy main system and the lower-layer multi-energy subsystem in the unit of t for the whole region. The constraint value may be calculated with reference to historical cumulative carbon emissions data for each main/sub-system in the region (e.g., with reference to a historical method using historical carbon emissions intensity as a reference).

And 4, step 4: inputting a total carbon emission constraint value distribution parameter of the regional multi-energy system, an upper region multi-energy main system and a lower region equipment output parameter of each multi-energy subsystem; setting an initial value of a coupling variable of a regional multi-energy system, and a parameter value and an iteration initial value of each multiplier of an improved target cascade analysis method.

And 5: and respectively solving the optimized scheduling problem of each multi-energy subsystem in the lower layer area, transmitting the solved coupling variable to the multi-energy main system of the power distribution network, the gas distribution network and the heat distribution network in the upper layer area for optimization, and transmitting the optimized real-time carbon emission of each multi-energy subsystem in the lower layer area to the multi-energy main system in the upper layer area so as to update the carbon emission constraint value of each multi-energy subsystem in the lower layer area by the whole decomposition of the area.

Step 6: after the multi-energy main system of the upper-layer power distribution network, the gas distribution network and the heat distribution network receives all variables transmitted by each multi-energy sub-system in the lower-layer area, the optimization problem of the multi-energy main system of the upper-layer area is solved respectively, real-time carbon emission amount after optimization of the multi-energy main system of the power distribution network, the gas distribution network and the heat distribution network is transmitted to the multi-energy main system of the upper-layer area, and therefore the carbon emission constraint value of the multi-energy main system of the upper-layer area, which is decomposed in the whole area, is updated.

In the invention, an improved target cascade analysis method is adopted to solve a complex regional multi-energy system double-layer dispersion coordination optimization scheduling model, which specifically comprises the following steps:

wherein the content of the first and second substances,

、

、

、

、

respectively, the multi-energy subsystems in the lower layer area

And connecting lines between the time interval and a power distribution network, a gas distribution network and a heat distribution network system in the upper-layer multi-energy-source main system transmit power auxiliary coupling variables, wherein the units are kW.

wherein the content of the first and second substances,

、

、

The unit of the tie line transmission power coupling variable between the time interval and each multi-energy subsystem in the lower layer area is kW;

、

、

A first order multiplier of a period lagrange penalty function;

、

、

The quadratic multiplier of the period lagrange penalty function.

And 7: checking the convergence condition of a distributed coordination optimization algorithm between the multi-energy main system in the upper layer area and each multi-energy subsystem in the lower layer area, and taking whether the coupling variable optimized by the upper/lower layer system and the difference value of the target function meet the precision requirement in the iterative optimization process as the convergence condition, wherein the method specifically comprises the following steps:

wherein the content of the first and second substances,

、

、

respectively representing the convergence precision of the coupling variable difference and the target function difference between the multi-energy main system in the upper layer area and each multi-energy subsystem in the lower layer area;

if the convergence criterion is met, stopping the iteration of the algorithm, and respectively outputting the optimal scheduling results of the multi-energy main system in the upper layer area and the multi-energy subsystems in the lower layer area; if the convergence criterion is not met, updating each multiplier parameter value of the improved target cascade analysis method, and returning to the step 5 to continue iterative solution;

specifically, when the convergence criterion is not satisfied, updating each multiplier parameter value of the improved target cascade analysis method according to the following formula, namely updating a quadratic term multiplier parameter value of the lagrangian penalty function:

wherein, in order to accelerate the convergence speed of the algorithm,

The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A regional multisystem double-layer dispersion optimization scheduling method considering a double-layer carbon emission optimization distribution model is characterized by comprising the following steps:

2. The regional multi-system double-layer decentralized optimization scheduling method considering the double-layer carbon emission optimization distribution model according to claim 1, wherein in the step 1, an upper-layer optimization objective function of an upper-layer multi-energy-source main system is established with a goal of minimizing network loss of a power distribution network, a gas distribution network and a heat distribution network:

wherein the content of the first and second substances,

an optimization objective function for the distribution network;

for the distribution network

Time interval circuit

The current of (a);

for branch of distribution network

The resistance of (1);

scheduling the period of the cycle for the day ahead;

the number of nodes of the power distribution network;

the number of branches of the power distribution network;

an optimization objective function for the gas distribution network;

for distributing gas in

Natural gas flow consumed by the pressurizer over time;

is a natural gas pipeline

Average flow over a period of time;

branch of pressurizer for gas distribution network

The natural gas pipeline damping coefficient of (1);

the number of the distribution pipe network branches is;

an optimized objective function for the heat distribution network;

for heat distribution net at

Time interval pipeline

The thermal power of (3);

for heat distribution net at

Time interval pipeline

Thermal power transmission loss coefficient of (1);

the number of nodes of the heat distribution pipe network;

the number of branches of the heat distribution pipe network.

3. The regional multi-system two-tier decentralized optimization scheduling method considering two-tier carbon emission optimization distribution model according to claim 1, wherein the upper-tier optimization constraint condition in step 1 includes:

wherein the content of the first and second substances,

for the distribution network

Time interval node

The active power injected;

for the distribution network

Time interval circuit

Active power of (d);

for the distribution network

Time interval node

The reactive power injected;

for the distribution network

Time interval circuit

The reactive power of (c);

for the distribution network

for the distribution network

Time interval nodes

Active power load of (2);

for the electrical energy storage of the distribution network side

A charging power of a period;

for the electrical energy storage of the distribution network side

Discharge power of a time period;

for the distribution network

Time period slave node

representing a multi-energy subsystem in an underlying region;

the number of each multi-energy subsystem in the lower layer area is collected;

for the distribution network

Segment line

The current of (a);

for branch of distribution network

The resistance of (1);

for branch of distribution network

A reactance of (d);

for branch of distribution network

An apparent capacity upper limit value of (d);

is a node

The square of the voltage of (c);

is a node

The square of the voltage of (c);

wherein the content of the first and second substances,

for the storage battery on the distribution network side

A storage capacity of the time period;

、

are binary 0-1 variables;

the maximum charge rate of the storage battery device is set;

the maximum discharge rate of the storage battery device;

storing energy for the accumulator means;

and

wherein the content of the first and second substances,

、

the upper limit climbing output constraint coefficient and the lower limit climbing output constraint coefficient of the coal-fired and diesel generator sets are respectively; subscript

Subscript of 1 or 3 for coal-fired unit

When the number is 2, the diesel engine group is represented;

wherein the content of the first and second substances,

and

are respectively a distribution network

wherein the content of the first and second substances,

、

、

、

scheduling the period of the cycle for the day ahead; all superscripts

4. The regional multi-system two-tier decentralized optimization scheduling method considering two-tier carbon emission optimization distribution model according to claim 1, wherein the upper-tier optimization constraint condition in step 1 includes:

wherein the content of the first and second substances,

for distributing gas in

The gas flow output by the natural gas source point of the time period;

and

respectively being natural gas pipelines

In that

Inlet gas flow and outlet gas flow for a time period;

representing a multi-energy subsystem in an underlying region;

the number of each multi-energy subsystem in the lower layer area is collected;

for distributing gas in

Time period slave node

for natural gas pipeline joint

In that

Natural gas load flow over a period of time;

for distributing gas in

Natural gas flow consumed by the pressurizer over time;

and

are respectively a gas distribution net

the natural gas pressurizer device is constrained in an inequality way:

wherein the content of the first and second substances,

branch of pressurizer for gas distribution network

The natural gas pipeline damping coefficient of (1);

is a natural gas pipeline

Average flow over a period of time;

is a natural gas pipeline

Time interval node

The pressure of (a);

is the compression factor of the pressurizer;

is a natural gas pipeline

Time interval node

The pressure of (a);

natural gas pipeline flow equality and inequality constraints:

wherein the content of the first and second substances,

is a natural gas pipe section

In that

Gas flow direction of the time period;

representing a pipe section

In that

Pipe constant of time interval, its value and length of pipe section

And diameter

(ii) related;

and

respectively natural gas pipe section

In that

and

are respectively natural gas nodes

Upper and lower pressure limits of (a);

is the natural gas flow index;

the gas network dynamic characteristic equality and inequality constraint:

wherein the content of the first and second substances,

is a natural gas pipeline

In that

Managing and storing time intervals;

and

are respectively pipelines

In that

is a pipe

In that

The initial value of the time interval is managed;

is a pipe

Natural gas pipe inventory after a cycle of operation;

is a natural gas pipeline

A set of (a);

scheduling the period of the cycle for the day ahead;

wherein the content of the first and second substances,

and

are respectively a gas distribution net

5. The regional multi-system two-tier decentralized optimization scheduling method considering two-tier carbon emission optimization distribution model according to claim 1, wherein the upper-tier optimization constraint condition in step 1 includes:

wherein the content of the first and second substances,

for heat distribution net at

Time interval node

The thermal power of the injection;

for heat distribution net at

Time interval pipeline

The thermal power of (3);

for heat distribution net at

Time interval nodes

Thermal power load of (2);

for heat distribution net at

Time interval node

The heating power of (a);

for heat distribution net at

for heat distribution net at

Time interval pipeline

Thermal power transmission loss coefficient of (1);

the heat source point output power is constrained by an inequality:

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

Heat output from heat source point of time intervalAn upper power limit and a lower power limit;

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

Time interval circuit

The upper limit value and the lower limit value of the thermal power flow;

wherein the content of the first and second substances,

and

are respectively provided with a heat distribution network

And the time interval is respectively connected with the upper limit value and the lower limit value of the thermal power transmitted by the hot water pipeline between the multi-energy subsystems in the lower layer area.

6. According to the claimsSolving 1 the regional multi-system double-layer decentralized optimization scheduling method considering the double-layer carbon emission optimization distribution model is characterized in that in the step 2, a lower-layer optimization objective function is established as an optimization objective by minimizing the economic operation cost of each multi-energy subsystem in the lower layer region

Comprises the following steps:

wherein the content of the first and second substances,

is the first in the lower layer region

The economic operating costs of the plurality of multi-energy subsystems;

is the first in the lower layer region

is the first in the lower layer region

Total operating cost for a time period;

the number of each multi-energy subsystem in the lower layer area is collected;

wherein the content of the first and second substances,

is the price of natural gas;

is the low calorific value of natural gas;

the heat production efficiency of natural gas consumed by the boiler;

the number of devices for micro gas turbines;

is as follows

Multiple multi-energy subsystem micro gas turbines in

An output power of the time period;

Thermal power input in a time period;

wherein the content of the first and second substances,

is the first in the lower layer region

、

、

、

、

、

、

、

、

、

respectively setting running cost parameters of photovoltaic equipment, a fan, a micro gas turbine, a storage battery, a gas storage tank, a cold storage tank and a heat storage tank equipment;

、

respectively a photovoltaic power and a fan

An output power of the time period;

、

、

、

The power of the absorbed energy of the time period;

、

、

、

The power of the released energy of the time period.

7. The regional multi-system double-layer decentralized optimization scheduling method according to claim 1, wherein in the step 2, the lower-layer optimization constraint condition includes a power balance constraint of power, natural gas, cold energy, heat energy and waste heat flue gas buses of each multi-energy subsystem in the lower-layer region:

wherein the content of the first and second substances,

、

respectively a photovoltaic power and a fan

An output power of the time period;

is as follows

Multiple multi-energy subsystem micro gas turbines in

An output power of the time period;

the number of devices for micro gas turbines;

is a storage battery device

Power of the released energy over a period of time;

is a storage battery device

The power of the absorbed energy of the time period;

、

An electrical power load consumed over a period of time;

、

、

、

respectively distribute power, gas, heat and cold in the upper region

、

、

、

respectively, the multi-energy subsystems in the lower layer area

Electrical, gas, hot, cold power loads for a period of time;

、

、

Power of the released energy over a period of time;

Thermal power load consumed over a period of time;

The waste heat power output in time intervals;

、

、

、

An output power of the time period;

Thermal power output in time intervals;

and

respectively a terminal of each multi-energy subsystem in the lower layer areaRegular and adjustable electrical loads;

、

、

、

、

and

respectively a conventional gas load and an adjustable gas load;

and

respectively a conventional cold load and an adjustable cold load;

、

converting factors for waste heat power generation;

for each multi-energy subsystem terminal in the lower layer area

Electrical loading of the time period.

8. The regional multi-system double-layer decentralized optimization scheduling method according to claim 1, wherein in the step 2, the lower-layer optimization constraint condition includes an energy balance constraint between energy supply devices in each multi-energy subsystem in the lower-layer region:

wherein the content of the first and second substances,

、

、

、

Time periodThe output power of (d);

、

An electrical power load consumed over a period of time;

An input power of the time period;

、

、

、

is a conversion factor of waste heat power generation.

9. The regional multi-system double-layer decentralized optimization scheduling method considering the double-layer carbon emission optimization distribution model according to claim 1, wherein in the step 2, the lower-layer optimization constraint condition includes an energy coupling constraint between energy supply devices in each multi-energy subsystem in the lower-layer region:

wherein the content of the first and second substances,

An output power of the time period;

The waste heat power input in time intervals;

An output power of the time period;

Thermal power input in a time period;

Thermal power output in time intervals;

The waste heat power output in time intervals.

10. The regional multi-system double-layer decentralized optimization scheduling method considering the double-layer carbon emission optimization distribution model according to claim 1, wherein in the step 2, the lower-layer optimization constraint condition includes an equality constraint and an inequality constraint of storage batteries, air storage tanks, cold storage tanks and heat storage tank devices in each multi-energy subsystem in the lower-layer region:

wherein the content of the first and second substances,

、

、

、

、

、

、

、

、

、

、

、

、

A storage capacity of the time period;

、

、

、

(ii) a charging power over a period of time;

、

、

、

Discharge power of a time period;

、

are binary 0-1 variables;

A minimum stored energy for a time period;

Maximum stored energy for a time period;

is rated capacity.

11. The regional multi-system double-layer decentralized optimization scheduling method according to claim 1, wherein in step 2, the lower-layer optimization constraint condition includes an inequality constraint of each energy supply device in each multi-energy subsystem in the lower-layer region:

wherein the content of the first and second substances,

and

An upper limit value and a lower limit value of output power of a time period;

and

、

、

、

respectively a waste heat boiler, a gas boiler, a photovoltaic system and a wind power system in each multi-energy subsystem in the lower layer areaThe equipment is at

An upper limit value of output power of the time period;

、

、

、

A lower limit value of the output power of the period.

12. The regional multi-system double-layer decentralized optimization scheduling method according to claim 1, wherein in step 2, the lower-layer optimization constraint condition includes inequality constraints of energy conversion devices in multi-energy subsystems in the lower-layer region:

wherein the content of the first and second substances,

、

、

、

An upper limit value of the period output power;

、

、

、

A lower limit value of the period output power.

13. The regional multi-system double-layer decentralized optimization scheduling method according to claim 1, wherein in step 2, the lower-layer optimization constraint condition includes an inequality constraint on link transmission power between each multi-energy subsystem in the lower-layer region and each distribution network in the upper-layer region:

wherein the content of the first and second substances,

、

、

、

respectively, the multi-energy subsystems in the lower layer area

14. The regional multi-system double-layer decentralized optimization scheduling method according to claim 1, wherein in step 2, the lower-layer optimization constraint condition includes a carbon emission constraint of each multi-energy subsystem in the lower-layer region based on real-time environment detection:

wherein the content of the first and second substances,

is as follows

Multiple multi-energy subsystem micro gas turbines in

An output power of the time period;

the number of devices for micro gas turbines;

scheduling the period of the cycle for the day ahead;

The waste heat power input in time intervals;

Thermal power output in time intervals;

Thermal power input in a time period; all superscripts

Both represent the number of the recorded data of the multi-energy main system in the upper layer area and each multi-energy subsystem in the lower layer area in the iterative optimization process,

in the lower layer regionThe total carbon emission quota of each multi-energy subsystem constrains an upper limit value,

15. The regional multi-system two-tier decentralized optimization scheduling method according to claim 1, wherein in step 2, the lower-tier optimization constraints include constraints of mobile electric, gas and cold loads of each multi-energy subsystem terminal in the lower-tier region:

wherein the content of the first and second substances,

scheduling the period of the cycle for the day ahead;

/

、

/

、

/

、

、

、

、

、

are binary variables from 0 to 1.

16. The regional multi-system double-layer decentralized optimization scheduling method according to claim 1, wherein in step 2, the lower layer optimization constraint condition includes constraint of a flexible indoor air conditioning power equivalent model for each multi-energy subsystem terminal user in the lower layer region:

wherein the content of the first and second substances,

is composed of

Predicted cold/heat load demand power at a time;

for each multi-energy subsystem in the lower layer region

Cold or hot power supplied to the end user at any time;

is composed of

and

and

、

respectively are the fluctuation parameters of the cold load and the heat load;

the period of the cycle is scheduled for the day ahead.

17. The regional multi-system double-layer decentralized optimization scheduling method according to claim 1, wherein in step 2, the lower-layer optimization constraint condition includes an indoor flexible hot water supply type load power equivalent model constraint of a building where each multi-energy subsystem terminal user is located in the lower-layer region:

wherein the content of the first and second substances,

demand power for the predicted hot water load;

is a hot water parameter;

is composed of

The cold water storage capacity at the moment;

is composed of

The storage temperature of the hot water at any moment;

is composed of

The temperature of cold water replacing hot water at any moment;

is composed of

and

all are hot water load fluctuation coefficients;

is composed of

and

are respectively as

Minimum and maximum allowable hot water storage temperatures;

scheduling periods for the day aheadA period of time.

18. The regional multi-system double-layer decentralized optimization scheduling method considering the double-layer carbon emission optimization distribution model according to claim 1, wherein the specific process of the step 3 is as follows:

wherein the content of the first and second substances,

for each multi-energy subsystem in the lower region

（

) A total carbon emission allowance constraint upper limit value;

、

the number of each multi-energy subsystem in the lower layer area is collected.

19. The regional multi-system double-layer decentralized optimization scheduling method considering the double-layer carbon emission optimization distribution model according to claim 1, wherein a specific method for solving the complex regional multi-energy system double-layer decentralized coordination optimization scheduling model by using the improved target cascade analysis method is as follows:

wherein the content of the first and second substances,

、

、

、

、

、

、

respectively, the multi-energy subsystems in the lower layer area

、

、

、

、

A first order multiplier of a period lagrange penalty function;

、

、

A quadratic multiplier of a period lagrange penalty function;

wherein the content of the first and second substances,

representing a multi-energy subsystem in an underlying region;

the number of each multi-energy subsystem in the lower layer area is collected;

the period of the cycle is scheduled for the day ahead.

20. The regional multi-system double-layer decentralized optimization scheduling method according to claim 19, wherein in step 7, whether the difference between the coupling variables and the objective function of the upper/lower layer system optimization in the iterative optimization process meets the accuracy requirement is used as a convergence condition, which is specifically as follows:

wherein the content of the first and second substances,

、

、

representing a multi-energy subsystem in an underlying region;

wherein the content of the first and second substances,