CN114285097A - Distributed energy storage aggregation participation power grid peak regulation strategy under multi-energy internet - Google Patents
Distributed energy storage aggregation participation power grid peak regulation strategy under multi-energy internet Download PDFInfo
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Abstract
The invention relates to a coordinated operation method of a distributed energy storage aggregation participation power grid, in particular to a peak regulation strategy of the distributed energy storage aggregation participation power grid under a multi-energy interconnection network. The access capability of renewable energy sources in the power distribution network is improved, the economic benefits of the power distribution network and users are increased, and powerful support is provided for the development of the intelligent power distribution network. The method specifically comprises the following steps: step 1, establishing a distributed energy storage aggregation participation power grid peak regulation overall control operation framework; step 2, establishing a distributed energy storage aggregation and multi-energy collaborative operation multi-objective optimization model; and 3, carrying out staged optimization solving on the distributed energy storage aggregation and multi-energy collaborative operation multi-target optimization model based on a heuristic intelligent algorithm.
Description
Technical Field
The invention relates to a coordinated operation method of a distributed energy storage aggregation participation power grid, in particular to a peak regulation strategy of the distributed energy storage aggregation participation power grid under a multi-energy interconnection network.
Background
The influence of the future high-proportion distributed energy access on the power grid is gradually obvious, and an energy storage system for organically integrating multiple types of energy storage to realize multi-energy complementation is a main form of the future energy storage system applied to the power grid. The distributed energy storage is used as a key technology for interconnection of a novel power grid and energy, so that the operation stability of the system can be improved, the electric energy quality is improved, the flexibility of power grid adjustment and the power supply reliability are improved, load peak load elimination and valley filling are realized, and the system needs to respond, adjust the peak and the like. However, the distributed energy storage single bodies have small capacity and unbalanced distribution, and have the problems of difficult system operation, management and operation and the like. How to solve the unified management and application of distributed energy storage and research the economic operation mechanism of different types of distributed energy storage resources. Therefore, a large-scale distributed energy storage aggregation method is provided, based on a cloud platform technology, coordinated operation of distributed energy storage and multiple energy sources of a power grid is achieved, and the method is a key problem and has great significance for improving the active supporting capability of the distributed energy storage of the power grid on the power grid.
At present, research on distributed energy storage aggregation peak shaving at home and abroad mainly focuses on the aspect of new energy peak shaving operation, and research on the distributed energy storage aggregation combined multi-energy peak shaving is relatively less. Through the research of the invention, the comprehensive application of various distributed energy storages can be realized, the matching of multiple energy sources and the peak valley of the power grid is realized, the balance of the supply and demand of the power grid is promoted, and the stable operation of the power grid is ensured. By means of a distributed energy storage aggregation method, the echelon utilization of various energy storage power supplies with different characteristics can be realized, the multi-energy active participation in the interactive operation of the power grid is realized to the maximum extent, and the efficient, stable and economic operation of the power distribution network is promoted. By means of a distributed energy storage operation mechanism and a cloud energy storage platform of project research, the distributed energy storage system can solve the problems of distributed grid connection and centralized application mechanism of various types of distributed energy resources, and further high-density distributed energy storage access and multi-energy global optimization management are achieved.
The research on distributed energy storage aggregation peak shaving at home and abroad is mainly focused on the aspect that a small number of distributed energy storage aggregations participate in the operation of a power grid, and the research on the large-scale distributed energy storage aggregation combined multi-energy peak shaving is relatively less. In the existing research, a battery energy storage technology is mostly used for tracking planned power generation, smoothing wind power output, improving the capacity of accessing wind power generation to a power grid, reducing wind abandon of a wind power plant, improving the utilization hours of wind power, receiving power grid dispatching in new energy grid-connected operation, and playing the roles of transient active output emergency response and transient electric compression emergency support. The method is applied to the field of power grid peak shaving, is limited to a local and small-scale range, and relatively few researches in the field of large-scale peak shaving are applied.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a strategy for participating in peak shaving of a power grid by distributed energy storage aggregation under a multi-energy Internet. The distributed energy storage device has the advantages that the operation reliability of the system can be effectively improved, the electric energy quality of the system can be improved, the access capacity of renewable energy sources in the power distribution network can be improved, the economic benefits of the power grid and users can be increased, and powerful support is provided for the development of the intelligent power distribution network.
Aiming at the problem that the distributed energy storage aggregation optimization scheduling of the regional power grid participates in the power grid peak shaving under multi-energy interconnection, the peak shaving operation characteristics and the influence factors of the existing distributed energy storage of the regional power grid are firstly researched, the distributed energy storage aggregation is established by integrating the operation parameters and the constraint conditions, the staged optimization solving strategy is proposed, a heuristic intelligent optimization algorithm method is used, the algorithm and the data of the existing scheduling system are fully utilized, and a feasible technical theoretical basis is provided for the implementation of the distributed energy storage participation regional power grid peak shaving.
In order to achieve the purpose, the technical scheme is adopted, and the situation that distributed energy storage cannot participate in power grid peak shaving at present is solved in a large-scale regional power grid system; the distributed energy storage monomers are aggregated into a plurality of groups, and distributed energy storage aggregation and multi-energy combined operation are realized to participate in power grid peak regulation in real time through staged optimization control and scheduling system coordination control, so that the distributed energy storage aggregation is participated in coordination control and stable operation of the power grid.
The method specifically comprises the following steps:
and 3, carrying out staged optimization solving on the distributed energy storage aggregation and multi-energy collaborative operation multi-target optimization model based on a heuristic intelligent algorithm.
(step 4, establishing a Matlab/Simulink platform for the distributed energy storage aggregation and multi-energy combined participation in a power grid peak regulation strategy;
and 5, performing simulation analysis on effectiveness of the distributed energy storage polymerization and multi-energy combined participation power grid peak regulation strategy, and verifying that the economy and stability of the power system peak regulation can be realized after more than 1000 distributed energy storage monomers are polymerized under the multi-energy interconnection. )
Further, the step 1 comprises:
step 1.1, establishing a distributed energy storage aggregation and multi-energy collaborative operation multi-objective optimization model, comprising the following steps: the system comprises a distributed energy storage monomer model, a distributed energy storage aggregation scheduling model and a multiple energy scheduling model;
step 1.2, (because the established multisource joint peak shaving optimization model of the power system is a nonlinear, multi-stage, large-scale optimization model with dynamic coupling relation and mixed continuous variables and discrete variables), distribution and staged solving rules are provided when the resolving strategy analysis is carried out;
step 1.3, a heuristic intelligent optimization algorithm is used, and the feasibility and the stability of the distributed energy storage aggregation and the multi-energy interconnection participating in the peak regulation of the power grid are proved by utilizing the algorithm and the data of the existing dispatching system. (further verification of the feasibility of the invention.)
Further, the step 2 comprises:
step 2.1, establishing a charge-discharge model of the energy storage monomer, and aggregating a plurality of energy storages into an equivalent group;
step 2.2, after all the stored energy is aggregated, equivalent concentrated rated power, equivalent concentrated rated capacity and equivalent concentrated charging and discharging efficiency are used for representing the whole distributed energy storage system;
step 2.3, (the energy storage polymer system is combined with other energy sources to jointly participate in system peak shaving, and can also independently participate in system peak shaving when the energy storage capacity reaches a certain scale), for the multi-source combined peak shaving optimization scheduling of the power grid, the following objective functions are established:
J＝Min
in the objective function, the first item represents the power generation cost of the thermal power generating unit in peak shaving scheduling; the second item is used for representing the generating cost of the hydroelectric generating set in peak regulation scheduling; the third item is that the wind power generation cost in peak shaving scheduling is represented so as to ensure that the system receives the wind power maximally under the condition that the operating condition meets, and the cost of a high-cost peak shaving power supply is not sacrificed; the fourth item represents the peak shaving cost of the pumped storage unit in peak shaving scheduling; the fifth item is to characterize the peak shaving cost of the energy storage polymer in peak shaving scheduling; the sixth item represents the starting and stopping peak regulation cost of the thermal power generating unit in peak regulation scheduling; characterizing peak shaving cost of the heat storage device in peak shaving scheduling;
in the formula: f. ofGi: a thermal power generation cost function; f. ofHi: a hydroelectric power generation cost function; f. ofwi: wind power generation cost function; cPu: peak shaving cost coefficient of the pumped storage unit; cN: energy storage polymer cost factor; cTOf: the starting and stopping cost coefficient of the thermal power generating unit; f. ofR: cost functions of heat storage and heat release power; lambda [ alpha ]GA、λH、λw、λP、 λN、λGB、λR: weighting coefficients of the peak shaving cost of each peak shaving power supply in the objective function;
inputting operation identification parameters of the water pumping and energy storage unit;
starting and stopping identification parameters of the thermal power generating unit;
the energy storage device comprises a thermal power generating unit, a hydroelectric generating unit, a wind power generating capacity, a peak shaving capacity of a pumped storage unit, an operating capacity of an energy storage polymer and a heat storage or release capacity of a heat storage device.
Further, the step 3 comprises:
step 3.1, obtaining relevant parameters comprises: the method comprises the following steps of (1) carrying out staged optimization solving on a distributed energy storage aggregation and multi-energy collaborative operation multi-target optimization model by adopting a control variable aggregation means;
3.2, determining a scheduling strategy of the high-cost peak shaving power supply, neglecting the operation cost of the conventional power supply, taking the peak shaving cost of the high-cost peak shaving power supply as the lowest target, and determining the scheduling strategy of each peak shaving power supply based on a heuristic intelligent algorithm to ensure that the peak shaving scheduling cost of the whole high-cost peak shaving power supply is the lowest;
3.3, under the condition that the high-cost peak shaving power supply is determined, solving the optimal scheduling of the conventional peak shaving power supply by taking the lowest running cost of the conventional peak shaving power supply as an optimal target; and obtaining a global optimal resolving strategy of the optimization problem according to the basic principle of staged optimization.
Compared with the prior art, the invention has the beneficial effects.
The distributed energy storage aggregation participation power grid peak regulation strategy based on the heuristic intelligent optimization algorithm under the multi-energy interconnection is characterized in that distributed energy storage monomers are aggregated into a plurality of groups in a large-scale regional power grid system aiming at the condition that the distributed energy storage can not participate in the power grid peak regulation at present, and the distributed energy storage aggregation and the multi-energy combined operation are realized to participate in the power grid peak regulation in real time through the staged optimization control and the coordinated control of a scheduling system, so that the distributed energy storage aggregation and the coordinated control and the stable operation of the power grid are realized.
The distributed energy storage polymerization system is convenient for commercial development, the requirement that a large-scale distributed energy storage polymerization platform and various energy sources are interconnected to participate in the operation of a power grid is higher along with the increase of the application of the distributed energy storage technology, the development of the coordination control method of the system has great requirements, and the distributed energy storage polymerization system has good commercial development prospect.
Drawings
The invention is further described with reference to the following figures and detailed description. The scope of the invention is not limited to the following expressions.
Fig. 1 is a block diagram of a distributed energy storage aggregation scheduling architecture.
Fig. 2 is a diagram of a peak shaver machine for an electric power system.
Fig. 3 is a block diagram of the basic steps of high cost peak shaver scheduling.
Fig. 4 is a solution strategy diagram based on a heuristic intelligent algorithm.
Fig. 5 is a diagram of the peak shaving effect of the distributed energy storage aggregation participating power grid.
Detailed Description
The invention provides a basic idea of a strategy for participating in power grid cooperative operation by distributed energy storage aggregation under multi-energy interconnection, which is as follows: aiming at the joint participation of the energy storage monomer polymerization and various energy sources in grid-connected peak regulation operation, a plurality of energy storage monomers are polymerized into a group, and the aim of the distributed energy storage polymerization and the interconnection of the various energy sources in a large scale to participate in the peak regulation control of a power grid is fulfilled by utilizing the staged optimization control and the control of an existing scheduling system.
As shown in fig. 1-5, the present invention comprises the steps of:
step 1), establishing a distributed energy storage aggregation participation power grid peak regulation overall control operation framework;
(1) establishing a distributed energy storage aggregation and multi-energy collaborative operation multi-objective optimization model: a distributed energy storage monomer model, a distributed energy storage aggregation scheduling model and a plurality of energy scheduling models are required to be established respectively.
Distributed energy storage monomer model:
in the formula: SOC0The SOC value is the initial time of energy storage; pchAnd PdisThe charging power and the discharging power between t and t-1 are respectively, and at most, one charging power and one discharging power are not 0; etachAnd ηdisRespectively charge and discharge efficiency; Δ t is a duration of charging and discharging; srateIs the rated capacity of the stored energy.
Constraint conditions of energy storage:
charging:
discharging:
in the formula: pirateRated charge-discharge power for the stored energy i; pichAnd PidisRespectively is the charge and discharge power of the energy storage i; SOCiThe current SOC value of the stored energy i.
After all the stored energy is aggregated, the whole distributed energy storage system can be represented only by equivalent concentrated rated power, equivalent concentrated rated capacity and equivalent concentrated charging and discharging efficiency. When each stored energy has the same charging and discharging duration under the rated power, the corresponding equivalent concentrated stored energy parameters are defined as follows:
the equivalent concentrated rated power is as follows:
in the formula: pallrateRated power P for equivalent concentrated energy storageirateThe rated power of the ith energy storage.
An equivalent concentrated rated capacity of
In the formula: sallrateRated capacity for equivalent centralized energy storage; sirateThe rated capacity of the ith stored energy.
The equivalent concentrated charging efficiency and the equivalent concentrated discharging efficiency are
In the formula: etaallchAnd ηalldisRespectively equivalent concentrated charging efficiency and equivalent concentrated discharging efficiency; etaichAnd ηidisThe charging efficiency and the discharging efficiency of the ith stored energy are respectively.
After the corresponding equivalent centralized parameters are defined, only one equivalent centralized energy storage is needed to represent all the energy storage during scheduling, and the power of each energy storage is distributed by adopting the following formula.
The charging and discharging power constraint of the distributed energy storage system is as follows:
the SOC constraint of the distributed energy storage system is as follows:
SOCmin≤SOCall≤SOCmax
SOCfirst＝SOClast
in the formula: pjiessAnd QjiessRespectively the energy storage active power and the reactive power installed at the node i in the jth load power interval; SOCallThe SOC value is equivalent centralized energy storage; SOCminAnd SOCmaxRespectively the minimum value and the maximum value allowed by the SOC of the distributed energy storage system; SOCfirstAnd SOClastRespectively an initial SOC and a final SOC of the equivalent concentrated energy storage.
Secondly, a pumped storage power station dispatching model:
Ef＝δPuEp≤Em
wherein: efAnd EpRespectively representing the generated energy of the pumped storage unit and the electric quantity required by the pumped load; emThe maximum allowable power generation of the pumped storage power station is limited by the storage capacity; deltaPuThe conversion efficiency of the pumped storage power station is improved. Then, per unit charge, 1/delta needs to be consumedPuOf electricity, i.e.The cost corresponding to a unit peak shaving capacity is: (1/delta)Pu-1)CPuWherein, CPuIs the average power generation cost of the system.
Constraint conditions of a pumped storage power station scheduling model:
output restraint of the pumped storage unit:
under the working condition of power generation, the requirements are met
Under the condition of pumping water, the requirement is met
Wherein:
and
respectively the generating power and the pumping power of the pumped storage unit, PPuMinAnd PPuMaxRespectively the minimum and maximum generating power of the pumped storage group,
the power points are power points when the pumped storage unit pumps water, and m is the number of the power points.
And (4) reservoir capacity constraint of an upper reservoir:
for any time period t, the following is satisfied:
wherein: etafAnd ηpThe average water quantity/electric quantity conversion constants during power generation and water pumping are respectively; c0Is the initial water quantity of the upper reservoir, CMaxAnd CMinRespectively upper reservoirs of pumped storage power stationsMaximum and minimum water amounts. The expression of the above formula is the restriction of the upper reservoir capacity in the scheduling period, and as the sum of the total storage capacity of the upper reservoir and the lower reservoir in the whole process is not changed, the storage capacity of the lower reservoir is actually limited.
And (3) water balance constraint:
and the water pumping amount and the water consumption for power generation in the dispatching period are balanced.
In the above expression, the output of the pumped storage group in each period of the scheduling cycle is used as a variable, and in the peak scheduling, that is, under the condition of the minimum operation capacity of the pumped storage group, the operation power of each period of each group is determined.
Thirdly, a heat storage adjustable load dispatching model:
the formula is calculated according to the law of thermodynamics:
Qt＝C×Mt×ΔT
wherein: q: representing the heat storage load of the heat storage tank; c: represents the specific heat of the thermal storage medium; m: represents the mass of the heat storage medium; Δ T: represents a temperature difference that causes a change in thermal load; t: representing a certain time period.
The mass of the heat storage medium is closely related to the volume of the heat storage load. Assuming that the heat storage load is regular, if the floor area of the heat storage load is S, the water level in the heat storage load at time T is T, and the equivalent temperature of water in the heat storage load at time T is TtReference temperature is T0Then, then
Mt＝ρ·S·h
ΔT＝Tt-T0
The above two types are brought into availability
Qt＝C·ρ·S·h·ΔT
Assuming that the temperature of water in the heat storage load is distributed in a gradient form, the sensors are sequentially T from top to bottomt 1、Tt 2、Tt 3、 Tt nThen the above formula can be changed to
After finishing, obtaining
Defining the equivalent temperature of water in the heat storage load at time t
Then there are:
Qt＝C·ρ·S·h·ΔTt (2-27)
in the formula, the heat storage load is related to parameters such as specific heat, density and volume of the medium, wherein the height of the medium can be measured, and the equivalent temperature can be controlled. The control system of the electric boiler normally realizes the control of the heat storage load by controlling the equivalent temperature.
Maximum heat storage capacity of heat storage load
Since the specific heat, density and floor area of water in the heat storage load are not changed during heat storage and heat supply, the water temperature is calculated to be the maximum value in the heat storage load according to the maximum value of the water level in the heat storage load
Qmax＝C·ρ·S·hmax·(Tmax-T0) (2-28)
Wherein h ismax: the height of the water level when the heat storage tank is filled with water; t ismax: the temperature value when all the water in the heat storage tank is hot water and the temperature of each layer is consistent to the maximum value.
Heat storage loaded heating power
According to the calculation formula of power and energy:
P·t＝Q
wherein: p: the heating power of the heat storage load; t: the heat supply power output period of the heat storage load; q: a heat supply load of the heat storage load.
Heating power P of heat storage load in t periodg(t)Can be calculated from the following formula:
Pg(t)·Δt＝Qout(t)-Qin(t)
wherein Q isout(t)、Qin(t)Respectively the heat input load and the heat output load of the heat storage load at the moment
It is assumed that the flow rates at the inlet and outlet of the thermal storage load at time t are Gin(t)、Gin(t)The unit is generally expressed in Kg/h, and the water flow velocity is respectively vin(t)、vout(t)The diameter of the tube is Din、DoutThen there is
According to the relation between the flow rate and the flow rate of hot water
Integrating the above formula to obtain
The low ebb electricity period at night is determined by the load condition in which the operation is required. Generally, a single heat storage mode or a simultaneous heat storage and heat supply mode of an electric boiler is adopted. But the load at night is controlled, otherwise, the excessive load at night influences the heat storage capacity of the system, and the excessive operation of the electric boiler on the next day can cause the increase of the operation cost.
Adopt the preferred joint heat supply mode of heat storage water tank daytime, adopt the heat storage water tank constant speed mode of releasing heat, guarantee that heat storage water tank evenly emits the heat, ensure simultaneously to exhaust the water tank heat during operation time period. When the constant-speed heat release quantity of the water tank is calculated, the operation in the peak avoiding electricity period of the electricity heat storage needs to be considered, and the heat storage water tank at the time period supplies heat in full quantity (namely, the heat storage water tank single-heat-supply mode), so that the operation cost of the electric boiler is reduced as much as possible.
(2) Because the established multi-source combined peak shaving optimization model of the power system is a large-scale optimization model which is nonlinear, multistage, has a dynamic coupling relation and is mixed by continuous variables and discrete variables, a distribution and staged solving rule is provided when the resolving strategy analysis is carried out, and the complexity of the optimization problem can be greatly reduced.
(3) A heuristic intelligent optimization algorithm is used, algorithms and data of the existing scheduling system are fully utilized, feasibility and stability of distributed energy storage aggregation and multi-energy interconnection participating in power grid peak regulation are proved, and feasibility of the method is further verified.
Step 2) establishing a distributed energy storage aggregation and multi-energy collaborative operation multi-objective optimization model
(1) Establishing a charge-discharge model of the energy storage monomer, and aggregating a plurality of energy storages into an equivalent group;
(2) after all the stored energy is aggregated, the whole distributed energy storage system can be represented only by equivalent concentrated rated power, equivalent concentrated rated capacity and equivalent concentrated charging and discharging efficiency;
(3) the energy storage polymer system is generally combined with other energy sources to participate in system peak shaving together, and can also participate in system peak shaving independently when the energy storage capacity reaches a certain scale. For the power grid multi-source combined peak regulation optimization scheduling problem, the following objective functions are established:
J＝Min
in the objective function, the first item represents the power generation cost of the thermal power generating unit in peak shaving scheduling; the second item is used for representing the generating cost of the hydroelectric generating set in peak regulation scheduling; the third item is that the wind power generation cost in peak shaving scheduling is represented so as to ensure that the system receives the wind power maximally under the condition that the operating condition meets, and the cost of a high-cost peak shaving power supply is not sacrificed; the fourth item represents the peak shaving cost of the pumped storage unit in peak shaving scheduling; the fifth item is to characterize the peak shaving cost of the energy storage polymer in peak shaving scheduling; the sixth item represents the starting and stopping peak regulation cost of the thermal power generating unit in peak regulation scheduling; and the seventh item represents the peak shaving cost of the heat storage device in peak shaving scheduling.
In the formula: f. ofGi: a thermal power generation cost function; f. ofHi: a hydroelectric power generation cost function; f. ofwi: wind power generation cost function; cPu: peak shaving cost coefficient of the pumped storage unit; cN: energy storage polymer cost factor; cTOf: the starting and stopping cost coefficient of the thermal power generating unit; f. ofR: cost functions of heat storage and heat release power; lambda [ alpha ]GA、λH、λw、λP、 λN、λGB、λR: weighting coefficients of the peak shaving cost of each peak shaving power supply in the objective function;
inputting operation identification parameters of the water pumping and energy storage unit;
starting and stopping identification parameters of the thermal power generating unit;
respectively a thermal power generating unit,The system comprises a hydroelectric generating set, a wind power generation capacity, a peak shaving capacity of a pumped storage set, an operation capacity of an energy storage polymer and a heat storage or release capacity of a heat storage device.
And 3) carrying out staged optimization solving on the distributed energy storage aggregation and multi-energy collaborative operation multi-target optimization model based on a heuristic intelligent algorithm.
(1) Relevant parameters such as daily load prediction results, regional power receiving plans, photovoltaic power prediction parameters, hydroelectric power generation plans, energy storage battery plans, thermal power generating unit equivalent parameters and the like are obtained to calculate corresponding equivalent conforming parameters, and a control variable aggregation means is adopted to carry out staged optimization solving on the distributed energy storage aggregation and multi-energy collaborative operation multi-target optimization model;
(2) determining a scheduling strategy of the high-cost peak shaving power supply, neglecting the operation cost of the conventional power supply, taking the peak shaving cost of the high-cost peak shaving power supply as the lowest target, and determining the scheduling strategy of each peak shaving power supply based on a heuristic intelligent algorithm to ensure that the peak shaving scheduling cost of the whole high-cost peak shaving power supply is the lowest;
the heuristic intelligent algorithm is a calculation model of a biological evolution process for simulating natural selection and genetic mechanism of Darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process. Heuristic intelligence algorithms start with a population that represents a set of possible potential solutions to the problem, and a population is composed of a certain number of individuals that are genetically encoded. Each individual is actually a chromosome-bearing entity. The chromosome is used as the main carrier of genetic material, namely a plurality of gene sets, the internal expression of the chromosome is a certain gene combination, the external expression of the shape of an individual is determined, after the initial generation population is generated, generation-by-generation evolution generates better and better approximate solutions according to the principle of survival and superior-inferior of the fitter, in each generation, the individual is selected according to the fitness of the individual in the problem domain, and the genetic operator is used for carrying out combination crossing and variation to generate a population representing a new solution set. The process leads the population of the next generation like natural evolution to be more suitable for the environment than the population of the previous generation, and the optimal individual in the population of the last generation can be used as the problem approximate optimal solution after being decoded.
1) And continuously optimizing the resolving mode of the control variable.
The power generation power of each generator in the thermal power generating unit is limited to PGminAnd PGmaxIn between, because the maximum and minimum values corresponding to each generator are different, the power intervals of each generator are generally different, and the value range is large, which increases the difficulty of the search performance of the genetic algorithm. The generating power raw data of the unit is changed as follows:
PG＝(PGmax-PGmin)×P*-PGmin
wherein, PGIs the raw power of the unit, P*Is a number with a range of values from 0 to 1. Through the change, the value of any generator at any moment is limited in the same value taking range. Binary coding is carried out on each unit power variable parameter to be solved in the range of 0-1 to obtain substrings, and then the substrings are connected into chromosomes.
2) And a resolving mode of the unit start-stop optimization control vector.
For the optimization of the start and stop of the unit, the start and stop of the unit can be expressed as 0-1, the variable '1' represents on, and the variable '0' represents off. As mentioned above, each unit start-stop variable can be represented by a vector with the same dimension as the research period, and because the unit start-stop constraint conditions are more, if the random generated group is still unfavorable for the resolving operation, the generated group is required to meet the unit start-stop constraint.
According to the characteristics of power grid peak regulation, the main problem is the voltage load problem of peak regulation, and in order to generate a feasible initial variable of the start-stop state of a unit, an initial vector is generated according to the following rule:
1. the initial element values of the start-stop vector of the unit are all 1;
2. introducing a unit shutdown period variable toff, toff∈[1,T]Randomly generating toff；
3. Introducing a unit shutdown time variable Toff，Toff＝T-TG on-TG offRandomly generating Toff；
4. And generating a feasible unit start-stop state initial variable vector according to the random variable value.
When heuristic intelligent search algorithm operation is carried out, variable t of each start-stop unit is subjected tooffAnd ToffAnd (6) coding is carried out. With such an encoding, the minimum on-time and minimum off-time constraints of the unit are contained in the binary string of decision variables, which can always be fully satisfied.
(3) Under the condition that the high-cost peak shaving power supply is determined, the optimal scheduling of the conventional peak shaving power supply is solved by taking the lowest operation cost of the conventional peak shaving power supply as an optimization target. According to the basic principle of staged optimization, a global optimal solution strategy of the optimization problem can be obtained;
and 4) establishing a Matlab/Simulink platform for jointly participating in a power grid peak regulation strategy by distributed energy storage aggregation and multiple energy sources.
And 5) carrying out simulation analysis on effectiveness of the distributed energy storage polymerization and multi-energy combined participation power grid peak regulation strategy, and verifying that the economy and the stability of participation in power system peak regulation can be realized after the distributed energy storage monomer is polymerized under the multi-energy interconnection.
Preferably, the distributed energy storage aggregation and the multi-energy interconnection jointly participate in the coordinated operation of the power grid, so that the economy and the stability of the power system peak regulation are ensured.
Preferably, a power grid peak regulation scheduling multi-objective optimization model considering the distributed energy storage aggregation effect is established, the model considers various distributed energy source forms such as a distributed energy storage aggregate, pumped storage, heat storage type adjustable load and the like, the model can be used for power grid peak regulation scheduling in the day-ahead, and the time resolution is less than 60 minutes.
Preferably, peak load regulation operating characteristics and influence factors of the existing distributed energy storage of the power grid are researched, a distributed energy storage aggregate is established by integrating operating parameters and constraint conditions, and a staged optimization solving strategy is provided.
Preferably, a heuristic intelligent search algorithm method is used, and the algorithm and data of the existing scheduling system are fully utilized, so that a feasible technical theoretical basis is provided for the implementation of the distributed energy storage participating in the peak load regulation of the power grid.
In fig. 1, an aggregator acquires the regulation and control authority of the power terminal device by signing a contract with a user, issues a regulation and control instruction as required, and adjusts the charging and discharging behaviors of the distributed energy storage. And at the moment, the operation, decision and regulation and control execution of the controlled equipment are all finished by the aggregator. The regulation mode is restricted by contracts, and higher controllability and reliability are shown by considering the operating characteristics and the charge states of different types of energy storage, for example, the distributed energy storage with insufficient electric quantity is charged preferentially, and the distributed energy storage with abundant energy is discharged preferentially.
In fig. 2, the difference between the peak-load regulation limit and the daily peak load is reserved for the system to rotate forward for use, and is generally 3% -5% of the rated capacity or total load of the maximum unit. The difference between the peak reduction limit and the daily minimum load is the negative rotational reserve of the system, typically taking 3% to 5% of the minimum load. The critical peak shaver threshold is the boundary between the normal pressure load peak shaver capacity and the high cost peak shaver capacity, which means that the minimum output level of the system can be reached when all the normal pressure load peak shaver capacity is put into use. When the lower peak regulation limit is below the critical peak regulation threshold, it represents that high-cost peak regulation means (such as pumped storage, energy storage polymer peak regulation, unit start and stop, etc.) need to be invested, and the peak regulation requirement of the system can be met.
In fig. 3, the first step of the optimization problem is an optimization problem of large-scale discrete variables, and an intelligent optimization search strategy is adopted to solve the optimization problem; the second step of optimization problem is the same as the conventional power grid peak shaving scheduling, and can be solved by using a software package in a scheduling automation system, so that the existing scheduling platform is utilized to the maximum extent. The key of the problem is how to solve the first step optimization problem, namely, the optimal solution of the variable set is obtained under the condition of meeting various constraints of peak shaving scheduling by taking the peak shaving cost of the high-cost peak shaving power supply as the lowest target.
In fig. 4, the heuristic intelligent algorithm is a calculation model of a biological evolution process simulating natural selection and genetic mechanism of darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process. The heuristic intelligence algorithm starts with a population representing the possible potential solution set of the problem, and a population is composed of a certain number of individuals that are genetically encoded. Each individual is actually a chromosome-bearing entity. The chromosome is used as the main carrier of genetic material, namely a plurality of gene sets, the internal expression of the chromosome is a certain gene combination, the external expression of the shape of an individual is determined, after the initial generation population is generated, generation-by-generation evolution generates better and better approximate solutions according to the principle of survival and the superiority and inferiority of fittest, in each generation, the individual is selected according to the fitness of the individual in a problem domain, and the genetic operator is used for carrying out combination crossing and mutation to generate a population representing a new solution set. The process leads the population of the next generation like natural evolution to be more suitable for the environment than the population of the previous generation, and the optimal individual in the population of the last generation can be used as the approximate optimal solution of the problem after decoding.
Fig. 5 is a diagram of the peak shaving effect of the distributed energy storage participation power grid.
(1) Only the distributed energy storage aggregation is considered to participate in the peak shaving of the power grid.
(2) Water pumping energy storage and adjustable heat storage device interconnected distributed energy storage polymerization participation power grid peak regulation
In order to simplify and analyze peak shaving of distributed energy storage in the scene, the energy storage device performs scheduling control according to the following strategy, a principle of preferentially entering and exiting is adopted for a heat storage device with small capacity and strong scheduling flexibility, and conversely, as few switching times as possible are guaranteed for a device with large capacity. And in the wind abandoning period, the power grid and the wind turbine generator are matched to store heat for the heat storage device, when the abandoned wind power is larger than the rated capacity of the heat storage device, the heat storage device is put into operation and the wind turbine generator stores heat, otherwise, the system abandons the wind and obtains electric energy from the power grid to store heat. And in the stage of not abandoning the wind, the heat storage device only supplies heat to meet the heat load requirement. The simulation time is 24h, and the time interval is 60 min.
(1) Before the energy storage polymer participates in peak regulation: air volume is abandoned at 3359 MWh; daily load peak-valley difference: the peak-to-valley difference is 3461MW, and the peak-to-valley difference rate is 25.82%. After the energy storage polymer participates in peak shaving: air volume 2359 MWh: daily load peak-valley difference: the peak-to-valley difference is 3051MW, which is 410MW lower than before peak regulation. The peak-to-valley difference rate was 23.04%.
(2) Under the condition that other conditions are not changed, the pumped storage is considered, the distributed energy storage polymerization of the interconnection of the adjustable heat storage devices participates in the peak regulation of the power grid, and after the pumped storage power station and the energy storage polymer participate in the peak regulation: (ii) a Daily load peak-valley difference: the peak-to-valley difference was 2335MW, which was 1126MW lower than before peak shaver. The peak-to-valley difference rate was 17.42%. Considering the heat storage device, after the pumped storage power station and the energy storage polymer participate in peak shaving: (ii) a Daily load peak-valley difference: the peak-valley difference is 1875MW, which is 1585MW lower than before peak regulation. The peak-to-valley difference rate was 13.9%.
The invention relates to a distributed energy storage aggregation participation power grid peak regulation strategy under a multi-energy interconnection network. The distributed energy storage system can realize the comprehensive application of various distributed energy storage, realize the matching of multiple energy sources and the peak valley of the power grid, promote the balance of the supply and demand of the power grid and ensure the stable operation of the power grid; by means of a distributed energy storage aggregation method of project research, the echelon utilization of energy storage power supplies with different characteristics can be realized, the active participation of multiple energy sources in power grid interactive operation is realized to the maximum extent, and the efficient, stable and economic operation of a power distribution network is promoted.
It should be understood that the detailed description of the present invention is only for illustrating the present invention and is not limited to the technical solutions described in the embodiments of the present invention, and those skilled in the art should understand that the present invention can be modified or substituted equally to achieve the same technical effects; as long as the use requirements are met, the invention is within the protection scope.
Claims (4)
1. Distributed energy storage polymerization participates in the power grid peak regulation strategy under the multi-energy interconnection network, and the method is characterized in that: the method comprises the following steps:
step 1, establishing a distributed energy storage aggregation participation power grid peak regulation overall control operation framework;
step 2, establishing a distributed energy storage aggregation and multi-energy collaborative operation multi-objective optimization model;
and 3, carrying out staged optimization solving on the distributed energy storage aggregation and multi-energy collaborative operation multi-target optimization model based on a heuristic intelligent algorithm.
2. The distributed energy storage and aggregation participation power grid peak regulation strategy under the multi-energy internet according to claim 1, characterized in that: the step 1 comprises the following steps:
step 1.1, establishing a distributed energy storage aggregation and multi-energy collaborative operation multi-objective optimization model, comprising the following steps: the system comprises a distributed energy storage monomer model, a distributed energy storage aggregation scheduling model and a multiple energy scheduling model;
step 1.2, providing a distribution and staged solving rule when carrying out solving strategy analysis;
and step 1.3, proving feasibility and stability of the distributed energy storage aggregation and multi-energy interconnection participating in power grid peak regulation by using a heuristic intelligent optimization algorithm and utilizing an algorithm and data of an existing scheduling system.
3. The distributed energy storage and aggregation participation power grid peak regulation strategy under the multi-energy internet according to claim 1, characterized in that: the step 2 comprises the following steps:
step 2.1, establishing a charge-discharge model of the energy storage monomer, and aggregating a plurality of energy storages into an equivalent group;
step 2.2, after all the stored energy is aggregated, equivalent concentrated rated power, equivalent concentrated rated capacity and equivalent concentrated charging and discharging efficiency are used for representing the whole distributed energy storage system;
step 2.3, for the multi-source combined peak regulation optimized scheduling of the power grid, establishing the following objective function:
in the objective function, the first item represents the power generation cost of the thermal power generating unit in peak shaving scheduling; the second item represents the generating cost of the hydroelectric generating set in the peak regulation scheduling; the third item is that the wind power generation cost in peak shaving scheduling is represented so as to ensure that the system receives the wind power maximally under the condition that the operating condition meets, and the cost of a high-cost peak shaving power supply is not sacrificed; the fourth item represents the peak shaving cost of the pumped storage unit in peak shaving scheduling; the fifth item represents the peak shaving cost of the energy storage polymer in peak shaving scheduling; the sixth item represents the peak regulation cost of starting and stopping the thermal power generating unit in peak regulation scheduling; characterizing peak shaving cost of the heat storage device in peak shaving scheduling;
in the formula: f. ofGi: a thermal power generation cost function; f. ofHi: a hydroelectric power generation cost function; f. ofwi: a wind power generation cost function; cPu: peak shaving cost coefficient of the pumped storage unit; cN: energy storage polymer cost factor; cTOf: the starting and stopping cost coefficient of the thermal power generating unit; f. ofR: cost functions of heat storage and heat release power; lambda [ alpha ]GA、λH、λw、λP、λN、λGB、λR: weighting coefficients of the peak shaving cost of each peak shaving power supply in the objective function;
inputting operation identification parameters of the pumped storage unit;
starting and stopping identification parameters of the thermal power generating unit;
the energy storage device comprises a thermal power generating unit, a hydroelectric generating unit, a wind power generating capacity, a peak shaving capacity of a pumped storage unit, an operating capacity of an energy storage polymer and a heat storage or release capacity of a heat storage device.
4. The distributed energy storage and aggregation participation power grid peak regulation strategy under the multi-energy internet according to claim 1, characterized in that: the step 3 comprises the following steps:
step 3.1, obtaining relevant parameters comprises: the method comprises the following steps of (1) carrying out staged optimization solving on a distributed energy storage aggregation and multi-energy collaborative operation multi-target optimization model by adopting a control variable aggregation means;
3.2, determining a scheduling strategy of the high-cost peak shaving power supply, neglecting the operation cost of the conventional power supply, taking the peak shaving cost of the high-cost peak shaving power supply as the lowest target, and determining the scheduling strategy of each peak shaving power supply based on a heuristic intelligent algorithm to ensure that the peak shaving scheduling cost of the whole high-cost peak shaving power supply is the lowest;
3.3, under the condition that the high-cost peak shaving power supply is determined, solving the optimal scheduling of the conventional peak shaving power supply by taking the lowest running cost of the conventional peak shaving power supply as an optimization target; and obtaining a global optimal solution strategy of the optimization problem according to the basic principle of staged optimization.
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