CN114266382A - Two-stage optimal scheduling method for cogeneration system considering thermal inertia - Google Patents

Two-stage optimal scheduling method for cogeneration system considering thermal inertia Download PDF

Info

Publication number
CN114266382A
CN114266382A CN202111414416.4A CN202111414416A CN114266382A CN 114266382 A CN114266382 A CN 114266382A CN 202111414416 A CN202111414416 A CN 202111414416A CN 114266382 A CN114266382 A CN 114266382A
Authority
CN
China
Prior art keywords
stage
power
scheduling
heat
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111414416.4A
Other languages
Chinese (zh)
Inventor
梁俊宇
袁兴宇
杨洋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Electric Power Research Institute of Yunnan Power Grid Co Ltd
Original Assignee
Electric Power Research Institute of Yunnan Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Electric Power Research Institute of Yunnan Power Grid Co Ltd filed Critical Electric Power Research Institute of Yunnan Power Grid Co Ltd
Priority to CN202111414416.4A priority Critical patent/CN114266382A/en
Publication of CN114266382A publication Critical patent/CN114266382A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a two-stage optimal scheduling method of a combined heat and power generation system considering thermal inertia. In the first stage, the structure and the operating characteristics of a heat supply network are considered, a model prediction control-based optimal scheduling model of the cogeneration system is established, and the output of controllable equipment in the day and the power grid interaction power strategy are optimized; and in the second stage, the minimum output adjustment quantity of each unit in the cogeneration system is taken as a target, the real-time prediction error of renewable energy and load is comprehensively considered, and the day scheduling strategy in the first stage is dynamically adjusted. Aiming at a CHP system in a grid-connected operation state, a thermoelectric coupling mechanism, heterogeneous energy characteristics and prediction errors are comprehensively considered, a two-stage coordination optimization scheduling strategy considering thermal inertia is provided, the prediction errors of renewable energy output and load are reduced through daily scheduling-real-time adjustment of two-stage coordination, a system scheduling plan is continuously perfected, and supply and demand balance is realized; meanwhile, the influence of renewable energy sources and load fluctuation on the system is reduced, and the energy efficiency is further improved.

Description

Two-stage optimal scheduling method for cogeneration system considering thermal inertia
Technical Field
The invention relates to the technical field of power markets, in particular to a two-stage optimal scheduling method for a cogeneration system considering thermal inertia.
Background
In recent years, the phenomena of energy shortage and environmental deterioration are becoming more serious, and how to improve the energy utilization rate and promote the maximum consumption of renewable energy is a concern of countries all over the world. A Combined Heat and Power (CHP) system promotes efficient utilization of energy by utilizing the characteristics of heat energy, electric energy complementation, peak shifting regulation and the like and combining flexible scheduling of an energy storage device, and has a wide development prospect.
The heat supply network and the heat load as important components of the CHP system have great potential in improving the system performance. The current research takes electric energy as a main body, focuses on power grid dispatching and flexible power supply, and improves energy efficiency by means of heat supply network transmission delay and heat load heat accumulation participation in system dispatching. But the influence of the output of renewable energy and the load fluctuation on the system operation is not considered in the system control process. A Model Predictive Control (MPC) method based on rolling optimization is provided for solving the problem of fluctuation of renewable energy sources and loads, a model predictive control algorithm forms a closed-loop system through feedback control, system output can be dynamically adjusted, calculation is simple, a control effect is good, robustness is strong, and the MPC method is widely applied to the fields of power system optimization, energy management and the like. Aiming at a CHP system in a grid-connected operation state, a thermoelectric coupling mechanism, heterogeneous energy characteristics and prediction errors are comprehensively considered, a two-stage coordination optimization scheduling strategy considering thermal inertia is provided, the prediction errors of renewable energy output and load are reduced through daily scheduling-real-time adjustment of two-stage coordination, a system scheduling plan is continuously perfected, and supply and demand balance is realized; meanwhile, the influence of renewable energy sources and load fluctuation on the system is reduced, and the energy efficiency is further improved.
Disclosure of Invention
Aiming at the problems that the prediction error of the output and the load of the renewable energy source is large and the influence of the renewable energy source and the load fluctuation on the system is high in the prior art, the application provides a two-stage optimal scheduling method of the cogeneration system considering thermal inertia so as to solve the problems in the prior art.
The application provides a two-stage optimal scheduling method of a combined heat and power generation system considering thermal inertia, which comprises the following steps:
s1, researching the operation characteristics of core equipment in the cogeneration system, establishing a cogeneration system mathematical model considering thermal inertia, and determining the volatility and uncertainty of renewable energy sources and loads;
s2, establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy sources and the load;
s3, acquiring thermoelectric load data and electricity price information of the area to be measured, and simulating the thermoelectric load data and the electricity price information of the area to be measured by using the scheduling model based on the two-stage optimization to obtain the two-stage optimized scheduling method of the cogeneration system considering thermal inertia.
Further, the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy and the load comprises:
the scheduling model based on the two-stage optimization comprises a daily scheduling stage and a daily real-time adjustment scheduling stage;
the in-day scheduling stage comprises a prediction model stage, a feedback correction stage and a rolling optimization stage;
the scheduling model based on the two-stage optimization comprises the following steps of;
in any scheduling period, according to the updated renewable energy and load data, predicting the renewable energy and load data in the future 4h to obtain a prediction result, and updating the running state of the equipment;
comparing the prediction result with the original data, calculating a prediction error weighted value, and correcting the next prediction result;
obtaining a future 4h dispatching plan by rolling optimization according to the corrected prediction result, and only executing the first 15min dispatching plan;
monitoring real-time data of renewable energy sources and loads, calculating and adjusting power, and feeding back the power to an energy management system;
adjusting the output plan of the previous stage every 5 min;
and if the time exceeds 15min, updating and feeding back the running state and the load data of the equipment, and repeating all the processes.
Further, the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy and the load comprises:
the predictive model stage comprises;
normalizing historical load data, establishing a CNN model, and inputting the normalization processing into the CNN model;
extracting historical load data characteristics by using the CNN model, and constructing a time sequence;
establishing a GRU model, and inputting the time sequence into the GRU model to obtain the output quantity and the weight distribution principle of the GRU model;
extracting a data feature vector by utilizing the GRU model through learning the CNN model;
establishing an Attention model, and iteratively updating a better weight parameter matrix by using the Attention model in combination with the output quantity of the GRU model and a weight distribution principle;
and establishing a better CNN-GRU prediction model based on the attention mechanism according to the better weight parameter matrix, predicting, outputting a prediction result, judging whether the evaluation requirement is met or not by combining with the comprehensive evaluation of the evaluation index, if so, outputting the comprehensive evaluation of the evaluation index, and if not, re-iterating and updating the better weight parameter matrix.
Further, the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy and the load comprises:
the formula of the feedback correction stage is as follows:
Figure BDA0003375386460000021
Figure BDA0003375386460000031
wherein the content of the first and second substances,
Figure BDA0003375386460000032
in order to be a sequence of residuals,
Figure BDA0003375386460000033
in order to predict the sequence(s),
Figure BDA0003375386460000034
is the original sequence.
Further, the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy and the load comprises:
the rolling optimization phase comprises the steps of,
the optimization objective function is:
Figure BDA0003375386460000035
the fuel cost formula is:
Figure BDA0003375386460000036
the cost formula of interaction with the power grid is as follows:
Figure BDA0003375386460000037
the formula of the operation and maintenance cost is as follows:
Figure BDA0003375386460000038
where F is the total cost of system operation, FfuelFor fuel cost, FgridFor interactive power costs with the grid, FmainFor operating maintenance costs; f. ofgasFor the price of natural gas, fgrid,bFor purchase of electricity price, fgrid,sThe price for electricity sale;
Figure BDA0003375386460000039
the unit capacity operation and maintenance costs of the gas turbine, the gas boiler, the storage battery and the heat storage tank are respectively; m is the scheduling control time domain of the rolling optimization, and delta t1 is the time interval of the scheduling phase in a day.
Further, the rolling optimization phase includes:
calculating constraint conditions in a rolling optimization stage;
the upper and lower limits of the gas boiler and the gas turbine are constrained as follows:
Figure BDA00033753864600000310
Figure BDA00033753864600000311
wherein the content of the first and second substances,
Figure BDA00033753864600000312
in order to be the capacity of the gas turbine,
Figure BDA00033753864600000313
is the gas boiler capacity;
the operation constraint of the storage battery is as follows:
Figure BDA0003375386460000041
wherein the content of the first and second substances,
Figure BDA0003375386460000042
is the battery capacity;
Figure BDA0003375386460000043
and
Figure BDA0003375386460000044
marking the charging and discharging states of the storage battery at the time t respectively;
Figure BDA0003375386460000045
and
Figure BDA0003375386460000046
respectively representing the maximum multiplying power of the charging power and the discharging power of the storage battery;
Figure BDA0003375386460000047
and
Figure BDA0003375386460000048
respectively are upper and lower limit coefficients of the storage battery state value;
the operation constraint of the heat storage tank is as follows:
Figure BDA0003375386460000049
wherein the content of the first and second substances,
Figure BDA00033753864600000410
the capacity of the heat storage tank;
Figure BDA00033753864600000411
and
Figure BDA00033753864600000412
respectively marking the heat absorption state and the heat release state of the heat storage tank at the time t;
Figure BDA00033753864600000413
and
Figure BDA00033753864600000414
respectively representing the maximum multiplying power of the heat absorption power and the heat release power of the heat storage tank;
Figure BDA00033753864600000415
and
Figure BDA00033753864600000416
upper and lower limit coefficients of the heat storage state value of the heat storage tank respectively;
the system and the main network interaction constraint is as follows:
Figure BDA00033753864600000417
wherein the content of the first and second substances,
Figure BDA00033753864600000418
is the maximum interaction power;
Figure BDA00033753864600000419
and
Figure BDA00033753864600000420
a status flag bit for indicating the power purchase and sale of the power network;
the energy transfer constraints are:
Figure BDA00033753864600000421
wherein the content of the first and second substances,
Figure BDA00033753864600000422
and
Figure BDA00033753864600000423
marking bits for states of energy input and output in a period t;
Figure BDA00033753864600000424
representing the input energy;
Figure BDA00033753864600000425
represents the transmittable electric power for the period t;
the system energy balance constraint is:
Figure BDA0003375386460000051
wherein the content of the first and second substances,
Figure BDA0003375386460000052
is the electrical load of the cogeneration system at time t;
Figure BDA0003375386460000053
the thermal power output by the cogeneration system at the moment t; h isheIs the efficiency of the heat exchanger.
Further, the adjusting and scheduling stage in real time in the day includes:
the real-time adjustment stage objective function in the day is as follows:
Figure BDA0003375386460000054
wherein D is the total adjustment of the system,
Figure BDA0003375386460000055
and
Figure BDA0003375386460000056
respectively the adjustment quantity, omega, of the system and the power grid in the real-time adjustment stage, the gas turbine, the storage battery, the gas boiler and the heat storage tank1And ω2Adjusting the weight coefficient for the electrothermal power, and omega1+ω2At 2 is the time interval of the real-time adjustment phase, 1.
Further, the adjusting and scheduling stage in real time in the day includes:
calculating constraint conditions of real-time adjustment and scheduling stages within a day;
the upper and lower limits of the gas boiler and the gas turbine are constrained as follows:
Figure BDA0003375386460000057
Figure BDA0003375386460000058
wherein the content of the first and second substances,
Figure BDA0003375386460000059
in order to be the capacity of the gas turbine,
Figure BDA00033753864600000510
is the gas boiler capacity;
the operation constraint of the storage battery is as follows:
Figure BDA00033753864600000511
wherein the content of the first and second substances,
Figure BDA00033753864600000512
is the battery capacity;
Figure BDA00033753864600000513
and
Figure BDA00033753864600000514
marking the charging and discharging states of the storage battery at the time t respectively;
Figure BDA00033753864600000515
and
Figure BDA00033753864600000516
respectively representing the maximum multiplying power of the charging power and the discharging power of the storage battery;
Figure BDA00033753864600000517
and
Figure BDA00033753864600000518
respectively are upper and lower limit coefficients of the storage battery state value;
the operation constraint of the heat storage tank is as follows:
Figure BDA0003375386460000061
wherein the content of the first and second substances,
Figure BDA0003375386460000062
the capacity of the heat storage tank;
Figure BDA0003375386460000063
and
Figure BDA0003375386460000064
respectively marking the heat absorption state and the heat release state of the heat storage tank at the time t;
Figure BDA0003375386460000065
and
Figure BDA0003375386460000066
respectively representing the maximum multiplying power of the heat absorption power and the heat release power of the heat storage tank;
Figure BDA0003375386460000067
and
Figure BDA0003375386460000068
upper and lower limit coefficients of the heat storage state value of the heat storage tank respectively;
the system and the main network interaction constraint is as follows:
Figure BDA0003375386460000069
wherein the content of the first and second substances,
Figure BDA00033753864600000610
is the maximum interaction power;
Figure BDA00033753864600000611
and
Figure BDA00033753864600000612
a status flag bit for indicating the power purchase and sale of the power network;
the energy transfer constraints are:
Figure BDA00033753864600000613
wherein the content of the first and second substances,
Figure BDA00033753864600000614
and
Figure BDA00033753864600000615
marking bits for states of energy input and output in a period t;
Figure BDA00033753864600000616
representing the input energy;
Figure BDA00033753864600000617
represents the transmittable electric power for the period t;
the system energy balance constraint is:
Figure BDA00033753864600000618
wherein the content of the first and second substances,
Figure BDA00033753864600000619
is the electrical load of the cogeneration system at time t;
Figure BDA00033753864600000620
the thermal power output by the cogeneration system at the moment t; h isheIs the efficiency of the heat exchanger.
The invention discloses a two-stage optimal scheduling method of a combined heat and power generation system considering thermal inertia. In the first stage, the structure and the operating characteristics of a heat supply network are considered, a model prediction control-based optimal scheduling model of the cogeneration system is established, and the output of controllable equipment in the day and the power grid interaction power strategy are optimized; and in the second stage, the minimum output adjustment quantity of each unit in the cogeneration system is taken as a target, the real-time prediction error of renewable energy and load is comprehensively considered, and the day scheduling strategy in the first stage is dynamically adjusted. The beneficial effects are that: the two-stage optimal scheduling method considering thermal inertia can improve the economical efficiency of system operation, wherein the improvement is more obvious in typical days in winter; by the two-stage optimization method of daily scheduling and real-time adjustment, a scheduling plan can be perfected, and the imbalance of supply and demand in a certain range is made up; by combining the thermal inertia of the system, the building can store heat or release heat according to the load demand and the time-of-use electricity price, so that the power fluctuation can be stabilized, and the thermoelectric complementation is promoted.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a flow chart of a two-stage optimal scheduling method for a combined heat and power generation system considering thermal inertia according to the present application;
FIG. 2 is a block diagram of the heating network of the present application;
FIG. 3 is a two-stage optimized scheduling model of the present application;
FIG. 4 is a CNN-GRU model structure based on the Attention mechanism of the present application;
FIG. 5 is a two-stage optimization control framework of the present application;
FIG. 6 is a power optimization result of the present application without regard to thermal inertia;
FIG. 7 is a power optimization result of the present application considering thermal inertia;
FIG. 8 shows the outlet temperature variation of the P1, P9, P12 pipes in two stages of the present application.
Detailed Description
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as exemplifications of systems and methods consistent with certain aspects of the application, as recited in the claims. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present application provides a two-stage optimal scheduling method for a cogeneration system considering thermal inertia, the method comprising:
s1, researching the operation characteristics of core equipment in the cogeneration system, establishing a cogeneration system mathematical model considering thermal inertia, and determining the volatility and uncertainty of renewable energy sources and loads;
s2, establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy sources and the load;
s3, acquiring thermoelectric load data and electricity price information of the area to be measured, and simulating the thermoelectric load data and the electricity price information of the area to be measured by using the scheduling model based on the two-stage optimization to obtain the two-stage optimized scheduling method of the cogeneration system considering thermal inertia.
Referring to fig. 2, fig. 2 is a simple structure of a heating network of the present application, which transmits heat power by using hot water as a medium, and includes a water supply pipeline, a water return pipeline and a node, the water supply pipeline and the water return pipeline have similar structures, and thus the water return pipeline is omitted in fig. 1. The operation mode of the heat supply network is a quality regulation mode, namely, the flow of the working medium is kept constant in the quality regulation mode, and the temperature of the working medium is in direct proportion to the heat. The quality adjusting mode considers the change of the heating network heating power link, namely the influence of the transmission delay of the working medium water flow in the pipeline. The temperature of supply water and the temperature of return water of a heat supply network are assumed to be fixed values, and the constraints of the heat supply network comprise node temperature fusion, node flow balance, transmission delay and heat energy loss.
Further, the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy and the load comprises:
referring to fig. 3, fig. 3 is a specific optimized scheduling model of the present application, and in order to reduce the influence of the volatility and uncertainty of renewable energy and load on the scheduling result, a scheduling model based on two-stage optimization is proposed herein, which includes an intra-day scheduling stage and an intra-day real-time adjustment scheduling stage. In the in-day scheduling stage, the starting and stopping state and the output plan of the in-day controllable equipment, the power grid electricity purchasing and selling state and the power purchasing and selling amount are optimized in a rolling mode based on an MPC method in combination with thermal inertia; in the in-day real-time adjustment stage, all the joint supply equipment is coordinated to take the minimum adjustment amount as a target on the basis of in-day scheduling plan output, the output of the equipment and the power grid purchase and sale electric quantity are continuously adjusted by monitoring and updating real-time data of renewable energy sources and loads, and finally a scheduling scheme is determined.
The scheduling model based on the two-stage optimization comprises a daily scheduling stage and a daily real-time adjustment scheduling stage;
due to the intermittent and random nature of renewable energy sources and load power, real-time data is difficult to predict. The scheduling stage in the day is an MPC-based economic scheduling model, and the core of the scheduling stage comprises three stages of a prediction model, rolling optimization and feedback correction, so that a scheduling plan of the current period is obtained.
The scheduling model based on the two-stage optimization comprises the following steps of;
in any scheduling period, according to the updated renewable energy and load data, predicting the renewable energy and load data in the future 4h to obtain a prediction result, and updating the running state of the equipment;
comparing the prediction result with the original data, calculating a prediction error weighted value, and correcting the next prediction result;
obtaining a future 4h dispatching plan by rolling optimization according to the corrected prediction result, and only executing the first 15min dispatching plan;
monitoring real-time data of renewable energy sources and loads, calculating and adjusting power, and feeding back the power to an energy management system;
adjusting the output plan of the previous stage every 5 min;
and if the time exceeds 15min, updating and feeding back the running state and the load data of the equipment, and repeating all the processes.
Further, the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy and the load comprises:
the predictive model stage comprises;
referring to fig. 4, fig. 4 is a flowchart of a prediction model of the present application, where the aging time of model prediction control in the present application is 15min, which belongs to short-term prediction (5-60min), currently, many researches on short-term prediction methods are available, including kalman filtering, time series, gray theory, regression analysis, etc., but there is a certain limitation in extracting valid historical data and preventing important information from being lost, so that a CNN-GRU short-term power load prediction method based on an attention mechanism is adopted herein.
Normalizing historical load data, establishing a CNN model, and inputting the normalization processing into the CNN model;
extracting historical load data characteristics by using the CNN model, and constructing a time sequence;
establishing a GRU model, and inputting the time sequence into the GRU model to obtain the output quantity and the weight distribution principle of the GRU model;
extracting a data feature vector by utilizing the GRU model through learning the CNN model;
establishing an Attention model, and iteratively updating a better weight parameter matrix by using the Attention model in combination with the output quantity of the GRU model and a weight distribution principle;
and establishing a better CNN-GRU prediction model based on the attention mechanism according to the better weight parameter matrix, predicting, outputting a prediction result, judging whether the evaluation requirement is met or not by combining with the comprehensive evaluation of the evaluation index, if so, outputting the comprehensive evaluation of the evaluation index, and if not, re-iterating and updating the better weight parameter matrix.
Further, the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy and the load comprises:
under the current wind power and photovoltaic prediction precision, the advanced MPC control cannot ensure that the wind power and photovoltaic output is the same as the prediction value, so that the deviation exists between the controllable distributed power output issued in advance and the actual active output. Therefore, a feedback correction link is needed, the current actual active output value of the system is used as an initial value of a new round of rolling optimization scheduling, closed-loop control is formed, uncertainty of the system, wind power and photovoltaic is overcome, the new round of active output predicted value is more practical, and accuracy is higher.
The formula of the feedback correction stage is as follows:
Figure BDA0003375386460000091
Figure BDA0003375386460000092
wherein the content of the first and second substances,
Figure BDA0003375386460000093
in order to be a sequence of residuals,
Figure BDA0003375386460000094
in order to predict the sequence(s),
Figure BDA0003375386460000095
is the original sequence.
Further, the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy and the load comprises:
the rolling optimization phase comprises the steps of,
the optimization objective function is:
Figure BDA0003375386460000096
the fuel cost formula is:
Figure BDA0003375386460000101
the cost formula of interaction with the power grid is as follows:
Figure BDA0003375386460000102
the formula of the operation and maintenance cost is as follows:
Figure BDA0003375386460000103
where F is the total cost of system operation, FfuelFor fuel cost, FgridFor interactive power costs with the grid, FmainFor operating maintenance costs; f. ofgasFor the price of natural gas, fgrid,bFor purchase of electricity price, fgrid,sThe price for electricity sale;
Figure BDA0003375386460000104
the unit capacity operation and maintenance costs of the gas turbine, the gas boiler, the storage battery and the heat storage tank are respectively; m is the scheduling control time domain of the rolling optimization, and delta t1 is the time interval of the scheduling phase in a day.
Further, the rolling optimization phase includes:
calculating constraint conditions in a rolling optimization stage;
the upper and lower limits of the gas boiler and the gas turbine are constrained as follows:
Figure BDA0003375386460000105
Figure BDA0003375386460000106
wherein the content of the first and second substances,
Figure BDA0003375386460000107
in order to be the capacity of the gas turbine,
Figure BDA0003375386460000108
is the gas boiler capacity;
the operation constraint of the storage battery is as follows:
Figure BDA0003375386460000109
wherein the content of the first and second substances,
Figure BDA00033753864600001010
is the battery capacity;
Figure BDA00033753864600001011
and
Figure BDA00033753864600001012
marking the charging and discharging states of the storage battery at the time t respectively;
Figure BDA00033753864600001013
and
Figure BDA00033753864600001014
respectively representing the maximum multiplying power of the charging power and the discharging power of the storage battery;
Figure BDA00033753864600001015
and
Figure BDA00033753864600001016
respectively are upper and lower limit coefficients of the storage battery state value;
the operation constraint of the heat storage tank is as follows:
Figure BDA0003375386460000111
wherein the content of the first and second substances,
Figure BDA0003375386460000112
the capacity of the heat storage tank;
Figure BDA0003375386460000113
and
Figure BDA00033753864600001120
respectively marking the heat absorption state and the heat release state of the heat storage tank at the time t;
Figure BDA0003375386460000114
and
Figure BDA0003375386460000115
respectively representing the maximum multiplying power of the heat absorption power and the heat release power of the heat storage tank;
Figure BDA0003375386460000116
and
Figure BDA0003375386460000117
upper and lower limit coefficients of the heat storage state value of the heat storage tank respectively;
the system and the main network interaction constraint is as follows:
Figure BDA0003375386460000118
wherein the content of the first and second substances,
Figure BDA0003375386460000119
is the maximum interaction power;
Figure BDA00033753864600001110
and
Figure BDA00033753864600001111
a status flag bit for indicating the power purchase and sale of the power network;
the energy transfer constraints are:
Figure BDA00033753864600001112
wherein the content of the first and second substances,
Figure BDA00033753864600001113
and
Figure BDA00033753864600001114
marking bits for states of energy input and output in a period t;
Figure BDA00033753864600001115
representing the input energy;
Figure BDA00033753864600001116
represents the transmittable electric power for the period t;
the system energy balance constraint is:
Figure BDA00033753864600001117
wherein the content of the first and second substances,
Figure BDA00033753864600001118
is the electrical load of the cogeneration system at time t;
Figure BDA00033753864600001119
the thermal power output by the cogeneration system at the moment t; h isheIs the efficiency of the heat exchanger.
Further, the adjusting and scheduling stage in real time in the day includes:
although roll optimization reduces the influence of the renewable energy output and the fluctuation and randomness of the load, the prediction error and the advanced prediction control process cause the deviation of the optimization scheme from the actual value of the system, thereby reducing the economy of the system. And introducing a real-time adjusting stage to perform ultra-short-time renewable energy output and load scheduling so as to reduce the influence of a prediction error and a prediction model on a prediction value. And the real-time adjustment is executed once every 5min, the output of the joint supply equipment is adjusted on the basis of the planned output value of the main joint supply equipment in the scheduling stage in the day, and the stage takes the minimum equipment adjustment amount as a target.
The real-time adjustment stage objective function in the day is as follows:
Figure BDA0003375386460000121
wherein D is the total adjustment of the system,
Figure BDA0003375386460000122
and
Figure BDA0003375386460000123
respectively the adjustment quantity, omega, of the system and the power grid in the real-time adjustment stage, the gas turbine, the storage battery, the gas boiler and the heat storage tank1And ω2Adjusting the weight coefficient for the electrothermal power, and omega1+ω2At 2 is the time interval of the real-time adjustment phase, 1.
Further, the adjusting and scheduling stage in real time in the day includes:
calculating constraint conditions of real-time adjustment and scheduling stages within a day;
the upper and lower limits of the gas boiler and the gas turbine are constrained as follows:
Figure BDA0003375386460000124
Figure BDA0003375386460000125
wherein the content of the first and second substances,
Figure BDA0003375386460000126
in order to be the capacity of the gas turbine,
Figure BDA0003375386460000127
is the gas boiler capacity;
the operation constraint of the storage battery is as follows:
Figure BDA0003375386460000128
wherein the content of the first and second substances,
Figure BDA0003375386460000129
is the battery capacity;
Figure BDA00033753864600001210
and
Figure BDA00033753864600001211
marking the charging and discharging states of the storage battery at the time t respectively;
Figure BDA00033753864600001212
and
Figure BDA00033753864600001213
respectively representing the maximum multiplying power of the charging power and the discharging power of the storage battery;
Figure BDA00033753864600001214
and
Figure BDA00033753864600001215
respectively are upper and lower limit coefficients of the storage battery state value;
the operation constraint of the heat storage tank is as follows:
Figure BDA0003375386460000131
wherein the content of the first and second substances,
Figure BDA0003375386460000132
the capacity of the heat storage tank;
Figure BDA0003375386460000133
and
Figure BDA0003375386460000134
respectively marking the heat absorption state and the heat release state of the heat storage tank at the time t;
Figure BDA0003375386460000135
and
Figure BDA0003375386460000136
respectively representing the maximum multiplying power of the heat absorption power and the heat release power of the heat storage tank;
Figure BDA0003375386460000137
and
Figure BDA0003375386460000138
upper and lower limit coefficients of the heat storage state value of the heat storage tank respectively;
the system and the main network interaction constraint is as follows:
Figure BDA0003375386460000139
wherein the content of the first and second substances,
Figure BDA00033753864600001310
is the maximum interaction power;
Figure BDA00033753864600001311
and
Figure BDA00033753864600001312
a status flag bit for indicating the power purchase and sale of the power network;
the energy transfer constraints are:
Figure BDA00033753864600001313
wherein the content of the first and second substances,
Figure BDA00033753864600001314
and
Figure BDA00033753864600001315
marking bits for states of energy input and output in a period t;
Figure BDA00033753864600001316
representing the input energy;
Figure BDA00033753864600001317
represents the transmittable electric power for the period t;
the system energy balance constraint is:
Figure BDA00033753864600001318
wherein the content of the first and second substances,
Figure BDA00033753864600001319
is the electrical load of the cogeneration system at time t;
Figure BDA00033753864600001320
the thermal power output by the cogeneration system at the moment t; h isheIs the efficiency of the heat exchanger.
In order to verify the effectiveness of the model, data such as thermoelectric load data, electricity price information and the like in a certain area in the north of China are selected for simulation, and the whole area is provided with: gas turbine (1 x 4000KW), gas boiler (1 x 4500KW), battery (1 x 2000KW), heat accumulation groove (1 x 2500KW), heat intercorrector (1 x 2000KW), heat supply network part includes 12 heat supply network transmission pipelines and 7 heat load nodes. The aging time delta t1 of model predictive control in the intra-day scheduling phase is 15min, and the control time length is 15min (the predicted time length and the control time length are assumed to be equal here). The scheduling duration of the real-time adjusting phase is 5min, and 3 seasons of 80 days in summer, 150 days in winter and 135 days in a transition season are selected for simulation (the power optimization result analysis part focuses on data in winter). The simulation platform is an Intel i7 CPU and an 8G memory, and CPLEX is called to solve the model under the compiling environment of MTLAB2017 b.
In order to analyze the influence of two-stage optimization, heat supply network characteristics and thermal inertia on the operation economy of a cogeneration system, four cases are designed: case I is a traditional case, without considering two-stage optimization and heat network characteristics and thermal inertia; case II considers only the heat supply network characteristics and the thermal inertia on the basis of case I; case III adopts a two-stage optimization strategy on the basis of case I; case IV is the scheduling method presented herein. The operating costs of the cogeneration systems of the four cases are shown in the following table.
Case(s) Two-stage optimization Heat net characteristics and thermal inertia Winter season (Summer) Transitional season
I × × 1784 509 751
II × √ 1730 487 730
III √ × 1754 475 726
IV √ √ 1697 451 699
As can be seen from the above table, case IV is economically optimal compared to the other three cases, which means that the operating cost of the cogeneration system can be reduced by using a two-stage optimization method or considering the heat grid characteristics and thermal inertia. The economy of winter system operation is significantly improved because of the greater winter heat load requirements, the more significant effect of heat grid characteristics and thermal inertia on operating costs, and the reduction of operating costs by the two-stage optimization method is not limited by seasons.
(1) Analysis of grid power optimization results
The output electric power of the main equipment of the cogeneration system in the typical winter day is shown in fig. 6 and 7. Wherein, fig. 6 is a two-stage power optimization result without considering system thermal inertia; fig. 7 is a two-stage optimization result considering system thermal inertia. The operation optimization results in the two stages comprise the optimization result of power interaction with the power grid, the optimization result of the output electric power of the gas turbine and the optimization result of the charge-discharge power of the storage battery.
As can be seen from fig. 7 and 8, the air temperature is low in winter, the gas turbine continuously operates to ensure that the system supplies electricity and heat energy, the interaction power between the system and the power grid is negative in the period from 00:00 to 09:00, and the load demand is low in the period, which indicates that the system sells a large amount of electric energy to the power grid to obtain economic benefits; in the period from 04:00 to 07:00, the power of the storage battery is positive, which indicates that the storage battery is charged, and in the period from 16:00 to 20:00, the interaction power of the system and the power grid is positive, the gas turbine runs in full load, and the power of the storage battery is negative, because the system load demand is increased in the period, the power generation of the gas turbine cannot meet the power purchasing of the electric load system, and the power purchasing of the electric grid and the storage battery are needed to ensure the electric power balance of the system. Meanwhile, the output plan of the equipment after real-time adjustment fluctuates around the daily scheduling plan, because the prediction error possibly existing in the daily scheduling stage is reduced by establishing a refined scheduling model in the real-time adjustment stage, and the imbalance of supply and demand in a certain range is made up.
Compared with fig. 7, it can be seen from fig. 8 that the direction of the power of the system interacting with the power grid changes during the time periods 16:00-17:00 and 19:00-20:00, because the direction of the interacting power mainly depends on the fluctuation of the load demand, and after the combination of the thermal inertia of the system, when the load demand is reduced, the thermal energy is stored in the building, and conversely, the thermal energy is released, so the fluctuation of the power of the system interacting with the power grid is reduced.
(2) Water temperature optimization result analysis
The two-stage P1, P9, P12 duct outlet temperature change is shown in fig. 8. It can be seen from fig. 8 that the temperature change law of each section of the pipeline is similar, but the temperature change law is shifted in time, because the transmission of the heat supply network has time delay, and the time delay is more obvious the farther the heat supply network is away from the heat source; in addition, when the electricity price is low, the water supply temperature is high; when the electricity price is in a peak, the temperature of the supplied water is reduced, which shows that the building stores heat or releases heat according to the time-of-use electricity price, and the complementation of heat energy and electric energy is promoted.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (8)

1. A two-stage optimal scheduling method for a combined heat and power generation system considering thermal inertia is characterized by comprising the following steps:
researching the operating characteristics of core equipment in a cogeneration system, establishing a cogeneration system mathematical model considering thermal inertia, and determining the volatility and uncertainty of renewable energy and load;
establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy sources and the load;
and acquiring thermoelectric load data and electricity price information of the area to be tested, and simulating the thermoelectric load data and the electricity price information of the area to be tested by using the scheduling model based on the two-stage optimization to obtain the two-stage optimized scheduling method of the cogeneration system considering thermal inertia.
2. The two-stage optimized scheduling method for a cogeneration system considering thermal inertia according to claim 1, wherein the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy sources and loads comprises:
the scheduling model based on the two-stage optimization comprises a daily scheduling stage and a daily real-time adjustment scheduling stage;
the in-day scheduling stage comprises a prediction model stage, a feedback correction stage and a rolling optimization stage;
the scheduling model based on the two-stage optimization comprises the following steps of;
in any scheduling period, according to the updated renewable energy and load data, predicting the renewable energy and load data in the future 4h to obtain a prediction result, and updating the running state of the equipment;
comparing the prediction result with the original data, calculating a prediction error weighted value, and correcting the next prediction result;
rolling and optimizing according to the corrected prediction result to obtain a future 4h scheduling plan, and only executing the first 15min scheduling plan;
monitoring real-time data of renewable energy sources and loads, calculating and adjusting power, and feeding back the power to an energy management system;
adjusting the output plan of the previous stage every 5 min;
and if the time exceeds 15min, updating and feeding back the running state and the load data of the equipment, and repeating all the processes.
3. The two-stage optimized scheduling method for a cogeneration system considering thermal inertia according to claim 2, wherein the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy sources and loads comprises:
the predictive model stage comprises;
normalizing historical load data, establishing a CNN model, and inputting the normalization processing into the CNN model;
extracting historical load data characteristics by using the CNN model, and constructing a time sequence;
establishing a GRU model, and inputting the time sequence into the GRU model to obtain the output quantity and the weight distribution principle of the GRU model;
extracting a data feature vector by utilizing the GRU model through learning the CNN model;
establishing an Attention model, and iteratively updating a better weight parameter matrix by using the Attention model in combination with the output quantity of the GRU model and a weight distribution principle;
and establishing a better CNN-GRU prediction model based on the attention mechanism according to the better weight parameter matrix, predicting, outputting a prediction result, judging whether the evaluation requirement is met or not by combining with the comprehensive evaluation of the evaluation index, if so, outputting the comprehensive evaluation of the evaluation index, and if not, re-iterating and updating the better weight parameter matrix.
4. The two-stage optimized scheduling method for a cogeneration system considering thermal inertia according to claim 2, wherein the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy sources and loads comprises:
the formula of the feedback correction stage is as follows:
Figure FDA0003375386450000021
Figure FDA0003375386450000022
wherein the content of the first and second substances,
Figure FDA0003375386450000023
in order to be a sequence of residuals,
Figure FDA0003375386450000024
in order to predict the sequence(s),
Figure FDA0003375386450000025
is the original sequence.
5. The two-stage optimized scheduling method for a cogeneration system considering thermal inertia according to claim 2, wherein the establishing a scheduling model based on two-stage optimization according to the volatility and uncertainty of the renewable energy sources and loads comprises:
the rolling optimization phase comprises the steps of,
the optimization objective function is:
Figure FDA0003375386450000026
the fuel cost formula is:
Figure FDA0003375386450000027
the cost formula of interaction with the power grid is as follows:
Figure FDA0003375386450000028
the formula of the operation and maintenance cost is as follows:
Figure FDA0003375386450000029
where F is the total cost of system operation, FfuelFor fuel cost, FgridFor interactive power costs with the grid, FmainFor operating maintenance costs; f. ofgasFor the price of natural gas, fgrid,bFor purchase of electricity price, fgrid,sThe price for electricity sale;
Figure FDA00033753864500000210
the unit capacity operation and maintenance costs of the gas turbine, the gas boiler, the storage battery and the heat storage tank are respectively; m is the scheduling control time domain of rolling optimization, and delta t1 is the time interval of the scheduling phase in a day.
6. A cogeneration system two-stage optimal scheduling method considering thermal inertia according to claim 5, wherein said rolling optimization stage comprises:
calculating constraint conditions in a rolling optimization stage;
the upper and lower limits of the gas boiler and the gas turbine are constrained as follows:
Figure FDA00033753864500000211
Figure FDA00033753864500000212
wherein the content of the first and second substances,
Figure FDA00033753864500000213
in order to be the capacity of the gas turbine,
Figure FDA00033753864500000214
is the gas boiler capacity;
the operation constraint of the storage battery is as follows:
Figure FDA0003375386450000031
wherein the content of the first and second substances,
Figure FDA0003375386450000032
is the battery capacity;
Figure FDA0003375386450000033
and
Figure FDA0003375386450000034
marking the charging and discharging states of the storage battery at the time t respectively;
Figure FDA0003375386450000035
and
Figure FDA0003375386450000036
respectively representing the maximum multiplying power of the charging power and the discharging power of the storage battery;
Figure FDA0003375386450000037
and
Figure FDA0003375386450000038
upper and lower limit coefficients of the storage battery state value are respectively;
the operation constraint of the heat storage tank is as follows:
Figure FDA0003375386450000039
wherein the content of the first and second substances,
Figure FDA00033753864500000310
the capacity of the heat storage tank;
Figure FDA00033753864500000311
and
Figure FDA00033753864500000312
respectively marking the heat absorption state and the heat release state of the heat storage tank at the time t;
Figure FDA00033753864500000313
and
Figure FDA00033753864500000314
respectively representing the maximum multiplying power of the heat absorption power and the heat release power of the heat storage tank;
Figure FDA00033753864500000315
and
Figure FDA00033753864500000316
upper and lower limit coefficients of the heat storage state value of the heat storage tank respectively;
the system and the main network interaction constraint is as follows:
Figure FDA00033753864500000317
wherein the content of the first and second substances,
Figure FDA00033753864500000318
is the maximum interaction power;
Figure FDA00033753864500000319
and
Figure FDA00033753864500000320
a status flag bit for indicating the power purchase and sale of the power network;
the energy transfer constraints are:
Figure FDA00033753864500000321
wherein the content of the first and second substances,
Figure FDA00033753864500000322
and
Figure FDA00033753864500000323
marking bits for states of energy input and output in a period t;
Figure FDA00033753864500000324
representing the input energy;
Figure FDA00033753864500000325
represents the transmittable electric power for the period t;
the system energy balance constraint is:
Figure FDA00033753864500000326
wherein the content of the first and second substances,
Figure FDA0003375386450000041
is the electrical load of the cogeneration system at time t;
Figure FDA0003375386450000042
the thermal power output by the cogeneration system at the moment t; h isheIs the efficiency of the heat exchanger.
7. The two-stage optimal scheduling method for a combined heat and power generation system considering heat inertia according to claim 2, wherein the adjusting the scheduling stage in real time within a day comprises:
the real-time adjustment stage objective function in the day is as follows:
Figure FDA0003375386450000043
wherein D is the total adjustment of the system,
Figure FDA0003375386450000044
and
Figure FDA0003375386450000045
respectively the adjustment quantity omega of the system and the power grid interaction and the adjustment quantity omega of the gas turbine, the storage battery, the gas boiler and the heat storage tank in the real-time adjustment stage1And ω2Adjusting the weight coefficient for the electrothermal power, and omega1+ω2At 2 is the time interval of the real-time adjustment phase, 1.
8. The two-stage optimal scheduling method for a combined heat and power generation system considering heat inertia according to claim 6, wherein the adjusting the scheduling stage in real time within a day comprises:
calculating constraint conditions of real-time adjustment and scheduling stages within a day;
the upper and lower limits of the gas boiler and the gas turbine are constrained as follows:
Figure FDA0003375386450000046
Figure FDA0003375386450000047
wherein the content of the first and second substances,
Figure FDA0003375386450000048
in order to be the capacity of the gas turbine,
Figure FDA0003375386450000049
is the gas boiler capacity;
the operation constraint of the storage battery is as follows:
Figure FDA00033753864500000410
wherein the content of the first and second substances,
Figure FDA00033753864500000411
is the battery capacity;
Figure FDA00033753864500000412
and
Figure FDA00033753864500000413
marking the charging and discharging states of the storage battery at the time t respectively;
Figure FDA00033753864500000414
and
Figure FDA00033753864500000415
respectively representing the maximum multiplying power of the charging power and the discharging power of the storage battery;
Figure FDA00033753864500000416
and
Figure FDA00033753864500000417
upper and lower limit coefficients of the storage battery state value are respectively;
the operation constraint of the heat storage tank is as follows:
Figure FDA0003375386450000051
and
Figure FDA0003375386450000052
respectively representing the maximum multiplying power of the heat absorption power and the heat release power of the heat storage tank;
Figure FDA0003375386450000053
and
Figure FDA0003375386450000054
upper and lower limit coefficients of the heat storage state value of the heat storage tank respectively;
the system and the main network interaction constraint is as follows:
Figure FDA0003375386450000055
wherein the content of the first and second substances,
Figure FDA0003375386450000056
is the maximum interaction power;
Figure FDA0003375386450000057
and
Figure FDA0003375386450000058
a status flag bit for indicating the power purchase and sale of the power network;
the energy transfer constraints are:
Figure FDA0003375386450000059
wherein the content of the first and second substances,
Figure FDA00033753864500000510
and
Figure FDA00033753864500000511
marking bits for states of energy input and output in a period t;
Figure FDA00033753864500000512
representing the input energy;
Figure FDA00033753864500000513
represents the transmittable electric power for the period t;
the system energy balance constraint is:
Figure FDA00033753864500000514
wherein the content of the first and second substances,
Figure FDA00033753864500000515
is the electrical load of the cogeneration system at time t;
Figure FDA00033753864500000516
the thermal power output by the cogeneration system at the moment t; h isheIs the efficiency of the heat exchanger.
CN202111414416.4A 2021-11-25 2021-11-25 Two-stage optimal scheduling method for cogeneration system considering thermal inertia Pending CN114266382A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111414416.4A CN114266382A (en) 2021-11-25 2021-11-25 Two-stage optimal scheduling method for cogeneration system considering thermal inertia

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111414416.4A CN114266382A (en) 2021-11-25 2021-11-25 Two-stage optimal scheduling method for cogeneration system considering thermal inertia

Publications (1)

Publication Number Publication Date
CN114266382A true CN114266382A (en) 2022-04-01

Family

ID=80825595

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111414416.4A Pending CN114266382A (en) 2021-11-25 2021-11-25 Two-stage optimal scheduling method for cogeneration system considering thermal inertia

Country Status (1)

Country Link
CN (1) CN114266382A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115979261A (en) * 2023-03-17 2023-04-18 中国人民解放军火箭军工程大学 Rotation scheduling method, system, equipment and medium for multi-inertial navigation system
CN117057491A (en) * 2023-10-13 2023-11-14 中宝电气有限公司 Rural area power supply optimization management method based on combination of MPC and energy storage system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115979261A (en) * 2023-03-17 2023-04-18 中国人民解放军火箭军工程大学 Rotation scheduling method, system, equipment and medium for multi-inertial navigation system
CN115979261B (en) * 2023-03-17 2023-06-27 中国人民解放军火箭军工程大学 Method, system, equipment and medium for round robin scheduling of multi-inertial navigation system
CN117057491A (en) * 2023-10-13 2023-11-14 中宝电气有限公司 Rural area power supply optimization management method based on combination of MPC and energy storage system
CN117057491B (en) * 2023-10-13 2024-02-02 中宝电气有限公司 Rural area power supply optimization management method based on combination of MPC and energy storage system

Similar Documents

Publication Publication Date Title
Pan et al. Optimal design and operation of multi-energy system with load aggregator considering nodal energy prices
Gu et al. Residential CCHP microgrid with load aggregator: Operation mode, pricing strategy, and optimal dispatch
Wang et al. Optimal operation of an integrated electricity-heat energy system considering flexible resources dispatch for renewable integration
CN108197768B (en) Energy system and pipe network layout joint optimization method
CN107958300A (en) A kind of more microgrid interconnected operation coordinated scheduling optimization methods for considering interactive response
Tan et al. A wind power accommodation capability assessment method for multi-energy microgrids
Yang et al. Economic-emission dispatch problem in integrated electricity and heat system considering multi-energy demand response and carbon capture Technologies
Chen et al. A water mass method and its application to integrated heat and electricity dispatch considering thermal inertias
CN112701687B (en) Robust optimization operation method of gas-electricity distribution network system considering price type combined demand response
Cui et al. Collaborative planning of distributed wind power generation and distribution network with large-scale heat pumps
Cheng et al. Optimal planning of multi-energy system considering thermal storage capacity of heating network and heat load
CN114266382A (en) Two-stage optimal scheduling method for cogeneration system considering thermal inertia
Chen et al. Optimal scheduling of combined heat and power units with heat storage for the improvement of wind power integration
CN116432824A (en) Comprehensive energy system optimization method and system based on multi-target particle swarm
CN114294708B (en) Method for adjusting heat storage of long-distance heat supply pipe network
CN112883630B (en) Multi-microgrid system day-ahead optimization economic dispatching method for wind power consumption
CN112862158B (en) Day-ahead optimal scheduling method and system for comprehensive energy system
Song et al. Dynamic economic dispatch with CHP and wind power considering different time scales
CN116341881B (en) Robust advanced scheduling method and system for electric-thermal system considering flexibility of heat supply network
Liu et al. Optimal dispatch of community integrated energy system considering comprehensive user satisfaction method
Zheng et al. Comparison and error analysis of off-design and design models of energy hubs
CN115758763A (en) Multi-energy flow system optimal configuration method and system considering source load uncertainty
Zhang et al. Multi-time scale co-optimization scheduling of integrated energy system for uncertainty balancing
Fang et al. Optimal operation strategy considering wind power accommodation in heating district
Wei et al. Power system flexibility scheduling model for wind power integration considering heating system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination