CN113919754B - Block chain-based distributed state estimation method for comprehensive energy system - Google Patents
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Abstract
The invention discloses a distributed state estimation method of a comprehensive energy system based on a block chain, which comprises the following steps: establishing a block chain network of the comprehensive energy system, collecting measurement values of all energy subsystem nodes, establishing a comprehensive energy system model comprehensively considering the energy coupling relation, establishing a state estimation model of the comprehensive energy system, realizing distributed state estimation and realizing real-time control and optimized operation of the comprehensive energy system; according to the distributed architecture based on the block chain, the industry barriers of all energy systems in the comprehensive energy system are broken through, on the premise that the credibility of information is guaranteed, the distributed solving method is combined, a large amount of measurement data in each subsystem does not need to be transmitted to the dispatching center, the information transmission quantity among nodes is reduced, the state estimation speed and efficiency are improved, meanwhile, the information privacy of all energy subsystems is protected, the measurement sampling values of different time scales are selected for each system of electricity, heat and gas to carry out state estimation, and the accuracy of the state estimation is improved.
Description
Technical Field
The invention relates to the technical field of comprehensive energy system state estimation, in particular to a distributed state estimation method of a comprehensive energy system based on a block chain.
Background
Under the severe background of energy situation, the overall use efficiency of energy is not high, the requirements for interconnection fusion and complementary integration of heterogeneous energy systems are increasingly urgent, as a new form of energy revolution, the comprehensive energy system can improve the utilization efficiency of energy and promote the consumption of renewable energy through energy cascade utilization and coordination optimization, meanwhile, the utilization flexibility of energy is enhanced, multiple heterogeneous energy sources in different subsystems can be associated by utilizing energy coupling equipment in the comprehensive energy system, and the stable operation of the comprehensive energy system can be guaranteed only through accurate and reliable state estimation.
At present, state estimation of an electric power system, a thermodynamic system and a natural gas system has a certain result, but a method under a single energy domain cannot be directly applied to a comprehensive energy system facing a multi-energy domain, the physical laws of each system of electricity, heat and gas are greatly different, physical quantities involved in the state estimation are different, meanwhile, each energy subsystem is relatively independent, a trust problem exists, centralized state estimation is difficult, and the characteristics of block chain decentralization, tampering prevention and the like can provide a distributed credible framework, so that the scene can be well matched.
The block chain solves the trust problem between the barriers of the heterogeneous energy systems in the comprehensive energy system, provides a trusted environment for the state estimation of the comprehensive energy system, but common solving methods in the prior art are not suitable for distributed scenes, and the finding of a suitable distributed solving mode becomes a key.
At present, a centralized method is mostly adopted for the research of the state estimation of the comprehensive energy system, information cannot be interacted in real time due to different management bodies among energy subsystems, a data centralized center needs to be established, the cost is high, the safety risk of information leakage and tampering exists, the centralized estimation is not suitable for a large-scale comprehensive energy system, the estimation accuracy and efficiency are low, in addition, the time scale characteristics of various energy sources such as electricity, heat and gas in the comprehensive energy system are different, the running time scale of the electric power system is in the minute level, the natural gas system is in the hour level, the thermodynamic system is in the several hour level, but the existing estimation method lacks consideration of multiple time scales, and therefore the estimation accuracy is influenced.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a distributed state estimation method for a block chain-based integrated energy system, which can better fit a state estimation scenario of the integrated energy system based on the characteristics of trusted security of the block chain and solve the problems of high cost and low security of the existing centralized method.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme: a distributed state estimation method of an integrated energy system based on a block chain comprises the following steps:
the method comprises the following steps: firstly, setting numbers for nodes, lines and energy coupling equipment in each energy subsystem through an energy management center of the comprehensive energy system, broadcasting, then obtaining complete topology information of the comprehensive energy system at each energy subsystem scheduling center through the energy management center, writing the complete topology information in a block, and establishing a block chain network of the comprehensive energy system;
step two: acquiring corresponding measured values required for energy network state estimation through each energy subsystem node, setting measurement step length by combining different energy time characteristics, generating blocks after a period is reached, and calling measurement information through an energy subsystem scheduling center to estimate the state quantity of each energy subsystem to obtain the operating characteristics of an electric system, a heat system and a gas system;
step three: firstly, establishing each energy subsystem model by combining the operation characteristics of an electricity system, a heat system and a gas system, describing the operation state of each energy system, establishing a coupling equipment model aiming at the coupling relation among the electricity system, the heat system and the gas system, and establishing a comprehensive energy system model comprehensively considering the energy coupling relation according to the characteristics of each energy subsystem;
step four: aiming at a comprehensive energy system model, considering the multi-time scale characteristic of the comprehensive energy system, establishing a comprehensive energy system state estimation model by adopting a weighted minimum absolute value method, aiming at the nonlinear constraint involved in the comprehensive energy system state estimation model, introducing an intermediate variable for linearization treatment, wherein the periodic omega calculation formula of the state estimation of the comprehensive energy system state estimation model is as follows:
ω＝ω e Δα e ＝ω g Δα g ＝ω h Δα h
wherein, ω is e 、ω g 、ω h Respectively representing the variation factors of the electric power, natural gas and thermal system, delta alpha e 、Δα g 、Δα h Changing periods for each system;
step five: in combination with a block chain architecture, an ADMM algorithm is deployed in a dispatching center of a subsystem by means of intelligent contract editability in a block chain, the preset trigger condition is that a measuring value of each node is received after a state estimation period, and the preset rule is that each energy subsystem utilizes the ADMM algorithm to solve a state estimation model, so that distributed state estimation is realized;
step six: and the energy management center of the comprehensive energy system obtains the state value of each energy subsystem according to the estimated value, grasps the operation conditions of the comprehensive energy system and each energy subsystem and realizes the real-time control and optimized operation of the comprehensive energy system.
The further improvement lies in that: in the first step, the node information set is sent to each energy scheduling center through each node of each energy subsystem, wherein the node information set comprises the subsystem where the node is located, a node incidence matrix and a line number connected with the node, and the set comprising the coupling energy type, the number of the connected nodes and the line number information connected with the node is directly sent to the energy management center through the energy coupling equipment.
The further improvement lies in that: in the second step, the called measurement information comprises a measurement value of an electric power system formed by node injection active power, node injection reactive power, node voltage amplitude, branch transmission active power and branch transmission reactive power, a measurement value of a thermodynamic system formed by node injection flow, node pressure, pipeline flow, node heat supply temperature and node heat return temperature, and a measurement value of a natural gas system formed by node pressure, pipeline gas flow and node load.
The further improvement lies in that: in the second step, the state quantities of the energy subsystems comprise a state quantity of an electric power system formed by a node voltage amplitude value and a phase angle, a state quantity of a thermodynamic system formed by a pipeline flow, a node heat supply temperature and a node heat return temperature, and a state quantity of a natural gas system formed by a node pressure.
The further improvement is that: in the third step, each energy subsystem model comprises an electric power model, a hydraulic power model, a thermal power model and a natural gas model, wherein the electric power model is as follows:
wherein the content of the first and second substances,
indicating that at time t node i injects active power,
indicating that node i injects reactive power, U, at time t i，t Representing the voltage amplitude, U, of node i at time t j，t Representing the voltage amplitude, G, of node j at time t ij Representing the conductance between node i and node j at time t, B ij Representing susceptance, θ, between node i and node j at time t ij Is the phase angle difference between node i and node j,
representing the active power transmitted by branch i at time t,
representing the reactive power transmitted by branch i at time t.
The further improvement lies in that: the hydraulic model is as follows:
the node inflow water quantity is equal to the sum of the node outflow water quantity and the node injection flow quantity:
the pipeline hydraulic loss is related to water flow, and the algebraic sum of the hydraulic losses of all pipelines in the whole heat supply network is 0:
∑p bl ＝0
p bl，t ＝p i，t -p j，t
wherein the content of the first and second substances,
and
respectively represents the total water flow flowing out of the node i and the total water flow flowing into the node i at the time t, m i，t Indicating node i injection flow, p bl Is the hydraulic loss vector of the pipeline l, K l To be the impedance coefficient of the pipe/,
for the flow of water in the pipe, p i，t And p j，t The water pressure at the head end node i and the tail end node j of the pipeline l.
The further improvement lies in that: the thermodynamic model is as follows:
wherein the content of the first and second substances,
represents node injection thermal power, c p Is the specific heat capacity of water, m i，t Indicating the amount of traffic injected by the node i,
indicating the temperature of the water supply at node i at time t,
the return water temperature of the node i at the time t is shown,
and
respectively represents the total water flow out of the node i and the total water flow into the node i at the time t,
for the mixed temperature at node i at time t,
the pipe end temperature at injection node i for time t,
and
representing the temperature at the nodes at the end and head of the pipeline at time t,
is the ambient temperature at time t, pi is the heat transfer factor per unit length of the pipe, L is the length of the pipe,
is the water flow in the pipeline.
The further improvement is that: the natural gas model comprises node load, branch flow and node pressure, the natural gas mass flow of an inflow node in the natural gas model is equal to the natural gas mass flow of an outflow node, and the natural gas model comprises the following specific steps:
wherein the content of the first and second substances,
the natural gas injection amount of the node m at the time t is shown,
and
indicating the injection and outflow rates of the pipe l,
representing the set of branches associated with node m,
and
the natural gas flow rates at the inlet and outlet of the pressurizing station a at the time t are respectively expressed, and the flow rate relationship between the inlet and the outlet of the pressurizing station is as follows:
wherein, delta a Representing the efficiency coefficient of the pressurizing station a, c g For thermal capacity of gas, T g Which is indicative of the temperature of the natural gas,
and
respectively representing the pressure intensity of an inbound node and an outbound node of the pressurizing station at the time t, wherein beta is an expansion coefficient;
the branch flow and node load are shown in the following formula:
wherein the content of the first and second substances,
indicating the natural gas flow of branch l at time t, K r Is the pipe constant, s m，n Representing the natural gas flow direction, and the m node pressure intensity pi at the time t m，t Greater than n node pressure pi n，t When s is m，n Taking the value of +1, otherwise taking the value of-1,
representing the load of node i at time t, N m Representing nodes associated with m nodes.
The further improvement lies in that: in the third step, the coupling equipment model is as follows:
CHP: cogeneration is an important coupling device in an integrated energy system, generates electric energy by consuming natural gas, and supplies heat by using the remaining heat:
wherein the content of the first and second substances,
and
respectively represent the electric power and the thermal power output by the CHP at the time t,
indicating the amount of natural gas consumed by CHP at time t, L g Is the heat value of the natural gas,
and
respectively representing CHP electricity generation efficiency, heat generation efficiency and loss efficiency;
P2G: converting redundant electric energy into pollution-free natural gas
Wherein, the first and the second end of the pipe are connected with each other,
indicating the flow of gas generated at time P2G,
expressed as the amount of power consumed by P2G at time t,
P2G energy conversion efficiency;
gas-fired boiler and electric boiler:
wherein the content of the first and second substances,
represents the thermal power generated at time GB,
expressed as the amount of natural gas consumed by GB at time t,
is the energy conversion efficiency of GB;
wherein the content of the first and second substances,
represents the thermal power generated by the EB at time t,
expressed as the amount of power consumed by EB at time t,
the energy conversion efficiency for EB;
a gas turbine:
wherein the content of the first and second substances,
indicating that the gas turbine is producing electrical power at time t,
expressed as the amount of natural gas consumed by the gas turbine at time t,
the energy conversion efficiency of the gas turbine;
a compressor: the gas compression is carried out by utilizing electric energy in a natural gas system, the pressure intensity is changed so as to change the flow rate,
wherein, the first and the second end of the pipe are connected with each other,
represents the electric power consumed by the compressor at time t,
representing the natural gas flow through the compressor at time t, B MC For the compressor outlet and inlet pressure ratios,
to the compressor efficiency, θ is a constant.
The further improvement is that: in the fifth step, according to the coupling device model, the algorithm principle of the ADMM is as follows:
converting the above formula into: a form w-Cv =0, wherein:
C＝[a 1 ，a 2 ，a 3 ，a 4 ，a 5 ，a 6 ，a 7 ] T
and converting the objective function of the comprehensive energy system state estimation model and the coupling equipment model constraint into the following formula by using an ADMM algorithm:
after each energy subsystem obtains basic data of a measurement value, the above formula is solved simultaneously and parallelly, the coupling variables w and v are shared with other subsystems, each subsystem updates the multiplier lambda after obtaining the coupling variables, and each energy subsystem is iterated continuously until a convergence criterion is met.
The invention has the beneficial effects that: the distributed architecture based on the block chain breaks industry barriers of all energy systems in the integrated energy system, combines a distributed solving method on the premise of guaranteeing credibility of information, does not need to transmit a large amount of measurement data in each subsystem to a dispatching center, reduces the information transmission quantity among nodes, improves the state estimation speed and efficiency, protects the information privacy of all energy subsystems, and aims at large time scale difference of all subsystems of electricity, heat and gas in the operation process, fastest change speed of an electric power system, slowest change speed of a thermodynamic system and centered change speed of a natural gas system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method in an embodiment of the invention;
FIG. 2 is a schematic diagram of an energy coupling relationship of an integrated energy system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an integrated energy system blockchain network according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Referring to fig. 1, 2 and 3, the embodiment provides a distributed state estimation method for an integrated energy system based on a block chain, including the following steps:
the method comprises the following steps: firstly, setting numbers for nodes, lines and energy coupling equipment in each energy subsystem through an energy management center of an integrated energy system and broadcasting, and sending node information to each energy scheduling center in a set mode through each node of each energy subsystem, wherein the node information comprises the subsystem where the node is located, a node incidence matrix and a line number connected with the node;
step two: acquiring corresponding energy network state estimation required quantity measured values by each energy subsystem node respectively, setting measurement step length by combining different energy time characteristics, generating a block after a period is reached, calling measurement information by an energy subsystem scheduling center to estimate state quantity of each energy subsystem, and obtaining operation characteristics of an electricity system, a heat system and a gas system, wherein the called measurement information comprises measurement values of an electric system formed by node injection active power, node injection reactive power, node voltage amplitude, branch transmission active power and branch transmission reactive power, measurement values of a thermal system formed by node injection flow, node pressure, pipeline flow, node heat supply temperature and node heat return temperature, and measurement values of a natural gas system formed by node pressure, pipeline gas flow and node load, and the state quantity of each energy subsystem comprises the state quantity of the electric system formed by node voltage amplitude and phase angle, the state quantity of the thermal system formed by pipeline flow, node heat supply temperature and node heat return temperature, and the state quantity of the natural gas system formed by node pressure;
step three: firstly, establishing each energy subsystem model by combining the operation characteristics of an electricity system, a heat system and a gas system, describing the operation state of each energy system, establishing a coupling equipment model aiming at the coupling relation among the electricity system, the heat system and the gas system, and establishing a comprehensive energy system model comprehensively considering the energy coupling relation according to the characteristics of each energy subsystem;
the power system in this embodiment is modeled for a common ac system, and the model is as follows:
wherein the content of the first and second substances,
indicating that node i injects active power at time t,
indicating that node i injects reactive power, U, at time t i，t Representing the magnitude of the voltage at node i at time t, U j，t Representing the voltage amplitude, G, of node j at time t ij Representing the conductance between node i and node j at time t, B ij Representing susceptance, θ, between node i and node j at time t ij Is the phase angle difference between node i and node j,
representing the active power transmitted by branch i at time t,
representing the reactive power transmitted by the branch circuit l at the moment t;
the thermodynamic system modeling of the embodiment comprises a thermodynamic model and a hydraulic model, wherein the hydraulic model is description of the state of a heat transfer medium in the thermodynamic system and mainly considers node pressure and node flow, the thermodynamic model mainly aims at temperature distribution of nodes and pipelines in the thermodynamic system, and main variables comprise node thermal power, node heat supply temperature and regenerative temperature, wherein the hydraulic model has the following formula:
the node inflow water quantity is equal to the sum of the node outflow water quantity and the node injection flow quantity:
the pipeline hydraulic loss is related to water flow, and the algebraic sum of the hydraulic losses of all pipelines in the whole heat supply network is 0:
∑p bl ＝0
p bl，t ＝p i，t -p j，t
wherein the content of the first and second substances,
and
respectively represents the total water flow flowing out of the node i and the total water flow flowing into the node i at the time t, m i，t Indicating node i injection flow, p bl Is the hydraulic loss vector of the pipeline l, K l In order to be the impedance coefficient of the pipe l,
for water flow in pipes, p i，t And p j，t The water pressure of a head end node i and a tail end node j of a pipeline l;
the thermodynamic model is as follows:
wherein the content of the first and second substances,
indicating node injection thermal power, c p Is the specific heat capacity of water, m i，t Indicating that the node i is injecting traffic,
indicating the temperature of the water supply at node i at time t,
the return water temperature of the node i at the time t is shown,
and
respectively representing the total water flow out of the node i and the total water flow into the node i at the moment t,
for the mixed temperature at node i at time t,
the pipe end temperature at injection node i for time t,
and
representing the temperature at the nodes at the end and head of the pipeline at time t,
is the ambient temperature at time t, pi is the heat transfer factor per unit length of the pipe, L is the length of the pipe,
water flow in the pipeline;
the embodiment establishes a natural gas system model relating to node load, branch flow and node pressure for the transmission characteristics of a natural gas system, wherein the mass flow of natural gas flowing into a node is equal to the mass flow of natural gas flowing out of the node, and the natural gas system model specifically comprises the following steps:
wherein the content of the first and second substances,
indicating the natural gas injection quantity at node m at time t,
and
indicating the injection and outflow rates of the pipe l,
representing the set of branches associated with node m,
and
the natural gas flow rates at the inlet and outlet of the pressurizing station a at the time t are respectively expressed, and the flow rate relationship between the inlet and the outlet of the pressurizing station is as follows:
wherein, delta a Representing the efficiency factor of the pressurizing station a, c g For gas heat capacity, T g Which is indicative of the temperature of the natural gas,
and
respectively representing the pressure intensity of an inbound node and an outbound node of the pressurizing station at the time t, wherein beta is an expansion coefficient;
the branch flow and node load are shown in the following formula:
wherein the content of the first and second substances,
indicating the natural gas flow, K, of branch l at time t r Is the pipe constant, s m，n The flow direction of natural gas is shown, and m node pressure intensity pi at t moment m，t Greater than n node pressure pi n，t When s is m，n Taking the value of +1, otherwise taking the value of-1,
represents the load of node i at time t, N m Representing nodes associated with m nodes;
the key of the comprehensive energy system for truly realizing the multi-energy complementation lies in various energy coupling devices, and the embodiment establishes a coupling device model aiming at the coupling relationship among electricity, heat and gas as follows:
CHP (CHP): cogeneration is an important coupling device in an integrated energy system, generates electric energy by consuming natural gas, and supplies heat by using the remaining heat:
wherein, the first and the second end of the pipe are connected with each other,
and
respectively represent the electric power and the thermal power output by the CHP at the time t,
indicating the amount of natural gas consumed by CHP at time t, L g Is the heat value of the natural gas,
and
respectively representing CHP electricity generation efficiency, heat generation efficiency and loss efficiency;
P2G: converting redundant electric energy into pollution-free natural gas
Wherein, the first and the second end of the pipe are connected with each other,
indicating the flow of gas generated at time P2G,
expressed as the amount of power consumed by P2G at time t,
P2G energy conversion efficiency;
gas-fired boilers and electric boilers:
wherein, the first and the second end of the pipe are connected with each other,
represents the thermal power generated at time GB,
expressed as the amount of natural gas consumed by GB at time t,
is an energy conversion efficiency of GB;
wherein, the first and the second end of the pipe are connected with each other,
represents the thermal power generated by EB at time t,
expressed as the amount of power consumed by EB at time t,
is the energy conversion efficiency of the EB;
a gas turbine:
wherein the content of the first and second substances,
indicating that the gas turbine is producing electrical power at time t,
expressed as the amount of natural gas consumed by the gas turbine at time t,
the energy conversion efficiency of the gas turbine;
a compressor: the gas compression is carried out in a natural gas system by utilizing electric energy, the pressure intensity is changed, the flow rate is changed,
wherein the content of the first and second substances,
represents the electric power consumed by the compressor at time t,
representing the natural gas flow through the compressor at time t, B MC For the compressor outlet and inlet pressure ratios,
θ is a constant for compressor efficiency;
step four: aiming at the comprehensive energy system model, considering the multi-time scale characteristic of the comprehensive energy system, establishing a comprehensive energy system state estimation model by adopting a weighted minimum absolute value method, and introducing an intermediate variable for linearization treatment aiming at nonlinear constraint involved in the comprehensive energy system state estimation model;
in order to solve the state of the integrated energy system in a certain time period, instead of the current time state, the time characteristics of the subsystems need to be considered, the variation cycle of the power system in the integrated energy system is shortest, the variation cycle of the natural gas system is second, and the variation cycle of the thermodynamic system is longest, so that in the cycle ω of state estimation, the time intervals of the measured values obtained by each subsystem are different, which is specifically as follows:
ω＝ω e Δα e ＝ω g Δα g ＝ω h Δα h
wherein, ω is e 、ω g 、ω h Respectively representing the variation factors of the electric power, natural gas and thermal system, delta alpha e 、Δα g 、Δα h The period is changed for each system.
After the sampling interval of the measurement values in the state estimation period is determined, a state estimation model is established based on the weighted absolute value minimum, the target is the weighted absolute value of the error between the minimum estimation value and the measurement value, and the specific objective function is as follows:
wherein D is e 、D g 、D h Representing the state errors of the electric power system, the natural gas system and the thermal system,
representing the state quantities, R, of the various energy subsystems e 、R g 、R h The covariance matrix of the measurement error of each subsystem is determined by the historical data set,
the measured values of each subsystem at the time t are respectively,
the estimated values of each subsystem at the time t are shown,
forming a comprehensive energy system state estimation model by the objective function and the comprehensive energy system model in the step three for the measurement equation of each subsystem;
in this embodiment, the state estimation model of the integrated energy system is nonlinear, and for the convenience of solution, an intermediate variable y is added to the present embodiment to decompose the nonlinear measurement equation into the following formula:
y′＝f(y)
y′＝Bx′+ξ y′
x＝g(x′)
and expressing the intermediate variable y in a nonlinear measurement equation of each energy subsystem, and finally obtaining the state quantity x in each energy subsystem, wherein the first formula and the third formula are solved by using an ADMM algorithm based on a weighted absolute minimum function, and the second formula and the fourth formula are locally subjected to nonlinear transformation in each energy subsystem.
Step five: in combination with a block chain architecture, an ADMM algorithm is deployed in a dispatching center of the subsystems by means of intelligent contract editability in the block chain, the preset triggering condition is that measuring values of all nodes are received after a state estimation period is reached, and the preset rule is that a state estimation model is solved for all energy subsystems by the ADMM algorithm, so that distributed state estimation is achieved;
the algorithm principle of the ADMM is as follows:
because the energy coupling equipment models are all equations and the coefficients are all constants, the following are specific:
the above formula can be converted into: w-Cv =0, wherein,
C＝[a 1 ，a 2 ，a 3 ，a 4 ，a 5 ，a 6 ，a 7 ] T
and converting the objective function and the coupling device equality constraint of the step four into the following formula by using an ADMM algorithm:
after each energy subsystem obtains basic data such as measurement values, the above formula is solved in parallel, the coupling variables w and v are shared with other subsystems, each subsystem updates the multiplier lambda after obtaining the coupling variables, and each energy subsystem iterates continuously until the convergence criterion is met;
λ k+1 ＝λ k +ρ(w-Cv)
step six: and the energy management center of the comprehensive energy system obtains the state values of all the energy subsystems according to the estimated values, grasps the operation conditions of the comprehensive energy system and all the energy subsystems and realizes the real-time control and the optimized operation of the comprehensive energy system.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A distributed state estimation method of an integrated energy system based on a block chain is characterized by comprising the following steps:
the method comprises the following steps: firstly, setting numbers for nodes, lines and energy coupling equipment in each energy subsystem through an energy management center of the comprehensive energy system, broadcasting, then obtaining complete topology information of the comprehensive energy system at each energy subsystem scheduling center through the energy management center, writing the complete topology information in a block, and establishing a block chain network of the comprehensive energy system;
step two: acquiring corresponding energy network state estimation required quantity measured values through each energy subsystem node, setting measurement step length by combining different energy time characteristics, generating blocks after a period is reached, and calling measurement information through an energy subsystem scheduling center to estimate the state quantity of each energy subsystem to obtain the running characteristics of an electric system, a thermal system and a gas system;
step three: firstly, establishing each energy subsystem model by combining the operation characteristics of an electric system, a thermal system and a gas system, describing the operation state of each energy system, establishing a coupling equipment model aiming at the coupling relation among the electric system, the thermal system and the gas system, and establishing a comprehensive energy system model comprehensively considering the energy coupling relation according to the characteristics of each energy subsystem;
step four: aiming at the comprehensive energy system model, considering the multi-time scale characteristics of the comprehensive energy system, establishing a comprehensive energy system state estimation model by adopting a weighted minimum absolute value method, aiming at the nonlinear constraint involved in the comprehensive energy system state estimation model, introducing an intermediate variable for linearization treatment, wherein the periodic omega calculation formula of the state estimation of the comprehensive energy system state estimation model is as follows:
ω＝ω e Δα e ＝ω g Δα g ＝ω h Δα h
wherein, ω is e 、ω g 、ω h Respectively representing the variation factors of the electric power, natural gas and thermal system, delta alpha e 、Δα g 、Δα h Changing periods for each system;
after the sampling interval of the measurement values in the state estimation period is determined, the state estimation model is established based on the weighted absolute value minimum, the target is the weighted absolute value of the error between the minimum estimation value and the measurement value, and the specific objective function is as follows:
wherein D is e 、D g 、D h Representing the state errors of an electric power system, a natural gas system and a thermal system,
representing the state quantity, R, of each energy subsystem e 、R g 、R h The covariance matrix of the measurement error of each subsystem is determined by the historical data set,
the measured values of the subsystems at the time t are respectively,
the estimated values of each subsystem at the time t are shown,
forming a comprehensive energy system state estimation model by the objective function and the comprehensive energy system model in the step three for the measurement equation of each subsystem;
the coupling device model is as follows:
CHP: cogeneration is an important coupling device in an integrated energy system, generates electric energy by consuming natural gas, and supplies heat by using the remaining heat:
wherein, the first and the second end of the pipe are connected with each other,
and
respectively represent the electric power and the thermal power output by the CHP at the time t,
indicating the amount of natural gas consumed by CHP at time t, L g Is the heat value of the natural gas,
and
respectively representing CHP electricity generation efficiency, heat generation efficiency and loss efficiency;
P2G: converting redundant electric energy into pollution-free natural gas
Wherein the content of the first and second substances,
indicating the flow of gas generated at time P2G,
expressed as the amount of power consumed by P2G at time t,
P2G energy conversion efficiency;
gas-fired boiler and electric boiler:
wherein the content of the first and second substances,
represents the thermal power generated at time GB,
expressed as the amount of natural gas consumed by GB at time t,
is the energy conversion efficiency of GB;
wherein the content of the first and second substances,
represents the thermal power generated by EB at time t,
expressed as the amount of power consumed by EB at time t,
energy conversion to EBEfficiency;
a gas turbine:
wherein the content of the first and second substances,
indicating that the gas turbine is producing electrical power at time t,
expressed as the amount of natural gas consumed by the gas turbine at time t,
the energy conversion efficiency of the gas turbine;
a compressor: the gas compression is carried out by utilizing electric energy in a natural gas system, the pressure intensity is changed so as to change the flow rate,
wherein, the first and the second end of the pipe are connected with each other,
represents the electric power consumed by the compressor at time t,
representing the natural gas flow through the compressor at time t, B MC For the compressor outlet and inlet pressure ratios,
to compressor efficiency, θ is a constant; step five: in combination with a block chain architecture, an ADMM algorithm is deployed in a dispatching center of a subsystem by means of intelligent contract editability in a block chain, and a preset triggering condition is state estimationAfter a period, measuring values of all nodes are received, and a preset rule is adopted for solving a state estimation model for each energy subsystem by using an ADMM algorithm, so that distributed state estimation is realized;
according to the coupling device model, the algorithm principle of the ADMM is as follows:
converting the above formula into: a form of w-Cv =0, wherein:
C＝[a 1 ,a 2 ,a 3 ,a 4 ,a 5 ,a 6 ,a 7 ] T
and converting the objective function of the comprehensive energy system state estimation model and the coupling equipment model constraint into the following formula by using an ADMM algorithm:
after each energy subsystem obtains basic data of a measurement value, the above formula is solved in parallel at the same time, the coupling variables w and v are shared with other subsystems, each subsystem updates the multiplier lambda after obtaining the coupling variables, and each energy subsystem is iterated continuously until the convergence criterion is met;
step six: and the energy management center of the comprehensive energy system obtains the state value of each energy subsystem according to the estimated value, grasps the operation conditions of the comprehensive energy system and each energy subsystem and realizes the real-time control and optimized operation of the comprehensive energy system.
2. The distributed state estimation method for the integrated energy system based on the blockchain according to claim 1, wherein the distributed state estimation method comprises the following steps: and in the first step, node information sets are sent to each energy scheduling center through each node of each energy subsystem, wherein each node information set comprises a subsystem where the node is located, a node incidence matrix and a line number connected with the node, and the sets comprising coupling energy types, the numbers of the connected nodes and the line number information connected with the node are directly sent to the energy management center through the energy coupling equipment.
3. The distributed state estimation method of the integrated energy system based on the block chain according to claim 1, characterized in that: in the second step, the called measurement information includes a measurement value of an electric power system composed of node injection active power, node injection reactive power, node voltage amplitude, branch transmission active power and branch transmission reactive power, a measurement value of a thermodynamic system composed of node injection flow, node pressure, pipeline flow, node heat supply temperature and node heat return temperature, and a measurement value of a natural gas system composed of node pressure, pipeline gas flow and node load.
4. The distributed state estimation method of the integrated energy system based on the block chain according to claim 1, characterized in that: in the second step, the state quantities of the energy subsystems comprise a state quantity of an electric power system formed by a node voltage amplitude value and a phase angle, a state quantity of a thermodynamic system formed by a pipeline flow, a node heat supply temperature and a node heat return temperature, and a state quantity of a natural gas system formed by a node pressure.
5. The distributed state estimation method for the integrated energy system based on the blockchain according to claim 1, wherein the distributed state estimation method comprises the following steps: in the third step, each energy subsystem model comprises an electric power model, a hydraulic power model, a thermal power model and a natural gas model, wherein the electric power model is as follows:
wherein the content of the first and second substances,
indicating that node i injects active power at time t,
indicating reactive power injected into node i at time tRate, U i,t Representing the magnitude of the voltage at node i at time t, U j,t Representing the voltage amplitude, G, of node j at time t ij Representing the conductance between node i and node j at time t, B ij Representing susceptance, θ, between node i and node j at time t ij Is the phase angle difference between node i and node j,
representing the real power transmitted by branch i at time t,
representing the reactive power transmitted by branch i at time t.
6. The distributed state estimation method of the integrated energy system based on the blockchain according to claim 5, wherein: the hydraulic model is as follows:
the node inflow water quantity is equal to the sum of the node outflow water quantity and the node injection flow quantity:
the hydraulic loss of the pipeline is related to water flow, and the algebraic sum of the hydraulic losses of the pipelines in the whole heat supply network is 0:
∑p bl ＝0
p bl,t ＝p i,t -p j,t
wherein, the first and the second end of the pipe are connected with each other,
and
respectively represent the outflow nodes at the time ti total water flow and node i total water flow, m i,t Indicating node i injection flow, p bl Is the hydraulic loss vector of the pipeline l, K l To be the impedance coefficient of the pipe/,
for the flow of water in the pipe, p i,t And p j,t The water pressure at the head end node i and the tail end node j of the pipeline l.
7. The distributed state estimation method of the integrated energy system based on the blockchain according to claim 5, wherein: the thermodynamic model is as follows:
wherein, the first and the second end of the pipe are connected with each other,
indicating node injection thermal power, c p Is the specific heat capacity of water, m i,t Indicating that the node i is injecting traffic,
indicates the temperature of the supplied water at the node i at time t,
the return water temperature of the node i at the time t is shown,
and
respectively representing the total water flow out of the node i and the total water flow into the node i at the moment t,
for the mixed temperature at node i at time t,
the pipe end temperature at injection node i for time t,
and
representing the temperature at the nodes at the end and head of the pipeline at time t,
is the ambient temperature at time t, pi is the heat transfer factor per unit length of the pipe, C is the length of the pipe,
is the water flow in the pipeline.
8. The distributed state estimation method for the integrated energy system based on the blockchain according to claim 5, wherein the distributed state estimation method comprises the following steps: the natural gas model comprises node load, branch flow and node pressure, the mass flow of natural gas flowing into a node in the natural gas model is equal to the mass flow of natural gas flowing out of the node, and the natural gas model comprises the following concrete steps:
wherein the content of the first and second substances,
indicating the natural gas injection quantity at node m at time t,
and
indicating the injection and outflow rates of the pipe l,
representing the set of branches associated with node m,
and
the natural gas flows at the inlet and outlet of the pressurizing station a at time t are respectively expressed, and the inlet and outlet flows of the pressurizing station are related as follows:
wherein, delta a Representing the efficiency factor of the pressurizing station a, c g For gas heat capacity, T g Which is indicative of the temperature of the natural gas,
and
respectively representing the pressures of an inbound node and an outbound node of the pressurizing station at the time t, wherein beta is an expansion coefficient;
the branch flow and node load are shown in the following formula:
wherein the content of the first and second substances,
indicating the natural gas flow, K, of branch l at time t r Is the pipe constant, s m,n Representing the natural gas flow direction, and the m node pressure intensity pi at the time t m,t Greater than n node pressure pi n,t When s is m,n Taking the value of +1, otherwise taking the value of-1,
representing the load of node i at time t, N m Representing nodes associated with m nodes.
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