CN116502921A - Park comprehensive energy system optimization management system and coordination scheduling method thereof - Google Patents
Park comprehensive energy system optimization management system and coordination scheduling method thereof Download PDFInfo
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Abstract
A low-carbon comprehensive energy system of a multi-energy complementary park and a management method thereof comprise an energy source flow generating module, an energy source flow conversion storage module and an energy source flow comprehensive demand response module; the energy source flow generation module is used for generating energy supply park demand users; including but not limited to utility grids representing tie line power, wind power, photovoltaic, superior gas grids representing natural gas input, and heat grids representing pipeline district heating; the energy source flow conversion storage module builds a system optimization model, converts and stores the energy generated by the energy source flow generation module into energy which can be transmitted in a park, and comprises the following steps: the energy coupling conversion unit comprises an electric conversion gas, an air source heat pump, a cogeneration and energy storage device; energy source comprehensive demand response module: from the point of change of the energy consumption curve at the load side, the energy consumption index in the form of electric heat is used for measuring the performance of the comprehensive demand response considering the replacement of electric energy, and the comprehensive demand response information flow is generated.
Description
Technical Field
The invention relates to an optimization management method, in particular to a park comprehensive energy system optimization management system and a coordination scheduling method thereof.
Background
The park-level integrated energysystem (PIES), also known as park micro-grid integrated energy system, refers to a terminal energy supply system integrating multiple energy sources such as electricity, heat, natural gas and the like in a specific space range. With the integrated development of energy, a great deal of PIES research and practice has been conducted in a plurality of countries. The accurate model is a precondition for the PIES optimized operation. Unlike a single electrical or thermal system, PIS has more diverse energy usage devices and decision-making bodies, and complex energy coupling relationships and interactive decision-making behaviors. In addition, PIES is subject to multiple uncertainties from different energy systems, which also presents a significant challenge to its modeling analysis. The optimization algorithm is a key to support the PIES optimization operation. The PIES optimization operation problem is essentially a non-convex nonlinear random optimization problem, and the solving difficulty is high.
CN111181201a discloses a multi-energy park dispatching method and system based on double-layer reinforcement learning, comprising obtaining dispatching controllable objects in a comprehensive energy system, namely a source side unit, a load side unit, an energy conversion unit and a storage unit; constructing a double-layer optimization decision model, wherein the double-layer optimization decision model comprises an upper reinforcement learning sub-model and a lower mixed integer linear programming sub-model; the upper reinforcement learning sub-model acquires the action variable information of the storage unit under the state variable information at the current moment and transmits the action variable information to the lower mixed integer linear programming sub-model; the lower mixed integer linear programming sub-model acquires corresponding rewarding variable and state variable information of the storage unit at the next moment, and feeds back the rewarding variable and the state variable information to the upper reinforcement learning sub-model; and iteratively executing the steps until the dispatching is finished.
CN111478326a discloses a comprehensive energy optimizing method and device based on model-free reinforcement learning, the method comprises: obtaining an energy supply instruction signal sample according to a preset comprehensive energy service business model; inputting energy supply guide signal samples into a preset neural network, performing network training according to a preset loss function, and acquiring energy exchange quantity of a park comprehensive energy system and a distribution network, wherein the preset loss function comprises a norm penalty item; carrying out rewarding simulation calculation according to the energy exchange quantity through a Monte Carlo algorithm to obtain an optimal energy supply guiding signal; substituting the optimal energy supply guide signal into a preset energy optimization model to obtain an optimal scheduling scheme, wherein the preset energy optimization model comprises a preset energy scheduling function and preset constraint conditions.
CN111950122a discloses a method for optimizing operation of a park comprehensive energy system, which considers the electricity, heat and gas of network multi-energy flows, and comprises the following steps: step one: mathematical modeling of the power distribution network; step two: mathematical modeling of a natural gas pipe network; step three: mathematical modeling of a heating power pipe network; step four: modeling the external characteristics of energy supply equipment; step five: constructing a mathematical optimization problem; step six: and solving an optimization problem. The invention has the beneficial effects that: 1. the method can realize the coordination and optimization of three energy systems of electric power, natural gas and heating power of the park comprehensive energy system; 2. the method provided by the patent can calculate network details (reactive power and node voltage in a power grid, node air pressure in a natural gas system, pipeline air flow, backwater temperature in a thermodynamic system, water pump power and the like) of each energy system, and analyze the influence of equipment access positions on the system.
CN112907098A discloses a multistage capacity allocation method and allocation system of a park comprehensive energy system, firstly analyzing the construction characteristics of a typical park comprehensive energy system, dividing the planning period of the park comprehensive energy system into a plurality of stages, gradually increasing the allocation capacity of each device in the park according to the annual growth characteristics of the load, and establishing a multistage capacity allocation model of the park comprehensive energy system; then, on the basis of the known park comprehensive energy system topology, establishing an operation model of each energy conversion device, and simultaneously considering various constraint conditions including energy balance, device output, energy storage charge and discharge and the like; secondly, an economic optimization objective function is established by considering the investment cost, the operation maintenance cost and the equipment residual value of the system; and finally, verifying the effectiveness and rationality of the model and the method on the capacity configuration of the park comprehensive energy system through calculation example analysis.
The solving methods adopted in the prior art comprise a mathematical programming method, a heuristic method, a reinforcement learning algorithm and the like, however, the methods have characteristics, but short plates exist in certain aspects, a set of solving optimization operation model is designed for PIES optimization operation problems, algorithms with good convergence are still to be researched, and particularly under the background of 'double carbon' and marketization transformation, the optimization operation target of the PIES needs to better consider both economy and low carbonization targets so as to improve the energy efficient operation of a comprehensive park.
Disclosure of Invention
The invention combines the research progress of PIES optimization operation of a park comprehensive energy system in the aspects of models, constraint conditions, algorithms and the like, and the technical scheme is as follows:
the low-carbon comprehensive energy system of the multi-energy complementary park comprises a plurality of unit area energy sub-systems, wherein each unit area energy sub-system is mutually communicated with other unit areas through an energy network; the system builds a multi-park comprehensive energy system cooperative operation framework by building an energy conversion balance matrix of the multi-park comprehensive energy system, and is characterized in that: the system comprises an energy source flow generation module, an energy source flow conversion storage module and an energy source flow comprehensive demand response module;
the energy source flow generation module is used for generating energy supply park demand users; including but not limited to utility grids representing tie line power, wind power, photovoltaic, superior gas grids representing natural gas input, and heat grids representing pipeline district heating;
the energy source flow conversion and storage module converts and stores the energy generated by the energy source flow generation module into energy which can be transmitted in a park, and the energy source conversion and storage module comprises: the energy coupling conversion unit comprises an electric conversion gas, an air source heat pump, a cogeneration and energy storage device;
the energy source comprehensive demand response module: from the point of change of the energy consumption curve at the load side, the energy consumption index in the form of electric heat is used for measuring the performance of the comprehensive demand response considering the replacement of electric energy, and the comprehensive demand response information flow is generated.
The invention also discloses a comprehensive park energy coordination scheduling method, which is characterized in that:
step1: the built system optimization model is adopted, the minimum running cost is taken as an objective function, and the control is carried out by using a penalty function according to each constraint condition;
step2: taking the objective function value as a fitness value, wherein the fitness function is F IES ＝min(F IDR +F W +F G +F EN +F MC +F EX )；
Step3: population initialization and parameter setting, including the current iteration time t=1, and the individual position x of the initial population fish i (t) population size NP, step size scaling factor Ct, maximum iteration number t max Leading inertial weight omega n Maximum guiding movement N max Foraging inertial weight ω f Maximum foraging speed V f And a maximum random spread velocity D max ；
Step4: calculating the fitness value of each fish individual;
step5: calculating three motion components of the individual fish by utilizing the motion mode step of the individual fish: guiding movement N i Foraging exercise F i Random diffusion motion D i ；
Step6: calculating the motions of individual fish according to the step of updating the fish positionAnd position x i (t)；
Step7: performing genetic operation on the individual according to the crossing operation step and the mutation operation step;
step8, calculating the fitness value of the new fish individuals after iteration, and carrying out selection operation by utilizing a selection operation Step to update the population;
Step9, comparing the change of the fitness value of the current fish individual, and dynamically updating the inertia weight of the fish individual;
step10, let t=t+1, update Step size scaling factor Ct, return to Step 3, until t reaches the set maximum iteration number t max 。
Drawings
FIG. 1 is a schematic diagram of a low-carbon integrated energy system for a multi-energy complementary park according to the present invention;
FIG. 2 is a flow chart of a low-carbon comprehensive energy management coordination method for a multi-energy complementary park in the invention;
FIG. 3 is a comparative graph of an embodiment of the present invention.
Detailed Description
Example 1
The low-carbon comprehensive energy system of the multi-energy complementary park comprises a plurality of unit area energy sub-systems, wherein each unit area energy sub-system is mutually communicated with other unit areas through an energy network;
the management of the multi-park integrated energy system and the distribution network generally belongs to different operation subjects, and each park also belongs to different management subjects, so that information barriers exist between each park and each other. Each park realizes the complementary mutual utilization of various energies through a mutual interconnection mode. The power flow distribution state of the power distribution network is influenced by the access load of each node, and particularly when optimal consumption calculation is carried out, the power load of certain nodes in the power network is directly influenced by the access position of each park to the power network, so that the running state of the power distribution network is influenced.
By comprehensively considering the factors, the invention firstly builds a multi-park comprehensive energy system cooperative operation framework which comprises a plurality of single-park energy structures, various energies generated in each single park can be used by themselves, on the other hand, energy interconnection and intercommunication can be realized by energy interaction between an electric power tie line and a thermal tie line and other interconnection parks, the optimal operation target of each park is achieved by information interaction between an information interaction server, and the information necessary for interaction between each park and a power distribution network is used for formulating an optimal operation scheme by an electric quantity quota dispatching center.
The energy conversion balance matrix of the multi-park comprehensive energy system is established by the multi-park comprehensive energy system cooperative operation framework, and is expressed as follows:
wherein: l (L) c 、L h And L c Respectively an electric load, a thermal load and a cold load; η (eta) T 、η gee 、η geh 、η gh The efficiency of the transformer, the power generation efficiency of the CHP unit, the heat generation efficiency of the CHP unit and the efficiency of the gas-fired boiler are respectively; p (P) e 、P ge 、P gh The electricity consumption of the CHP unit and the gas consumption of the gas boiler are respectively: r is R echar 、P edis 、P hchar 、P hdis The charging and discharging power of the electric energy storage system and the charging and discharging power of the thermal energy storage system are respectively; p (P) PVe 、P PVh Respectively generating power and heating power for distributed energy sources; p (P) ei For consuming electric power, P, for electric refrigerating units hi The heat power is consumed for the absorption refrigerating unit; p (P) eic 、P hic The cold power is respectively produced by an electric refrigerating unit and an absorption refrigerating unit; p (P) exe 、P exh For the electric power, thermal power flowing into the energy station.
The expression includes the energy interaction part EX vector between systems. In a multi-park integrated energy system, energy is transmitted through interconnection lines among parks, but for stable operation of the system, balance among various energy transmitted among each other should be ensured, and for the whole system, the following should be ensured:
wherein omega is the set of parks that interact with all of the energy in the system;
P exe，j 、P exh，j respectively, the electric power and the thermal power of the interaction of the park j.
On the basis of the energy conversion balance matrix, energy balance among various parks is ensured, and a detailed description is given to the comprehensive energy system:
the integrated energy system comprises an energy source flow generation module, an energy source flow conversion storage module, an energy source flow transmission module, an energy source flow integrated demand response module and an energy source flow integrated demand response information module;
1. the energy source flow generation module is used for generating energy sources including, but not limited to, a utility grid representing tie line power, wind power, photovoltaic, an upper air grid representing natural gas input, a heat supply network representing pipeline central heating, and the like;
(1) The utility power is used as important electric energy supply guarantee of the multi-park comprehensive energy system, and can be supplemented when the power supply equipment in the system cannot meet the electric load demands of users, so that the demands of electric loads are met, and a single park is generally connected into a utility power grid through a voltage transformation device. With the expansion of the scale of the multi-park comprehensive energy system, the access of the system affects the running state of the utility grid, and in order to ensure the safe and economic running of the utility grid, the supply distribution of the utility grid needs to be optimized. For the optimal supply of the utility grid to increase the solving speed of the optimal supply of the utility grid and ensure the optimality thereof, we first model the utility grid to obtain a good convergence speed so as to meet the requirement of the optimal supply solving: a utility grid model:
wherein V is i Square the voltage amplitude at node i; i ij Squaring the current amplitude of the line ij; p (P) ij 、Q ij Active power and reactive power on line ij respectively; r is (r) ij 、x ij The resistance and reactance of the line ij respectively; p is p j 、q j Active power and reactive power injected into the node j respectively; p (P) jk 、Q jk Active and reactive power g on their line jk for node k connected to node j, respectively j 、b j The conductance and susceptance of node j, respectively.
The utility network is reconstructed through the switches of the tie switch and the sectionalizer in the utility grid, and the power distribution network is still a radial network in order to reduce short-circuit current in the network reconstruction process, and the existence of zero injection isolated nodes is prevented to ensure the connectivity and the effectiveness of the network, so that the radial network meets the following conditions:
In n l Is the total number of network nodes; n is n s Counting root nodes; z ij A variable of 0-1 is used for representing the on-off state of the line ij, 0 represents disconnection, and 1 represents connection.
(2) Wind power model
Considering wind turbine generator output uncertainty, it can be described by the following function:
wherein v is wind speed; phi is a shape parameter; θ is a scale parameter. According to the wind speed distribution function, calculating wind power generation power as follows:
wherein: g wpp，t The power generation power of the wind turbine generator at the time t is obtained; v t The natural wind speed at the moment t; v in 、v out The wind speeds are cut-in and cut-out respectively; v r Is the rated wind speed; g r Is rated output power.
(3) Hydrogen energy storage device charge-discharge model:
in the method, in the process of the invention,the residual electric quantity at the time t and the time t-1 of the hydrogen energy storage system are respectively>Supplying hydrogen load power directly; alpha E2H 、α H2E 、α H2G The efficiency of hydrogen synthesis by electrolysis hydrogen production, fuel cell power generation and hydrogen synthesis methane are respectively; n (N) ELE 、N FC 、N H2G The number of the electrolytic tank, the hydrogen fuel cell and the H2G device are respectively;The active power consumed by the ith electrolytic tank, the hydrogen fuel cell and the H2G device at the time t is respectively;The minimum capacity and the maximum capacity of the hydrogen energy storage system are respectively; Δt is the run length.
(4) Photovoltaic model
Photovoltaic is a device for converting solar energy into electric energy through a semiconductor technology, the power generation output of the device is influenced by temperature and solar illumination, and under standard test conditions, the photovoltaic power output expression is as follows:
Wherein PSTC is the maximum output power of the photovoltaic;is flatThe radiation intensity of solar energy under the equal time length;Is the intensity of solar radiation; alpha P Is the temperature coefficient of the power; tc is the temperature of the photovoltaic cell at the current time; tc, STC is the photovoltaic cell temperature.
2. An energy source stream conversion storage module converts and stores the energy generated by the energy source stream generation module into transportable energy in a campus, comprising: an energy coupling conversion unit such as an electric conversion gas, an air source heat pump, a cogeneration and energy storage device and the like;
1) Modeling a combined heat and power functional device, taking natural gas as input, firstly generating power through a gas turbine to supply electric energy to users, and then recycling the generated waste heat by a waste heat boiler to generate heat; because of its energy cascade utilization characteristics, its characteristics are embodied in a mathematical model:
P GT，h (t)＝P GT，c (t)(1-σ GT -σ loss )/σ GT
P h (t)＝P GT，h (t)σ rcc σ h
p in the formula GT，c (t) -represents the power generated by the gas turbine during the t period, kW;
P GT，h (t) -represents the total amount of waste heat resources discharged by the gas turbine in t time periods, kW;
P h (t) -represents the total output heat power of the waste heat boiler in the period of t, kW;
σ h - representing the utilization efficiency of waste heat resources of a waste heat boiler,%;
σ GT -representing gas turbine power generation efficiency,%;
σ loss - -representing heat loss efficiency,%;
σ rec - -representing the recovery utilization rate of waste heat resources in the power generation process of the gas turbine by the waste heat boiler,%.
2) Modeling of electric-to-gas wind power absorption capacity characteristics with waste heat recovery
The electric conversion gas can strengthen energy coupling between electricity, and the price difference of different periods of the electric energy is utilized, so that surplus wind energy is converted into natural gas to supply load or store the natural gas when the electric load is low, and the electric load is supplied to the electric load through the thermoelectric combined functional device in the electric load peak period, thereby ensuring the economic operation of the electric conversion gas and improving the wind power absorption capacity. The electric conversion gas is divided into two stages of electric hydrogen production and methanation reaction, and from the aspect of chemical reaction, the methanation reaction is an exothermic reaction under the action of a catalyst, and the sintering deactivation of the catalyst can be caused due to the fact that the temperature is too high, so that the productivity efficiency is affected. Therefore, the reaction heat is usually directly emitted into the air to ensure the constant reaction temperature, thereby causing heat pollution and resource waste. Considering the recycling of reaction heat, the invention is used for supplying domestic hot water, extends the means of electricity-to-gas conversion to consume wind power, and promotes the cascade high-efficiency utilization of energy.
The calculation formula of the methane yield and the reaction exotherm in unit time is as follows:
In the middle ofIndicative of hydrogen production rate, nm 3 /(3.47kW·h)；
Represents the methane production rate of hydrogen, nm 3 /(3.47kW·h)；
The heat release amount of methane produced by hydrogen is represented as kW.h;
Q p2g represents the calorific value of methane and the exothermic amount of methanation reaction, mJ/m 3 ；
Represents the gas density of hydrogen and methane, g/m 3 ；
P p2g Representing the electric energy consumed by the electric conversion gas, kW;
η p2g，h the heat required for the removal cycle is indicated, the proportion of the heat of reaction injected into the heat supply network,%.
The electric-to-gas wind power absorption capacity characteristic model for waste heat recovery is as follows:
P h，p2g (t)＝P p2g (t)·η h
P g，p2g (t)＝P p2g (t)·η p2g
wherein: p (P) g，p2g (t)、P h，p2g (t)、P p2g (t) represents the gas production, heating and power consumption of the electric conversion gas in the period t,%; η (eta) h 、η p2g The waste heat recovery rate and the electric conversion gas operation efficiency are shown.
After the waste heat recovery is considered, the value of electricity-to-gas wind power absorption is extended, on one hand, the waste wind power absorption is used as an electric load for absorbing surplus wind power in a load valley period, and on the other hand, the waste heat recovery is used as a heat source for supplying heat in a coordinated manner with cogeneration, so that the electric heat energy is coupled more closely, and meanwhile, the wind power absorption space is extended.
3) Modeling of heat pump variable-working-condition operation characteristics
The invention introduces the heat pump and the combined heat and power functional device to supply heat together, and increases the flexibility of source side electric heating energy supply modes. When the heat pump works under a heating working condition, the low-temperature heat source is air, the heat of the air low-temperature heat source is absorbed by the evaporator, then the water temperature in the water tank rises to form steam, then the low-temperature steam obtained by the evaporator flows to the compressor to be compressed to form high-temperature high-pressure gas, the generated high-temperature gas is condensed by the condenser under the action of the refrigerant, meanwhile, the heat is transferred to the indoor unit to supply indoor heat supply through the fan coil, and the condensed liquid is subjected to drying treatment after flowing through the liquid storage tank, the drying filter and the expansion valve, so that a heat supply cycle is completed. The heat pump variable-working-condition model is established according to the variable-working-condition operation characteristics of the heat pump, and is shown as follows:
Wherein: η (eta) ASHP (t) represents the heat production efficiency,%;
T out (t) represents an outdoor ambient temperature, DEG C, for a period of t;
the power consumption of the air source heat pump in the t period is represented as kW;
and the heating power of the air source heat pump in the period t is shown as kW.
4) A gas water heating boiler. The mathematical model of the gas boiler is as follows:
in the method, in the process of the invention,the actual output thermal power of the gas boiler; f (F) GB Actually inputting gas flow into a gas boiler; η (eta) GB Is the heat efficiency of the gas boiler.
5) Modeling of heat storage device
The adopted heat storage device comprises a heat storage tank and an electric storage device, and a mathematical model expression of the heat storage device is established as follows:
wherein: s is S es (t)、S es (t-1) respectively representing the energy storage states of the electric energy storage tank and the heat storage tank in the time periods t and t-1, and kW.h;
P es，cha (t-1)、P es，dis (t-1) represents the charging and discharging power of the electric energy storage and heat storage tank in the period of t-1, and kW;
β loss representing the self-loss rate of the electric energy storage and heat storage tank,%;
Δt represents a scheduling time interval, h;
η es，cha 、η es，ddis respectively representing the energy storage efficiency and the energy release efficiency of the electric energy storage tank and the heat storage tank,%;
A. b-represents 0 and 1 variables, and the electric energy storage and heat storage tank is charged when a=1, and discharged when b=1.
3. The energy source flow comprehensive demand response module includes: schedulable resources representing the load demands of the multi-energy users including, but not limited to, heat, gas, mechanical, cold load, and interruptible, diverted, and converted load multi-energy users.
The electric power price type demand response is a demand side project which influences the user's energy consumption behavior by price means. The price type demand response is specifically that electricity prices of the time period of each day are distributed to users in advance, the users autonomously select whether to reduce energy consumption when the price is high or not on the basis of the received energy price signals, and energy consumption is increased when the price is low, so that the energy consumption cost of the users is reduced, and the economy is improved. The real-time electricity price has a certain time delay characteristic on a communication system and a price signal transmission, and the air network and the heat network of the comprehensive energy system, so that the applicability of the real-time electricity price to the comprehensive energy system is low, the time-sharing electricity price updating period is long, namely the electricity price time period information of one day is distributed to a user path, and the communication requirement is low.
The change of the electricity price determines the magnitude of the load change amount of a user to a certain extent, namely the change rate of the electricity price and the magnitude of the load change amount have a certain correlation, the correlation is a price elastic coefficient, and the energy source flow comprehensive demand response information module responds to the comprehensive demand response under the action of the time-division electricity price to generate a comprehensive demand response information flow.
The invention adopts multi-period response in time-sharing electricity price, namely the time-sharing electricity price is specifically three different electricity prices of peak-to-valley, and the maximum factors influencing the time-to-peak electricity price are the self-elasticity coefficients in the independent period and the mutual-elasticity coefficients in the different periods, so that under the background of the time-to-peak electricity price, the price type demand response model can be modeled as follows:
wherein:
P e，0 (t): t time period demand response item current load electricity consumption, kW;
ΔP e (t): representing the electric load demand response call quantity of a user in a period t, kW;
P e，1 (t): and the actual power consumption after the user t-period demand response is represented, and kW.
The total amount of the power load is larger at night, so that the energy demand of the load schedulable resource is reduced to a certain extent by adopting a higher energy price, the total amount of the power load is smaller in the early morning, and the energy demand of the schedulable resource is increased to a certain extent by adopting a lower energy price, so that the time-sharing electricity price has a better peak clipping and valley filling effect.
In addition, since the price type demand response of the transferable load is only a transfer of the energy usage period, the total amount of energy usage needs to be kept constant during the scheduling period, and thus the price type demand response is constrained as follows:
P el0 (t) represents a t-period electrical load predictive value, kW;
ΔP e，mov (t) represents a user t period electric transfer load change amount, kW;
P ell (t) represents the electricity consumption amount after the electric load is transferred in the period of t of the user, and kW;
ΔP max (t)、ΔP min (t) user transferable load t period response limit, kW;
x t an auxiliary variable indicating whether load transfer occurs in the user t period, indicating that transfer occurs at 1;
C mov the compensation price of the electric load transfer of the unit power of the user is expressed as the element/(kW.h).
The excitation type demand response model signs a call contract for the power grid and the user, the contract indicates that the excitation compensation mode is used for compensating the reduction of the energy consumption of the user, the energy supply economy of the excitation type demand response model and the user is improved, and the excitation type demand response model is used for compensating the reduction of the electricity consumption in the traditional sense. The heat energy in the comprehensive energy system also has excitation reduction characteristics, and the human body has a certain degree of perception blurring attribute on the change of indoor temperature, so that the heat supply temperature is changed within a certain temperature range, and the change of the thermal comfort of the human body is not great. The temperature range can be equivalent converted into the heat reduction amount for the user heat load to carry out excitation reduction compensation, and the schedulable characteristic of the charge side energy consumption characteristic is fully utilized. The main reasons of wind abandoning of the comprehensive energy system are contradiction between peak and valley time periods of an electric heating curve and electric heating supply contradiction represented by heat electricity determining operation constraint of combined heat and power, so that excitation type demand response is carried out on a heat load, the contradiction between electric heating supply and demand can be directly relieved to a certain extent, and wind power grid-connected consumption is assisted. Heating heat load was chosen herein as the subject of study.
Because the combined heat and power supply is in a heat load peak period at night, the combined heat and power supply has higher heat power because the combined heat and power supply works under the operation constraint of 'fixed heat and power supply', the excitation type demand response to the heat load is adopted in a certain range, a certain amount of heat load is reduced on the premise of not influencing the physical perception of a user, the heat output of the combined heat and power supply is reduced, meanwhile, the forced electric output of the combined heat and power supply at night is equivalently reduced, and the wind power grid-connected dissipation is assisted. The thermal load excitation type demand response in the integrated energy system is modeled as follows.
P h，cut (t) represents a user heat reducible load change amount during the t period, and a negative value represents a reduction, kW;
P h，cut，max (t)、P h，cut，min (t) represents a user t period heat reducible load change amount, kW;
y t an auxiliary variable indicating whether or not heat load reduction occurs in the user t period, indicating that reduction occurs at 1;
C cut the compensation price of the heat load reduction per unit power of the user is represented by the element/(kW.h).
Under the premise of considering electric heating, electric energy conversion, heterogeneous energy price factors and conversion efficiency deviation of a coupling unit, the equivalent comprehensive electricity price and gas price are obtained, and a user can directly select an energy supply mode by comparing the comprehensive electricity price and gas price, so that self energy economy is improved. When the comprehensive electricity price is higher than the gas price, the multifunctional user selects gas to reduce electricity, and when the comprehensive electricity price is relatively low, the multifunctional user can increase the electricity demand to reduce the use of natural gas, so that the benefit maximization of the multifunctional user and the electric heat conversion are the same. Based on the calorific value equivalent principle and the energy conservation law, the convertible load is modeled as follows:
ΔP echange Representing the amount of change in electrical load after considering the replacement of electrical energy, kW;
ΔP echange1 representing the amount of change in electrical load at the time of the corresponding occurrence of gas-to-electricity conversion, kW;
ΔP echange2 representing the amount of change in electrical load corresponding to the occurrence of the thermo-electric conversion, kW;
ΔP gchange representing the amount of change in the gas load at the time of the corresponding occurrence of the gas-electricity conversion, kW;
ΔP hchange representing the amount of change in thermal load corresponding to the occurrence of the thermo-electric conversion, kW;
ΔP change，max 、ΔP change，min an upper and lower limit value representing an amount of change in the electrically switchable load, kW;
η eg 、η eh represents the electric-to-gas, electric-to-heat conversion coefficient, percent, obtained by the calorific value equivalent theorem;
P in 、P out representing an energy input and output matrix of the comprehensive energy system;
c-represents a system energy coupling conversion matrix, and represents the internal energy distribution and conversion efficiency of the comprehensive energy system;
alpha and beta represent energy distribution coefficients, and determine flow direction proportion distribution coefficients of energy in the coupling equipment and the load,%;
η represents the energy conversion coefficient, determining the efficiency of IES in the energy conversion process,%.
4. Energy source flow comprehensive demand response information module
From the point of change of the energy consumption curve at the load side, the energy consumption index in the form of electric heat three energy sources is used for measuring the performance of the comprehensive demand response considering the replacement of electric energy, and the energy source comprehensive demand response information module expression is as follows:
Wherein:
P el0 (t) represents the electrical load, kW, before the integrated demand response is implemented for period t;
P el2 (t) represents the electrical load, kW, after the integrated demand response is implemented for period t;
P gl0 (t) represents the gas load before the integrated demand response is performed in period t, kW;
P gl2 (t) represents the gas load, kW, after the integrated demand response is performed in period t;
P hl0 (t) represents the thermal load, kW, before the integrated demand response is implemented for period t;
P hl2 (t) represents the thermal load, kW, after the integrated demand response is implemented for period t;
ΔP e (t) represents an amount of change in electric power for implementing the integrated demand response in the t period, kW;
ΔP g (t) represents the amount of change in the gas amount for implementing the integrated demand response in the t period, kW;
ΔP h (t) represents the amount of change in heat for implementing the integrated demand response for the period t, kW;
S C indicating the comprehensive energy satisfaction index,%.
And the energy utilization period is changed through comprehensive demand response or the abandoned wind power is converted into other forms of energy to be utilized, so that the grid-connected wind power consumption is assisted.
Example 2
The comprehensive demand response is considered to realize the comprehensive park energy coordination scheduling method, and the method is based on the low-carbon comprehensive energy system of the multi-energy complementary park.
1. Because of factors such as poor energy supply flexibility, mismatching of source charge supply and demand curves, contradiction between peak and valley time periods of electric heating load curves and the like caused by 'heat and electricity fixing' operation constraint of combined heat and power in the comprehensive energy system, the problem of wind electricity absorption in the comprehensive energy system taking the combined heat and power as a core is caused, and in order to solve the problem, the economic performance of the operation of the comprehensive energy system is improved. The comprehensive operation cost comprises system gas purchase cost, electric energy interaction cost with a superior main network, coupling equipment operation maintenance cost, demand response project cost, environment cost and abandoned wind punishment cost, and the objective function modeling is specifically as follows:
F IES ＝min(F IDR +F W +F G +F EN +F MC +F EX )。
Wherein:
F IES representing the total running cost of the system;
F IDR representing the cost of the demand response project, and the element;
F W the punishment cost of the wind power abandon phenomenon of the system is represented;
F G representing the fuel cost of the system multi-energy coupling unit;
F EN the environmental cost of the polluted gas generated by the operation of the system multi-energy coupling unit is represented;
F MC representing the operation and maintenance cost of the system multi-energy coupling unit;
f EX representing the online purchasing cost of the upper cascade, which is equal to the difference between the purchasing cost and the selling cost.
(1) Demand response project cost:
(2) Wind abandoning punishment cost:
P w (t) represents the non-network power of the wind turbine generator system in the period t, and kW; c (C) w Representing unit abandoned wind punishment cost, and meta/(kW.h); t-represents a scheduling period, taken as 24h.
(3) Fuel cost:
P buyg (t) represents a period t system purchasing natural gas power, kW;
Δt represents a scheduling time interval, h;
represents the low heat value of natural gas, kW.h/m 3 ；
Representing the unit purchase cost of the natural gas of the system, yuan/m 3 。
(4) Environmental cost:
α p represents the unit treatment cost of the p-th pollutant, and the unit treatment cost is Yuan/kW;
Q p represents the p-th unit power pollutant emission amount, g/(kW.h);
q-represents the contaminant species;
P i (t) represents the power of the ith unit in t period, kW;
P buy (t) represents that the cascade network line transmits electric power to the comprehensive energy system for a period of t, kW;
(5) Unit operation maintenance cost:
wherein:
C i representing the unit power operation and maintenance cost of the ith coupling equipment, wherein the unit/(kW.h);
P j (t) represents the operation power of the j-th wind turbine generator system, and kW;
C p2g representing the unit power operation and maintenance cost of the electric conversion device, and the unit/(kW.h);
P es (t) represents the operating power of the energy storage device at time t, kW;
C es and the unit power operation and maintenance cost of the energy storage device is represented as unit/(kW.h).
(6) Electric energy interaction cost:
C buy representing the unit power tie line energy purchasing cost, and the unit/(kW.h);
P sell (t) the comprehensive energy system in the period of t transmits the electric power to the upper-level tie line, kW; the unit power selling cost is expressed as Yuan/(kW.h).
2. Considering safety operation constraint conditions of various devices in park
(1) Overall system energy balance constraint
From the whole perspective of the demand side, every period j, the demand side electricity and heat load satisfies:
wherein:the sum of the demand side fixed electrical load and the active demand response electrical load is the demand side total electrical load;A fixed electrical load that does not have an active demand response characteristic;An electrical load for consumer heat pump consumption having an active demand response characteristic;Is the total load of the heat on the demand side;The heat load from the energy hub, which is consumed by the user, has an active demand response characteristic.
(2) Demand side user thermal system constraints
Since the electric energy and heat energy of the user need to satisfy:
wherein:n, j is the thermocouple energy efficiency ratio;The lower limit and the upper limit of the electric power of the thermocouple are adopted.
In order to ensure the thermal comfort of users, the heating temperature in operation is limited in a proper interval, namely:
wherein:the allowable lower and upper temperature limits, respectively.
(3) Functional device operation constraints
In the method, in the process of the invention,upper and lower output limits of the j-th heat (cold) supply (storage) device respectively;climbing speed for the j-th device.
(4) Thermoelectric coupling constraint
In which Q m And P n The thermal and electrical yields of the units respectively; τ is the annual average heat ratio reference value of the unit.
(5) Energy storage operation constraint
Wherein:representing the upper limit and the lower limit of electric energy storage and energy storage power in the t period of time, and kW; />
Representing the upper limit and the lower limit of the electric energy storage and supply power in the t period, and kW;
S cs，max (t)、S cs，min (t) represents the electrical energy storage energy state in the t periodUpper and lower state limit,%;
S cs (0)、S cs (24) And (3) representing the energy state of the energy storage device at the beginning and the end of the scheduling period,%.
(6) Power balance constraint
Electric power balance constraint
Wherein: p (P) LD，t The electric load is at the time t; p (P) chp，i，t The electric output of the ith cogeneration unit at the t moment; p (P) wind T is the actual output of the wind farm; p (P) pv，t The actual output of the photovoltaic power is provided; p (P) G，i，t The electric output of the ith thermal power generating unit at the moment t; p (P) EB，t The actual output of the electric boiler; thermal power balance constraint
Wherein H is LD，t Is the thermal load at time t; h EB，t Is the thermal load of the electric boiler.
(7) Line tide constraint
P l min ≤P l，t ≤P l max
Wherein; p (P) l min The tide of the line I is taken off line; p (P) l，t The tide of the line I at the moment t; p (P) l max And (5) uploading the power flow of the line I.
(8) Comprehensive energy satisfaction constraints:
to restrict the user's behavior of putting on a machine to maliciously cut down the load transfer and thereby to make the user friendly, the amount of load change on the load side is constrained by the user's comprehensive satisfaction.
Indicating a minimum of user aggregate satisfaction, preferably 0.9.
3. The invention adopts an improved fish swarm algorithm to realize the coordinated scheduling of energy sources in the comprehensive park, each individual fish represents a potential solution in a solution space (assumed to be n-dimensional), and the food position is the required global optimal solution. Each fish body continuously updates its own position through three motion modes of guiding motion, foraging motion and random diffusion motion, and iterates in the solution space until the termination condition is met to output the optimal solution.
In the algorithm model, the motion of each individual fish i is composed of 3 parts, which are expressed as follows:
wherein: n (N) i Guiding movement for fish individuals affected by other fish; f (F) i Foraging movements that result from food affecting individual fish; d (D) i Randomly diffusing motion for the individual fish per se:
(1) Guiding movement N i
Wherein: alpha i Is the guiding direction; n (N) max Is the maximum guiding movement; omega n ∈[O，1]Inertial weight for guiding motion twice;for the last guiding movement.
α i Representing the combined effect of surrounding fish individuals and the current optimal fish individuals on fish individual i, expressed as: alpha i ＝α i local +α i target
Wherein: alpha i local Guiding surrounding fish individualsA direction; alpha i target The guiding direction of the fish individuals is optimized before the fish individuals are guided.
(2) Foraging exercise F i
F i ＝β i V f +ω f F i old
Wherein: beta i Is the foraging direction; v (V) f Is the maximum foraging movement; omega f ∈[0，1]Inertial weight for two foraging movements; f (F) i old Is the last foraging movement.
β i Representing the combined effect of food and current optimal fish individuals on fish individual i, expressed as:
β i ＝β i food +β i ibest
wherein: beta i food The direction of food for individual fish; beta i ibest The guiding direction of the fish individuals is the current optimal fish individuals.
(3) Random diffusion motion D i
D i ＝D max δ
Wherein: d (D) max Is the maximum random diffusion motion; delta epsilon [ -1,1]Is a random diffusion direction. In the fish swarm algorithm, the individual fishes have certain random motion, so that the diversity of the population can be increased, but as the iteration number increases, the better the position of the fishes is, the smaller the random diffusion motion of the fishes is. Therefore, a coefficient decreasing with the iteration number is added in the random diffusion motion,
I.e.
Wherein: t is t max Is the maximum number of iterations.
2. Updating the position of fish
Wherein: ct is the step size scaling factor, UB j 、LB j The upper and lower bounds of the decision variable, respectively, and NV is the dimension of the decision variable.
3. Crossover operation
Wherein, r is [1 ], NP]NP is population size; and r.noteq.i, x r，m Is different from x i，m M-th dimension element, C of individual r Is the crossover probability.
4. Mutation operation
The mutation operation refers to the adjustment of elements in parent individuals.
Wherein: x is x gbest，m The m-th dimension element, x of the current optimal fish body p，m 、x q，m Is different from x i，m M-th dimension element of the two fish individuals, mu is the mutation probability.
5. Selection operation
Wherein:representing iteratively updated fish.
Comparing parent individualsAnd post-iteration individual->And selecting the individual with the better fitness value as the parent of the next iteration.
Since a large number of invalid iterations can occur in the optimization process of the fish swarm algorithm, in order to improve the situation, in the optimization process, fish individuals are classified into two types according to the change of the fitness value of the fish individuals: an individual whose fitness value is improved and an individual whose fitness value is reduced. If the fitness value of the ith individual after iteration is optimized, the inertia weight of the next iteration is kept unchanged; if the fitness value of the ith individual after iteration becomes worse, the inertia weight of the ith individual in the next iteration is reset to zero, namely omega n ＝0，ω f ＝0。
In order to ensure that the algorithm not only has a wide search space in the early stage, but also can accelerate the convergence rate in the later stage, the step-length scaling factor Ct is subjected to nonlinear transformation by the following formula, so that the value of the step-length scaling factor Ct is reduced along with the increase of the iteration times:
the method comprises the following specific steps:
step1, adopting the built system optimization model, taking the minimum running cost as an objective function, and controlling by using a penalty function according to each constraint condition;
step2: taking the objective function value as a fitness value, wherein the fitness function is F IES ＝min(F IDR +F W +F G +F EN +F MC +F EX )；
Step3: population initialization and parameter setting, including the current iteration time t=1, initial seedIndividual position x of group fish i (t) population size NP, step size scaling factor Ct, maximum iteration number t max Leading inertial weight omega n Maximum guiding movement N max Foraging inertial weight ω f Maximum foraging speed V f And a maximum random spread velocity D max ；
Step4: calculating the fitness value of each fish individual;
step5: calculating three motion components of the individual fish by utilizing the motion mode step of the individual fish: guiding movement N i Foraging exercise F i Random diffusion motion D i ；
Step6: calculating the motions of individual fish according to the step of updating the fish positionAnd position x i (t)；
Step7: performing genetic operation on the individual according to the crossing operation step and the mutation operation step;
Step8: calculating the fitness value of the new fish individuals after iteration, performing selection operation by using the selection operation step, and updating the population;
step9: comparing the change of the adaptation value of the current fish individual, and dynamically updating the inertia weight of the fish individual by adopting a formula 1;
step10: let t=t+1, update step size scaling factor Ct using equation 2, return to 3 steps until t is met to set maximum number of iterations t max 。
Example 3
Three parks that the example adopted divide into two parts of electric power system and thermodynamic system, and electric power system is IEEE39 node electric wire netting, and thermodynamic system is 14 node heating networks, and electric power system part contains 2 cogeneration units, 6 thermal power generating units, 1 wind power plant and 1 photovoltaic power plant. Wherein, cogeneration unit 1 is located grid node 35 and heat supply network node 1, cogeneration unit 2 is located grid node 38 and heat supply network node 8, photovoltaic power plant is located grid node 32, and wind farm is located grid node 35. Parameters of the cogeneration unit, the thermal power unit and the like are shown in the following table:
the thermodynamic system part comprises 2 heat sources, 14 heat exchange stations and 26 heat supply pipelines. The relevant parameters of the pipeline are shown in the following table:
the capacity of the two electric boilers is 100MW, and the two electric boilers are respectively arranged in the cogeneration unit 1 and the cogeneration unit 2, and other electric-heat combined system parameters are shown in the following table
The scheduling period is 24h, and the unit scheduling time is 1h.
In order to illustrate the influence of the heat supply network thermal dynamic characteristics and the flexible thermal load on the running condition of the electric-thermal combined system, 2 different working scenes are set for comparison and analysis.
Scene 1: the operation of the electric-thermal combination system is not considered by the thermodynamic characteristics of the heating network and the flexible heat load.
Scene 2: the electric-thermal combined system taking the thermodynamic characteristics of the heating network and the flexible heat load into consideration operates.
Calculation example results
The model is solved in the shoal of fish algorithm and the improved shoal of fish algorithm respectively, the population scale is set to be 50, the iteration number is 50, and the operation result is shown in figure 3. As can be seen from FIG. 3, the improved shoal algorithm is based on the inertial weight ω n 、ω f And the dynamic update of the step size scaling factor Ct reduces a large number of invalid iterations and improves the convergence speed. The optimal operation results under different scenes are shown in the following table.
Through the example simulation, the photovoltaic is completely absorbed, and no light rejection phenomenon is generated. As can be seen from the above table, the air volume of the case 2 was reduced from 75.53mw·h to 49.63mw·h, and 25.87mw·h was reduced, as compared with the case 1. But the running cost is increased from 284.11 ten thousand yuan to 286.04 ten thousand yuan, and 1.93 ten thousand yuan. According to the analysis of the thermodynamic characteristics of the heating network, the reason for the reduction of the air discarding quantity may be that after the thermodynamic characteristics and the flexible heat load of the heating network are considered, the peak of the heat output of the cogeneration unit is avoided to a certain extent, and the wind power peak is relieved. However, the thermodynamic characteristics of the heating network include heat loss, so that the heat output of the cogeneration unit is increased, the power generation cost is increased, and the running cost of the combined electric and heat system is increased.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. The low-carbon comprehensive energy system of the multi-energy complementary park comprises a plurality of unit area energy sub-systems, wherein each unit area energy sub-system is mutually communicated with other unit areas through an energy network; the system builds a multi-park comprehensive energy system cooperative operation framework by building an energy conversion balance matrix of the multi-park comprehensive energy system, and is characterized in that: the system comprises an energy source flow generation module, an energy source flow conversion storage module and an energy source flow comprehensive demand response module;
the energy source flow generation module is used for generating energy supply park demand users; including but not limited to utility grids representing tie line power, wind power, photovoltaic, superior gas grids representing natural gas input, and heat grids representing pipeline district heating;
The energy source flow conversion storage module builds a system optimization model, converts and stores the energy generated by the energy source flow generation module into energy which can be transmitted in a park, and comprises the following steps: the energy coupling conversion unit comprises an electric conversion gas, an air source heat pump, a cogeneration and energy storage device;
the energy source comprehensive demand response module: from the point of change of the energy consumption curve at the load side, the energy consumption index in the form of electric heat is used for measuring the performance of the comprehensive demand response considering the replacement of electric energy, and the comprehensive demand response information flow is generated.
2. The multi-energy complementary campus low-carbon integrated energy system of claim 1, wherein: the system optimization model comprises a thermoelectric combined functional device model, an electric-to-gas wind power absorption capacity characteristic modeling with waste heat recovery, a heat pump variable working condition operation characteristic modeling, a gas boiler mathematical model and a heat storage device modeling.
3. The multi-energy complementary campus low-carbon integrated energy system of claim 2, characterized in that: the combined heat and power functional device model takes natural gas as input, firstly generates power through a gas turbine to supply electric energy to users, and then the generated waste heat is recycled by a waste heat boiler to generate heat; because of its energy cascade utilization characteristics, its characteristics are embodied in a mathematical model:
P GT，h (t)＝P GT，c (t)(1-σ GT -σ loss )/σ GT
P h (t)＝P Gt，h (t)σ rcc σ h
P in the formula GT，e (t) -representing the power generated by the gas turbine during a period t, kW;
P GT，h (t) -representing the total amount of waste heat resources discharged by the gas turbine in t time periods, kW;
P h (t) -representing the total output heat power of the waste heat boiler in the period t, kW;
σ h -representing the efficiency of waste heat boiler waste heat resource utilization,%;
σ GT -representing the power generation efficiency,%;
σ loss -representing heat loss efficiency,%;
σ rec -representing the recovery utilization rate of waste heat resources in the power generation process of the gas turbine by the waste heat boiler,%.
4. The multi-energy complementary campus low-carbon integrated energy system of claim 2, characterized in that: the price difference of electric energy sources in different time periods is utilized, surplus wind energy is converted into natural gas to supply load or store through electric conversion when the electric load is low, and the natural gas is supplied to the electric load through a combined heat and power function device in the time period of electric load peak; the electric conversion gas is divided into two stages of electric hydrogen production and methanation reaction, wherein the methanation reaction is an exothermic reaction under the action of a catalyst, and is used for supplying domestic hot water, and a means of expanding the electric conversion gas to consume wind power so as to promote the cascade high-efficiency utilization of energy.
5. The multi-energy complementary campus low-carbon integrated energy system of claim 2, characterized in that: from the point of change of the energy consumption curve at the load side, the energy consumption index in the form of electric heat three energy sources is used for measuring the performance of the comprehensive demand response considering the replacement of electric energy, and the energy source comprehensive demand response information module expression is as follows:
Wherein:
P el0 (t) represents the electrical load, kW, before the integrated demand response is implemented for period t;
P el2 (t) represents the electrical load, kW, after the integrated demand response is implemented for period t;
P gl0 (t) represents the time period tApplying a gas load before comprehensive demand response, kW;
P gl2 (t) represents the gas load, kW, after the integrated demand response is performed in period t;
P gl0 (t) represents the thermal load, kW, before the integrated demand response is implemented for period t;
P hl2 (t) represents the thermal load, kW, after the integrated demand response is implemented for period t;
ΔP e (t) represents an amount of change in electric power for implementing the integrated demand response in the t period, kW;
ΔP g (t) represents the amount of change in the gas amount for implementing the integrated demand response in the t period, kW;
ΔP h (t) represents the amount of change in heat for implementing the integrated demand response for the period t, kW;
S C indicating the comprehensive energy satisfaction index,%.
6. The comprehensive park energy coordination scheduling method is based on the multi-energy complementary park low-carbon comprehensive energy system as claimed in claim 1, and is characterized in that:
step1, adopting a system optimization model, taking the minimum running cost as an objective function, and controlling by using a penalty function according to each constraint condition;
step2, taking the objective function value as the fitness value, and the fitness function is as follows
F IES ＝min(F IDR +F W +F G +F EN +F MC +F EX ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein:
F IES representing the total running cost of the system;
F IDR representing the cost of the demand response project, and the element;
F W The punishment cost of the wind power abandon phenomenon of the system is represented;
F G representing the fuel cost of the system multi-energy coupling unit;
F EN the environmental cost of the polluted gas generated by the operation of the system multi-energy coupling unit is represented;
F MC representing the running maintenance cost of the system multi-energy coupling unit；
F EX Representing the online purchasing cost of the upper cascade, which is equal to the difference between the purchasing cost and the selling cost.
Step3, initializing the population and setting parameters, wherein the population initialization and the parameter setting comprise the current iteration times t=1 and the individual positions x of the initial population fish i (t) population size NP, step size scaling factor Ct, maximum iteration number t max Leading inertial weight omega n Maximum guiding movement N max Foraging inertial weight ω f Maximum foraging speed V f And a maximum random spread velocity D max ；
Step4, calculating the fitness value of each individual fish;
step5, calculating three motion components of the individual fish by utilizing the motion mode Step of the individual fish, namely guiding the motion N i Foraging exercise F i Random diffusion motion D i ；
Step6, calculating the motions of the individual fish according to the Step of updating the fish positionAnd position x i (t); step7, carrying out genetic operation on the individual according to the cross operation Step and the mutation operation Step;
step8, calculating the fitness value of the new fish individuals after iteration, and carrying out selection operation by utilizing a selection operation Step to update the population;
Step9, comparing the change of the fitness value of the current fish individual, and dynamically updating the inertia weight of the fish individual;
step10, let t=t+1, update Step size scaling factor Ct, return to Step 3 Step until t reaches the set maximum iteration number t max 。
7. A non-volatile storage medium comprising a stored program, wherein the program when run controls a device in which the non-volatile storage medium resides to perform the method of claim 6.
8. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of claim 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116804467A (en) * | 2023-08-03 | 2023-09-26 | 广州永昱节能科技股份有限公司 | Intelligent control system and method for multi-energy complementary central heating |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112749462A (en) * | 2020-12-31 | 2021-05-04 | 西安交通大学 | Method, system and equipment for optimizing current operation parameters of comprehensive energy system |
| CN112785065A (en) * | 2021-01-27 | 2021-05-11 | 国网江苏省电力有限公司扬州供电分公司 | Comprehensive energy system planning method and system based on hybrid artificial fish swarm algorithm |
| CN115241931A (en) * | 2022-09-23 | 2022-10-25 | 国网浙江省电力有限公司宁波供电公司 | Garden comprehensive energy system scheduling method based on time-varying electrical carbon factor curve |
| CN115660142A (en) * | 2022-09-09 | 2023-01-31 | 国网福建省电力有限公司漳州供电公司 | Source-load-storage coordination optimization scheduling method for park comprehensive energy system |
| CN116167483A (en) * | 2022-11-17 | 2023-05-26 | 河海大学 | Park comprehensive energy system robust scheduling method considering stepped demand response |
-
2023
- 2023-06-09 CN CN202310682730.3A patent/CN116502921B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112749462A (en) * | 2020-12-31 | 2021-05-04 | 西安交通大学 | Method, system and equipment for optimizing current operation parameters of comprehensive energy system |
| CN112785065A (en) * | 2021-01-27 | 2021-05-11 | 国网江苏省电力有限公司扬州供电分公司 | Comprehensive energy system planning method and system based on hybrid artificial fish swarm algorithm |
| CN115660142A (en) * | 2022-09-09 | 2023-01-31 | 国网福建省电力有限公司漳州供电公司 | Source-load-storage coordination optimization scheduling method for park comprehensive energy system |
| CN115241931A (en) * | 2022-09-23 | 2022-10-25 | 国网浙江省电力有限公司宁波供电公司 | Garden comprehensive energy system scheduling method based on time-varying electrical carbon factor curve |
| CN116167483A (en) * | 2022-11-17 | 2023-05-26 | 河海大学 | Park comprehensive energy system robust scheduling method considering stepped demand response |
Non-Patent Citations (2)
| Title |
|---|
| 朱共山 等: "《能源互联网技术与产业》", vol. 1, 30 April 2017, 上海科学技术出版社, pages: 4 - 5 * |
| 贺兴 等: "《电力系统大数据与数字孪生系统》", vol. 1, 30 September 2022, pages: 77 - 78 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116804467A (en) * | 2023-08-03 | 2023-09-26 | 广州永昱节能科技股份有限公司 | Intelligent control system and method for multi-energy complementary central heating |
| CN116804467B (en) * | 2023-08-03 | 2024-03-01 | 东营市星创园区运营管理有限公司 | Intelligent control system and method for multi-energy complementary central heating |
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