Regulation and control method based on multi-source flexible resource regulation model

Abstract

The invention relates to a regulation and control method based on a multi-source flexible resource regulation model, and belongs to the technical field of power systems. The regulation and control method comprises the following steps: acquiring real-time operation data of a plurality of different types of energy resources in a power system; establishing a mathematical model for different types of energy resources based on the acquired data; performing simulation prediction on the operation state of the power system by using the mathematical model; an optimal scheduling strategy is provided according to a simulation result, and optimal configuration and regulation of source-load-storage resources are realized; modeling can be accurately carried out on adjustment characteristics and operation modes of various types of flexible resources, and accurate mastering and efficient utilization of the resource regulation and control capability are achieved. Through research and application of an advanced mathematical modeling method, the accuracy and flexibility of coordination control among different types of flexible resources are improved, an optimization strategy can be dynamically adjusted according to real-time data and prediction information, stable operation of a power system is ensured, and economic benefits and social benefits are maximized at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a control method based on a multi-source flexible resource regulation model. Background Art

[0002] By quickly responding to and regulating flexible loads, deeply exploring power demand-side resources, and expanding the regulation capabilities of the existing source-network-load system, potential safety hazards brought by UHV faults in the power grid can be effectively addressed, providing guarantees for the supply-demand balance of the regional power grid and maintaining the safe and stable operation of the power grid. Flexible loads refer to loads that have the ability to actively participate in the operation control of the power grid, can interact with the power grid in terms of energy, and have flexible characteristics. Their flexible characteristics are reflected in being flexibly variable within a certain time range without significantly affecting the user's power consumption experience. Due to their large quantity, fast response speed, and flexible control, flexible loads have become an important dispatching and regulation resource for solving supply-demand contradictions, driving the traditional "supply following demand" regulation mode to gradually transform into "supply-demand interaction" and "demand following supply" modes. Under this background, relevant research on demand response is urgent and necessary. The flexibility characteristics of load-side resources should be considered, and this characteristic should be fully utilized to promote the consumption of new energy power, which is an important issue to be solved in the current power development.

[0003] Based on this, this case is proposed. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a control method based on a multi-source flexible resource regulation model to achieve precise control of the multi-source flexible resource regulation model and improve the accuracy and flexibility of regulating different types of flexible resources.

[0005] To achieve the above purpose, the technical solution of the present invention is as follows:

[0006] A control method based on a multi-source flexible resource regulation model includes the following steps:

[0007] S01. Obtain the real-time operation data of multiple different types of energy resources in the power system;

[0008] S02. Establish a mathematical model for different types of energy resources based on the obtained data;

[0009] S03. Use the mathematical model to simulate and predict the operation state of the power system to evaluate the stability and efficiency of the system under various scenarios;

[0010] S04. Propose an optimized dispatching strategy according to the simulation results to achieve the optimal allocation and control of source-load-storage resources.

[0011] Further, the process of establishing the mathematical model includes: statistically analyzing the historical data of each energy resource to determine its probability distribution characteristics; and training and optimizing the model parameters using machine learning algorithms.

[0012] Further, the mathematical model is:

[0013]

[0014] In the formula, F represents the total supply-demand interaction flexibility of the system; f flex (t) represents the flexibility contribution of the flexible load at time t; f stor (t) represents the flexibility contribution of all energy storage devices at time t; f conv (t) represents the flexibility contribution of the energy conversion unit at time t; f renew (t) represents the contribution of renewable energy to the system flexibility at time t; T represents the total number of planned time periods;

[0015] f flex (t) = L flex,max (t) - L flex,min (t);

[0016] In the formula, L flex,max (t) and L flex,min (t) respectively represent the maximum and minimum adjustable power levels of the flexible load at time t, L flex,max (t) = min(P max , P current + ΔP up ), P max is the maximum power of the device, P current is the current power of the device, ΔP up is the maximum upward adjustment range allowed according to user comfort and service quality, L flex,min (t) = max(P min , P current - ΔP down ), P min is the minimum power of the device, ΔP down is the maximum downward adjustment range allowed according to user comfort and service quality;

[0017]

[0018] In the formula, k represents different types of energy storage devices, and respectively represent the maximum charging power and minimum discharging power of the k-th type of energy storage device at time t;

[0019]

[0020] Wherein, j represents different types of energy conversion units, and respectively represent the maximum and minimum output capabilities of the j-th energy conversion unit at time t;

[0021] P rated is the rated power of the device; P environmental (t) is the maximum allowable output based on the current environmental conditions; P maintenance (t) is the maximum allowable output based on the current maintenance status; P technical_limit is the minimum power level at which the device can operate stably, P economic (t) is the minimum recommended output power based on the current economic conditions;

[0022] f renew (t) = Flexibility Reserve renewable (t);

[0023] Wherein, Flexibility Reserve renewable (t) refers to the amount of flexibility reserve reserved at time t to cope with the prediction error of renewable energy.

[0024] Furthermore, the mathematical model needs to satisfy the power balance constraint:

[0025] ∑ i P i (t) - ∑ l L l (t) = 0;

[0026] Wherein, P i (t) represents the net output of the i-th energy production or conversion unit at time t; L l (t) represents the demand of the l-th load demand at time t.

[0027] Furthermore, the mathematical model needs to satisfy the energy storage operation constraint. For each type of energy storage k:

[0028]

[0029] Wherein, and respectively represent the minimum and maximum values of the energy state of the k-th type of energy storage device; E k (t) represents the energy state of the k-th type of energy storage device at time t; P stor,k (t) represents the net output power of the k-th type of energy storage device at time t.

[0030] Furthermore, the mathematical model needs to satisfy the operation constraints of the energy conversion unit. For each type of energy conversion unit j:

[0031]

[0032] In the formula, C j (t) represents the output capacity of the j-th type of energy conversion unit at time t.

[0033] Furthermore, the mathematical model needs to satisfy user comfort and service quality:

[0034]

[0035] S m (t) represents the satisfaction score of the m-th factor affecting user comfort at time t; w m represents the weight of the m-th factor.

[0036] Furthermore, the simulation and prediction process includes: simulating the impact of different scenarios on the system stability and formulating corresponding emergency plans accordingly. The different scenarios include one or more of normal operating conditions, extreme weather conditions, market electricity price fluctuations, and load mutations.

[0037] The advantages of the present invention are as follows: It can accurately model the regulation characteristics and operation modes of various types of flexible resources (such as distributed energy, energy storage devices, and adjustable loads, etc.), so as to achieve precise grasp and efficient utilization of the regulation capabilities of these resources. By researching and applying advanced mathematical modeling methods, this method not only improves the accuracy and flexibility of coordinated control between different types of flexible resources, but also can dynamically adjust and optimize strategies according to real-time data and prediction information to ensure the stable operation of the power system, while maximizing economic and social benefits. This comprehensive analysis and modeling method provides the power system with stronger adaptability and higher operation efficiency, especially important in the face of complex market environments and changing energy supply conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the basic architecture of the integrated energy system - power distribution network source - network - load - storage coordination in the embodiment;

[0039] Figure 2 It is a regulation method based on a multi-source flexible resource regulation model in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The present invention will be further described in detail below in conjunction with embodiments. It should be understood that the orientation or positional relationships indicated by terms such as "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. in the text are based on the orientation or positional relationships shown in the coordinate system of the attached drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0041] As Figure 1 shown, the integrated energy system (hereinafter referred to as IES) is the basic architecture for the coordination of power grid source-network-load-storage, and includes three major categories: an energy production unit, an energy conversion unit, and an energy storage unit. Among them, the energy production unit is the core component of the IES, which is responsible for converting various natural resources into available energy forms. The energy conversion unit is the key link for realizing the conversion of different forms of energy, and can realize the conversion between multiple energy forms such as electric energy and thermal energy, and electric energy and mechanical energy. The energy storage unit plays an important role in balancing energy supply and demand and improving system stability in the IES. Together, they build a complete and efficient energy utilization chain. The energy production unit includes one or more of a power grid, a natural gas station, wind power, and photovoltaic power generation; the energy conversion unit includes one or more of a gas turbine, a gas boiler, a heat pump, a heat exchanger, power-to-gas, an air conditioner, an electric chiller, an absorption chiller, electrolytic hydrogen production, a hydrogen fuel cell, and a carbon capture device; the energy storage device includes one or more of an electric storage, a thermal storage, a cold storage, a natural gas storage, and a hydrogen storage. By excavating and analyzing the regulation characteristics and operation modes of the flexible resources of the source-network-load-storage, and carrying out modeling, the accurate grasp and efficient utilization of the regulation capabilities of these resources can be realized.

[0042] As Figure 2 shown, the regulation method based on the multi-source flexible resource regulation model includes the following steps:

[0043] S01. Obtain the real-time operation data of multiple different types of energy resources in the power system;

[0044] S02. Based on the obtained data, establish a mathematical model for different types of energy resources. The process of establishing the mathematical model includes statistically analyzing the historical data of each energy resource to determine its probability distribution characteristics; using machine learning algorithms to train and optimize the model parameters;

[0045] S03. Use the mathematical model to simulate and predict the operating state of the power system to evaluate the stability and efficiency of the system under various scenarios. The simulation and prediction process includes: simulating the impact of different scenarios on the system stability and formulating corresponding emergency plans accordingly. The different scenarios include one or more of normal operating conditions, extreme weather conditions, market electricity price fluctuations, and load mutations.

[0046] S04. Propose an optimized dispatching strategy based on the simulation results to achieve the optimal allocation and regulation of source-load-storage resources.

[0047] In step S02, when designing the mathematical model based on the flexibility of supply-demand interaction, the focus is on maximizing the flexibility and adaptability of the system to more effectively integrate intermittent renewable energy and improve the stability and reliability of the power system. The mathematical model is as follows:

[0048]

[0049] In the formula, F represents the total supply-demand interaction flexibility of the system; f flex (t) represents the flexibility contribution of flexible loads at time t; f stor (t) represents the flexibility contribution of all energy storage devices at time t; f conv (t) represents the flexibility contribution of the energy conversion unit at time t; f renew (t) represents the contribution of renewable energy to the system flexibility at time t; T represents the total number of planned time periods;

[0050] f flex (t) = L flex,max (t) - L flex,min (t);

[0051] In the formula, L flex,max (t) and L flex,min (t) respectively represent the maximum and minimum adjustable power levels of flexible loads at time t. L flex,max (t) = min(P max , P current + ΔP up ), P max is the maximum power of the device, P current is the current power of the device, ΔP up is the maximum upward adjustment range allowed according to user comfort and service quality. L flex,min (t) = max(P min , P current - ΔP down ), P min is the minimum power of the device, and ΔP down is the maximum downward adjustment range allowed according to user comfort and service quality;

[0052]

[0053] In the formula, k represents different types of energy storage devices, and respectively represent the maximum charging power and the minimum discharging power of the k-th type of energy storage device at time t;

[0054]

[0055] In the formula, j represents different types of energy conversion units, and respectively represent the maximum and minimum output capabilities of the j-th type of energy conversion unit at time t;

[0056] P rated is the rated power of the device; P environmental (t) is the maximum allowable output based on the current environmental conditions; P maintenance (t) is the maximum allowable output based on the current maintenance status; P technical_limit is the minimum power level at which the device can operate stably, P economic (t) is the minimum recommended output power based on the current economic conditions;

[0057] f renew (t) = Flexibility Reserve renewable (t);

[0058] In the formula, Flexibility Reserve renewable (t) refers to the amount of flexibility reserve reserved at time t to cope with the prediction error of renewable energy.

[0059] This model needs to satisfy the following constraint conditions.

[0060] 1. Power balance constraint:

[0061] ∑ i P i (t) - ∑ l L l (t) = 0;

[0062] In the formula, P i (t) represents the net output of the i-th energy production or conversion unit at time t; L l (t) represents the demand of the l-th load demand at time t.

[0063] 2. Energy storage operation constraints. For each type of energy storage, its charge and discharge rate limits and capacity limits need to be considered. For each type of energy storage k:

[0064]

[0065] Wherein, and respectively represent the minimum and maximum values of the energy state of the k-th type of energy storage device; E k (t) represents the energy state of the k-th type of energy storage device at time t; P stor,k (t) represents the net output power of the k-th type of energy storage device at time t.

[0066] 3. Energy conversion unit operation constraints. Each energy conversion unit has its specific operation range and efficiency curve. For each type of energy conversion unit j:

[0067]

[0068] Wherein, C j (t) represents the output capacity of the j-th type of energy conversion unit at time t.

[0069] 4. User comfort and quality of service (ensuring that the adjustment will not seriously affect the user experience, especially when it comes to devices directly affecting users such as air conditioners and refrigeration):

[0070]

[0071] S m (t) represents the satisfaction score of the m-th factor affecting user comfort at time t; w m represents the weight of the m-th factor.

[0072] Based on the above control method, this embodiment also proposes a control system, including a data acquisition module, a model construction and analysis module, a simulation prediction module, an optimal scheduling module, and a human-machine interaction interface. Among them, the data acquisition module is used to collect real-time operation data of various energy resources; the model construction and analysis module is used to establish and update mathematical models; the simulation prediction module is used to simulate and predict the system operation state; the optimal scheduling module is used to generate and execute optimal scheduling strategies; the user interaction interface allows operators to view the system state, adjust parameters, and manually intervene in scheduling decisions.

[0073] The control method provided by this embodiment, by making full use of multi-source flexible resources such as flexible loads, not only improves the ability of the power grid to respond to emergencies, but also promotes the efficient consumption of new energy power, providing technical support for realizing a more intelligent and flexible power grid operation.

[0074] The above embodiments are only used to explain the concept of the present invention, rather than limiting the protection scope of the rights of the present invention. Any non-substantive modification of the present invention using this concept shall fall within the protection scope of the present invention.

Claims

1. A control method based on a multi-source flexible resource regulation model, characterized in that: The following steps are involved: S01. Obtain real-time operating data of multiple different types of energy resources in the power system; S02. Establish mathematical models for different types of energy resources based on the acquired data; S03. Use the mathematical model to simulate and predict the operating status of the power system to evaluate the stability and efficiency of the system under various scenarios; S04. Propose an optimization scheduling strategy based on the simulation results to achieve optimal configuration and regulation of source-load-storage resources.

2. A control method based on a multi-source flexible resource regulation model as claimed in claim 1, characterized in that: The process of establishing the mathematical model includes: conducting statistical analysis on the historical data of various energy resources to determine their probability distribution characteristics; and using machine learning algorithms to train and optimize model parameters.

3. A control method based on a multi-source flexible resource adjustment model as claimed in claim 2, characterized in that: The mathematical model is: In the formula, F represents the total supply-demand interaction flexibility of the system; f flex (t) represents the flexibility contribution of the flexible load at time t; f stor (t) represents the flexibility contribution of all energy storage devices at time t; f conv (t) represents the flexibility contribution of the energy conversion unit at time t; f renew (t) represents the contribution of renewable energy to system flexibility at time t; T represents the total number of planned time periods; f flex (t)=L flex,max (t)-L flex,min (t); Where, L flex,max (t) and L flex,min (t) represent the maximum and minimum adjustable power levels of the flexible load at time t, L flex,max (t) = min(P max ,P current +ΔP up ), P max is the maximum power of the device, P current is the current power of the device, ΔP up is the maximum increase allowed based on user comfort and service quality, L flex,min (t) = max(P min ,P current -ΔP down ), P min is the minimum power of the device, ΔP down It is the maximum downward adjustment allowed based on user comfort and service quality; In the formula, k represents different types of energy storage devices, and They represent the maximum charging power and minimum discharging power of the k-th energy storage device at time t respectively; In the formula, j represents different energy conversion unit types, and They represent the maximum and minimum output capacities of the j-th energy conversion unit at time t respectively; P rated is the rated power of the equipment; P environmental (t) is the maximum allowable output based on current environmental conditions; P maintenance (t) is the maximum allowable output based on the current maintenance status; P technical_limit is the minimum power level at which the device can operate stably, P economic (t) is the minimum recommended output power based on current economic conditions; f renew (t)=Flexibility Reserve renewable (t); In the formula, Flexibility Reserve renewable (t) refers to the flexibility reserve reserved at time t to cope with renewable energy forecast errors.

4. A control method based on a multi-source flexible resource adjustment model as claimed in claim 3, characterized in that: The mathematical model needs to satisfy the power balance constraint: ∑ i P i (t)-∑ l L l (t)=0; Where P i (t) represents the net output of the i-th energy production or conversion unit at time t; L l (t) represents the demand of the lth load demand at time t.

5. The control method based on the multi-source flexible resource regulation model as claimed in claim 3, characterized in that: The mathematical model needs to satisfy the energy storage operation constraints. For each energy storage type k: In the formula, and They represent the minimum and maximum energy states of the k-th energy storage device respectively; E k (t) represents the energy state of the k-th energy storage device at time t; P stor,k (t) represents the net output power of the kth type of energy storage device at time t.

6. A control method based on a multi-source flexible resource regulation model as claimed in claim 3, characterized in that: The mathematical model needs to satisfy the energy conversion unit operation constraints. For each energy conversion unit j: In the formula, C j (t) represents the output capacity of the j-th energy conversion unit at time t.

7. A control method based on a multi-source flexible resource adjustment model as claimed in claim 3, characterized in that: The mathematical model needs to meet the user comfort and service quality: S m (t) represents the satisfaction score of the mth factor affecting user comfort at time t; w m represents the weight of the mth factor.

8. The control method based on the multi-source flexible resource adjustment model according to claim 1, characterized in that: The simulation prediction process includes: simulating the impact of different scenarios on system stability and formulating corresponding emergency plans accordingly. The different scenarios include one or more of normal operating conditions, extreme weather conditions, market electricity price fluctuations, and load mutations.

9. A control method based on a multi-source flexible resource regulation model as described in claims 1 to 8, characterized in that: The method is implemented by a control system, comprising: Data acquisition module, used to collect real-time operation data of various energy resources; Model building and analysis module, used to build and update mathematical models; A simulation prediction module is used to simulate and predict the system operation status; The optimization scheduling module is used to generate and execute optimization scheduling strategies.

10. A control method based on a multi-source flexible resource adjustment model as claimed in claim 9, characterized in that: The control system also includes a user interface that allows operators to view system status, adjust parameters, and manually intervene in scheduling decisions.

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