A microgrid self-scheduling method and system with energy storage device for active backup are provided

Abstract

The application discloses a kind of self-scheduling method and system of microgrid containing energy storage equipment providing active backup, based on the system framework diagram of self-scheduling technology of microgrid containing energy storage equipment providing active backup, the scheduling process analysis of self-scheduling of microgrid containing energy storage equipment providing active backup, establishes decision dependent uncertainty set;According to the history information of random factor, establish renewable energy and load demand uncertainty set;With the minimum operation cost in the expected scenario as the goal, a multi-stage robust optimization model of microgrid containing energy storage equipment providing active backup is established;Establish the feasibility range of scheduling scheme that meets safety and economy, establish the mixed integer programming scheduling model of microgrid containing energy storage equipment providing active backup, solve the positive and negative backup capacity provided by each period energy storage microgrid for external system or main grid under maintaining its optimal scheduling, the optimal safety decision range of each period decision and the day-ahead optimal power injection curve ensuring system safety.

Description

Technical Field

[0001] This invention belongs to the field of microgrid application technology for energy storage and renewable energy, specifically relating to a microgrid self-dispatch method and system that provides active backup and includes energy storage devices. Background Technology

[0002] Since 1990, global carbon dioxide emissions from the power system and industry have been increasing at a rate of 2.3% per year, exacerbating a series of problems such as climate change and environmental pollution. The global consensus on addressing climate change and promoting sustainable development includes: significantly reducing the carbon intensity of the power grid, actively developing renewable energy, and transforming the current energy sector into a clean, low-carbon, environmentally friendly, safe, and efficient energy system.

[0003] Due to the influence of weather factors such as wind speed and solar intensity, renewable energy power generation, represented by wind and solar power, exhibits characteristics such as high intermittency, strong volatility, high randomness, poor controllability, and low prediction accuracy. This leads to increasingly prominent issues in the balance of energy supply and demand and security in the power system, posing a significant challenge to the safe, stable, and reliable operation of the power system. Therefore, it is urgently necessary to efficiently integrate a high proportion of renewable energy into the power grid while ensuring its safe and stable operation.

[0004] Energy storage devices, as the most representative flexible and adjustable resource, can store and transfer energy through charging and discharging functions. They can remove the constraint that power production, transmission, and consumption must be completed simultaneously; fully leverage the complementary functions of multiple resource coupling; improve the renewable energy absorption rate and power quality; and provide strong support for renewable energy grid integration. Therefore, they are a key technology for improving the security, economy, and flexibility of the power system and for building a new energy system.

[0005] Microgrids are small-scale power systems that primarily utilize renewable energy generation and employ energy storage devices for regulation to meet load demands. They possess complete power generation, distribution, and consumption functions. Microgrids can serve as a buffer between renewable energy sources and the main power grid, effectively mitigating the impact of the randomness and intermittency of renewable energy on the external grid. They improve renewable energy penetration, power quality, power supply reliability, and energy efficiency, providing new opportunities for the application of renewable energy in power systems, thereby maximizing economic and environmental benefits.

[0006] Currently, research on microgrid systems incorporating energy storage and renewable energy primarily focuses on dispatching issues with power balance as the main concern. The main grid plays a crucial role in ensuring the safe and stable operation of the system by providing sufficient backup services to these energy storage systems to compensate for potential fluctuations. In this case, the system is considered a backup consumer. However, this operating mode increases the backup burden on the main grid and the difficulty of its dispatching, and the backup potential of systems with energy storage devices is not fully utilized, making it not an economically reliable solution. As a highly efficient and fast-responding device, energy storage has the ability to quickly provide positive and negative backups to the system, and the reliability of the backups provided is comparable to that provided by traditional generating units, ensuring the safe and stable operation of the system. However, the understanding of microgrid systems incorporating energy storage and renewable energy as backup providers is still significantly lacking.

[0007] Therefore, further optimizing energy storage functions, actively promoting the application of energy storage technology in the standby auxiliary market, fully tapping the potential of energy storage, enhancing the flexibility of grid operation, and improving the economic efficiency of the system are important directions for the development of new power systems and important means to cope with the challenges of large-scale uncertain power injection. Summary of the Invention

[0008] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a self-dispatch method and system for microgrids containing energy storage devices that provides active backup. This addresses the technical problem that when a microgrid containing energy storage provides backup to external systems or the main grid, it increases the backup burden and dispatching difficulty of the main grid, and fails to fully realize the backup potential of the microgrid containing energy storage. This invention provides technical support for the feasibility of microgrids containing energy storage as backup providers, offers a safe and economical dispatching scheme for microgrids containing energy storage, provides effective positive and negative backup capacity schemes for external systems or the main grid, provides decision-making basis for the planning scheme of microgrids containing energy storage, and promotes the consumption of new energy and the decarbonization process.

[0009] The present invention adopts the following technical solution:

[0010] A self-dispatch method for microgrids with energy storage devices that provides active backup includes the following steps:

[0011] S1. Construct a system framework for microgrid self-dispatch technology that provides active backup and includes energy storage devices;

[0012] S2. Analyze the scheduling process of a microgrid with energy storage devices that provides active backup in the system framework of microgrid self-dispatch technology, and establish a decision-dependent uncertainty set for the uncertainty of positive and negative backup demand; establish the uncertainty set of renewable energy and load demand based on the historical information of the acquired random factors.

[0013] S3. Obtain the key parameters of the microgrid system, construct an objective function with the goal of minimizing the operating cost under the desired scenario, and construct a multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup based on the renewable energy and load demand uncertainty set obtained in step S2.

[0014] S4. Based on the multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup obtained in step S3, establish a scheduling scheme that meets the feasibility and security requirements of the microgrid system in each time period using its constraint structure characteristics.

[0015] S5. Based on the multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup obtained in step S3 and the scheduling scheme obtained in step S4, establish a day-ahead scheduling model of the microgrid with energy storage equipment that provides active backup, and solve the decision feasibility range, optimal positive and negative backup capacity and optimal power injection curve of the microgrid system. Based on the obtained results, realize the self-schedule of the microgrid.

[0016] Preferably, in step S2, the uncertain set of renewable energy and load demand specifically refers to:

[0017]

[0018] Where t is the current scheduling period; and Power output for wind and solar power during period t; and The upper and lower limits of wind power output during time period t; and The upper and lower limits of photovoltaic power output during time period t; d t For load demand during period t; and These represent the upper and lower limits of load demand during time period t; and For the positive and negative backup requirements during time period t; and The positive and negative reserve capacity limits for time period t.

[0019] Preferably, in step S3, the objective function is specifically:

[0020]

[0021] Where T is the set of scheduling time periods; τ is the length of the time period; The unit price of electricity purchased from the main grid by the microgrid system during time period t; The unit price of electricity sold from the microgrid system to the main grid during time period t; For time period t, the day-ahead power injection from the microgrid to the main grid (positive or negative); and The unit prices for positive and negative reserve during time period t; and For time period t, the amount of wind and solar power curtailed. The energy storage charging and discharging power during time period t; and The positive and negative reserve capacity limits for time period t.

[0022] Preferably, the constraints satisfied by the microgrid system scheduling operation include:

[0023] Power balance constraints, charging / discharging power limits, upper and lower bound constraints on energy storage levels, dynamic equations for energy storage SOC, constraints on wind and solar curtailment, positive and negative reserve limits, and power exchange limits between microgrids and the main grid.

[0024] Preferably, power balance constraints:

[0025]

[0026] Charge / discharge power limits:

[0027]

[0028] Energy storage energy level upper and lower bound constraints:

[0029]

[0030] Energy storage SOC dynamic equation:

[0031]

[0032] Wind curtailment constraints:

[0033]

[0034] Discard light constraint:

[0035]

[0036] Positive and negative reserve positive value limit:

[0037]

[0038] Power exchange limitations between microgrid and main grid:

[0039]

[0040] Among them, Ω t It is an uncertain set; The energy storage charging and discharging power during time period t; and For time period t, the amount of wind and solar power curtailed. and Power output for wind and solar power during period t; For time period t, the day-ahead power injection from the microgrid to the main grid (positive or negative); and For time period t, the positive and negative reserve capacity limits are defined; d t For load demand during period t; and For the positive and negative backup requirements during time period t; and These represent the charging and discharging power limits of the energy storage system during time period t; E t The energy level of the energy storage system at the end of time period t; and These represent the upper and lower bounds of the energy level of the energy storage system at the end of time period t; and The upper and lower limits of the power exchanged with the main network during time period t; and For time period t, the positive and negative reserve capacity limits are defined. For time period t, the quantity is uncertain. Let h(·) and h be the realized values ​​of the uncertain quantity from time period 1 to time period T. -1 (·) is an auxiliary function.

[0041] Preferably, the auxiliary function h(·) and its inverse function are:

[0042]

[0043] Where τ is the length of a time period; η c and η d , respectively, are the charge / discharge efficiency coefficients; x is the independent variable of the auxiliary variable, corresponding to the discharge and charge power p of the energy storage device. t .

[0044] Preferably, in step S4, the specific range of scheduling schemes that satisfy feasibility and security in each time period of the multi-stage robust optimization model of the microgrid system is as follows:

[0045]

[0046] in, These are the minimum and maximum values ​​of the safe range at the end of time period t-1, respectively. For time period t, the day-ahead power injection from the microgrid to the main grid (positive or negative); d t and These represent the upper and lower limits of load demand during time period t; and For time period t, the positive and negative reserve capacity limits are defined. This represents the lower limit of wind power output during time period t. This represents the lower limit of photovoltaic power output during time period t. and These represent the charging and discharging power limits of the energy storage system during time period t; E t-1 and , , represent the upper and lower bounds of the energy level of the energy storage system at the end of time period t-1; min and max are the minimum and maximum value functions, respectively; h(·) is an auxiliary function.

[0047] Preferably, step S5 specifically includes:

[0048] The system operating parameters and uncertainty set parameters are obtained, and the day-ahead reserve supply model is solved in one go through the solver to obtain the decision feasibility range, optimal positive and negative reserve capacity and optimal power injection curve of the microgrid system.

[0049] Preferably, the objective function that minimizes the operating cost within the scheduling cycle is:

[0050]

[0051] The power exchange restrictions between the microgrid and the main grid are as follows:

[0052]

[0053] Positive and negative reserve values ​​are limited to:

[0054]

[0055] The prerequisite for the existence of a solution in the system is:

[0056]

[0057] The safe range for energy storage levels is:

[0058]

[0059] Where T is the set of scheduling time periods; τ is the length of a time period; The unit price of electricity purchased from the main grid by the microgrid system during time period t; The unit price of electricity sold from the microgrid system to the main grid during time period t; For time period t, the day-ahead power injection from the microgrid to the main grid (positive or negative); and The unit prices for positive and negative reserve during time period t; and For time period t, the amount of wind and solar power curtailed. The energy storage charging and discharging power during time period t; and For the positive and negative reserve capacity limits during time period t; Ω t It is an uncertain set; and The upper and lower limits of the power exchanged with the main network during time period t; and These represent the upper and lower limits of load demand during time period t; and These are the charging and discharging power limits for the energy storage system during time period t; This represents the lower limit of wind power output during time period t. This represents the lower limit of photovoltaic power output during time period t. These are the minimum and maximum values ​​of the safe range at the end of time period t-1, respectively. and , , represent the upper and lower bounds of the energy level of the energy storage system at the end of time period t-1; min and max are the minimum and maximum value functions, respectively; h(·) is an auxiliary function.

[0060] Secondly, embodiments of the present invention provide a microgrid self-dispatch system with energy storage devices that provides active backup, characterized in that it includes:

[0061] The system module constructs a system framework for microgrid self-dispatch technology that includes energy storage devices and provides active backup.

[0062] The data module analyzes the scheduling process of a microgrid with energy storage devices that provides active backup within the system framework of microgrid self-dispatch technology. It establishes a decision-dependent uncertainty set for the uncertainties of positive and negative backup demand. Based on the acquired historical information of random factors, it establishes uncertainty sets for renewable energy and load demand.

[0063] The function module obtains key parameters of the microgrid system, constructs an objective function with the goal of minimizing the operating cost under the desired scenario, and builds a multi-stage robust optimization model of the microgrid with energy storage equipment based on the obtained renewable energy and load demand uncertainty set to provide active backup.

[0064] The optimization module, based on the obtained multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup, utilizes its constraint structure characteristics to establish a scheduling scheme for the microgrid system that meets feasibility and security requirements at various time periods.

[0065] The scheduling module establishes a day-ahead scheduling model for a microgrid with energy storage that provides active backup, based on the obtained multi-stage robust optimization model and scheduling scheme. It solves for the decision feasibility range, optimal positive and negative backup capacity, and optimal power injection curve of the microgrid system, and realizes microgrid self-schedule based on the results.

[0066] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described method for providing active backup in a microgrid with energy storage.

[0067] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described microgrid self-dispatch method for providing active backup with energy storage devices.

[0068] Compared with the prior art, the present invention has at least the following beneficial effects:

[0069] This paper proposes a self-dispatch method for microgrids with energy storage devices that provide active backup. Leveraging an understanding of microgrid system dispatching methods and a deep dive into the backup capabilities of energy storage systems, a system framework diagram of this technology is established. This diagram accurately describes the main functions of each component in a microgrid with energy storage providing backup, as well as the dispatching logic and process. Based on the system framework diagram, the paper analyzes the dispatching process of a microgrid with energy storage providing backup and establishes a decision-dependent uncertainty set for the range of positive and negative backup demand uncertainties. Based on historical data of stochastic factors in the microgrid system, uncertainty sets for renewable energy (wind power, photovoltaic, etc.) and load demand are established to describe the distribution and changes of stochastic factors. This paper presents a system framework diagram of a microgrid self-dispatch technology with energy storage that provides active backup, and two types of uncertainty sets. A multi-stage robust optimization model for the microgrid with energy storage that provides active backup is established. This model comprehensively considers the dispatch process of the microgrid with energy storage providing backup, the physical characteristics of each physical device, and the operating conditions of the microgrid system, and can reasonably reflect the dispatch characteristics of the microgrid with energy storage providing active backup. Through analysis of the constraint structure in the multi-stage model of the microgrid with energy storage that provides active backup, an effective method is proposed. This method can provide the effective positive and negative backup capacity that ensures the safe operation of the microgrid system and can cope with uncertain backup demands, the day-ahead power injection curve that ensures the safe operation of the microgrid system, and the safe dispatch range for decision-making in each time period. If the physical devices in the microgrid system are reasonably scheduled within the safe range, the positive and negative reserve requirements of the external system or the main grid are within the given positive and negative reserve capacity range, and the main grid provides power to the microgrid according to the power injection curve, then the realization of any random factor in the uncertainty set can be guaranteed to be handled. Based on the positive and negative reserve capacity, the day-ahead power injection curve, and the safe scheduling range of the decision given above, a hybrid integer programming scheduling model for microgrids with energy storage devices that provides active backup can be established. This model aims to optimize the operating cost under the desired scenario.

[0070] Furthermore, by acquiring the equipment composition of the microgrid system and establishing a system framework diagram for microgrid self-dispatch technology that provides active backup and includes energy storage devices, technical support can be provided for the self-dispatch of microgrids that provide active backup and include energy storage devices.

[0071] Furthermore, based on the scheduling process and method of reserve provision, a decision-dependent uncertainty set is established for the range of uncertainties in positive and negative reserve demand.

[0072] Furthermore, obtaining relevant information such as equipment parameters, uncertain parameters (electricity load, wind power output, photovoltaic output), and economic parameters can provide data support for the self-dispatch of microgrids with energy storage devices that provide active backup.

[0073] Furthermore, an uncertainty set is established for random factors such as electricity load, wind power output, and photovoltaic output to reflect the range of variation of uncertainties and make it consistent with the actual situation.

[0074] Furthermore, taking the minimization of operating costs under the desired scenario within the scheduling cycle as the objective function, and comprehensively considering the operational requirements of self-scheduled microgrids with energy storage providing active backup, a multi-stage robust optimization model for microgrids with energy storage providing active backup is established. This model is a conceptual model; theoretically, the scheduling scheme obtained by this model can guarantee the security and feasibility of the scheduling scheme and improve its operational economy. However, since this model needs to consider the feasibility under scenarios with an infinite number of random factors, it cannot directly obtain an economically feasible scheduling scheme.

[0075] Furthermore, through the analysis of a multi-stage robust optimization model of a microgrid with energy storage providing active backup, the feasible range for ensuring the safe operation of the dispatch scheme, the positive and negative backup capacities for ensuring system safety and feasibility, and the power injection curve from the main grid to the microgrid are presented. For microgrid systems with energy storage under different parameters, if it can be guaranteed that each main grid can install the optimal power injection curve to provide power to the microgrid in the day-ahead and that the dispatch schemes for each time period are within the safe decision range, then any positive or negative backup demand from the external system or the main grid within the positive and negative backup capacity range, and any realized value of random variables in the future time period (within a given uncertainty set), can be handled by the microgrid system with energy storage.

[0076] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0077] In summary, this invention provides an economically feasible dispatching scheme for microgrids with active backup and energy storage devices, and provides technical support for the economic feasibility of planning schemes for microgrids with active backup and energy storage devices.

[0078] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0079] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the following description of the relative embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0080] Figure 1 A system framework diagram of a microgrid self-dispatch technology with energy storage devices for active backup provided by the present invention;

[0081] Figure 2 This is a schematic diagram of the process of the present invention;

[0082] Figure 3 This is a flowchart illustrating the operation and scheduling process of the present invention.

[0083] Figure 4 This is a block diagram of an electronic device according to an embodiment of the present invention;

[0084] Figure 5 A diagram showing the upper and lower limits of load demand and the expected load demand scenario.

[0085] Figure 6 This diagram shows the upper and lower limits of wind power output and the expected scenario for wind power output.

[0086] Figure 7 Diagram showing the upper and lower limits of photovoltaic power output and expected photovoltaic power output scenarios;

[0087] Figure 8 The graph shows the day-ahead reserve capacity and power injection curves obtained based on the method proposed in this invention.

[0088] Figure 9 The robust feasible region diagram of energy storage level is obtained based on the method proposed in this invention;

[0089] Figure 10 This is a schematic diagram of a computer device provided according to an embodiment of the present invention. Detailed Implementation

[0090] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0091] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0092] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0093] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0094] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0095] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0096] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0097] Please see Figure 1A microgrid with energy storage devices that provides active backup includes an energy storage system, electrical loads (including detachable loads, portable loads, and flexible loads), and renewable energy power plants such as wind and solar power. This microgrid is connected to the main grid system and can exchange energy with it, meaning it can both purchase and sell electricity to the main grid. Furthermore, the energy storage system, electrical loads, and renewable energy sources such as wind and solar power are connected via the same bus, enabling direct energy exchange within the microgrid. This means the loads can obtain electricity from the energy storage system and the renewable energy power plants; the energy storage system can supply electricity to the loads and also obtain electricity from the renewable energy power plants; and the renewable energy power plants can supply electricity to the loads and the energy storage system.

[0098] The specific structural features of the microgrid with active backup and energy storage devices provided by this invention are as follows:

[0099] Battery energy storage systems include battery packs, battery management systems, power conversion systems, collection lines, and other auxiliary equipment, used for the storage, conversion, and release of electrical energy;

[0100] A photovoltaic power station includes a photovoltaic array, a charge and discharge controller, a battery, and an inverter. It uses the photovoltaic effect at the semiconductor interface to directly convert light energy into electrical energy.

[0101] Wind power generation includes wind turbine generators, towers supporting the generators, battery charging controllers, inverters, unloaders, grid connection controllers, and battery banks, which convert the kinetic energy of wind into electrical energy.

[0102] Electrical loads are categorized into various industrial loads, agricultural loads, transportation loads, and residential loads, consuming electrical energy to enable the operation of industry, agriculture, transportation, and daily life. The total load of a power system is the sum of the total power consumed by all electrical equipment within the system.

[0103] Compared to the power system or other market participants, microgrid systems with energy storage have lower capacity and therefore participate in the market as price takers. Decision variables are divided into two categories based on the different stages of dispatch: day-ahead decision variables and real-time decision variables. Day-ahead decision variables include increasing positive / negative reserve capacity to the main grid or external systems (gray solid line) and the day-ahead power injection curve (orange solid line). Real-time decision variables include the amount of positive / negative reserve provided to the main grid (green dashed line) and dispatch decisions within the microgrid (black dashed line), such as the charging / discharging power of energy storage and the amount of renewable energy output discarded. The dispatch process is described below.

[0104] In the current phase, microgrids with energy storage, acting as independent backup suppliers, will engage in two transactions with the main grid:

[0105] 1) Positive / negative reserve capacity provided to external systems or the main power grid;

[0106] 2) The power injection curve represents the power received by the microgrid from the main grid. This is an important resource for ensuring system feasibility. The following two points need to be explained regarding the power injection curve.

[0107] First, the concept of a "power injection curve" is introduced primarily to distinguish it from the "exchange power" concept in the standby consumer model. Both refer to the amount of power exchanged between the microgrid and the main grid, with positive values ​​indicating the main grid provides power to the microgrid (buying power from the main grid), and negative values ​​indicating the microgrid provides surplus power to the main grid (selling power to the main grid). The key difference is that the power injection curve is a deterministic curve determined day-ahead, rather than over a specific region. This means that the exchange power between the microgrid and the main grid remains constant during dispatch, and the main grid does not need to provide additional backup services. Therefore, the dispatching difficulty and burden on the main grid are reduced accordingly.

[0108] Secondly, even if the system does not consider providing backup, the power injection profile may still be necessary to ensure the feasibility of the system.

[0109] Once the day-ahead decision is made, the microgrid with energy storage can not only achieve optimal self-dispatch in the real-time phase in response to any uncertainty, but also provide backup services to the main grid within the day-ahead given reserve capacity.

[0110] This invention provides a microgrid with active backup and energy storage devices that can cope with uncertain renewable energy output and uncertain load demand without the need for backup support from the main grid, and achieve optimal self-dispatch; it can also provide backup services to external systems (or the main grid) when backup demand is uncertain, thereby increasing microgrid revenue and reducing total operating costs.

[0111] Please see Figure 2 The present invention provides a microgrid self-dispatch method with energy storage devices that provides active backup, comprising the following steps:

[0112] S1. Establish a system framework diagram for microgrid self-dispatch technology that provides active backup and includes energy storage devices;

[0113] Specifically, it includes:

[0114] (1) The composition of a microgrid system with energy storage as a backup provider;

[0115] (2) The operation mode includes a microgrid system with energy storage to provide backup.

[0116] In the current phase, microgrids with energy storage, acting as independent backup suppliers, will engage in two transactions with the main grid:

[0117] 1) Positive / negative reserve capacity provided to external systems or the main power grid;

[0118] 2) The power injection curve from which the microgrid obtains (provides) power to the main grid is an important resource for meeting system feasibility requirements.

[0119] The power injection curve is a deterministic curve determined day-ahead, not a specific region. Once the day-ahead decision is made, the microgrid with energy storage can not only achieve optimal self-dispatch for any uncertainty in the real-time phase, but also provide backup services to the main grid within the day-ahead given reserve capacity.

[0120] S2. Based on the system framework diagram, analyze the scheduling process of providing backup for microgrids with energy storage, and establish a decision-dependent uncertainty set for the uncertainty of positive and negative backup demand; based on the historical information of the acquired random factors, establish uncertainty sets for renewable energy sources such as wind power and photovoltaics, as well as load demand.

[0121] Specifically, it includes:

[0122] (1) Decision depends on uncertainty set

[0123]

[0124] Where t is the current scheduling period; and For the positive and negative backup requirements during time period t; and The positive and negative reserve capacity limits for time period t.

[0125] (2) Obtain the parameters of the uncertainty set, including the upper and lower limits of wind power output in time period t. and upper and lower limits of photovoltaic power output during period t and Load demand upper and lower limits during period t and And establish uncertain sets for wind power output, photovoltaic power output, and load demand.

[0126]

[0127]

[0128] in, and For wind and solar power output during period t; d t Let t represent the load demand during the time period.

[0129] S3. Obtain key system parameters and, with the goal of minimizing operating costs under the desired scenario, establish a multi-stage robust optimization model for a microgrid containing energy storage devices as a backup provider under the uncertainty set obtained in step S2.

[0130] Specifically, it includes:

[0131] (1) Energy storage system parameters, such as the upper and lower limits of energy storage capacity, the energy storage system charge and discharge efficiency coefficient, and the energy storage system maximum charge and discharge power;

[0132] (2) Operating parameters, such as total scheduling cycle, length of each scheduling period, and power exchanged with the main network;

[0133] (3) Economic parameters, such as the price at which the microgrid system buys electricity from the main grid and the price at which it sells electricity to the main grid, as well as the positive and negative reserve prices.

[0134] With the goal of minimizing operating costs under desired scenarios, a multi-stage robust optimization model for microgrids with energy storage devices that provide active backup is established.

[0135] 1) Objective function

[0136] The goal of the multi-stage robust optimization model for microgrids with energy storage that provides active backup is to minimize the system operating cost during the scheduling cycle.

[0137]

[0138] The first part of the above formula represents the cost (benefit) of power injection from the microgrid system to the main grid, and the second part represents the benefit of the microgrid system providing backup to the main grid or external systems.

[0139] Where T is the set of scheduling time periods; τ is the length of a time period; The unit price of electricity purchased from the main grid by the microgrid system during time period t; The unit price of electricity sold from the microgrid system to the main grid during time period t; For time period t, the day-ahead power injection from the microgrid to the main grid (positive or negative); and The unit prices for positive and negative reserve during time period t; and For time period t, the amount of wind and solar power curtailed. The energy storage charging and discharging power during time period t; and The positive and negative reserve capacity limits for time period t.

[0140] 2) Operational constraints

[0141] For a microgrid with energy storage to provide active backup and operate under self-dispatch capability, it needs to meet a series of conditions:

[0142] Equation (2) is a power balance constraint, which requires that the algebraic sum of the energy storage charging / discharging power, the curtailed wind and solar power, and the power injection value from the main grid must be equal to the algebraic sum of the uncertain wind power output, the uncertain photovoltaic power output, the uncertain load demand, and the uncertain positive and negative reserve demand.

[0143]

[0144] Equation (3) represents the charge / discharge power limit, which restricts the limits of the discharge and charge power of the energy storage system:

[0145]

[0146] Equation (4) represents the upper and lower bounds of the energy storage level, which are the physical operation requirements in actual operation.

[0147]

[0148] Equation (5) is the dynamic equation for the energy storage SOC (state of charge):

[0149]

[0150] Among them, h(·) and h -1 (·) is an auxiliary function that has a monotonically decreasing property.

[0151]

[0152] Equation (7) is the wind curtailment constraint:

[0153]

[0154] Equation (8) is the light-wasting constraint:

[0155]

[0156] Equation (9) represents the positive and negative reserve positive value limit:

[0157]

[0158] Equation (10) defines the power exchange limits between the microgrid and the main grid, which sets the lower and upper limits for power exchange between the microgrid system and the main grid:

[0159]

[0160] Among them, Ω t It is an uncertain set; The energy storage charging and discharging power during time period t; and For time period t, the amount of wind and solar power curtailed. and Power output for wind and solar power during period t; For time period t, the day-ahead power injection from the microgrid to the main grid (positive or negative); and For time period t, the positive and negative reserve capacity limits are defined; d t For load demand during period t; and For the positive and negative backup requirements during time period t; and These represent the charging and discharging power limits of the energy storage system during time period t; E t E represents the energy level of the energy storage system at the end of time period t. t and These represent the upper and lower bounds of the energy level of the energy storage system at the end of time period t; and The upper and lower limits of the power exchanged with the main network during time period t; and For time period t, the positive and negative reserve capacity limits are defined. The uncertainty matrix for time period t This represents the realized value of the uncertain quantity from time period 1 to time period T. and η represents the charging and discharging power of the energy storage system during time period t; c and η d Let represent the charging and discharging efficiency coefficients for time period t; x is the independent variable of the auxiliary variable, corresponding to the discharging and charging power p of the energy storage device. t .

[0161] S4. Based on the multi-stage robust optimization model obtained in step S3, and using its constraint structure characteristics, establish the range formula (11) of the scheduling scheme that satisfies the feasibility and security of the microgrid system in each time period.

[0162]

[0163] in, These are the minimum and maximum values ​​of the safe range at the end of time period t-1, respectively. For time period t, the day-ahead power injection from the microgrid to the main grid (positive or negative); d t and These represent the upper and lower limits of load demand during time period t; and For time period t, the positive and negative reserve capacity limits are defined. This represents the lower limit of wind power output during time period t. This represents the lower limit of photovoltaic power output during time period t. and These represent the charging and discharging power limits of the energy storage system during time period t; E t-1 and , , represent the upper and lower bounds of the energy level of the energy storage system at the end of time period t-1; min and max are the minimum and maximum value functions, respectively; h(·) is an auxiliary function.

[0164] Specifically, it includes:

[0165] First, for the sake of brevity, the decision variables for the real-time stage are abbreviated as:

[0166]

[0167] To simplify the constraint structure and facilitate subsequent analysis, E is selected. t As a core variable, it replaces the real-time stage decision variable in the constraints. and The specific details are as follows.

[0168] First, according to equations (5) and (6), This is equivalent to equation (13).

[0169]

[0170] Based on equations (3), (13), and the monotonically decreasing characteristic of the auxiliary function h(·), we can obtain

[0171]

[0172] Similarly, according to equations (2) and (13), Transform into

[0173]

[0174] Furthermore, according to equation (15), constraint (7) can be transformed into equation (16).

[0175]

[0176] Rearranging equation (16) yields

[0177]

[0178] From equations (8) and (17), to ensure If it is not empty, then constraint (18) must be satisfied.

[0179]

[0180] With E t -E t-1 Rearranging equation (18) for the variables, we get

[0181]

[0182] The above analysis shows that, compared with real-time stage decision variables... and The relevant constraints can be transformed into those related to E. t -E t-1 The relevant constraints. Therefore, by transforming the constraints, they can be omitted. and variable.

[0183] For the sake of brevity, we introduce equation (20).

[0184]

[0185] Then, using equations (4), (14), (19), and (20), equations (2) to (8) can be rewritten as follows:

[0186]

[0187] If a feasible solution satisfies constraints (2)-(8), then equation (21) should be non-empty. That is to say...

[0188]

[0189] Specifically, in each time period t All of them should be less than Therefore, we can obtain three sets of inequalities.

[0190]

[0191]

[0192]

[0193] Based on equations (23)-(25), the following conclusions are obtained.

[0194] From constraints (3)-constraint (4), constraint (7) and constraint (8), we can see that the inequality in the formula always holds.

[0195] In equation (24), the inequality is related to the state variable E. t Since it is irrelevant, equation (24) can be considered a prerequisite. For any uncertain realization, equation (24) should always be satisfied, therefore it can be further concluded that equation (26) must hold.

[0196]

[0197] By rearranging equation (26), we can see that equation (27) is satisfied.

[0198]

[0199] In equation (25), the inequality is related to E. t-1 This is related to the scope of E. t Sufficient conditions for the existence of a feasible solution.

[0200] For any uncertain realization, the inequality in equation (25) should hold. Then we have

[0201]

[0202] Note that the inequality in equation (28) is only to ensure E t When the range is not empty, E t-1 In addition to the constraints, E t-1 It also needs to satisfy its own physical constraints (4). Then further...

[0203]

[0204] Equation (29) gives the feasible range of E for the time period t (single time period) to guarantee the requirements of unpredictability and multi-stage robustness. In order to guarantee the feasibility of the solution throughout the entire scheduling period, Equation (29) should be extended to the entire scheduling period.

[0205] According to recursion theory, for the time interval t=T, we have and For the time period t = T-1, we can further obtain the formula based on formula (29).

[0206]

[0207] For t = T - 2, we have

[0208]

[0209] According to recursion theory, equation (32) should be satisfied for each time period.

[0210]

[0211] S5, please refer to Figure 3 Based on the multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup obtained in step S3 and the range of scheduling schemes obtained in step S4, a day-ahead scheduling model of the microgrid with energy storage equipment that provides active backup is established, and the decision feasibility range, optimal positive and negative backup capacity and optimal power injection curve of the microgrid system are directly solved.

[0212] Equation (33) is the objective function, which requires minimizing the operating cost within the scheduling cycle, including the cost (benefit) of power injection from the microgrid system to the main grid and the benefit of the microgrid system providing reserves to the main grid or external systems.

[0213]

[0214] Equation (34) represents the power exchange limit between the microgrid and the main grid, which restricts the lower and upper limits of power exchange between the microgrid system and the main grid:

[0215]

[0216] Equation (35) represents the positive and negative reserve positive value limit:

[0217]

[0218] The prerequisite for the system to have a solution in equation (36) is:

[0219]

[0220] Equation (37) represents the safe range of energy storage levels:

[0221]

[0222] Where T is the set of scheduling periods; τ is a time period; The unit price at which the microgrid system purchases electricity from the main grid; The unit price for electricity sold from the microgrid system to the main grid; For the day-ahead power injection from the microgrid to the main grid (positive) and (negative); and For positive and negative reserve unit prices; and This refers to the amount of wind and solar power that are forfeited. For energy storage charging and discharging power; and Positive and negative reserve capacity limits; Ω t It is an uncertain set; and The upper and lower limits of the power exchanged with the main network; and These are the upper and lower limits of load demand; and These are the charging and discharging power limits for the energy storage system, respectively. This represents the lower limit of wind power output. This represents the lower limit of photovoltaic power output. These are the minimum and maximum values ​​of the safe range for the time period t-1, respectively. and , , represent the upper and lower bounds of the energy level of the energy storage system during time period t-1; min and max are the minimum and maximum value functions, respectively; h(·) is an auxiliary function.

[0223] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0224] In another embodiment of the present invention, a microgrid self-dispatch system with energy storage devices that provides active backup is provided. This system can be used to implement the above-mentioned microgrid self-dispatch method with energy storage devices that provides active backup. Specifically, the microgrid self-dispatch system with energy storage devices that provides active backup includes a system module, a data module, a function module, an optimization module, and a scheduling module.

[0225] Among them, the system module constructs a system framework for microgrid self-dispatch technology that includes energy storage devices to provide active backup;

[0226] The data module analyzes the scheduling process of a microgrid with energy storage devices that provides active backup within the system framework of microgrid self-dispatch technology. It establishes a decision-dependent uncertainty set for the uncertainties of positive and negative backup demand. Based on the acquired historical information of random factors, it establishes uncertainty sets for renewable energy and load demand.

[0227] The function module obtains key parameters of the microgrid system, constructs an objective function with the goal of minimizing the operating cost under the desired scenario, and builds a multi-stage robust optimization model of the microgrid with energy storage equipment based on the obtained renewable energy and load demand uncertainty set to provide active backup.

[0228] The optimization module, based on the obtained multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup, utilizes its constraint structure characteristics to establish a scheduling scheme for the microgrid system that meets feasibility and security requirements at various time periods.

[0229] The scheduling module establishes a day-ahead scheduling model for a microgrid with energy storage that provides active backup, based on the obtained multi-stage robust optimization model and scheduling scheme. It solves for the decision feasibility range, optimal positive and negative backup capacity, and optimal power injection curve of the microgrid system, and realizes microgrid self-schedule based on the results.

[0230] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used to provide the operation of a self-dispatch method for microgrids with active backup and energy storage devices, including:

[0231] This paper constructs a system framework for microgrid self-dispatch technology with energy storage devices providing active backup. It analyzes the dispatching process of the microgrid with energy storage devices providing active backup within this framework, establishing a decision-dependent uncertainty set for the uncertainties of positive and negative backup demand. Based on historical information of acquired random factors, it establishes uncertainty sets for renewable energy and load demand. Key parameters of the microgrid system are obtained, and an objective function is constructed with the goal of minimizing operating costs under the desired scenario. A multi-stage robust optimization model for the microgrid with energy storage devices providing active backup is constructed based on the obtained uncertainty sets of renewable energy and load demand. Based on the obtained multi-stage robust optimization model, a dispatching scheme that satisfies feasibility and safety for each time period of the microgrid system is established using its constraint structure characteristics. Based on the obtained multi-stage robust optimization model and the obtained dispatching scheme, a mixed-integer programming day-ahead dispatching model for the microgrid with energy storage devices providing active backup is established. The decision feasibility range, optimal positive and negative backup capacity, and optimal power injection curve of the microgrid system are solved, and microgrid self-dispatch is achieved based on the results.

[0232] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that more specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0233] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0234] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0235] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the microgrid self-dispatch method with energy storage device providing active backup in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0236] This paper constructs a system framework for microgrid self-dispatch technology with energy storage devices providing active backup. It analyzes the dispatching process of the microgrid with energy storage devices providing active backup within this framework, establishing a decision-dependent uncertainty set for the uncertainties of positive and negative backup demand. Based on historical information of acquired random factors, it establishes uncertainty sets for renewable energy and load demand. Key parameters of the microgrid system are obtained, and an objective function is constructed with the goal of minimizing operating costs under the desired scenario. A multi-stage robust optimization model for the microgrid with energy storage devices providing active backup is constructed based on the obtained uncertainty sets of renewable energy and load demand. Based on the obtained multi-stage robust optimization model, a dispatching scheme that satisfies feasibility and safety for each time period of the microgrid system is established using its constraint structure characteristics. Based on the obtained multi-stage robust optimization model and the obtained dispatching scheme, a mixed-integer programming day-ahead dispatching model for the microgrid with energy storage devices providing active backup is established. The decision feasibility range, optimal positive and negative backup capacity, and optimal power injection curve of the microgrid system are solved, and microgrid self-dispatch is achieved based on the results.

[0237] Please see Figure 10 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the microgrid self-dispatch method with energy storage devices providing active backup in this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the microgrid self-dispatch system with energy storage devices providing active backup in this embodiment. To avoid repetition, details are omitted here.

[0238] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 10This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0239] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, CPUs, graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic units, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0240] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0241] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0242] Any references to memory, databases, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0243] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0244] Please see Figure 4 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0245] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 2 The steps are shown in the figure.

[0246] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0247] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0248] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0249] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0250] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0251] A microgrid system, including energy storage, wind farm, and photovoltaic units, is planned to be constructed in a microgrid park. The project aims to obtain relevant information for dispatching the microgrid system with energy storage equipment to provide active backup. Table 1 shows the main parameters of the microgrid system. Figure 5 , Figure 6 and Figure 7 It displays information related to load demand, wind power output, and photovoltaic power output.

[0252] Table 1 Main parameters of the microgrid system

[0253]

[0254] Table 1 provides scheduling parameters for 24 time periods, with each period lasting one hour. This microgrid system can exchange energy with the gateway, meaning it can buy or sell electricity to the gateway, with a maximum purchase capacity of 4MW and a maximum sales capacity of 4MW. Additionally, the microgrid system includes energy storage devices for energy transfer, with a physical energy level range of [2, 7.6] MWh. Initially, the energy storage devices have an energy level of 4.8 MWh, a maximum charge / discharge power of 2MW, and a charge / discharge efficiency of 90%.

[0255] Figure 5 , Figure 6 and Figure 7 It involves 24 time periods, with each time period having a length of 1, including upper and lower limits of load demand, expected load demand, upper and lower limits of wind power output, expected wind power output, upper and lower limits of photovoltaic output, and expected photovoltaic output.

[0256] Based on the above parameters and uncertainties, the scheduling performance of this method in the day-ahead phase is as follows: Figure 8 and Figure 9 As shown.

[0257] The recent dispatch results are as follows Figure 8 As shown, the orange solid line is the power injection curve, and the heights of the green and blue bar regions represent the maximum negative and positive reserve capacity that the microgrid can provide, respectively.

[0258] The power injection curve is determined in the day-ahead phase, and based on this curve, the main grid provides (obtains) specific power values ​​to (from) the microgrid in the real-time phase. Calculations of the microgrid dispatch problem show that when the injected power is zero in each time period in this case, there is no feasible dispatch scheme for the microgrid. This means that the injected power is necessary for the stability and security of the microgrid. This is because energy storage can only transfer energy and cannot generate energy, and therefore may not be able to fill the power difference between wind power output and load demand. Furthermore, from... Figure 8The trend of the orange solid line (power injection curve) shows that when the PJM market electricity price is low, the microgrid tends to buy electricity from the main grid (positive power injection), and vice versa. Therefore, it can be proven that the dispatching results obtained based on the method proposed in this invention meet the economic requirements.

[0259] in addition, Figure 8 This demonstrates the day-ahead positive and negative reserve capacity that a microgrid with energy storage, based on the methods proposed in this chapter, can provide to the main grid or external systems. From Figure 8 As can be seen, microgrids with energy storage can provide sufficient positive and negative backup capacity, indicating that the system has great potential in providing backup services. Figure 8 For any positive or negative reserve demand within the given reserve capacity, the microgrid can find a feasible scheduling scheme according to the method proposed in this invention.

[0260] Finally, the range of feasible scheduling solutions is analyzed, i.e., the energy level feasible / safe domain of the energy storage device, such as... Figure 9 As shown. According to Figure 9 From this, we can draw the following conclusions: First, due to operational constraints, especially cross-time coupling constraints, the feasible region for energy storage capacity is usually smaller than its physical boundary. Second, Figure 9 The fact that the feasible region is non-empty indicates that for Figures 5 to 7 Arbitrary output values ​​of renewable energy and Figure 8 For any reserve demand within the medium reserve capacity range, a microgrid always has a feasible solution that satisfies all operational constraints. For operators, the robust feasible region is extremely convenient; they only need to make appropriate decisions within the feasible region at each time period to effectively cope with complex system constraints and uncertainties.

[0261] In summary, the present invention provides a microgrid self-dispatch method and system with energy storage devices that provides active backup. The microgrid with energy storage can act as a backup provider, providing backup auxiliary services to external systems or the main grid, thereby increasing the total revenue of the microgrid.

[0262] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0263] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0264] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0265] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0266] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0267] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0268] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0269] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0270] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0271] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0272] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A microgrid self-dispatch method with active backup and energy storage devices, characterized in that, Includes the following steps: S1. Construct a system framework for microgrid self-dispatch technology that provides active backup and includes energy storage devices; S2. Analyze the scheduling process of a microgrid with energy storage devices that provides active backup in the system framework of microgrid self-dispatch technology, and establish a decision-dependent uncertainty set for the uncertainty of positive and negative backup demand; establish the uncertainty set of renewable energy and load demand based on the historical information of the acquired random factors. S3. Obtain the key parameters of the microgrid system, construct an objective function with the goal of minimizing the operating cost under the desired scenario, and build a multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup based on the renewable energy and load demand uncertainty set obtained in step S2. The specific objective function is as follows: in, T A set of scheduling periods; τ The duration of the time period; for t The unit price of electricity purchased from the main grid by the time-period microgrid system; for t The unit price of electricity sold from the microgrid system to the main grid during specific time periods; for t Day-ahead power injection from microgrids to the main grid; and for t Unit prices for positive and negative standby periods; and for t Wind and solar power curtailment during specific time periods; for t Time-limited energy storage charging and discharging power; and for t Time-based positive and negative reserve capacity limits; The constraints that the microgrid system scheduling and operation must meet include: Power balance constraints, charging / discharging power limits, upper and lower bound constraints on energy storage levels, energy storage SOC dynamic equations, wind and solar curtailment constraints, positive and negative reserve limits, and power exchange limits between microgrids and the main grid. S4. Based on the multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup obtained in step S3, establish a scheduling scheme that satisfies feasibility and security for each time period of the microgrid system using its constraint structure characteristics. The specific range of the scheduling schemes that satisfy feasibility and security for each time period of the multi-stage robust optimization model of the microgrid system is as follows: in, , They are respectively t The minimum and maximum values ​​of the safe range at the end of the -1 period; for t The microgrid injects day-ahead power into the main grid from positive to negative directions; and for t Upper and lower limits of load demand during different time periods; and for t Time-based positive and negative reserve capacity limits; for t Lower limit of wind power output during certain time periods; for t Lower limit of photovoltaic output during the period; and They are respectively t Time-limited charging and discharging power of energy storage systems; and They are respectively t -1 is the upper and lower bounds of the energy level of the energy storage system at the end of the time period; min and max are functions for taking the minimum and maximum values, respectively; For auxiliary functions; S5. Based on the multi-stage robust optimization model of the microgrid with energy storage equipment that provides active backup obtained in step S3 and the scheduling scheme obtained in step S4, establish a day-ahead scheduling model of the microgrid with energy storage equipment that provides active backup, and solve the decision feasibility range, optimal positive and negative backup capacity and optimal power injection curve of the microgrid system. Based on the obtained results, realize the self-schedule of the microgrid.

2. The microgrid self-dispatch method with energy storage device providing active backup as described in claim 1, characterized in that, In step S2, the uncertain set of renewable energy and load demand is specifically as follows: in, This is the current scheduling period; and for t Wind and solar power output during certain periods; and for t Upper and lower limits of wind power output during different time periods; and for t Upper and lower limits of photovoltaic power output during different time periods; for t Time-of-day load demand; and for t Upper and lower limits of load demand during different time periods; and for t Positive and negative backup demand during different time periods; and for t Time-based positive and negative backup capacity limits.

3. The microgrid self-dispatch method with energy storage device providing active backup as described in claim 1, characterized in that, Power balance constraints: Charge / discharge power limits: Energy storage energy level upper and lower bound constraints: Energy storage SOC dynamic equation: Wind curtailment constraints: Discard light constraint: Positive and negative reserve positive value limit: Power exchange limitations between microgrid and main grid: in, It is an uncertain set; for t Time-limited energy storage charging and discharging power; and for t Wind and solar power curtailment during specific time periods; and for t Wind and solar power output during certain periods; for t The microgrid injects day-ahead power into the main grid from positive to negative directions; and for t Time-based positive and negative reserve capacity limits; for t Time-of-day load demand; and for t Positive and negative backup demand during different time periods; and They are respectively t Time-limited charging and discharging power of energy storage systems; for t Energy level of energy storage system at the end of the period; and They are respectively t Upper and lower bounds of the energy level of the energy storage system at the end of the period; and for t Upper and lower limits of power exchanged between the main network and the time period; and for t Time-based positive and negative reserve capacity limits; for t Uncertainty over time; For the uncertain quantity from time period 1 to T The realized value of the time period, as well as This is an auxiliary function.

4. The microgrid self-dispatch method with energy storage device providing active backup according to claim 3, characterized in that, Auxiliary functions Its inverse function is: in, τ The length of a time period; and These are the charge / discharge efficiency coefficients, respectively. The independent variable is an auxiliary variable, corresponding to the discharge and charging power of the energy storage device. p t .

5. The microgrid self-dispatch method with energy storage device providing active backup according to claim 1, characterized in that, Step S5 is as follows: The system operating parameters and uncertainty set parameters are obtained, and the day-ahead reserve supply model is solved in one go through the solver to obtain the decision feasibility range, optimal positive and negative reserve capacity and optimal power injection curve of the microgrid system.

6. The microgrid self-dispatch method with energy storage device providing active backup according to claim 5, characterized in that, The objective function that minimizes the operating cost within the scheduling cycle is: The power exchange restrictions between the microgrid and the main grid are as follows: Positive and negative reserve values ​​are limited to: The prerequisite for the existence of a solution in the system is: The safe range for energy storage levels is: in, T A set of scheduling periods; τ The length of a time period; for t The unit price of electricity purchased from the main grid by the time-period microgrid system; for t The unit price of electricity sold from the microgrid system to the main grid during specific time periods; for t The microgrid injects day-ahead power into the main grid from positive to negative directions; and for t Unit prices for positive and negative standby periods; and for t Wind and solar power curtailment during specific time periods; for t Time-limited energy storage charging and discharging power; and for t Time-based positive and negative reserve capacity limits; It is an uncertain set; and for t Upper and lower limits of power exchanged between the main network and the time period; and for t Upper and lower limits of load demand during different time periods; and They are respectively t Time-limited charging and discharging power of energy storage systems; for t Lower limit of wind power output during certain time periods; for t Lower limit of photovoltaic output during the period; , They are respectively t The minimum and maximum values ​​of the safe range at the end of the -1 period; and They are respectively t -1 is the upper and lower bounds of the energy level of the energy storage system at the end of the time period; min and max are functions for taking the minimum and maximum values, respectively; This is an auxiliary function.

7. A microgrid self-dispatch system with energy storage devices providing active backup, characterized in that, include: The system module constructs a system framework for microgrid self-dispatch technology that includes energy storage devices and provides active backup. The data module analyzes the scheduling process of a microgrid with energy storage devices that provides active backup within the system framework of microgrid self-dispatch technology. It establishes a decision-dependent uncertainty set for the uncertainties of positive and negative backup demand. Based on the acquired historical information of random factors, it establishes uncertainty sets for renewable energy and load demand. The function module obtains key parameters of the microgrid system and constructs an objective function with the goal of minimizing operating costs under the desired scenario. Based on the obtained renewable energy and load demand uncertainty set, it constructs a multi-stage robust optimization model of the microgrid with energy storage devices to provide active backup. The specific objective function is as follows: in, T A set of scheduling periods; τ The duration of the time period; for t The unit price of electricity purchased from the main grid by the time-period microgrid system; for t The unit price of electricity sold from the microgrid system to the main grid during specific time periods; for t The microgrid injects day-ahead power into the main grid from positive to negative directions; and for t Unit prices for positive and negative standby periods; and for t Wind and solar power curtailment during specific time periods; for t Time-limited energy storage charging and discharging power; and for t Time-based positive and negative reserve capacity limits; The constraints that the microgrid system scheduling and operation must meet include: Power balance constraints, charging / discharging power limits, upper and lower bound constraints on energy storage levels, energy storage SOC dynamic equations, wind and solar curtailment constraints, positive and negative reserve limits, and power exchange limits between microgrids and the main grid. The optimization module, based on the obtained multi-stage robust optimization model of the microgrid with energy storage devices providing active backup, utilizes its constraint structure characteristics to establish scheduling schemes that satisfy feasibility and security for each time period of the microgrid system. The specific range of scheduling schemes that satisfy feasibility and security for each time period in the multi-stage robust optimization model of the microgrid system is as follows: in, , They are respectively t The minimum and maximum values ​​of the safe range at the end of the -1 period; for t The microgrid injects day-ahead power into the main grid from positive to negative directions; and for t Upper and lower limits of load demand during different time periods; and for t Time-based positive and negative reserve capacity limits; for t Lower limit of wind power output during certain time periods; for t Lower limit of photovoltaic output during the period; and They are respectively t Time-limited charging and discharging power of energy storage systems; and They are respectively t -1 is the upper and lower bounds of the energy level of the energy storage system at the end of the time period; min and max are functions for taking the minimum and maximum values, respectively; For auxiliary functions; The scheduling module establishes a day-ahead scheduling model for a microgrid with energy storage that provides active backup, based on the obtained multi-stage robust optimization model and scheduling scheme. It solves for the decision feasibility range, optimal positive and negative backup capacity, and optimal power injection curve of the microgrid system, and realizes microgrid self-schedule based on the results.

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