CN111008739A - Optimal regulation and control and income distribution method and system for cogeneration virtual power plant - Google Patents
Optimal regulation and control and income distribution method and system for cogeneration virtual power plant Download PDFInfo
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Abstract
A method and a system for optimal regulation and income distribution of a combined heat and power generation virtual power plant are provided, wherein the method comprises the following steps: s10, establishing a CHP-VPP model comprising a wind power generation system, a photovoltaic power generation system, a cogeneration system, an energy storage system and a controllable load; s20, constructing an objective optimization function with the maximum profit of the CHP-VPP system as an objective; s30, solving the optimal output of each distributed unit in the CHP-VPP system through a particle swarm algorithm, and determining the optimal operation mode of the system in a scheduling period; and S40, realizing fair profit distribution of each distributed unit in the CHP-VPP system by improving a xiapril value method. The invention can obtain the optimal output of each distributed unit, determine the optimal operation mode of the whole system in the scheduling period and realize the optimal benefit distribution.
Description
Technical Field
The invention relates to a method and a system for optimal regulation and control and income distribution of a cogeneration virtual power plant, in particular to a CHP-VPP optimal scheduling model based on a cooperative game, which is solved by a particle swarm algorithm to obtain the optimal output of each distributed unit, determine the optimal operation mode of the whole system in a scheduling period and realize the optimal income distribution based on an improved Shapley value method.
Background
Over the past decade, the installed capacity of renewable energy power generation has increased dramatically. By 2020, the cumulative capacity of wind power generation and solar power generation in China will reach 205GW and 150GW, respectively. Large-scale integrated Distributed Energy Sources (DERs) for renewable energy power generation mitigate energy shortages to some extent. However, in winter, especially in the "norththree" region, high heat demand is typically produced by Combined Heat and Power (CHP) units in a traditional "on-demand" mode of operation. In other words, a large amount of electrical energy is produced as a byproduct. In order to keep the safe and stable operation of the power grid, the amount of abandoned wind and abandoned light is greatly increased. By aggregating distributed energy generation, Energy Storage Systems (ESS) and various loads, a Virtual Power Plant (VPP) can make full use of DER, reduce fluctuations in renewable energy generation, and improve the reliability of the power supply. In the prior art, for example, CN201510311963, an economic scheduling method for interconnected micro-grids based on cooperative game dynamic alliance structure division, where a VPP aggregates renewable energy resources including distributed power generation, ESS and controllable load, realizes optimal energy management using an empire and national sense competition algorithm and a particle swarm algorithm, respectively, integrates Demand Response (DR) into the VPP, and reduces total operating cost to the maximum extent while maintaining power quality, or employs a certain random scheduling method to suppress uncertainty of VPP operation.
To improve the renewable energy capacity of the grid, cogeneration starts to participate in the optimal scheduling of the VPP. In the prior art, a VPP random model is established to meet the local heat supply demand response, and an interval and certainty combined optimization method is provided for the VPP containing a CHP unit so as to optimize the VPP profit and manage uncertainty. Existing CHP-containing VPP optimal scheduling methods typically operate with the primary goal of maximizing overall system profit. In the prior art, for example, CN201510630844, an allocation method of fixed power transmission cost of a wind power grid-connected system based on a sharey value, or CN201310738190, an improved sharey value method allocation method of a micro-load game, provides an allocation method of fixed power transmission cost of a wind power grid-connected system based on a sharey value, considers the influence of a wind power grid-connected environment on the allocation of the fixed cost of the power transmission system, and makes the cost allocation result meet fairness, stability and financial settlement balance as much as possible; the wind power output has certain randomness, and a Monte Carlo method is adopted to simulate the wind power output scene, but the introduction of a large number of scenes can increase the calculation burden.
In existing transmission systems, fixed costs are typically apportioned by load size. However, this approach easily causes cross subsidy between loads, cannot provide economic incentive, cannot promote reasonable distribution of loads of the whole network, and cannot achieve the optimization goal of optimizing and saving social resources. In the prior art, for example, CN201510641609, a fixed cost allocation method for a power transmission system based on a cooperative game and a DEA, which provides a fixed cost allocation method for a power transmission system based on a cooperative game and a DEA, the method establishes a alliance game model under a Data Envelope Analysis (DEA) framework, from the perspective of multi-attribute decision, provides allocation of fixed cost of the power transmission system by using a kernel method based on the cooperative game and the data envelope analysis, and calculates the fixed cost allocation of the power transmission system in the DEA alliance game under the condition of guaranteeing regional constraint. However, since different energy networks coexist and are operated by different entities with different interests and decision objectives, the choice of the optimal solution is influenced by the conflict of interests among the different entities.
On this background, how to analyze the profit behaviors in the energy transaction in the multi-energy system and the game relations among the subjects and the influence on the market balance, how to realize the optimal scheduling of the power and heat networks to optimize the resource allocation, and simultaneously satisfy the benefits of all agents and the power and heat balance of the whole system becomes a technical problem to be solved in the field.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for optimizing, regulating and allocating revenue of a cogeneration virtual power plant, which comprises the following specific scheme:
a method for optimizing regulation and control and income distribution of a cogeneration virtual power plant comprises the following steps:
s10: establishing a CHP-VPP model comprising a wind power generation system, a photovoltaic power generation system, a cogeneration system, an energy storage system and a controllable load;
s20: constructing an objective optimization function with the maximum profit of the CHP-VPP system as a target;
s30: solving the optimal output of each distributed unit in the CHP-VPP system through a particle swarm algorithm, and determining the optimal operation mode of the system in a scheduling period;
s40: and the fair profit distribution of each distributed unit in the CHP-VPP system is realized by improving a xiapril value method.
Preferably, the step S10 includes the following steps:
s101: carrying out processing on uncertainty of wind speed by utilizing Weibull distribution to construct a wind power generation model;
s102: processing the uncertainty of illumination by utilizing Beta distribution to construct a photovoltaic power generation model;
s103: establishing a CHP electric heating output model according to the electric heating coupling characteristic of the CHP;
s104: respectively establishing an electricity storage unit model and a heat storage unit model according to the charge state;
s105: an incentive-based demand response model is established.
Preferably, the step S20 includes the following steps:
s201: formulating a CHP-VPP operation mode based on a cooperative game theory;
s202: determining an objective function of the system;
s203: the constraints of the system are determined.
Preferably, the step S30 includes the following steps:
s301: inputting power generation parameters and parameters of each distributed unit in a typical day, and setting particle swarm algorithm parameters;
s302: initializing a population speed, a position and a target value;
s303: updating the speed and the position of the population;
s304: considering the constraint of each distributed unit in the whole system, continuously adjusting the output of each unit, and calculating the maximum profit of the whole system;
s305: judging whether an iteration termination condition is met or the maximum iteration frequency is reached, if so, performing step S306; if not, step S203 is carried out again;
s306: and finishing the iteration and outputting the result.
Preferably, the step S40 includes the following steps:
s401: determining a traditional revenue distribution scheme;
s402: adjusting the weight of each distributed unit according to the thermoelectric ratio;
s403: an improved revenue allocation scheme is determined.
A cogeneration virtual power plant optimal regulation and revenue distribution system, the system comprising the following modules:
the system comprises an establishing module, a control module and a control module, wherein the establishing module is used for establishing a CHP-VPP model comprising a wind power generation system, a photovoltaic power generation system, a cogeneration system, an energy storage system and a controllable load;
the system comprises a construction module, a selection module and a control module, wherein the construction module is used for constructing an objective optimization function with the maximum profit of a CHP-VPP system as an objective;
the determining module is used for solving the optimal output of each distributed unit in the CHP-VPP system through a particle swarm algorithm and determining the optimal operation mode of the system in a scheduling period;
and the distribution module is used for realizing fair profit distribution of each distributed unit in the CHP-VPP system by improving a xiapril value method.
Preferably, the establishing module comprises the following sub-modules:
the wind power generation model building submodule processes uncertainty of wind speed by utilizing Weibull distribution to build a wind power generation model;
the photovoltaic power generation model building submodule processes the uncertainty of illumination by utilizing Beta distribution to build a photovoltaic power generation model;
the CHP electric heating output model building submodule builds a CHP electric heating output model according to the electric heating coupling characteristic of the CHP;
the electric storage unit model and the heat storage unit model building sub-modules respectively build an electric storage unit model and a heat storage unit model according to the charge state;
and the demand response model building submodule is used for building a demand response model based on excitation.
Preferably, the building block comprises the following sub-modules:
the operation mode submodule is used for formulating a CHP-VPP operation mode based on a cooperative game theory;
the objective function submodule is used for determining an objective function of the system;
and the constraint condition submodule is used for determining the constraint condition of the system.
Preferably, the determination module comprises the following sub-modules:
the setting submodule is used for inputting power generation parameters of a typical day and parameters of each distributed unit and setting parameters of a particle swarm algorithm;
the initialization submodule is used for initializing the speed, the position and the target value of the population;
the updating submodule is used for updating the population speed and the population position and updating the population speed and the population position again based on the condition that the output of the judging submodule is negative;
the unit adjusting submodule is used for continuously adjusting the output of each unit based on the considered constraint of each distributed unit in the whole system and calculating the maximum profit of the whole system;
the judgment submodule judges whether the iteration termination condition is met or the maximum iteration frequency is reached;
and the output submodule is used for finishing iteration and outputting a result based on the condition that the output of the judgment submodule is yes.
Preferably, the distribution module comprises the following sub-modules:
a receiving submodule for determining a conventional revenue allocation scheme;
the weight adjusting submodule adjusts the weight of each distributed unit according to the thermoelectric ratio;
an allocation submodule for determining an improved revenue allocation scheme.
A storage medium storing a computer executable program for performing the cogeneration virtual power plant optimal regulation and revenue allocation method as described above when the computer executable program is run.
An electronic device comprising a processor, a memory, the memory storing a computer executable program, the computer executable program when executed, is for performing the cogeneration virtual power plant optimization regulation and revenue sharing method as described above.
According to the invention, WPP, PV, CHP, ESS and IBDR are collected in CHP-VPP, a cooperative game model is introduced, and a random scheduling optimization model is established for the CHP-VPP. In order to promote the utilization of renewable energy and enable the cogeneration unit to operate efficiently, an improved Shapley value method is provided to realize optimal profit distribution, fair profit distribution can be realized, the efficiency of the cogeneration unit is improved, and the reduction of renewable energy is reduced. Specifically, the following technical effects can be obtained: the method comprises the following steps that (1) according to the characteristics of a distributed unit and a network topological structure, a CHP-VPP concept is established on a thermoelectric coupling network; considering the uncertainty of renewable energy power generation, establishing a cooperative game model to realize the optimal thermoelectric coupling scheduling of the CHP-VPP; (3) an improved Shapley value method is provided to realize fair profit distribution of CHP-VPP agents, improve the efficiency of a CHP unit and reduce the reduction of renewable energy.
Drawings
Fig. 1 is a flowchart of a cogeneration virtual power plant optimal regulation and revenue distribution method.
FIG. 2 is a flow chart for building a CHP-VPP model including a wind power generation system, a photovoltaic power generation system, a cogeneration system, an energy storage system, and a controllable load.
FIG. 3 is a flow chart for constructing an objective optimization function targeting a maximum profit for the CHP-VPP system.
FIG. 4 is a flow chart for determining the optimal operation mode of the system in the scheduling period by solving the optimal output of each distributed unit in the CHP-VPP system through a particle swarm optimization.
FIG. 5 is a flow chart for achieving fair revenue distribution among distributed units in a CHP-VPP system through an improved xiapril value method.
FIG. 6 is a schematic illustration of the region of the thermocouple feasible operation region.
FIG. 7 is a schematic diagram of an integrated cogeneration virtual power plant system.
Detailed Description
Referring to fig. 1, the invention provides a method for optimizing regulation and control and income distribution of a cogeneration virtual power plant, which comprises the following steps:
s10: establishing a CHP-VPP model comprising a wind power generation system, a photovoltaic power generation system, a cogeneration system, an energy storage system and a controllable load;
s20: constructing an objective optimization function with the maximum profit of the CHP-VPP system as a target;
s30: solving the optimal output of each distributed unit in the CHP-VPP system through a particle swarm algorithm, and determining the optimal operation mode of the system in a scheduling period;
s40: and the fair profit distribution of each distributed unit in the CHP-VPP system is realized by improving a xiapril value method.
Preferably, the step S10 includes the following steps:
s101: the uncertainty of the wind speed is processed by utilizing Weibull distribution, and a wind power generation model is constructed, and the specific process is as follows:
the output power of a WPP is usually determined by the wind speed v, which is described by Weibull distribution, so that the probability density function f (v) of the wind speed is
Wherein the content of the first and second substances,
in order to be a parameter of the shape,
is a scale parameter. Therefore, the actual wind power output power PWPP(t) is
Wherein，vin，voutAnd vratedRespectively cut-in, cut-out and rated wind speed, PrIs the rated power.
S102: the uncertainty of illumination is processed by utilizing Beta distribution, and a photovoltaic power generation model is constructed, wherein the concrete process is as follows:
photovoltaic output power PPV(t) is mainly influenced by the illumination intensity θ, and the uncertainty of the illumination intensity is usually described by using a Beta distribution.
Wherein Γ (·) is a Gamma function, ξ and ψ are scale functions of Beta function
PPV(t)＝ηPVSpvθ(t) (0.4)
η thereinPVIs the energy conversion efficiency, SpvIs the total area of the PV panel.
S103: according to the electric heating coupling characteristic of the CHP, a CHP electric heating output model is established, and the specific process is as follows:
the thermoelectric output of the CHP units is interdependent. CHP units are mainly of two types: back pressure CHP and suction condensing CHP. The strongly coupled nature of the back pressure CHP allows for a fixed thermoelectric ratio. In contrast, the pumped condensing CHP is more flexible because its thermoelectric output can be adjusted within a certain range, as shown in fig. 6. The invention selects the extraction-condensation CHP, and the area of the feasible operation interval of thermoelectric coupling can be represented by a group of inequalities:
wherein, PCHP(t)，QCHP(t) are the electrical and thermal output of the CHP unit, respectively. A, B, C and D are boundary points of the operation area. c. Cab，cbc，cadThe slopes of the segments AB, BC, and AD, respectively.
The upper and lower boundaries of the CHP unit power generation respectively,
respectively the upper and lower boundaries of heat generation by the CHP unit.
S104: respectively establishing an electricity storage unit model and a heat storage unit model according to the state of charge, and the specific process is as follows:
in the CHP-VPP, the ESS is mainly composed of two parts: an electricity storage system EESS and a heat storage system HESS. Electricity storage system
The EESS charging characteristics may be expressed as
Wherein the SoCEESS(t) represents the state of charge of the EESS.
Indicating the amount of charge. The dynamic behavior of the discharge can be expressed as:
wherein the content of the first and second substances,
indicating the amount of discharge.
The total electric quantity output of the electricity storage system EESS is as follows:
wherein the bilateral variables
And
respectively, the charge and discharge states of the EESS.
The total heat output of the heat storage system HESS is:
wherein the content of the first and second substances,
and
respectively, heat storage capacity and heat release capacity, bilateral variable
And
respectively, the heat storage system HESS stores heat and releases heat.
S105: establishing a demand response model based on excitation, which comprises the following specific processes:
end users adjust their power usage behavior during peak hours, such as turning on/off water heaters, air conditioners, electric heaters, etc. there are a total of I loads participating in the IBDR, and the total equivalent output power △ L provided by the IBDRIB(t) can be represented as
Wherein △ Li(t) represents load reduction of the load i.
Preferably, the step S20 includes the following steps:
s201: the CHP-VPP operation mode is formulated based on the cooperative game theory, and the specific process is as follows:
each power generation unit in the CHP-VPP is assigned to different operators, and in the case where each operator is not cooperative, the overall optimization of the system may conflict with the situation where each operator seeks to maximize their own benefits. In the study herein, each operator took a cooperative mode and could obtain higher revenue than in the non-cooperative mode (simulation results have been demonstrated). In the mode of cooperation of each power generation unit, the whole system still needs to meet the requirements of electricity and heat load. On the basis, by reasonably analyzing the information (including parameters such as cost, income and the like) of each unit, an optimal operation strategy is respectively made for each unit, so that the overall income of the system is further optimized.
S202: determining an objective function of the system, which comprises the following specific processes:
a large-scale CHP-VPP system comprises WPP, PV, CHP, EESS, HESS and IBDR, and as wind power and photovoltaic power generation have priority grid-connected power generation rights, in order to maximize the total income of the whole VPP system, more clean energy power generation is required to be consumed by grid connection as much as possible, and the utilization rate of clean energy is improved. However, the output of WPP and PV is intermittent. In order to reduce the uncertainty of the output of the renewable energy, the ESS is adopted to provide standby service for the renewable energy unit. The end user's heat demand is supplied primarily by the CHP unit. The IBDR may be considered a virtual power generation unit that may reduce net load. The invention designs an economics model of electric heat coordination optimization scheduling by taking CHP-VPP system as an object, the CHP-VPP trades electric energy with a large power grid integrally according to time-of-use electricity price, therefore, the total profit of the CHP-VPP is as follows:
where S (t) represents the set of all decision variables, re(t) is the time of use price, Pbid(t) Total contract Power, Qsum(t) Total Heat production, ΠIB(t) controllable load cost, [ pi ]S(t) is the cost of energy storage, ΠCHP(t) CHP operating cost, #p(t) is penalty cost. In the present invention, the outputs of WPP, PV and CHP are decision variables. The main gains are derived from the electrical energy supplied to the grid and the thermal energy given to the users. The major costs include the IBDR cost, ESS energy storage cost, CHP operating cost, and penalty cost. The IBDR cost comes from the contract made by the end user in advance, and the operation cost of WPP, PV and HESS is shortThe cost is low in the period and can be ignored. Furthermore, not all of the generated electrical energy can be sold to a large power grid. The CHP-VPP is penalized if there is an imbalance between actual power generation and contract power. While the CHP unit is controllable, we assume that the actual heat production matches exactly the contract heating value. The above mentioned benefits and costs can be expressed as:
Psum(t)＝PWPP(t)+PPV(t)+PEESS(t)+PCHP(t) (0.12)
Qsum(t)＝QHESS(t)+QCHP(t) (0.13)
ΠS(t)＝ASPEESS(t)+BS(0.15)
wherein r ise(t) is the time of use electricity price; r ish(t) is the heat rate of the heat network; psum(t) and Qsum(t) total electrical and thermal output of the CHP-VPP, respectively; pbid(t) Total contract Power αIBAnd βIBCost coefficients for the IBDR, respectively; chi shape0，χ1，χ2， χ3Hexix-4Is the cost factor of the CHP; a. thesAnd BsIs the cost factor of the EESS; r isp，
And
is the unbalanced electric quantity △ PUGPenalty electricity prices.
S203: determining the constraint conditions of the system, and the specific process is as follows:
in order to ensure the safe and stable operation of the whole system, the constraint conditions to be met include:
(1) power network constraints
Since there are many types of power generation units in the CHP-VPP, and power generation characteristics (power generation characteristics) are different from each other, the output of each power generation unit should be converted into direct-current (direct-current) for power convergence (power conversion). Therefore, the direct current power flow model is adopted to calculate the line power flow. The power network constraints include two main parts: supply-demand balance constraints and power flow constraints. The WPP, PV, CHP and EESS should all generate power in contract to meet load demands. If there is still an imbalance between the power supply and demand, the CHP-VPP should exchange additional power with the main grid. The constraint of electric balance can be written as
PWPP(t)+PPY(t)+PEESS(t)+PCHP(t)+ΔPUG(t)＝LO(t)-ΔLIB(t) (0.18)
Wherein L is0(t) represents the load demand, △ LIB(t) represents the total equivalent output power provided by the IBDR. To protect the line, the power flow on the jth line should satisfy:
wherein the content of the first and second substances,
and
respectively, the lower and upper bounds of the current for branch j.
(2) Constraint of the thermodynamic network:
since the main focus of the heating network is to meet the source side heating demand, the present invention employs constant current control. This indicates that the water flow and water pressure are constant and only the temperature is controllable. Heat flow Q in the pipe ww(t) can be represented as
Wherein
And
is the mass flow rate at the beginning and end of the pipe w, the temperature at the beginning and end of the pipe w being
C is the specific heat capacity of water;
is the mass flow rate through the conduit w.
Taking into account heat losses and transport delays τ
Where κ is the loss coefficient, FwIs a characteristic factor characterized by the length and cross-sectional area of the conduit. Therefore, to ensure the heat generation amount QCHP(t) and thermal load QL(t) balance of
(3) Electric energy storage system EESS operation constraint
Since EESS cannot be charged and discharged simultaneously, therefore
Wherein the bilateral variables
And
respectively, the charge and discharge states of the EESS.
In addition, the charge and discharge rates must not exceed limits, for EESS there are
Wherein
Respectively the upper and lower limits of SoC;
respectively, the upper and lower limits of the charging rate;
respectively, the upper and lower limits of the discharge rate.
(4) HESS operating constraints for thermal energy storage system
Because HESS cannot be charged and discharged simultaneously, so
Furthermore, the charge and discharge rate cannot exceed a limit. For HESS, there are
Wherein
Respectively the upper and lower limits of SoC in HESS;
upper and lower limits of HESS charge rate, respectively;
respectively, the upper and lower limits of the HESS discharge rate.
(5) Unit output and system operation constraints
For a CHP unit, its operating region can be represented by a set of inequalities as described above in equation 1.5. In addition, the electric heating force and the climbing are restricted as
Wherein the content of the first and second substances,
the upper and lower limits of CHP power generation are respectively.
The upper and lower limits of heat production of CHP are provided.
Respectively, the constraints of the uphill and downhill slopes.
(6) Demand response constraints
The IBDR is considered as part of the CHP-VPP, and its load shedding can be considered as a virtual power generation unit. To avoid excessive fluctuations in load, the IBDR should satisfy the following constraints
Wherein the content of the first and second substances,
is the cumulative IBDR upper limit.
(7) System backup constraints
To ensure reliable operation of the CHP-VPP, the system should set the corresponding reverse capacity, i.e., spin-up/spin-down reserve, which can be expressed as
Wherein the content of the first and second substances,
P sumand (t) is the upper limit and the lower limit of the total generated energy respectively. EpsilonLIs the load reserve factor.
Is the upward rotation standby factor of WPP and PV,
is the downward rotation standby factor for WPP and PV.
Preferably, the step S30 includes the following steps:
s301: and inputting power generation parameters, time-of-use electricity prices and parameters of each distributed unit of a typical day, and setting parameters of a particle swarm algorithm. The power generation parameters comprise wind speed data, illumination intensity data, electric load data and heat load data of typical days, and the particle swarm algorithm parameters comprise a population scale, iteration times, an inertia weight factor and a learning factor.
S302: initializing a population speed, a position and a target value;
s303: updating the population speed and the population position, and the specific process is as follows:
and solving the CHP-VPP scheduling model by adopting a particle swarm optimization algorithm. The particle swarm optimizer uses a velocity location model to optimize the entire space. Iterating each particle to update its velocity v and position x
Wherein p isid(t) is the historical optimal position of the current particle; p is a radical ofgd(t) is the historical optimum position for the entire population of particles. w is an inertia weight factor; c. C1And c2Is a learning factor, r1And r2Is a random number between 0 and 1. The parameters of the particle swarm optimization algorithm are set as follows: the population size is 200, the iteration number is 100, the inertia weight factor is 0.75, and the learning factor is 1.3.
S304: considering the constraint of each distributed unit in the whole system, continuously adjusting the output of each unit, and calculating the maximum profit of the whole system;
s305: judging whether an iteration termination condition is met or the maximum iteration frequency is reached, if so, performing step S306; if not, step S203 is carried out again;
s306: and finishing the iteration and outputting the result.
Preferably, the step S40 includes the following steps:
s401: a conventional revenue allocation scheme is determined.
In order to ensure the fairness of profit sharing of each unit, a cooperative game method can be adopted. The invention adopts a Shapley value-based method as a classical cooperative game method. It not only provides flexibility for agents to collaborate with others, but also reduces distribution losses. During profit sharing, all possible DER combinations need to be considered. It is therefore necessary to calculate the virtual profit associated with all combinations of DER. For a VPP containing U cells, there are a total of combinations, except for one empty combination. Note that the cooperative game focuses only on the outcome of the cooperation, and does not consider the details of the bargaining between each cell. The superiority of cooperative gaming is mainly embodied in two aspects: (1) the profit of the league should be greater than the sum of the profits of the participating individuals operating independently prior to the cooperation, (2) in the cooperation mode, the profit of the individuals in the league should be greater than the profit prior to the cooperation.
The traditional sharey value method is a common method for solving the problem of cooperative gaming. In the profit sharing process, the marginal profit of the cooperation is shared according to the joining of new individuals. Thus, units that provide more resources for a portfolio should be allocated more profits. Define Ω ═ {1,2,3, …, U } as the set of all units, where U is the total number of units, allotting the profit Π of individual UuCan be expressed as
Wherein U is the total number of each unit, βhvRepresenting a bilateral variable for the remaining unit v ∈ Ω \ u in federation h. II typehuMarginal benefits before and after joining a federation for unit u
S402: and adjusting the weight of each distributed unit according to the thermoelectric ratio.
The core of the invention is to develop a new method for solving a cooperative game model, and the model can comprehensively consider individual game behaviors in the CHP-VPP. The traditional Shapley method assigns a uniform weight of 1/U to all individuals, ignoring the characteristic differences of each individual. However, since the heat-to-electricity ratio is a determining factor that affects the profit sharing of an actual CHP unit. Therefore, the system should properly activate CHP units with high thermoelectric ratios. Therefore, an improved Shapley value method is proposed to allocate the profit of CHP-VPP equally, and to adjust the weight of the individual u
α thereinuIs a thermoelectric ratio, and
the new weight is therefore △ epsilonu＝εu-1/U (0.33)
Wherein the content of the first and second substances,
s403: an improved revenue allocation scheme is determined.
For individual u, the new profit allocated is
The improved sharley-valued approach retains the advantages of the traditional approach and reduces the limitations in capturing the personal characteristics of each agent. The proposed method achieves fair profit sharing and reduces the possibility of an agent exiting the federation, maintaining the security and stability of the entire system.
As shown in the figure 7, an integrated cogeneration virtual power plant system formed by coupling a power system and a heating pipe network system is constructed on the basis of a modified IEEE30 node power system and a 14-node heating pipe network system. In the power network, WPP, PV, CHP1, CHP2 and EESS are connected to power nodes E7, E13, E18, E26 and E28, respectively. The heat sources HESS, CHP1 and CHP2 are connected to the heat supply nodes H1, H4 and H12, respectively. Decision variables S (t) including PWPP(t),PPV(t),PCHP1(t),QCHP1(t),PCHP2(t),QCHP2(t) of (d). Each CHP is provided with a generator, and the climbing speed is 60 MW/h. The electricity market employs time of use electricity prices, as shown in table 1 below:
TABLE 1
The charge and discharge prices all follow the market price of electricity and punish the price of electricity
And
the price is $40/MWh, $100/MWh, the heat sale price is $13/MWh, and wind power and photovoltaic data are from actual data of China. Typical parameter v of a wind turbinein＝3m/s, vout＝25m/s,vrated12.5 m/s. The shape parameter and the scale parameter can be calculated from historical data:
psi-8.54. The scene simulation method can be used for simulating WPP and PV output, errors are 8% and 6%, typical heat requirements and electricity requirements can be met, and wind electricity predicted output and photovoltaic power generation predicted output can be obtained.
Considering the size and nature of individuals in CHP-VPP, there are four major components: WPP, PV, CHP1 and CHP 2. According to the cooperative game theory, 15 union combinations are shared. All scheduling results and corresponding profits are obtained by using a particle swarm optimization algorithm, and the results correspond to the results that none of the four are cooperative and the four are cooperative with each other.
In the mode of the non-cooperative game, nash equilibrium is resolved to (4088.92MW,865.92MW, (4927.98MW,4617.68MW), (5400.51MW,2652.47 MW)). Total profit is $ 1.18X 106. When all subjects collaborated, nash equilibrium was resolved to (3968.01MW,780.99MW, (3573.54MW,3949.84MW), (4556.84MW,3419.48MW))) with a total profit of $1.361 × 106Gold in the United states. In a cooperative mode, WPP and PV sell more power to the grid, while CHP sells less. The light abandoning rate of the abandoned wind is respectively reduced by 16.7 percent and 17.4 percent. By using the sharey value method, the profit of each distributed unit in the cooperative gaming mode is higher than that in the non-cooperative mode.
In fact, CHP gives way to the profit of the entire system, which indicates that cogeneration provides more profit for renewable energy power generation while guaranteeing profit. The CHP is unfair in allocating profits based only on its heat and power generation. Therefore, the improved Shapley value method provided by the invention can really realize fair profit distribution for the whole CHP-VPP system. By using the modified sharley value method, the WPP and PV units share part of the profit with the CHP units in terms of thermoelectric ratio. At the same time, the profit per unit is still higher than that in the non-cooperative mode, which maintains the stability of the entire CHP-VPP system and improves the efficiency of CHP units.
When the system is in the non-cooperative mode, the overall profit is low, especially for certain incorrect combinations. The improved sharley value approach may enable fair profit sharing and result in a more stable system. In addition, reasonable resource allocation may guide the broker's scheduling to reduce the generation of renewable energy.
The invention also provides a system for optimizing, regulating and controlling the cogeneration virtual power plant and distributing the income, which comprises the following modules:
the system comprises an establishing module, a control module and a control module, wherein the establishing module is used for establishing a CHP-VPP model comprising a wind power generation system, a photovoltaic power generation system, a cogeneration system, an energy storage system and a controllable load;
the system comprises a construction module, a selection module and a control module, wherein the construction module is used for constructing an objective optimization function with the maximum profit of a CHP-VPP system as an objective;
the determining module is used for solving the optimal output of each distributed unit in the CHP-VPP system through a particle swarm algorithm and determining the optimal operation mode of the system in a scheduling period;
and the distribution module is used for realizing fair profit distribution of each distributed unit in the CHP-VPP system by improving a xiapril value method.
Preferably, the establishing module comprises the following sub-modules:
the wind power generation model building submodule processes uncertainty of wind speed by utilizing Weibull distribution to build a wind power generation model;
the photovoltaic power generation model building submodule processes the uncertainty of illumination by utilizing Beta distribution to build a photovoltaic power generation model;
the CHP electric heating output model building submodule builds a CHP electric heating output model according to the electric heating coupling characteristic of the CHP;
the electric storage unit model and the heat storage unit model building sub-modules respectively build an electric storage unit model and a heat storage unit model according to the charge state;
and the demand response model building submodule is used for building a demand response model based on excitation.
Preferably, the building block comprises the following sub-modules:
the operation mode submodule is used for formulating a CHP-VPP operation mode based on a cooperative game theory;
the objective function submodule is used for determining an objective function of the system;
and the constraint condition submodule is used for determining the constraint condition of the system.
Preferably, the determination module comprises the following sub-modules:
the setting submodule is used for inputting power generation parameters, time-of-use electricity prices and distributed unit parameters of a typical day and setting particle group algorithm parameters;
the initialization submodule is used for initializing the speed, the position and the target value of the population;
the updating submodule is used for updating the population speed and the population position and updating the population speed and the population position again based on the condition that the output of the judging submodule is negative;
the unit adjusting submodule is used for continuously adjusting the output of each unit based on the considered constraint of each distributed unit in the whole system and calculating the maximum profit of the whole system;
the judgment submodule judges whether the iteration termination condition is met or the maximum iteration frequency is reached;
and the output submodule is used for finishing iteration and outputting a result based on the condition that the output of the judgment submodule is yes.
Preferably, the distribution module comprises the following sub-modules:
a receiving submodule for determining a conventional revenue allocation scheme;
the weight adjusting submodule adjusts the weight of each distributed unit according to the thermoelectric ratio;
an allocation submodule for determining an improved revenue allocation scheme.
In the optimal regulation and control and income distribution system of the cogeneration virtual power plant, the specific working processes of each module and each submodule are correspondingly the same as the steps in the optimal regulation and control and income distribution method of the cogeneration virtual power plant.
The invention provides a storage medium, which stores a computer executable program, wherein the computer executable program is used for executing the optimal regulation and control and income distribution method of the cogeneration virtual power plant when in operation.
The invention provides electronic equipment, which comprises a processor and a memory, wherein the memory stores a computer executable program, and the computer executable program is used for executing the optimal regulation and control and the income distribution method of the cogeneration virtual power plant when running
The present invention is explained in detail with reference to the above examples, but the present invention is not limited to the above detailed processes and compositions. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed.
Claims (10)
1. A method for optimizing regulation and control and income distribution of a cogeneration virtual power plant is characterized by comprising the following steps:
s10: establishing a CHP-VPP model comprising a wind power generation system, a photovoltaic power generation system, a cogeneration system, an energy storage system and a controllable load;
s20: constructing an objective optimization function with the maximum profit of the CHP-VPP system as a target;
s30: solving the optimal output of each distributed unit in the CHP-VPP system through a particle swarm algorithm, and determining the optimal operation mode of the system in a scheduling period;
s40: and the fair profit distribution of each distributed unit in the CHP-VPP system is realized by improving a xiapril value method.
2. The optimal regulation and control of a cogeneration virtual power plant and revenue sharing method of claim 1, wherein said step S10 includes the steps of:
s101: carrying out processing on uncertainty of wind speed by utilizing Weibull distribution to construct a wind power generation model;
s102: processing the uncertainty of illumination by utilizing Beta distribution to construct a photovoltaic power generation model;
s103: establishing a CHP electric heating output model according to the electric heating coupling characteristic of the CHP;
s104: respectively establishing an electricity storage unit model and a heat storage unit model according to the charge state;
s105: an incentive-based demand response model is established.
3. The optimal regulation and control of a cogeneration virtual power plant and revenue sharing method of claim 1, wherein said step S20 includes the steps of:
s201: formulating a CHP-VPP operation mode based on a cooperative game theory;
s202: determining an objective function of the system;
s203: the constraints of the system are determined.
4. The optimal regulation and control of a cogeneration virtual power plant and revenue sharing method of claim 1, wherein said step S30 includes the steps of:
s301: inputting power generation parameters, time-of-use electricity prices and parameters of each distributed unit of a typical day, and setting particle swarm algorithm parameters;
s302: initializing a population speed, a position and a target value;
s303: updating the speed and the position of the population;
s304: considering the constraint of each distributed unit in the whole system, continuously adjusting the output of each unit, and calculating the maximum profit of the whole system;
s305: judging whether an iteration termination condition is met or the maximum iteration frequency is reached, if so, performing step S306; if not, step S203 is carried out again;
s306: and finishing the iteration and outputting the result.
5. The optimal regulation and control of a cogeneration virtual power plant and revenue sharing method of claim 1, wherein said step S40 includes the steps of:
s401: determining a traditional revenue distribution scheme;
s402: adjusting the weight of each distributed unit according to the thermoelectric ratio;
s403: an improved revenue allocation scheme is determined.
6. A cogeneration virtual power plant optimal regulation and revenue distribution system, comprising the following modules:
the system comprises an establishing module, a control module and a control module, wherein the establishing module is used for establishing a CHP-VPP model comprising a wind power generation system, a photovoltaic power generation system, a cogeneration system, an energy storage system and a controllable load;
the system comprises a construction module, a selection module and a control module, wherein the construction module is used for constructing an objective optimization function with the maximum profit of a CHP-VPP system as an objective;
the determining module is used for solving the optimal output of each distributed unit in the CHP-VPP system through a particle swarm algorithm and determining the optimal operation mode of the system in a scheduling period;
and the distribution module is used for realizing fair profit distribution of each distributed unit in the CHP-VPP system by improving a xiapril value method.
7. The cogeneration virtual power plant optimal regulation and control and revenue distribution system of claim 6, wherein the building module includes the following sub-modules:
the wind power generation model building submodule processes uncertainty of wind speed by utilizing Weibull distribution to build a wind power generation model;
the photovoltaic power generation model building submodule processes the uncertainty of illumination by utilizing Beta distribution to build a photovoltaic power generation model;
the CHP electric heating output model building submodule builds a CHP electric heating output model according to the electric heating coupling characteristic of the CHP;
the electric storage unit model and the heat storage unit model building sub-modules respectively build an electric storage unit model and a heat storage unit model according to the charge state;
and the demand response model building submodule is used for building a demand response model based on excitation.
8. The cogeneration virtual power plant optimal regulation and control and revenue distribution system of claim 6, wherein the building module includes the following sub-modules:
the operation mode submodule is used for formulating a CHP-VPP operation mode based on a cooperative game theory;
the objective function submodule is used for determining an objective function of the system;
and the constraint condition submodule is used for determining the constraint condition of the system.
9. The cogeneration virtual power plant optimal regulation and control and revenue distribution system of claim 6, wherein the determination module includes the following sub-modules:
the setting submodule is used for inputting power generation parameters, time-of-use electricity prices and parameters of all distributed units of a typical day and setting particle swarm algorithm parameters;
the initialization submodule is used for initializing the speed, the position and the target value of the population;
the updating submodule is used for updating the population speed and the population position and updating the population speed and the population position again based on the condition that the output of the judging submodule is negative;
the unit adjusting submodule is used for continuously adjusting the output of each unit based on the considered constraint of each distributed unit in the whole system and calculating the maximum profit of the whole system;
the judgment submodule judges whether the iteration termination condition is met or the maximum iteration frequency is reached;
and the output submodule is used for finishing iteration and outputting a result based on the condition that the output of the judgment submodule is yes.
10. The cogeneration virtual power plant optimization, regulation and revenue distribution system of claim 6, wherein the distribution module includes the following sub-modules:
a receiving submodule for determining a conventional revenue allocation scheme;
the weight adjusting submodule adjusts the weight of each distributed unit according to the thermoelectric ratio;
an allocation submodule for determining an improved revenue allocation scheme.
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| JP7221370B1 (en) | 2021-12-06 | 2023-02-13 | 株式会社日立パワーソリューションズ | power management device |
| JP2023083633A (en) * | 2021-12-06 | 2023-06-16 | 株式会社日立パワーソリューションズ | Power management apparatus |
| CN117196173A (en) * | 2023-07-27 | 2023-12-08 | 河海大学 | Virtual power plant distributed scheduling method considering operation risk and network transmission |
| CN117196173B (en) * | 2023-07-27 | 2024-04-09 | 河海大学 | Virtual power plant distributed scheduling method considering operation risk and network transmission |
| CN117439080A (en) * | 2023-12-20 | 2024-01-23 | 国网山东省电力公司营销服务中心(计量中心) | Scheduling method, system, storage medium and equipment of virtual power plant |
| CN117439080B (en) * | 2023-12-20 | 2024-04-12 | 国网山东省电力公司营销服务中心(计量中心) | Scheduling method, system, storage medium and equipment of virtual power plant |
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