CN114386256A - Regional electric heating system optimal scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics - Google Patents

Regional electric heating system optimal scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics Download PDF

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CN114386256A
CN114386256A CN202111626524.8A CN202111626524A CN114386256A CN 114386256 A CN114386256 A CN 114386256A CN 202111626524 A CN202111626524 A CN 202111626524A CN 114386256 A CN114386256 A CN 114386256A
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许烽
万灿
裘鹏
陆翌
陶远超
陆承宇
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Zhejiang University ZJU
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a regional electric heating system optimization scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics, and belongs to the field of comprehensive energy system operation optimization. The method comprises the following steps: a multi-energy equipment power model and an electric heating network model are established, and electric heating flexibility supply and demand constraint are established based on equipment regulation characteristics and by considering source-load double-side uncertainty; and (4) considering the time correlation of the flexibility constraint and the thermal inertia of the heat supply network, and constructing an economic operation model with the aim of minimizing the sum of the external energy purchase cost, the system operation cost and the light abandoning penalty. The method considers the time correlation of the multi-energy flexibility and the thermal inertia of the heat supply network, improves the precision degree of the system and the accuracy of a model simulation result, provides conditions for coping with the fluctuation of electric heating loads and the uncertainty of new energy, provides reference for further researching the effect of the thermoelectric coupling characteristic on the flexibility of the optimized dispatching of the power system, and accords with the development trend that the single electric energy of the current power network tends to the multi-energy coupling.

Description

Regional electric heating system optimal scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics
Technical Field
The invention relates to a regional electric heating system optimization scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics, and belongs to the field of comprehensive energy system operation optimization.
Background
The rapid increase in energy demand presents a significant environmental challenge. To achieve the goal of carbon neutralization, various countries have come to have a series of policies to control carbon emissions. One effective approach is to vigorously develop renewable energy sources and simultaneously promote the synergistic complementation of multiple energy sources. The regional electric heating system adopts distributed power generation, renewable energy and energy storage technology, promotes supply and demand interaction of various energy forms, and can improve regional energy utilization efficiency and renewable energy consumption level.
With the increasing installed capacity of renewable energy, renewable energy such as wind and light is continuously developed and utilized. The power output and user load of a high proportion of renewable energy sources have severe fluctuations and uncertainties that pose severe challenges to the operation of regional thermoelectric systems. Aiming at the problem, the invention provides an optimal scheduling method for a regional electric heating system, which considers flexibility constraint of electric heating equipment and heat supply network characteristics.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a regional electric heating system optimization scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics, which provides conditions for coping with electric heating load volatility and new energy uncertainty, and has certain guiding significance for optimizing scheduling of a regional electric heating system, improving system operation safety margin and realizing safe and economic operation.
In order to achieve the purpose, the invention adopts the following technical scheme:
firstly, constructing a power model of the multi-energy equipment based on the working principle and characteristics of the multi-energy equipment, and determining parameters such as the thermoelectric output ratio, the rated power and the like of elements such as a cogeneration unit, a gas boiler, a distributed gas unit, a storage battery and the like; establishing a flexibility provision constraint in view of the plant regulation characteristic; secondly, considering the thermal inertia of a heat supply network, establishing an electric heating network model, and writing an electric power and thermal power balance equation; constructing a distributed photovoltaic and electric heating load probability interval based on historical data to obtain flexibility requirement constraint; and establishing an optimization model taking the lowest sum of external energy purchase cost, system operation cost and light abandonment penalty as an objective function, wherein the model comprises flexibility supply constraint and flexibility demand constraint, and solving the optimization model by using an interior point method so as to obtain an optimal operation scheduling scheme of the regional electric heating system.
(1) Modeling method of multi-energy equipment power model and electric heating network model
By analyzing the physical structures of important multifunctional equipment such as a cogeneration unit, a gas boiler, a distributed gas unit, a storage battery and the like and considering the operation characteristics of different types of thermocouple elements, an electric heating operation characteristic model of the cogeneration unit, a gas boiler operation model, a distributed gas unit operation model and a storage battery characteristic model are established, an electric heating network model is established by considering the thermal inertia of a heat supply network, and a foundation is laid for the construction of subsequent flexibility constraints and the optimal economic operation scheduling calculation of an electric heating system.
(2) Method for constructing source-load bilateral uncertainty model
According to the collection of historical data and the research of literature data, the randomness and the volatility of distributed photovoltaic output are described by a Beta distribution function, and the randomness and the volatility of electric heating load are described by a normal distribution function. And constructing a probability interval of photovoltaic output and electric heating load based on source-load bilateral uncertainty.
(3) Optimal economic operation scheduling scheme considering flexibility constraint
Constructing a flexible supply constraint based on the multi-energy equipment power model and the electric heating network model; according to collection of photovoltaic output historical data and research of literature data, by fitting historical distributed photovoltaic output and electric heat load data curves, probability distribution function parameters of distributed photovoltaic output and electric heat load are determined, and flexibility requirement constraint is constructed. And (3) an optimization model taking the lowest sum of the external energy purchasing cost, the system operation cost and the light abandoning penalty as an objective function and solving by using an interior point method so as to obtain an optimal economic operation scheduling scheme.
In the foregoing technical solution, further, the economic operation model with the minimum sum of the external energy purchase cost, the system operation cost, and the light abandonment penalty as a target specifically includes:
the objective function of the model is
Figure BDA0003440155160000031
Figure BDA0003440155160000032
Where C is the total cost, Δ t is the scheduling interval,
Figure BDA0003440155160000033
in order to purchase the energy cost from the outside,
Figure BDA0003440155160000034
in order to achieve the cost of the operation,
Figure BDA0003440155160000035
penalizing cost for light abandonment;
Figure BDA0003440155160000036
respectively the output of a cogeneration unit, a diesel generator, a gas boiler and distributed photovoltaic at the moment t,
Figure BDA0003440155160000037
for the power to be charged to the battery at time t,
Figure BDA0003440155160000038
for the discharge power of the battery at time t, celec，cgasRespectively the unit price of electricity and gas purchased from the outside, deltaCHP，δCGU，δGB，δPV，δBESRespectively is the running unit price of a cogeneration unit, a diesel generator, a gas boiler, a photovoltaic power generator and a storage battery,
Figure BDA0003440155160000039
in order to purchase the electric quantity for the outside,
Figure BDA00034401551600000310
the amount of the gas purchased outside the gas pool,
Figure BDA00034401551600000311
the maximum value of the distributed photovoltaic output at the moment t;
the optimal objective function satisfies the following flexibility constraint;
Figure BDA0003440155160000041
Figure BDA0003440155160000042
Figure BDA0003440155160000043
in the formula, λe，λhRepresenting the volatility of the electrical load and the thermal load, respectively; variables tagged with up represent upward flexibility supply/demand, variables tagged with dn represent downward flexibility supply/demand, SOCt-1The state of charge of the battery at time t-1,
Figure BDA0003440155160000044
at the maximum value of the number of the first,SOCat its minimum, ZBUIs the battery capacity, ηdcFor discharge efficiency, ηchIn order to achieve a high charging efficiency,
Figure BDA0003440155160000045
is the maximum value of the discharge power of the storage battery,
Figure BDA0003440155160000046
for the maximum value of the charging power,
Figure BDA0003440155160000047
is the maximum power value, P, of the cogeneration unitCHPIs the minimum value of the power of the cogeneration unit, rCHPFor the climbing rate of the cogeneration unit,
Figure BDA0003440155160000048
is the maximum value of the power of the diesel generator, CGUPis the minimum value of the power of the diesel generator, rCGUIs the climbing rate of the diesel generator,
Figure BDA0003440155160000049
is the maximum value of the power of the gas-fired boiler, GBHis the minimum value of the power of the gas boiler, rGBIs the climbing rate of the gas-fired boiler,
Figure BDA00034401551600000410
is the CHP hotspot ratio, Pload,tFor the electrical load at time t, Hload,tIs the thermal load at time t.
The invention has the beneficial effects that:
the invention provides a modeling method of a multi-energy device and an electric heating network, which simplifies a physical model of a thermoelectric coupling device with a complex internal structure into a mathematical model considering the energy conversion relation between thermoelectricity; the method comprises the steps of describing randomness and volatility of an electric heating load and distributed photovoltaic by using a probability density function, and constructing a probability interval of photovoltaic output and the electric heating load; and the optimal economic operation scheduling scheme considering the time correlation of the flexibility constraint and the thermal inertia of the heat supply network is provided, and the solution is realized by utilizing an interior point method.
The invention breaks through the conventional thought that the traditional power system only considers the uncertainty of the electric energy, adds the uncertainty of the heat load and the corresponding multi-energy equipment into a model, considers the time correlation of the multi-energy flexibility and the thermal inertia of a heat network, improves the precision of the system and the accuracy of a model simulation result, provides conditions for coping with the fluctuation of the electric heat load and the uncertainty of new energy, provides reference for further researching the action of the thermoelectric coupling characteristic on the flexibility of optimizing and dispatching of a regional electric heating system, and accords with the development trend that the single electric energy tends to the multi-energy coupling of the current power network.
Drawings
FIG. 1 is a regional electric heating system frame;
fig. 2 is a schematic flow chart of a calculation method for optimizing scheduling of a regional electric heating system in consideration of flexibility of electric heating equipment and characteristics of a heat supply network.
Detailed Description
Fig. 1 shows a frame of the area electric heating system of the present embodiment.
Fig. 2 is a schematic flow chart of a method for optimizing and scheduling a district heating system in consideration of flexibility constraints of heating devices and characteristics of a heat supply network according to the present invention.
(1) Multifunctional equipment and modeling method of electric heating network
Combined heat and power generating unit
The combined heat and power generation unit can simultaneously supply two energy sources of electric energy and heat energy, is a coupling point of a combined heat and power system at the source side, can utilize the hypodynamia steam exhausted after a steam turbine applies work, heats circulating hot water through a heat exchanger, and supplies heat to a heat user for heating and other purposes, and the total utilization rate of the steam is high, so that the total efficiency including the power generation efficiency and the heat supply efficiency reaches more than 85%, and the environmental pollution is effectively reduced. The cogeneration unit generally has types such as back pressure unit, extraction and condensation formula unit, and different grade type units have different operating characteristics, and when adopting the back pressure unit, the relation between the electric heat power that cogeneration unit can provide can be expressed as:
PCHP_Heat＝λCHPPCHP_ele
wherein, PCHP_eleFor electric power of cogeneration units, PCHP_HeatThe heat power of the cogeneration unit; lambda [ alpha ]CHPThe heat-power ratio of the cogeneration unit is regarded as a fixed positive value, and the value is between 0 and 1.
Diesel generating set
The diesel generator set is a power generation device which takes diesel oil as fuel and a diesel engine as a prime motor, has the advantages of short starting time, convenient operation and maintenance, less investment, wide application range and the like, can be used as a common generator set to provide electricity for production and living for some areas far away from a power grid or industrial and mining enterprises, or can be used as a standby generator set to ensure reliable and continuous power supply for hospitals, airports and important industrial production enterprises. The output power of the diesel generator set meets the following constraints:
Figure BDA0003440155160000061
Figure BDA0003440155160000062
Figure BDA0003440155160000063
the subscript i is the number of the small diesel engine set;
Figure BDA0003440155160000064
respectively outputting active power and reactive power for the small diesel engine set;
Figure BDA0003440155160000065
the active output of the small diesel engine set is the upper limit and the lower limit;
Figure BDA0003440155160000066
Figure BDA0003440155160000067
the upper limit and the lower limit of the reactive power output of the small diesel engine set are set;
Figure BDA0003440155160000068
the climbing capability of the small diesel engine set is up and down.
Thirdly gas boiler
The boiler is a heat exchange device which heats water (or water vapor) to a set state by utilizing heat energy generated by other energy sources. The gas boiler uses gas (mostly natural gas) as main fuel, converts chemical energy into internal energy, and provides hot water or hot steam for life production. The heat that central heating energy station cogeneration unit produced probably is not enough to satisfy user's demand, generally has other heat energy apparatus for producing, mainly is gas boiler, and its model of exerting oneself satisfies following restraint:
Figure BDA0003440155160000071
Figure BDA0003440155160000072
Figure BDA0003440155160000073
Figure BDA0003440155160000074
the natural gas power consumed by the gas boiler at the moment t;
Figure BDA0003440155160000075
the thermal output of the gas boiler at the time t; etaGBProviding heat to the gas boiler with efficiency;
Figure BDA0003440155160000076
H GBthe upper limit and the lower limit of the thermal output of the gas boiler;
Figure BDA0003440155160000077
the climbing capability of the gas boiler is the climbing capability of the gas boiler.
Fourthly, storage battery
Due to the improvement of the permeability of the distributed renewable energy, in order to stabilize the randomness and the fluctuation of the output of the renewable energy and improve the voltage quality, certain energy storage equipment is generally required to be configured. The state of charge represents the remaining capacity of energy storage, and the dynamic process is shown as the following formula:
Figure BDA0003440155160000078
in the formula:
Figure BDA0003440155160000079
respectively representing the discharging power and the charging power of the energy storage system at the moment t;
Figure BDA00034401551600000710
respectively representing the maximum discharge and charge power of the stored energy;
Figure BDA00034401551600000711
the charge states of the energy storage system at the time t +1 and the time t are respectively;
Figure BDA00034401551600000712
ηch、ηdcrespectively representing the self-loss rate, the charging efficiency and the discharging efficiency of the energy storage device;
Figure BDA00034401551600000713
is the rated capacity of the stored energy.
Power distribution network linear power flow model
A radial power distribution network linearization power flow model is adopted, and the branch loss of the power distribution network is ignored in the simplification process.
Figure BDA00034401551600000714
In the formula: j → k represents the set of nodes k to which power flows from node j; pijAnd QijRespectively representing the active power and the reactive power flowing from the node i to the node j; pjAnd QjRespectively the active power and the reactive power of the load flowing to the node j; rijAnd XijThe resistance and reactance of branch ij are respectively; vi、VjThe voltage amplitudes of node i and node j, respectively.
Sixth, thermal inertia model
The hot water needs a certain time to flow from the first section to the tail end of the pipeline, a certain time delay also exists when the hot water flows from the first heat source station to the heat exchange station for heat energy distribution, the total length of the heat distribution pipeline can reach dozens of kilometers, the heat energy is generated from the heat source and is transmitted to a user for some time, dozens of minutes or even hours, and the time delay of a heat supply network needs to be considered when a scheduling plan is made.
Figure BDA0003440155160000081
In the formula: n is a positive integer; mpIs the total mass of hot water in the pipe p; ρ is the density of water; m ispIs the mass flow of the hot water in the pipeline p; dpAnd LpThe diameter and length of the pipe p, respectively; Δ t is the scheduling time interval.
(2) Method for constructing source-load bilateral uncertainty model
In order to construct flexibility requirement constraints, on the basis of establishing a power model of the multi-energy equipment, the fluctuation and randomness of source load are considered, and a probability distribution model of distributed photovoltaic, thermal load and electrical load is constructed.
Distributed photovoltaic probability distribution model
Photovoltaic power generation is a technology for directly converting solar radiation energy into electrical energy by using the photovoltaic effect of a solar cell semiconductor material. Therefore, the output power of the distributed photovoltaic varies with the intensity of the illumination on the interface. The illumination intensity is a variable with obvious day and night property and strong randomness, and according to the investigation on collected photovoltaic output historical data and literature data, the probability distribution of the illumination intensity can be considered to meet Beta distribution, and the output power of distributed photovoltaic and the illumination intensity present an approximately proportional relation. Therefore, the output power of distributed photovoltaics is also analyzed in this way, namely:
Figure BDA0003440155160000091
wherein P is the distributed photovoltaic output power, PmaxAt its maximum. Alpha and Beta are two shape parameters of Beta distribution respectively, and the calculation formula is as follows:
Figure BDA0003440155160000092
Figure BDA0003440155160000093
where μ is the expectation of the distribution of Γ, σ2Is the variance. Values of alpha and beta can be obtained through historical typical day data, and uncertainty of output of the distributed photovoltaic can be described according to the probability density function.
Probability distribution model of electric load
The power of the electrical load fluctuates randomly on a time scale, and according to the investigation on historical load data and literature data, the probability distribution of the electrical load can be considered to satisfy the normal distribution, that is:
Figure BDA0003440155160000094
in the formula, muPAnd σPTo represent the variance of the active power versus the desired value, muQAnd σQIs used to represent the variance and expected value of the active power.
Thirdly, heat load probability distribution model
The thermal load and the electrical load have a certain correlation, and the probability distribution of the thermal load on a time scale can be described by using normal distribution, but the parameters are different due to the longer time scale of the change of the thermal load.
(3) Optimal economic operation scheduling scheme considering flexibility constraint
The uncertainty of the source load on both sides can influence the operation of the system, and the requirement of flexibility is brought to the system. In order to fully utilize the adjusting capacity of the energy supply of the multi-energy equipment in the system, a regional electric heating system optimization scheduling scheme considering the flexibility constraint of the electric heating equipment and the heat supply network characteristics is provided. The objective function of the optimal operating scheme is
Figure BDA0003440155160000101
Figure BDA0003440155160000102
Where C is the total cost, Δ t is the scheduling interval,
Figure BDA0003440155160000103
in order to purchase the energy cost from the outside,
Figure BDA0003440155160000104
to runThe cost of the process is reduced, and the cost of the process,
Figure BDA0003440155160000105
penalizing cost for light abandonment;
Figure BDA0003440155160000106
respectively serving as a cogeneration unit, a diesel generator, a gas boiler and distributed photovoltaic power output at the t moment,
Figure BDA0003440155160000107
for the power to be charged to the battery at time t,
Figure BDA0003440155160000108
for the discharge power of the battery at time t, celec，cgasRespectively the unit price of electricity and gas purchased from the outside, deltaCHP，δCGU，δGB，δPV，δBESRespectively is the running unit price of a cogeneration unit, a diesel generator, a gas boiler, a photovoltaic power generator and a storage battery,
Figure BDA0003440155160000109
in order to purchase the electric quantity for the outside,
Figure BDA00034401551600001010
the amount of the gas purchased outside the gas pool,
Figure BDA00034401551600001011
the maximum value of the distributed photovoltaic output at the moment t;
the optimal objective function satisfies the following flexibility constraint;
Figure BDA00034401551600001012
Figure BDA0003440155160000111
Figure BDA0003440155160000112
in the formula, λe，λhRepresenting the volatility of the electrical load and the thermal load, respectively; variables tagged with up represent upward flexibility supply/demand, variables tagged with dn represent downward flexibility supply/demand, SOCt-1The state of charge of the battery at time t-1,
Figure BDA0003440155160000113
at the maximum value of the number of the first,SOCat its minimum, ZBUIs the battery capacity, ηdcFor discharge efficiency, ηchIn order to achieve a high charging efficiency,
Figure BDA0003440155160000114
is the maximum value of the discharge power of the storage battery,
Figure BDA0003440155160000115
for the maximum value of the charging power,
Figure BDA0003440155160000116
is the maximum value of the power of the cogeneration unit, CHPPis the minimum value of the power of the cogeneration unit, rCHPFor the climbing rate of the cogeneration unit,
Figure BDA0003440155160000117
is the maximum value of the power of the diesel generator, CGUPis the minimum value of the power of the diesel generator, rCGUIs the climbing rate of the diesel generator,
Figure BDA0003440155160000118
is the maximum value of the power of the gas-fired boiler, GBHis the minimum value of the power of the gas boiler, rGBIs the climbing rate of the gas-fired boiler,
Figure BDA0003440155160000119
is the CHP hotspot ratio, Pload,tFor the electrical load at time t, Hload,tIs the thermal load at time t.
The flexibility constraint with time correlation is introduced, the requirement of flexibility supply is always greater than the requirement of flexibility, and the regional electric heating system can better cope with source-charge bilateral uncertainty fluctuation. The optimization problem is a mixed integer nonlinear optimization problem, and an interior point method can be used for solving the optimization model.
The above description of the embodiments of the present invention is provided in conjunction with the accompanying drawings, and not intended to limit the scope of the present invention, and all equivalent models or equivalent algorithm flows made by using the contents of the present specification and the accompanying drawings are within the scope of the present invention by applying directly or indirectly to other related technologies.

Claims (2)

1. A regional electric heating system optimization scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics is characterized by comprising the following steps: firstly, constructing a power model of the multi-energy equipment based on the working principle and characteristics of the multi-energy equipment; considering the thermal inertia of a heat supply network, establishing an electric heating network model; establishing a flexibility provision constraint in view of the plant regulation characteristic; then, a distributed photovoltaic and electric heating load probability interval is constructed based on historical data, a source-load bilateral uncertainty model is established, and flexibility requirement constraint is obtained; and finally, considering the time correlation of the flexibility constraint and the thermal inertia of the heat supply network, constructing an economic operation model with the minimum sum of the external energy purchasing cost, the system operation cost and the light abandoning penalty as a target, wherein the model comprises a flexibility supply constraint and a flexibility demand constraint, and solving the model to obtain an optimized scheduling scheme of the regional electric heating system.
2. The optimal scheduling method of a regional electric heating system considering flexibility constraint of electric heating equipment and characteristics of a heat supply network according to claim 1, wherein the economic operation model aiming at minimizing sum of external energy purchase cost, system operation cost and light abandonment penalty is specifically as follows:
the objective function of the model is
Figure FDA0003440155150000011
Figure FDA0003440155150000012
Where C is the total cost, Δ t is the scheduling interval,
Figure FDA0003440155150000013
in order to purchase the energy cost from the outside,
Figure FDA0003440155150000014
in order to achieve the cost of the operation,
Figure FDA0003440155150000015
penalizing cost for light abandonment; pt CHP，
Figure FDA0003440155150000016
Pt PVRespectively the output of a cogeneration unit, a diesel generator, a gas boiler and distributed photovoltaic at the moment t,
Figure FDA0003440155150000017
for the power to be charged to the battery at time t,
Figure FDA0003440155150000018
for the discharge power of the battery at time t, celec，cgasRespectively the unit price of electricity and gas purchased from the outside, deltaCHP，δCGU，δGB，δPV，δBESThe running unit price of the cogeneration unit, the diesel generator, the gas boiler, the photovoltaic and the storage battery is Pt EXTTo purchase electric quantity, Ft EXTThe amount of the gas purchased outside the gas pool,
Figure FDA0003440155150000021
the maximum value of the distributed photovoltaic output at the moment t;
the optimal objective function satisfies the following flexibility constraint;
Figure FDA0003440155150000022
Figure FDA0003440155150000023
Figure FDA0003440155150000024
in the formula, λe，λhRepresenting the volatility of the electrical load and the thermal load, respectively; variables tagged with up represent upward flexibility supply/demand, variables tagged with dn represent downward flexibility supply/demand, SOCt-1The state of charge of the battery at time t-1,
Figure FDA0003440155150000025
at the maximum value of the number of the first,SOCat its minimum, ZBUIs the battery capacity, ηdcFor discharge efficiency, ηchIn order to achieve a high charging efficiency,
Figure FDA0003440155150000026
is the maximum value of the discharge power of the storage battery,
Figure FDA0003440155150000027
for the maximum value of the charging power,
Figure FDA0003440155150000028
is the maximum value of the power of the cogeneration unit, CHPPis the minimum value of the power of the cogeneration unit, rCHPFor the climbing rate of the cogeneration unit,
Figure FDA0003440155150000029
is the maximum value of the power of the diesel generator, CGUPis the minimum value of the power of the diesel generator, rCGUIs the climbing rate of the diesel generator,
Figure FDA00034401551500000210
is the maximum value of the power of the gas-fired boiler, GBHis the minimum value of the power of the gas boiler, rGBIs the climbing rate of the gas-fired boiler,
Figure FDA00034401551500000211
is the CHP hotspot ratio, Pload,tFor the electrical load at time t, Hload,tIs the thermal load at time t.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116341881A (en) * 2023-05-29 2023-06-27 山东大学 Robust advanced scheduling method and system for electric-thermal system considering flexibility of heat supply network
CN116317110B (en) * 2023-01-17 2023-11-14 中国电力科学研究院有限公司 Power grid dispatching operation previewing method and system considering source load bilateral fluctuation

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116317110B (en) * 2023-01-17 2023-11-14 中国电力科学研究院有限公司 Power grid dispatching operation previewing method and system considering source load bilateral fluctuation
CN116341881A (en) * 2023-05-29 2023-06-27 山东大学 Robust advanced scheduling method and system for electric-thermal system considering flexibility of heat supply network
CN116341881B (en) * 2023-05-29 2023-08-18 山东大学 Robust advanced scheduling method and system for electric-thermal system considering flexibility of heat supply network

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