A virtual power plant modeling method considering an electrolytic hydrogen production system

Abstract

The present invention provides a virtual power plant modeling method considering an electrolytic hydrogen production system, comprising the following steps: constructing virtual models of an electrolytic hydrogen production system, new energy, demand response, gas turbines, and energy storage; aggregating the above virtual models to generate a virtual power plant model with the minimization of the total system dispatching cost as the total objective function, and constructing constraint conditions for the virtual power plant model. The present invention can make up for the deficiencies of existing VPP modeling methods in considering the types of distributed resources and related costs, and provide a reference for the application of electrolytic hydrogen production systems in VPPs and the research on the economic dispatching of VPPs aggregating multiple distributed resources to participate in the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and particularly to a virtual power plant modeling method considering an electrolytic hydrogen production system. Background Art

[0002] In recent years, with the increasing proportion of new energy in the power system, the uncertainty of its output has made the adjustable resources of the system increasingly scarce. At the same time, with the rise of flexible resources such as large-scale adjustable loads, distributed power sources, and energy storage on the distribution and consumption sides, through a virtual power plant (VPP), their aggregation management can be realized, enabling them to participate in grid regulation, which can fundamentally solve the problem of insufficient grid regulation resources. As a controllable load, electrolytic hydrogen production can not only increase the consumption of new energy, but also participate in electric energy storage and power auxiliary regulation. In addition, its product hydrogen, as an ideal clean energy source, has a high calorific value and no pollution during combustion, and has a large demand and wide application in industrial production and hydrogen-powered vehicles. The energy system characterized by renewable energy hydrogen production will be one of the important directions for the construction of the future energy Internet. In existing VPP research, there are studies on aggregating demand response, wind power, and photovoltaic power to establish a VPP model to study the revenue maximization scheme, etc. However, the established models rarely consider the electrolytic hydrogen production system, and have not comprehensively considered the costs of abandoned energy (abandoned wind and light), environmental pollution, and the costs of conventional thermal power units outside the VPP, etc. Summary of the Invention

[0003] The purpose of the present invention is to provide a virtual power plant modeling method considering an electrolytic hydrogen production system to solve the problems raised in the above background art.

[0004] The present invention is realized through the following technical solutions: A virtual power plant modeling method considering an electrolytic hydrogen production system includes the following steps:

[0005] Construct virtual models of an electrolytic hydrogen production system, new energy, demand response, gas turbines, and energy storage;

[0006] After aggregating the above virtual models, a virtual power plant model with the minimum total system dispatching cost as the total objective function is generated, and the constraint conditions of the virtual power plant model are constructed.

[0007] Optionally, the virtual model of the electrolytic hydrogen production system consists of a first objective function and a first set of constraint conditions;

[0008] The first objective function includes the hydrogen production amount function of the electrolyzer per unit time:

[0009]

[0010] In the formula, The hydrogen production of the electrolyzer of the $i$-th electrolytic hydrogen production system in the $t$-th period, is the hydrogen production rate of the electrolyzer, $P$ el,it is the electric power consumed by the electrolyzer of the $i$-th electrolytic hydrogen production system in the $t$-th period;

[0011] The calculation function of the power consumption of the compressor, the input hydrogen volume, and the pressure compression ratio:

[0012]

[0013] In the formula, $P$ com,it is the electric power consumed by the compressor of the $i$-th electrolytic hydrogen production system in the $t$-th period, is the specific heat capacity constant of hydrogen, $T$ in is the hydrogen temperature input to the compressor, $\kappa$ is the isentropic exponent of hydrogen, $\eta$ com is the working efficiency of the compressor, $P$ out is the hydrogen pressure output from the compressor, $P$ in is the hydrogen pressure input to the compressor;

[0014] The internal pressure function of the hydrogen storage tank:

[0015]

[0016] In the formula, $P_{rs}$ it is the internal pressure of the hydrogen storage tank of the $i$-th electrolytic hydrogen production system in the $t$-th period, is the reference value of the hydrogen capacity of the hydrogen storage tank, is the internal temperature of the hydrogen storage tank, is the molar mass of hydrogen, is the hydrogen load of the $i$-th electrolytic hydrogen production system in the $t$-th period;

[0017] The operation and maintenance cost function:

[0018]

[0019] In the formula: is the operation and maintenance cost of the electrolytic hydrogen production system, is the operation and maintenance cost coefficient of the electrolytic hydrogen production system, is the number of electrolytic hydrogen production systems, $P$ P2H,it is the total power consumption of the $i$-th electrolytic hydrogen production system in the $t$-th period;

[0020] The first set of constraint conditions includes the constraint function of the electrolyzer:

[0021] $0\leq P$ el,it $\leq P$ el,max

[0022] $|P$ el,i(t+1) $-P$ el,it|≤P el,ramp

[0023] Wherein: P el,max is the maximum electric power that the electrolyzer can consume, and P el,ramp is the maximum upward and downward ramp electric power of the electrolyzer;

[0024] Constraint function of the compressor:

[0025] 0 ≤ P com,it ≤ P com,max

[0026] Wherein: P com,max is the maximum electric power that the compressor can consume;

[0027] Constraint function of the hydrogen storage tank:

[0028]

[0029] Wherein: Prs min is the minimum allowable internal air pressure of the hydrogen storage tank, and Prs max is the maximum allowable internal air pressure of the hydrogen storage tank.

[0030] Optionally, the new energy virtual model consists of a second objective function and a second constraint condition group;

[0031] The second objective function includes the cost function of new energy:

[0032]

[0033] The second constraint condition group includes the wind power output constraint:

[0034]

[0035] (i = 1, 2,... N W )

[0036] Photovoltaic power output constraint:

[0037]

[0038] (i = 1, 2,... N PV )

[0039] Wherein, is the actual output of the i-th wind turbine in the t-th time period, is the predicted maximum output of the i-th wind turbine in the t-th time period, and N W is the number of wind turbines participating in the dispatching, is the actual output of the i-th photovoltaic unit in the t-th time period, The predicted maximum output of the i-th photovoltaic unit in the t-th period, N PV The number of photovoltaic units participating in the dispatching, C W,PV The cost of wind and light curtailment, γ W The wind curtailment penalty factor, γ PV The light curtailment penalty factor.

[0040] Optionally, the demand response virtual model consists of a third objective function and a third set of constraint conditions; the third objective function includes a demand response cost function:

[0041]

[0042] The third set of constraint conditions includes:

[0043]

[0044] In the formula, C DR is the demand response cost, N DR is the number of demand response loads, D it is the load change of the i-th demand response load in the t-th period, r i2 is the quadratic term coefficient of the i-th demand response cost, r i1 is the linear term coefficient of the i-th demand response cost, D it,max is the maximum allowable change of the i-th demand response load in the t-th period.

[0045] Optionally, the gas turbine virtual model consists of a fourth objective function and a fourth set of constraint conditions;

[0046] The fourth objective function includes a gas turbine unit cost function:

[0047]

[0048] The fourth set of constraint conditions includes the gas turbine unit output constraint:

[0049] P GT,min ≤P GT,it ≤P GT,max

[0050]

[0051] In the formula, C GT is the coal consumption cost of the gas turbine unit, N GT is the number of gas turbine units, b i0 、b i1 、b i2 are the coal consumption cost coefficients of the i-th gas turbine, P GT,it is the active power output of the i-th gas turbine in the t-th period, is the maximum upward ramping power of the i-th gas turbine unit, is the maximum downward ramping power of the i-th gas turbine unit.

[0052] Optionally, the energy storage virtual model consists of a fifth objective function and a fifth set of constraint conditions;

[0053] The fifth objective function includes an energy storage device output function:

[0054]

[0055] An energy storage device charge and discharge function:

[0056]

[0057] The fifth set of constraint conditions includes energy storage power and charge and discharge power constraints:

[0058] SOC i,min ≤SOC it ≤SOC i,max

[0059] P SE,i,min ≤P SE,it ≤P SE,i,max

[0060] In the formula, C SE is the output cost of the energy storage device, N SE is the number of energy storage devices, λ SE is the cost coefficient of the energy storage device, P SE,it is the charge and discharge power of the i-th energy storage device at the t-th time period, SOC it is the power of the i-th energy storage device at the t-th time period, η c is the charging efficiency of the energy storage device, η d is the discharging efficiency of the energy storage device, SOC i,min is the minimum allowable power of the i-th energy storage device, SOC i,max is the maximum allowable power of the i-th energy storage device, P SE,i,min is the maximum charging power of the i-th energy storage device, P SE,i,max is the maximum discharging power of the i-th energy storage device.

[0061] Optionally, the total objective function of the generated virtual power plant is:

[0062]

[0063] In the formula, N s is the number of new energy typical scenarios, p s is the probability of the s-th new energy typical scenario occurring, C GThe total dispatching cost of thermal power units is expressed as:

[0064]

[0065] C G is the total dispatching cost of thermal power units, N G is the number of thermal power units, T is the number of time periods within the dispatching cycle, I it is the on-line status of the i-th unit in the t-th time period, SU it is the start-up cost of the i-th unit in the t-th time period, SD it is the shutdown cost of the i-th unit in the t-th time period, F Gi (P it ) is the fuel cost of the i-th unit;

[0066] The net dispatching revenue of the virtual power plant is expressed as:

[0067]

[0068] In the formula, is the operation and maintenance cost of the electrolytic hydrogen production system, C W,PV is the cost of curtailed wind and curtailed light, C DR is the demand response cost, C GT is the coal consumption cost of the gas turbine unit, is the CO2 emission cost of the gas turbine unit, C SE is the output cost of the energy storage device, E VPP The total revenue of the virtual power plant dispatching is expressed as:

[0069]

[0070] where λ t is the day-ahead electricity price in the t-th time period.

[0071] Optionally, the constraint conditions of the constructed virtual power plant model include: the first constraint condition group, the second constraint condition group, the third constraint condition group, the fourth constraint condition group, the fifth constraint condition group, and other constraint condition groups.

[0072] Optionally, the other constraint condition group includes the active power output constraint of thermal power units:

[0073] P i,min ·I it ≤P it ≤P i,max ·I it

[0074] In the formula: P i,min is the lower limit of the active power output of the i-th unit, Pi,max is the upper limit of the active power output of the i-th unit;

[0075] Ramp rate constraint of thermal power unit:

[0076]

[0077] In the formula: is the maximum upward ramp power of the i-th unit's output, is the maximum downward ramp power of the i-th unit's output;

[0078] Start-up and shut-down time constraint of thermal power unit:

[0079]

[0080] In the formula: is the continuous operation time of the i-th unit before the t-th period, T i on is the minimum continuous operation time of the i-th unit, is the continuous shutdown time of the i-th unit before the t-th period, T i off is the minimum continuous shutdown time of the i-th unit;

[0081] Start-up and shut-down status constraint of thermal power unit:

[0082] I it -I i(t-1) =Y it -Z it

[0083] (t = 1, 2,...T)

[0084] In the formula: I i0 is the on-line status of the i-th unit at the initial moment of scheduling;

[0085] Maximum transmission power constraint of the line:

[0086] -P Li,max ≤P L,it ≤P Li,max

[0087] In the formula: P Li,max is the maximum allowable transmission power of the i-th line, P L,it is the transmission power of the i-th line in the t-th period;

[0088] Power balance constraint:

[0089]

[0090] In the formula: P VPP,tis the total output of the virtual power plant in the t-th period;

[0091] Reserve capacity constraint:

[0092]

[0093] In the formula: P VPP,t,max is the maximum output of the virtual power plant in the t-th period, and R t is the reserve capacity of the system in the t-th period.

[0094] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0095] The virtual power plant modeling method considering the electrolytic hydrogen production system provided by the present invention aggregates the electrolytic hydrogen production system, new energy (wind power and photovoltaic), demand response, gas turbine, and energy storage to form a VPP, and establishes a VPP model with the goal of minimizing the total system dispatching cost. This method innovatively considers the electrolytic hydrogen production system with broad development prospects into the VPP, and studies the influence of the hydrogen load demand curve on the VPP revenue, cost, and new energy consumption. In addition, the model takes into account both the VPP revenue and the cost of conventional thermal power units, and simultaneously considers the impact of CO2 emissions on the environment. This model is easy to solve, can make up for the deficiencies of existing VPP modeling methods in considering the types of distributed resources and related costs, and provides a reference for the application of the electrolytic hydrogen production system in the VPP and the research on the economic dispatching of the VPP aggregating multiple distributed resources to participate in the power system. Brief Description of the Drawings

[0096] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only the preferred embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0097] Figure 1 is the flowchart of a virtual power plant modeling method considering the electrolytic hydrogen production system provided by the present invention

[0098] Figure 2 is the schematic diagram of the electrolytic hydrogen production system provided by the present invention;

[0099] Figure 3 is the schematic diagram of the predicted output of wind power and photovoltaic provided by the present invention;

[0100] Figure 4 is the schematic diagram of the wind power output scenario before reduction provided by the present invention;

[0101] Figure 5 is the schematic diagram of the photovoltaic output scenario before reduction provided by the present invention;

[0102] Figure 6 Schematic diagram of typical wind power output scenarios provided by the present invention;

[0103] Figure 7 Schematic diagram of typical photovoltaic output scenarios provided by the present invention;

[0104] Figure 8 Schematic diagram of load curve provided by the present invention;

[0105] Figure 9 Schematic diagram of hydrogen load demand curve provided by the present invention;

[0106] Figure 10 Schematic diagram of system output curve provided by the present invention;

[0107] Figure 11 Schematic diagram of output curves of each unit inside the virtual power plant provided by the present invention;

[0108] Figure 12 Schematic diagram of load curve considering before and after power-to-hydrogen provided by the present invention;

[0109] Figure 13 Schematic diagram of load curve before and after demand response provided by the present invention;

[0110] Figure 14 Schematic diagram of charge-discharge power and remaining capacity of energy storage provided by the present invention. Detailed implementation manners

[0111] In order to make the objectives, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0112] In the following description, numerous specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other instances, in order to avoid confusion with the present invention, some well-known technical features are not described.

[0113] It should be understood that the present invention can be implemented in different forms and should not be construed as limited to the embodiments presented herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the present invention to those skilled in the art.

[0114] The purpose of the terms used herein is only to describe specific embodiments and is not a limitation of the present invention. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, specify the presence of the stated features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups. As used herein, the term "and / or" includes any and all combinations of the associated listed items.

[0115] To thoroughly understand the present invention, detailed structures will be presented in the following description to illustrate the technical solutions proposed by the present invention. The optional embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention may also have other embodiments.

[0116] See Figure 1 , a virtual power plant modeling method considering an electrolytic hydrogen production system, comprising the following steps:

[0117] S1. Construct virtual models of an electrolytic hydrogen production system, new energy, demand response, gas turbine and energy storage;

[0118] For the virtual model of the electrolytic hydrogen production system, it consists of three sub-virtual models: an electrolyzer, a compressor and a hydrogen storage tank. Each sub-virtual model is composed of a corresponding first objective function and a first set of constraint conditions.

[0119] For the virtual model of the electrolyzer, in this embodiment, the electrolyzer model selects alkaline electrolysis technology. Under steady state, the hydrogen production rate per unit time of the electrolyzer is a proportional function of the electrolyzer's electric power. The specific functional relationship is expressed as follows:

[0120]

[0121] In the formula, is the hydrogen production amount of the electrolyzer of the i-th electrolytic hydrogen production system at the t-th time period, is the hydrogen production rate of the electrolyzer, and P el,it is the electric power consumed by the electrolyzer of the i-th electrolytic hydrogen production system at the t-th time period;

[0122] For the virtual model of the compressor, the compressor compresses the hydrogen generated by the electrolyzer, and its power consumption is related to the input hydrogen amount and the pressure compression ratio:

[0123]

[0124] In the formula, P com,it is the electric power consumed by the compressor of the i-th electrolytic hydrogen production system at the t-th time period, is the specific heat capacity constant of hydrogen, T in is the temperature of hydrogen input into the compressor, κ is the isentropic exponent of hydrogen, η com is the working efficiency of the compressor, P out is the hydrogen pressure output from the compressor, P in is the hydrogen pressure input into the compressor;

[0125] For the virtual model of the hydrogen storage tank, the hydrogen storage tank stores high-pressure hydrogen compressed by the compressor, and the amount of hydrogen storage can be reflected by its internal pressure. The internal pressure of the hydrogen storage tank is expressed as:

[0126]

[0127] In the formula, Prs it is the internal pressure of the hydrogen storage tank of the i-th electrolytic hydrogen production system at the t-th time period, is the reference value of the hydrogen capacity of the hydrogen storage tank, is the internal temperature of the hydrogen storage tank, is the molar mass of hydrogen, is the hydrogen load of the i-th electrolytic hydrogen production system at the t-th time period;

[0128] Operation and maintenance cost function:

[0129]

[0130] In the formula: is the operation and maintenance cost of the electrolytic hydrogen production system, is the operation and maintenance cost coefficient of the electrolytic hydrogen production system, is the number of electrolytic hydrogen production systems, P P2Hit is the total power consumption of the i-th electrolytic hydrogen production system at the t-th time period, expressed as:

[0131] P P2H,it =P el,it +P com,it

[0132] For the constraint function of the electrolyzer, the electric power consumed by the electrolyzer should satisfy the upper and lower power limits and the ramping constraint:

[0133] 0 ≤ P el,it ≤ P el,max

[0134] |P el,i(t+1) -P el,it | ≤ P el,ramp

[0135] In the formula: P el,max is the maximum electric power that the electrolyzer can consume, P el,ramp is the maximum upward and downward ramping electric power of the electrolyzer;

[0136] The electric power consumed by the compressor shall satisfy the upper and lower limit constraints of power:

[0137] 0 ≤ P com,it ≤ P com,max

[0138] Where: P com,max is the maximum electric power that the compressor can consume;

[0139] The constraint function of the hydrogen storage tank. The internal air pressure of the hydrogen storage tank shall satisfy the upper and lower limit constraints of air pressure and the equality of air pressure at the initial and final time periods:

[0140]

[0141] Where: Prs min is the minimum allowable internal air pressure of the hydrogen storage tank, and Prs max is the maximum allowable internal air pressure of the hydrogen storage tank.

[0142] Furthermore, for the new energy virtual model, it is composed of the second objective function and the second constraint condition group. In specific implementation, the stochastic programming method is used to simulate the uncertainty of wind power and photovoltaic power output. First, a large number of new energy output scenarios are simulated by the Monte Carlo stochastic sampling method, and then they are reduced to 5 typical scenarios based on the backward scenario reduction technology, and the corresponding probabilities are obtained. The obtained typical new energy output is used as the predicted output of the new energy that composes the virtual power plant. The cost of the new energy power station is mainly concentrated in the construction investment. Once built, the cost of power generation is relatively low and can be ignored compared with thermal power units. With the large-scale grid connection of distributed wind power and photovoltaics, the phenomena of wind curtailment and PV curtailment are becoming more and more serious. Considering the costs of wind curtailment and PV curtailment of wind power and photovoltaics, in order to consume as much new energy as possible and reduce the amount of wind curtailment and PV curtailment. Therefore, the second objective function includes the cost function of new energy:

[0143]

[0144] The second constraint condition group includes the wind power output constraint:

[0145]

[0146] (i = 1, 2,... N W )

[0147] The photovoltaic power output constraint:

[0148]

[0149] (i = 1, 2,... N PV )

[0150] Where, is the actual output of the i-th wind turbine in the t-th time period, is the predicted maximum output of the i-th wind turbine in the t-th time period, N W is the number of wind turbines participating in the dispatch, is the actual output of the i-th photovoltaic unit in the t-th time period, is the predicted maximum output of the i-th photovoltaic unit in the t-th time period, N PV is the number of photovoltaic units participating in the dispatch, C W,PV is the cost of wind and light curtailment, γ W is the wind curtailment penalty factor, γ PV is the light curtailment penalty factor.

[0151] Furthermore, for the demand response virtual model, it mainly considers that users respond to relevant incentive policies, that is, incentive-based demand response, which is used as a component of the virtual power plant. Users actively respond to incentive policies, timely reduce the electricity load and obtain certain economic compensation. The demand response virtual model consists of a third objective function and a third set of constraint conditions;

[0152] The third objective function includes a demand response cost function:

[0153]

[0154] The third set of constraint conditions includes:

[0155]

[0156] In the formula, C DR is the demand response cost, N DR is the number of demand response loads, D it is the load change of the i-th demand response load in the t-th time period, r i2 is the quadratic coefficient of the i-th demand response cost, r i1 is the linear coefficient of the i-th demand response cost, D it,max is the maximum allowable change of the i-th demand response load in the t-th time period.

[0157] Furthermore, the virtual model of the gas turbine consists of a fourth objective function and a fourth set of constraint conditions;

[0158] During the dispatch process, the gas turbine unit will generate coal consumption cost and CO2 emission cost. Therefore, the fourth objective function includes a gas turbine unit cost function:

[0159]

[0160] The fourth set of constraint conditions includes the output constraint of the gas turbine unit:

[0161] P GT,min ≤P GT,it ≤P GT,max

[0162]

[0163] wherein, C GT is the coal consumption cost of the gas turbine unit, N GT is the number of gas turbine units, b i0 、b i1 、b i2 are the coal consumption cost coefficients of the i-th gas turbine, P GT,it is the active power output of the i-th gas turbine at the t-th time period, is the maximum upward ramp power of the i-th gas turbine unit, is the maximum downward ramp power of the i-th gas turbine unit.

[0164] For the energy storage model, the energy storage virtual model is composed of a fifth objective function and a fifth constraint condition group; due to the uncertainty and intermittency of wind power and photovoltaic power, the actual values of their outputs may be different from the corresponding predicted values. Therefore, it is necessary to have a certain energy storage configuration in the virtual power plant to compensate for the fluctuations in the output power of new energy, so that the output of the virtual power plant is stable, providing flexibility for the power system and ensuring the security of the power system. The energy storage device will cause certain damage and aging during the charging and discharging process. Considering the output cost of the energy storage device, the fifth objective function includes the energy storage device output function:

[0165]

[0166] Energy storage device charging and discharging function:

[0167]

[0168] The fifth constraint condition group includes energy storage power and charging and discharging power constraints:

[0169] SOC i,min ≤SOC it ≤SOC i,max

[0170] P SE,i,min ≤P SE,it ≤P SE,i,max

[0171] wherein, C SE is the output cost of the energy storage device, N SE is the number of energy storage devices, λ SE is the cost coefficient of the energy storage device, P SE,it$P_{i,t}$ is the charging and discharging power of the $i$-th energy storage device in the $t$-th time period, and SOC it $SOC_{i,t}$ is the power of the $i$-th energy storage device in the $t$-th time period, and $\eta$ c $\eta_{ch}$ is the charging efficiency of the energy storage device, and $\eta$ d $\eta_{dch}$ is the discharging efficiency of the energy storage device, and SOC i,min $SOC_{min,i}$ is the minimum allowable power of the $i$-th energy storage device, and SOC i,max $SOC_{max,i}$ is the maximum allowable power of the $i$-th energy storage device, and $P$ SE,i,min $P_{ch,max,i}$ is the maximum charging power of the $i$-th energy storage device, and $P$ SE,i,max $P_{dch,max,i}$ is the maximum discharging power of the $i$-th energy storage device.

[0172] S2. After aggregating the above virtual models, a virtual power plant model with the minimum total system scheduling cost as the total objective function is generated, and the constraint conditions of the virtual power plant model are constructed.

[0173] For the virtual power plant model established by the present invention, while expecting the net profit of the virtual power plant to be as large as possible, it is expected that the scheduling cost of conventional thermal power units is as small as possible. The net profit of the virtual power plant is obtained by subtracting the power generation operation cost from the power generation and grid connection income of each distributed energy source inside it. Therefore, the objective function considering the scheduling cost of thermal power units and the net profit of the virtual power plant is

[0174]

[0175] In the formula, $N$ s is the number of new energy typical scenarios, and $p$ s is the probability of the occurrence of the $s$-th new energy typical scenario.

[0176] The power generation operation cost of thermal power units consists of two parts, namely the fuel cost and the unit start-stop cost. The fuel cost can be regarded as a quadratic function of the power generation power, and the coal consumption cost coefficient is determined by the characteristics of the unit itself. The unit start-stop cost is determined by the start-stop state and the cost coefficient. In addition, with the proposal and attention of "carbon peak" and "carbon neutrality" in China, the present invention considers the CO2 emissions, and converts the CO2 emissions into the CO2 emission cost as a part of the total cost of thermal power units. A 0-1 variable is introduced to represent the online state of the unit, as well as the start-up and shutdown states. Therefore, the total scheduling cost $C$ of thermal power units G , is expressed as:

[0177]

[0178] $C$ G is the total scheduling cost of thermal power units, $N$ G is the number of thermal power units, $T$ is the number of time periods in the scheduling cycle, and $I$ it is the online state of the $i$-th unit in the $t$-th time period, and $SU$ itThe start-up cost of the i-th unit in the t-th period, SD it The shutdown cost of the i-th unit in the t-th period, F Gi (P it ) is the fuel cost of the i-th unit;

[0179] The fuel cost is a quadratic function of the power generation, expressed as:

[0180] F Gi (P it ) = a i2 ·(P it ) 2 + a i1 ·P it + a i0

[0181] In the formula, P it is the active power output of the i-th unit in the t-th period, a i2 , a i1 , a i0 are the coal consumption cost coefficients of the i-th unit, e Gi is the CO2 emission coefficient of the i-th unit, c G is the CO2 emission cost coefficient of the thermal power unit.

[0182] The start-up cost SU it The expression is:

[0183] SU it = c SUi ·Y it

[0184] In the formula: c SUi is the start-up cost coefficient of the i-th unit, Y it is the start-up status of the i-th unit in the t-th period.

[0185] The shutdown cost SD it The expression is:

[0186] SD it = c SDi ·Z it

[0187] In the formula: c SDi is the shutdown cost coefficient of the i-th unit, Z it is the shutdown status of the i-th unit in the t-th period (1 means shutdown, 0 means not shutdown).

[0188] is the net dispatch revenue of the virtual power plant, which is expressed as:

[0189]

[0190] In the formula, is the operation and maintenance cost of the electrolytic hydrogen production system, C W,PV is the curtailment cost of wind and solar power, C DR is the demand response cost, C GT is the coal consumption cost of the gas turbine unit, is the CO2 emission cost of the gas turbine unit, C SE is the output cost of the energy storage device, E VPP is the total revenue of the virtual power plant dispatching, which is expressed as:

[0191]

[0192] where λ t is the day-ahead electricity price in the t-th period.

[0193] Optionally, the constraint conditions of the constructed virtual power plant model include: the first constraint group, the second constraint group, the third constraint group, the fourth constraint group, the fifth constraint group, and other constraint groups.

[0194] Optionally, the other constraint groups should also satisfy the relevant constraints of thermal power units, safety constraints, and power balance constraints and reserve constraints in the system;

[0195] Therefore, the other constraint groups also include the active power output constraint of thermal power units:

[0196] P i,min ·I it ≤P it ≤P i,max ·I it

[0197] In the formula: P i,min is the lower limit of the active power output of the i-th unit, P i,max is the upper limit of the active power output of the i-th unit;

[0198] Thermal power unit output ramp rate constraint:

[0199]

[0200] In the formula: is the maximum upward ramp power of the output of the i-th unit, is the maximum downward ramp power of the output of the i-th unit;

[0201] Thermal power unit start-stop time constraint:

[0202]

[0203] In the formula: is the duration for which the \(i\)-th unit has been in the on state before the \(t\)-th period, \(T\) i on is the minimum continuous on-time of the \(i\)-th unit is the duration for which the \(i\)-th unit has been in the off state before the \(t\)-th period, \(T\) i off is the minimum continuous off-time of the \(i\)-th unit

[0204] Start-stop state constraints of thermal power units

[0205] \(I\) it \(-I\) i(t-1) \(= Y\) it \(-Z\) it

[0206] (\(t = 1, 2, \cdots, T\))

[0207] In the formula: \(I\) i0 is the on-line state of the \(i\)-th unit at the initial scheduling moment

[0208] Maximum transmission power constraint of the line

[0209] \(-P\) Li,max \(\leq P\) L,it \(\leq P\) Li,max

[0210] In the formula: \(P\) Li,max is the maximum allowable transmission power of the \(i\)-th line, \(P\) L,it is the transmission power of the \(i\)-th line in the \(t\)-th period

[0211] Power balance constraint

[0212]

[0213] In the formula: \(P\) VPP,t is the total output of the virtual power plant in the \(t\)-th period

[0214] Reserve capacity constraint

[0215]

[0216] In the formula: \(P\) VPP,t,max is the maximum output of the virtual power plant in the \(t\)-th period, \(R\) t is the reserve capacity of the system in the \(t\)-th period

[0217] For the established virtual power plant model with an electrolytic hydrogen production system, a 6-node system is taken as an example for verification

[0218] The 6-node system contains 3 conventional thermal power units, located at nodes 1, 2, and 6 respectively; 2 loads, located at nodes 3 and 4 respectively; and 11 transmission lines. The virtual power plant contains 2 wind turbine units, 1 photovoltaic unit, 2 electrolytic hydrogen systems, 1 demand response load, 4 gas turbine units and 3 energy storage power stations. Among them, the installed capacities of the wind turbine units are 50MW and 10MW, located at nodes 3 and 5 respectively, the installed capacity of the photovoltaic unit is 50MW, located at node 4; the electrolytic hydrogen systems are located at nodes 2 and 4 respectively, and the reference values of the hydrogen load demand are 3kg and 5kg; the demand response load is located at node 5, and the quadratic coefficient and the linear coefficient of the cost are 0.1 $ / MW and 20 $ / MW respectively; the cost coefficient of the energy storage power station is 1.29 $ / MW. The curtailment penalty factors for wind and light are set at 100 $ / MW, and the day-ahead electricity price is set at 53.8 $ / MW.

[0219] Programming and modeling are carried out in MATLAB R2016, and the solution is obtained through the CPLEX solver. The solution results are as Figures 2 - 14 shown. It can be seen from the results that the established model is effective and reasonable. The proposed virtual power plant modeling method with electrolytic hydrogen systems can make up for the deficiencies in considering the types of distributed resources and related costs in virtual power plant modeling, and provide a reference for the research on virtual power plants aggregating various distributed resources to participate in the economic dispatch of power systems.

[0220] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A virtual power plant modeling method considering an electrolytic hydrogen production system, characterized in that Including the following steps: Construct virtual models of an electrolytic hydrogen production system, new energy, demand response, gas turbines, and energy storage; After aggregating the above virtual models, generate a virtual power plant model with the minimum total system scheduling cost as the total objective function, and construct the constraint conditions of the virtual power plant model; The virtual model of the electrolytic hydrogen production system consists of a first objective function and a first set of constraint conditions; The first objective function includes a hydrogen production amount function of the electrolyzer per unit time: In the formula, is the hydrogen production amount of the electrolyzer of the i-th electrolytic hydrogen production system in the t-th period, is the hydrogen production rate of the electrolyzer, P el,it is the electric power consumed by the electrolyzer of the i-th electrolytic hydrogen production system in the t-th period; A calculation function of the power consumption of the compressor, the input hydrogen amount, and the gas pressure compression ratio: where P com,it is the electric power consumed by the compressor of the i-th electrolytic hydrogen production system during the t-th period, is the specific heat capacity constant of hydrogen, T in is the temperature of the hydrogen input into the compressor, κ is the isentropic exponent of hydrogen, η com is the working efficiency of the compressor, P out is the hydrogen pressure output from the compressor, P in is the hydrogen pressure input into the compressor; The internal gas pressure function of the hydrogen storage tank: Where Prs it is the internal air pressure of the hydrogen storage tank of the i-th electrolytic hydrogen production system in the t-th period, is the reference value of the hydrogen capacity of the hydrogen storage tank, is the internal temperature of the hydrogen storage tank, is the molar mass of hydrogen, is the hydrogen load of the i-th electrolytic hydrogen production system in the t-th period; The operation and maintenance cost function: Wherein: is the operation and maintenance cost of the electrolytic hydrogen production system, is the operation and maintenance cost coefficient of the electrolytic hydrogen production system, is the number of electrolytic hydrogen production systems, P P2H,it is the total power consumption of the i-th electrolytic hydrogen production system in the t-th period; The first set of constraint conditions includes a constraint function of the electrolyzer: 0 ≤ P el,it ≤ P el,max |P el,i(t+1) -P el,it |≤P el,ramp Where: P el,max is the maximum electric power that the electrolyzer can consume, and P el,ramp is the maximum upward and downward ramping electric power of the electrolyzer; The constraint function of the compressor: 0 ≤ P com,it ≤ P com,max Where: P com,max is the maximum electric power that the compressor can consume; The constraint function of the hydrogen storage tank: where: Prs min is the minimum allowable internal pressure of the hydrogen storage tank, and Prs max is the maximum allowable internal pressure of the hydrogen storage tank.

2. A virtual power plant modeling method considering an electrolytic hydrogen production system according to claim 1, characterized in that, The virtual model of new energy consists of a second objective function and a second set of constraint conditions; The second objective function includes a cost function of new energy: The second set of constraint conditions includes a wind power output constraint: (i = 1, 2,... N W ) A photovoltaic power output constraint: (i = 1, 2,... N PV ) Wherein, is the actual output of the i-th wind turbine in the t-th time period, is the predicted maximum output of the i-th wind turbine in the t-th time period, N W is the number of wind turbines participating in the dispatching, is the actual output of the i-th photovoltaic unit in the t-th time period, is the predicted maximum output of the i-th photovoltaic unit in the t-th time period, N PV is the number of photovoltaic units participating in the dispatching, C W,PV is the cost of curtailed wind and curtailed light, γ W is the wind curtailment penalty factor, γ PV is the light curtailment penalty factor.

3. A virtual power plant modeling method considering an electrolytic hydrogen production system according to claim 2, characterized in that The virtual model of demand response consists of a third objective function and a third set of constraint conditions; The third objective function includes a demand response cost function: The third set of constraint conditions includes: Wherein, C DR is the demand response cost, N DR is the number of demand response loads, D it is the load change of the i-th demand response load in the t-th period, r i2 is the quadratic term coefficient of the i-th demand response cost, r i1 is the linear term coefficient of the i-th demand response cost, D it,max is the maximum allowable change of the i-th demand response load in the t-th period.

4. A virtual power plant modeling method considering an electrolytic hydrogen production system according to claim 3, characterized in that, The virtual model of the gas turbine consists of a fourth objective function and a fourth set of constraint conditions; The fourth objective function includes a gas turbine unit cost function: The fourth set of constraint conditions includes a gas turbine unit output constraint: P GT,min ≤P GT,it ≤P GT,max where C GT is the coal consumption cost of the gas turbine unit, N GT is the number of gas turbine units, b i0 , b i1 , b i2 are the coal consumption cost coefficients of the i-th gas turbine, P GT,it is the active power output of the i-th gas turbine at the t-th time period, is the maximum upward ramp power of the i-th gas turbine unit, is the maximum downward ramp power of the i-th gas turbine unit.

5. A virtual power plant modeling method considering an electrolytic hydrogen production system according to claim 4, characterized in that The virtual model of energy storage consists of a fifth objective function and a fifth set of constraint conditions; The fifth objective function includes an energy storage device output function: The charge and discharge function of the energy storage device: The fifth set of constraint conditions includes energy storage power and charge and discharge power constraints: SOC i,min ≤SOC it ≤SOC i,max P SE,i,min ≤P SE,it ≤P SE,i,max Where, C SE is the output cost of the energy storage device, N SE is the number of energy storage devices, λ SE is the cost coefficient of the energy storage device, P SE,it is the charge and discharge power of the i-th energy storage device in the t-th period, SOC it is the electricity quantity of the i-th energy storage device in the t-th period, η c is the charging efficiency of the energy storage device, η d is the discharging efficiency of the energy storage device, SOC i,min is the minimum allowable electricity quantity of the i-th energy storage device, SOC i,max is the maximum allowable electricity quantity of the i-th energy storage device, P SE,i,min is the maximum charging power of the i-th energy storage device, P SE,i,max is the maximum discharging power of the i-th energy storage device.

6. A virtual power plant modeling method considering an electrolytic hydrogen production system according to claim 5, characterized in that The total objective function of the generated virtual power plant is: where N s is the number of typical scenarios of new energy, p s is the probability of the s-th typical scenario of new energy occurring, and C G is the total cost of thermal power unit scheduling, expressed as: N G is the number of thermal power units, T is the number of time periods within the scheduling cycle, and I it is the online status of the i-th unit in the t-th time period, SU it is the start-up cost of the i-th unit in the t-th time period, SD it is the shutdown cost of the i-th unit in the t-th time period, F Gi (P it ) is the fuel cost of the i-th unit; is the virtual power plant dispatching net revenue, which is expressed as: Wherein, is the operation and maintenance cost of the electrolytic hydrogen production system, C W,PV is the cost of curtailed wind and solar power, C DR is the cost of demand response, C GT is the coal consumption cost of the gas turbine unit, is the CO2 emission cost of the gas turbine unit, C SE is the output cost of the energy storage device, E VPP is the total revenue of the virtual power plant dispatching, which is expressed as: where λ t is the day-ahead electricity price in the t-th period.

7. A virtual power plant modeling method considering an electrolytic hydrogen production system according to claim 6, characterized in that, The constraint conditions of the constructed virtual power plant model include: the first set of constraint conditions, the second set of constraint conditions, the third set of constraint conditions, the fourth set of constraint conditions, the fifth set of constraint conditions, and other sets of constraint conditions.

8. A virtual power plant modeling method considering an electrolytic hydrogen production system according to claim 7, characterized in that The other sets of constraint conditions include a thermal power unit active power output constraint: P i,min ·I it ≤P it ≤P i,max ·I it Where: P i,min is the lower limit of the active power output of the i-th unit, and P i,max is the upper limit of the active power output of the i-th unit; The thermal power unit output ramp constraint: where: is the maximum upward ramp rate of the output of the i-th unit, is the maximum downward ramp rate of the output of the i-th unit; The thermal power unit start-stop time constraint: Where: is the duration that the i-th unit has been in the on state continuously before the t-th period, T i on is the minimum continuous on-time of the i-th unit, is the duration that the i-th unit has been in the off state continuously before the t-th period, T i off is the minimum continuous off-time of the i-th unit; The thermal power unit start-stop state constraint: I it -I i(t-1) = Y it -Z it (t = 1, 2,... T) The maximum transmission power constraint of the line: -P Li,max ≤P L,it ≤P Li,max Where: P Li,max is the maximum transmission power allowed for the i-th line, and P L,it is the transmission power of the i-th line during the t-th period; The power balance constraint: Where: P VPP,t is the total output of the virtual power plant in the t-th period; The reserve capacity constraint: Where: P VPP,t,max is the maximum output of the virtual power plant in the t-th period, and R t is the reserve capacity of the system in the t-th period.

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