A method and system for controlling operation of power resource aggregate

Abstract

The present invention discloses a method and system for controlling the operation of an electric power resource aggregate, wherein the method includes establishing output models of uncontrollable distributed units, controllable distributed units and load models of adjustable loads respectively based on a target resource aggregate; constructing a cooperative operation model of the target resource aggregate according to the output model and the load model; constructing a joint optimization constraint of the cooperative operation model based on the supply and demand balance constraints of the uncontrollable distributed units, the output upper and lower limit constraints of the controllable distributed units and the load constraints of the adjustable load; calculating the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraint to obtain the optimal operation control scheme of each participant in the target resource aggregate. The method provided by the present invention fully considers the complementary characteristics of multi-factor distributed resources and realizes the optimized operation control of the power system.

Description

Technical Field

[0001] The present invention relates to the field of power system control, and in particular to a method and system for controlling the operation of a power resource aggregate. Background Art

[0002] In the context of building a new power system, distributed renewable energy has ushered in a new era of rapid development and extensive grid connection. However, due to the small capacity and scattered distribution of distributed energy units, they cannot directly participate in energy transactions in the power market. To this end, new power system resource aggregates such as energy aggregates and virtual power plants have become an effective way to solve this problem. By integrating multiple distributed energy sources, the advantages of energy sources can be complemented, thereby reducing the risk of distributed resources in the region participating independently in the power market.

[0003] As resource aggregation entities present diversified characteristics, how to effectively solve the problem of distributed resource cooperation has become the focus of current research. In existing technologies, most studies focus on the operation control of power systems and the optimal scheduling of distributed resources at a single scale. These studies often only focus on a single dimension of the power system and ignore the comprehensive consideration of multi-dimensional factors. Summary of the invention

[0004] The present invention aims to provide a method and system for controlling the operation of an electric power resource aggregate, so as to solve the technical problem of how to optimize the control of an electric power system.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides an operation control method of a power resource aggregate, comprising:

[0006] Based on the target resource aggregate, the output models of uncontrollable distributed units, controllable distributed units and the load model of adjustable load are established respectively;

[0007] constructing a cooperative operation model of the target resource aggregation according to the output model and the load model, wherein the cooperative operation model is designed to maximize energy utilization efficiency by calculating a power upper limit at each time step;

[0008] Constructing the joint optimization constraints of the cooperative operation model based on the supply and demand balance constraints of the uncontrollable distributed units, the upper and lower limit constraints of the output of the controllable distributed units, and the load constraints of the adjustable loads;

[0009] The cooperative operation model is calculated according to the particle swarm optimization algorithm and the joint optimization constraints to obtain the optimal operation control plan of each participant in the target resource aggregate.

[0010] As one of the preferred solutions, the cooperative operation model of the target resource aggregation is constructed according to the output model and the load model, including:

[0011] Determining a cooperation goal according to the output model and the load model, wherein the cooperation goal includes maximizing energy utilization;

[0012] Based on the physical characteristics of each participant in the target resource aggregate, corresponding responsibilities are assigned to each participant, wherein the uncontrollable distributed unit provides renewable energy as an energy supplier, the controllable distributed unit balances the supply and demand relationship as an energy regulator, and the adjustable load supports the stable operation of the power system as a demand-side response;

[0013] Generate a cooperation mechanism according to the cooperation goal and the corresponding responsibilities of each of the participants, wherein the cooperation mechanism includes information sharing, coordinated scheduling and loss compensation;

[0014] A cooperative operation model of the target resource aggregate is constructed based on the cooperation target and the cooperation mechanism.

[0015] As one of the preferred solutions, the cooperative operation model of the target resource aggregation is constructed according to the output model and the load model, including:

[0016] Determining a cooperation goal according to the output model and the load model, wherein the cooperation goal includes maximizing energy utilization;

[0017] Based on the physical characteristics of each participant in the target resource aggregate, corresponding responsibilities are assigned to each participant, wherein the uncontrollable distributed unit provides renewable energy as an energy supplier, the controllable distributed unit balances the supply and demand relationship as an energy regulator, and the adjustable load supports the stable operation of the power system as a demand-side response;

[0018] Generate a cooperation mechanism according to the cooperation goal and the corresponding responsibilities of each of the participants, wherein the cooperation mechanism includes information sharing, coordinated scheduling and loss compensation;

[0019] A cooperative operation model of the target resource aggregate is constructed based on the cooperation target and the cooperation mechanism.

[0020] As one of the preferred solutions, the uncontrollable distributed units include distributed wind power and distributed photovoltaics, the controllable distributed units include gas units and energy storage systems, and the adjustable loads include transferable loads and interruptible loads;

[0021] The output models of the uncontrollable distributed units and the controllable distributed units and the load model of the adjustable load are respectively established:

[0022] Acquire historical operation data of the distributed wind power, the distributed photovoltaic, the gas generator set, the energy storage system, the transferable load and the interruptible load;

[0023] Based on the historical operation data, a distributed wind power output model, the distributed photovoltaic model, a gas unit model, an energy storage system model, a transferable load model and an interruptible load model are respectively constructed.

[0024] As one preferred solution, after calculating the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraint to obtain the optimal operation control solution of each participant in the target resource aggregate, the method further includes:

[0025] generating a cooperative game strategy according to the cooperative operation model, and calculating a resource optimization index based on the cooperative game strategy;

[0026] Evaluate the operation control effect of each participant in the cooperative operation model according to the resource optimization index, and output the corresponding configuration of each participant as the optimization result;

[0027] Based on the optimization result, relevant parameters of the target resource aggregate in actual operation are controlled.

[0028] Another embodiment of the present invention provides an electric resource aggregation operation control system, including:

[0029] The first building module is used to establish output models of uncontrollable distributed units, controllable distributed units and load models of adjustable loads based on the target resource aggregate;

[0030] A second building module is used to build a cooperative operation model of the target resource aggregation according to the output model and the load model, wherein the cooperative operation model is designed to maximize energy utilization efficiency by calculating a power upper limit for each time step;

[0031] A constraint module, used to construct a joint optimization constraint of the cooperative operation model based on the supply and demand balance constraint of the uncontrollable distributed unit, the upper and lower limit constraints of the output of the controllable distributed unit, and the load constraint of the adjustable load;

[0032] The calculation module is used to calculate the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraint to obtain the optimal operation control plan of each participant in the target resource aggregate.

[0033] As a preferred solution, the first building block is used to:

[0034] Determining a cooperation goal according to the output model and the load model, wherein the cooperation goal includes maximizing energy utilization;

[0035] Based on the physical characteristics of each participant in the target resource aggregate, corresponding responsibilities are assigned to each participant, wherein the uncontrollable distributed unit provides renewable energy as an energy supplier, the controllable distributed unit balances the supply and demand relationship as an energy regulator, and the adjustable load supports the stable operation of the power system as a demand-side response;

[0036] Generate a cooperation mechanism according to the cooperation goal and the corresponding responsibilities of each of the participants, wherein the cooperation mechanism includes information sharing, coordinated scheduling and loss compensation;

[0037] A cooperative operation model of the target resource aggregate is constructed based on the cooperation target and the cooperation mechanism.

[0038] As one preferred solution, the first building block is further used for:

[0039] Acquire historical operation data of each of the participants in the target resource aggregate;

[0040] Determine the objective function according to the cooperation goal, determine the constraint conditions according to the cooperation mechanism, and construct a mathematical model of the target resource aggregate;

[0041] The mathematical model is optimized according to the historical operation data to obtain the cooperative operation model.

[0042] As one of the preferred solutions, the uncontrollable distributed units include distributed wind power and distributed photovoltaics, the controllable distributed units include gas units and energy storage systems, and the adjustable loads include transferable loads and interruptible loads;

[0043] The first building block is further used for:

[0044] Acquire historical operation data of the distributed wind power, the distributed photovoltaic, the gas generator set, the energy storage system, the transferable load and the interruptible load;

[0045] Based on the historical operation data, a distributed wind power output model, the distributed photovoltaic model, a gas unit model, an energy storage system model, a transferable load model and an interruptible load model are respectively constructed.

[0046] As one preferred solution, the computing module is further used for:

[0047] generating a cooperative game strategy according to the cooperative operation model, and calculating a resource optimization index based on the cooperative game strategy;

[0048] Evaluate the operation control effect of each participant in the cooperative operation model according to the resource optimization index, and output the corresponding configuration of each participant as the optimization result;

[0049] Based on the optimization result, relevant parameters of the target resource aggregate in actual operation are controlled.

[0050] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0051] 1) By modeling different types of distributed resources separately, the present invention can more accurately understand the characteristics and output capacity of various types of resources, so as to flexibly schedule them according to needs in actual operation, avoid waste or overuse of resources, and significantly improve resource utilization efficiency;

[0052] 2) The present invention incorporates uncontrollable, controllable and adjustable loads into a unified management framework, and coordinates the output of various resources in the form of a cooperative operation model, which helps to balance system supply and demand, reduce grid fluctuations, and improve the stability and reliability of the entire system;

[0053] 3) With the rapid development of renewable energy and distributed generation technology, the power system is facing more and more uncertainty and complexity. The present invention enhances the system's ability to flexibly respond to external changes and improves the system's adaptability by dynamically adjusting the output configuration of each resource. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of a method for controlling the operation of a power resource aggregate in one embodiment of the present invention;

[0055] Figure 2 It is a structural diagram of an electric resource aggregate operation control system in one embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0058] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0059] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0060] An embodiment of the present invention provides a method for controlling the operation of a power resource aggregate. For details, see Figure 1 , Figure 1 The flowchart of the power resource aggregation operation control method provided in one embodiment of the present invention is shown. It includes steps S1 to S4:

[0061] S1. Based on the target resource aggregate, the output models of the uncontrollable distributed units and the controllable distributed units and the load model of the adjustable load are established respectively;

[0062] When constructing the output model of the target resource aggregate, different types of resources (uncontrollable distributed units, controllable distributed units and adjustable loads) need to be modeled separately.

[0063] Preferably, in one embodiment of the present invention, the uncontrollable distributed units include distributed wind power and distributed photovoltaics, the controllable distributed units include gas units and energy storage systems, and the adjustable loads include transferable loads and interruptible loads;

[0064] The output models of the uncontrollable distributed units and the controllable distributed units and the load model of the adjustable load are respectively established:

[0065] Acquire historical operation data of the distributed wind power, the distributed photovoltaic, the gas generator set, the energy storage system, the transferable load and the interruptible load;

[0066] Based on the historical operation data, a distributed wind power output model, the distributed photovoltaic model, a gas unit model, an energy storage system model, a transferable load model and an interruptible load model are respectively constructed.

[0067] First, the uncontrollable distributed units are analyzed. Specifically, the output deviation of new energy has a great impact on the operation of resource aggregation. The present invention specifically analyzes the output of two typical new energy sources, distributed wind power and distributed photovoltaic power.

[0068] The predicted wind power output is:

[0069] ,

[0070] in, , The wind turbines are The day-ahead output forecast and actual output value for each time period (assuming 1 day of 24 hours is 24 time periods); For wind power Time period forecast error.

[0071] The predicted photovoltaic output is:

[0072] ,

[0073] In the formula, , The photovoltaic power generation units are The day-ahead output forecast and actual output for the time period; For photovoltaic generators Time period forecast error.

[0074] Next, we analyze the controllable distributed units. Specifically, the power generation cost of the gas unit during the operation phase is:

[0075] ,

[0076] In the formula, For gas units The cost of electricity generation during the time period; For gas units Output power during the time period;

[0077] , , are the power generation cost coefficients of gas-fired units respectively.

[0078] The energy storage system has bidirectional characteristics of charging and discharging, and can realize applications such as load balancing and frequency regulation:

[0079] ,

[0080] In the formula, For energy storage systems The charging and discharging power of the time period; It is the maximum value of charging and discharging power of the energy storage system.

[0081] Finally, we analyze the flexible load. The flexible load (FL) in the resource aggregate mainly includes transferable load and interruptible load. The transferable load is expressed as:

[0082] ,

[0083] In the formula, Indicates that the transferable load is The actual load during the time period; Indicates that the transferable load is The amount of load transfer during the period; Indicates that the transferable load is The amount of adjustment for the time period; , Respectively represent the transferable load in The upper and lower limits of load adjustment for the time period; is the total number of time periods.

[0084] The interruptible load can be expressed as:

[0085] ,

[0086] In the formula, After interruptible load shedding The actual load during the time period; For interruptible loads The original load of the period; For interruptible loads The amount of load reduction during the period.

[0087] S2. constructing a cooperative operation model of the target resource aggregate according to the output model and the load model, wherein the cooperative operation model is designed to maximize energy utilization efficiency by calculating a power upper limit for each time step;

[0088] Preferably, in one embodiment of the present invention, constructing the cooperative operation model of the target resource aggregation according to the output model and the load model includes:

[0089] Determining a cooperation goal according to the output model and the load model, wherein the cooperation goal includes maximizing energy utilization;

[0090] Based on the physical characteristics of each participant in the target resource aggregate, corresponding responsibilities are assigned to each participant, wherein the uncontrollable distributed unit provides renewable energy as an energy supplier, the controllable distributed unit balances the supply and demand relationship as an energy regulator, and the adjustable load supports the stable operation of the power system as a demand-side response;

[0091] Generate a cooperation mechanism according to the cooperation goal and the corresponding responsibilities of each of the participants, wherein the cooperation mechanism includes information sharing, coordinated scheduling and loss compensation;

[0092] A cooperative operation model of the target resource aggregate is constructed based on the cooperation target and the cooperation mechanism.

[0093] In terms of the interaction between the subject and the load, wind power generation, photovoltaic power generation, and gas-fired power generation not only need to provide electricity to the power grid, but also serve various load resources and energy storage systems; the power grid itself also has the task of supplying electricity to these load resources and energy storage systems.

[0094] Preferably, in an embodiment of the present invention, the cooperative operation model of the target resource aggregate is constructed based on the cooperation mechanism, including:

[0095] Acquire historical operation data of each of the participants in the target resource aggregate;

[0096] Determine the objective function according to the cooperation goal, determine the constraint conditions according to the cooperation mechanism, and construct a mathematical model of the target resource aggregate;

[0097] The mathematical model is optimized according to the historical operation data to obtain the cooperative operation model.

[0098] Specifically, a cooperative operation model and operation control optimization model are constructed by combining historical operation data such as wind power output, photovoltaic output, gas unit output, charging and discharging power and capacity data of energy storage systems, and adjustable load data.

[0099] The historical operation data includes operation data, electricity price data, and equipment price data; wherein, the operation data includes: the benchmark values ​​of wind and solar power output and gas unit output in the day-ahead scheduling planning stage, as well as the charge and discharge power, capacity benchmark value, and adjustable load benchmark value of the energy storage system; the actual values ​​of wind and solar power output and gas unit output in the real-time operation stage, as well as the charge and discharge power, capacity actual value, and adjustable load actual value of the energy storage system.

[0100] The electricity price data include: the purchase and sale prices of electricity in the day-ahead market and the intraday market;

[0101] The equipment price data include: operation and maintenance costs of wind turbines and photovoltaic units, and charging and discharging loss costs of energy storage systems.

[0102] The goal is to maximize energy utilization, and the objective function is as follows:

[0103] ,

[0104] ,

[0105] ,

[0106] In the formula, To consider the overall economic benefits of the cooperative operation model after total operating costs; Trading revenues with the grid for cooperative operation models; The total operating cost of the cooperative operation model; and The cooperative operation model is The power purchased and sold from the power grid during the period; and The cooperative operation model is The price of electricity purchased from the power grid and the price of electricity sold during the period; , , , They are respectively the wind power operating costs, photovoltaic operating costs, gas unit operating costs, energy storage system operating costs, and the compensation paid by the cooperative operation model to adjustable loads participating in demand response.

[0107] The following is a detailed calculation of various costs and compensations during the operation of the cooperative operation model:

[0108] ,

[0109] In the formula, , are the linear cost coefficients of operation and maintenance of wind turbines and photovoltaic units respectively; , are the on-grid electricity prices for wind turbines and photovoltaic units respectively; is the charging and discharging cost loss coefficient of the energy storage system; , are the compensation factors for interruptible load and transferable load respectively; , They are the states of interruptible load and transferable load, respectively. A value of 0 indicates that no reduction or transfer occurs, while a value of 1 indicates the opposite.

[0110] S3. Constructing the joint optimization constraints of the cooperative operation model based on the supply and demand balance constraints of the uncontrollable distributed units, the upper and lower limit constraints of the output of the controllable distributed units, and the load constraints of the adjustable loads;

[0111] In order to ensure the safe and stable operation of the cooperative operation model, relevant operation constraints are set, including supply and demand balance constraints, upper and lower limit constraints on the output of gas units, load constraints and energy storage system operation constraints.

[0112] Supply and demand balance constraints:

[0113] ,

[0114] In the formula, , The cooperative operation model is The power purchased and sold from the power grid during the period; After adjusting for adjustable load The actual load during the period.

[0115] Gas unit power output upper and lower limit constraints:

[0116] ,

[0117] Where: , The gas units are Positive and negative reserves provided during the time period; , They are the upper and lower limits of the output of the gas unit respectively.

[0118] Load constraints include transferable load constraints and interruptible load constraints. The transferable load constraints are expressed as:

[0119] ,

[0120] In the formula, After adjusting for adjustable load The initial load of the time period.

[0121] The interruptible load constraint is expressed as:

[0122] ,

[0123] In the formula, For interruptible loads The upper limit of load reduction during the period.

[0124] Energy storage system operation constraints:

[0125] ,

[0126] In the formula, For energy storage systems State of charge during the time period; is the initial charge state of the energy storage system; , are the maximum and minimum values ​​of the energy storage charge state, respectively.

[0127] According to the objectives and constraints of power system operation optimization, a suitable modeling method is selected to establish a cooperative operation model of the power system. The model is trained using historical operation data to obtain suitable model parameters.

[0128] S4. Calculate the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraint to obtain the optimal operation control plan of each participant in the target resource aggregate.

[0129] Preferably, in one embodiment of the present invention, after calculating the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraint to obtain the optimal operation control scheme of each participant in the target resource aggregate, it also includes:

[0130] generating a cooperative game strategy according to the cooperative operation model, and calculating a resource optimization index based on the cooperative game strategy;

[0131] Evaluate the operation control effect of each participant in the target resource aggregate according to the resource optimization index, and output the corresponding configuration of each participant as the optimization result;

[0132] Based on the optimization result, relevant parameters of the target resource aggregate in actual operation are controlled.

[0133] From the perspective of cooperative game, different resources participate in cooperative operation in the hope of obtaining their own minimum resource optimization index, which means lower resource input and higher operating efficiency, thus ensuring the benefits of the overall operation of the system. If the resource optimization index obtained by the resources participating in the cooperation is lower than the minimum resource optimization index, the cooperation is interrupted and cannot be achieved. Therefore, it is necessary to consider the marginal participation contribution and output deviation risk to re-quantify the resource optimization index of the cooperative operation model.

[0134] Specifically, in one embodiment of the present invention, generating a cooperative game strategy according to the cooperative operation model, and calculating a resource optimization index based on the cooperative game strategy include:

[0135] Calculating the marginal participation contribution of each participant in the target resource aggregation according to the cooperative operation model;

[0136] Based on the cooperative operation model, the planned operation data of each participant in the target resource aggregate is obtained, and the adjustment risk coefficient of the uncontrollable distributed unit is calculated in combination with the obtained actual operation data of each participant;

[0137] Calculate resource optimization index according to the marginal participation contribution and the adjustment risk coefficient;

[0138] A cooperative game strategy is generated based on the resource optimization index to evaluate the optimization control effect of the cooperative operation model.

[0139] Specifically, in one embodiment of the present invention, the calculating the marginal participation contribution of each participant in the target resource aggregation according to the cooperative operation model includes:

[0140] According to the Shapley value method, the game relationship between different participants is expressed and the marginal participation contribution is established, which is expressed by the formula:

[0141] ,

[0142] ,

[0143] in, For all parties involved The contribution coefficient of Indicates the cooperative operation model, For the total benefit of the cooperative operation model, For Deleting members from The subsequent income; for The weighting factor of for The marginal contribution value of participation is For all parties involved.

[0144] Specifically, in one embodiment of the present invention, the adjustment risk coefficient includes the proportion of the adjustment cost of each participant in the cooperative operation model to the total adjustment cost, which is expressed by the formula:

[0145] ,

[0146] ,

[0147] in, is the risk factor for adjustment, For the participants in the cooperative operation model The deviation adjustment cost; , , , They are the grid-connected benefits of distributed wind power, distributed photovoltaic power, gas-fired units and energy storage systems; , , , Respectively in The planned output of distributed wind power, distributed photovoltaic and gas-fired units during the period, as well as the planned charging and discharging power of the energy storage system; .、 , , Respectively in The actual output of distributed wind power, distributed photovoltaic and gas-fired units during the period, as well as the actual charging and discharging power of the energy storage system.

[0148] Specifically, in one embodiment of the present invention, the resource optimization index is expressed by the formula:

[0149] ,

[0150] in, , They are the participants in the cooperative operation model. The initial resource optimization index and the comprehensive resource optimization index considering the marginal participation contribution and the risk factor of adjustment; For participants historical benefits; , They are respectively marginal participation contribution and risk coefficient of adjustment, and are decision values ​​for determining the size of individual benefits of resources;

[0151] The generating of the cooperative game strategy based on the resource optimization index to evaluate the optimization control effect of the cooperative operation model includes:

[0152] Allocate benefits to each participant in the cooperative operation model according to the resource optimization index to obtain the actual benefits of each participant after optimization;

[0153] The actual benefit is compared with the historical benefit to evaluate the optimization control effect of the cooperative operation model.

[0154] Specifically, the benefits of adjustable resources within the cooperative operation model are allocated according to the comprehensive resource optimization index, so as to evaluate the optimization effect of the operation control of each resource in the cooperative operation model:

[0155] ,

[0156] Where: Considering the marginal contribution and risk of participants in the cooperative operation model The benefits of , it means that the operation and control effect of the cooperative operation model is good, otherwise, the operation and control effect of the cooperative operation model does not meet the requirements.

[0157] With reference to the optimization results, the relevant parameters of the cooperative operation model in actual operation are adjusted, such as the output of distributed resource generators, energy storage systems, and scheduling strategies for adjustable loads, so as to rationally plan the adjustable capacity of resources and reduce the operating energy consumption of the cooperative operation model during cooperative scheduling, thereby improving the overall economy of power system operation control.

[0158] Another embodiment of the present invention provides a power resource aggregation operation control system. For details, see Figure 2 , Figure 2 The structure diagram of the power resource aggregation operation control system provided in one embodiment of the present invention is shown. It includes: a first construction module 11, a second construction module 12, a constraint module 13, and a calculation module 14, wherein:

[0159] The first building module 11 is used to establish output models of uncontrollable distributed units, controllable distributed units and load models of adjustable loads based on the target resource aggregate;

[0160] A second construction module 12 is used to construct a cooperative operation model of the target resource aggregation according to the output model and the load model, wherein the cooperative operation model is designed to maximize energy utilization efficiency by calculating a power upper limit at each time step;

[0161] A constraint module 13, configured to construct a joint optimization constraint of the cooperative operation model based on the supply and demand balance constraint of the uncontrollable distributed unit, the output upper and lower limit constraints of the controllable distributed unit, and the load constraint of the adjustable load;

[0162] The calculation module 14 is used to calculate the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraint to obtain the optimal operation control solution of each participant in the target resource aggregate.

[0163] Preferably, in one embodiment of the present invention, the first building block is used to:

[0164] Determining a cooperation goal according to the output model and the load model, wherein the cooperation goal includes maximizing energy utilization;

[0165] Based on the physical characteristics of each participant in the target resource aggregate, corresponding responsibilities are assigned to each participant, wherein the uncontrollable distributed unit provides renewable energy as an energy supplier, the controllable distributed unit balances the supply and demand relationship as an energy regulator, and the adjustable load supports the stable operation of the power system as a demand-side response;

[0166] Generate a cooperation mechanism according to the cooperation goal and the corresponding responsibilities of each of the participants, wherein the cooperation mechanism includes information sharing, coordinated scheduling and loss compensation;

[0167] A cooperative operation model of the target resource aggregate is constructed based on the cooperation target and the cooperation mechanism.

[0168] Preferably, in one embodiment of the present invention, the first building block is further used for:

[0169] Acquire historical operation data of each of the participants in the target resource aggregate;

[0170] Determine the objective function according to the cooperation goal, determine the constraint conditions according to the cooperation mechanism, and construct a mathematical model of the target resource aggregate;

[0171] The mathematical model is optimized according to the historical operation data to obtain the cooperative operation model.

[0172] Preferably, in one embodiment of the present invention, the uncontrollable distributed units include distributed wind power and distributed photovoltaics, the controllable distributed units include gas units and energy storage systems, and the adjustable loads include transferable loads and interruptible loads;

[0173] The first building block is further used for:

[0174] Acquire historical operation data of the distributed wind power, the distributed photovoltaic, the gas generator set, the energy storage system, the transferable load and the interruptible load;

[0175] Based on the historical operation data, a distributed wind power output model, the distributed photovoltaic model, a gas unit model, an energy storage system model, a transferable load model and an interruptible load model are respectively constructed.

[0176] Preferably, in one embodiment of the present invention, the calculation module is further used for:

[0177] generating a cooperative game strategy according to the cooperative operation model, and calculating a resource optimization index based on the cooperative game strategy;

[0178] Evaluate the operation control effect of each participant in the cooperative operation model according to the resource optimization index, and output the corresponding configuration of each participant as the optimization result;

[0179] Based on the optimization result, relevant parameters of the target resource aggregate in actual operation are controlled.

[0180] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0181] 1) By modeling different types of distributed resources separately, the present invention can more accurately understand the characteristics and output capacity of various types of resources, so as to flexibly schedule them according to needs in actual operation, avoid waste or overuse of resources, and significantly improve resource utilization efficiency;

[0182] 2) The present invention incorporates uncontrollable, controllable and adjustable loads into a unified management framework, and coordinates the output of various resources in the form of a cooperative operation model, which helps to balance system supply and demand, reduce grid fluctuations, and improve the stability and reliability of the entire system;

[0183] 3) With the rapid development of renewable energy and distributed generation technology, the power system is facing more and more uncertainty and complexity. The present invention enhances the system's ability to flexibly respond to external changes and improves the system's adaptability by dynamically adjusting the output configuration of each resource.

[0184] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for controlling the operation of an electric power resource aggregate, characterized in that: The following steps are involved: Based on the target resource aggregate, the output models of uncontrollable distributed units, controllable distributed units and the load model of adjustable load are established respectively; constructing a cooperative operation model of the target resource aggregation according to the output model and the load model, wherein the cooperative operation model is designed to maximize energy utilization efficiency by calculating a power upper limit at each time step; Constructing the joint optimization constraints of the cooperative operation model based on the supply and demand balance constraints of the uncontrollable distributed units, the upper and lower limit constraints of the output of the controllable distributed units, and the load constraints of the adjustable loads; Calculating the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraints to obtain the optimal operation control scheme of each participant in the target resource aggregate; Wherein, after calculating the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraint to obtain the optimal operation control scheme of each participant in the target resource aggregate, it also includes: generating a cooperative game strategy according to the cooperative operation model, and calculating a resource optimization index based on the cooperative game strategy; Evaluate the operation control effect of each participant in the cooperative operation model according to the resource optimization index, and output the corresponding configuration of each participant as the optimization result; Based on the optimization result, controlling relevant parameters of the target resource aggregate in actual operation; The resource optimization index is expressed as: ， in, , They are the participants in the cooperative operation model. The initial resource optimization index and the comprehensive resource optimization index considering the marginal participation contribution and the risk factor of adjustment, For the participants The historical benefits Indicates the cooperative operation model, for The marginal contribution value of participation is is the risk factor for adjustment.

2. The power resource aggregation operation control method according to claim 1, characterized in that: The step of constructing a cooperative operation model of the target resource aggregation according to the output model and the load model includes: Determining a cooperation goal according to the output model and the load model, wherein the cooperation goal includes maximizing energy utilization; Based on the physical characteristics of each participant in the target resource aggregate, corresponding responsibilities are assigned to each participant, wherein the uncontrollable distributed unit provides renewable energy as an energy supplier, the controllable distributed unit balances the supply and demand relationship as an energy regulator, and the adjustable load supports the stable operation of the power system as a demand-side response; Generate a cooperation mechanism according to the cooperation goal and the corresponding responsibilities of each of the participants, wherein the cooperation mechanism includes information sharing, coordinated scheduling and loss compensation; A cooperative operation model of the target resource aggregate is constructed based on the cooperation target and the cooperation mechanism.

3. The power resource aggregation operation control method according to claim 2, characterized in that: The cooperative operation model of the target resource aggregation body is constructed based on the cooperative mechanism, including: Acquire historical operation data of each of the participants in the target resource aggregate; Determine the objective function according to the cooperation goal, determine the constraint conditions according to the cooperation mechanism, and construct a mathematical model of the target resource aggregate; The mathematical model is optimized according to the historical operation data to obtain the cooperative operation model.

4. The method for controlling the operation of an electric power resource aggregation system according to claim 1, characterized in that: The uncontrollable distributed units include distributed wind power and distributed photovoltaics, the controllable distributed units include gas units and energy storage systems, and the adjustable loads include transferable loads and interruptible loads; The output models of the uncontrollable distributed units and the controllable distributed units and the load model of the adjustable load are respectively established: Acquire historical operation data of the distributed wind power, the distributed photovoltaic, the gas generator set, the energy storage system, the transferable load and the interruptible load; Based on the historical operation data, a distributed wind power output model, the distributed photovoltaic model, a gas unit model, an energy storage system model, a transferable load model and an interruptible load model are respectively constructed.

5. An electric power resource aggregate operation control system, characterized in that: Includes the following modules: The first building module is used to establish output models of uncontrollable distributed units, controllable distributed units and load models of adjustable loads based on the target resource aggregate; A second building module is used to build a cooperative operation model of the target resource aggregation according to the output model and the load model, wherein the cooperative operation model is designed to maximize energy utilization efficiency by calculating a power upper limit for each time step; A constraint module, used to construct a joint optimization constraint of the cooperative operation model based on the supply and demand balance constraint of the uncontrollable distributed unit, the upper and lower limit constraints of the output of the controllable distributed unit, and the load constraint of the adjustable load; A calculation module, used to calculate the cooperative operation model according to the particle swarm optimization algorithm and the joint optimization constraint to obtain the optimal operation control plan of each participant in the target resource aggregate; Wherein, the computing module is also used for: generating a cooperative game strategy according to the cooperative operation model, and calculating a resource optimization index based on the cooperative game strategy; Evaluate the operation control effect of each participant in the cooperative operation model according to the resource optimization index, and output the corresponding configuration of each participant as the optimization result; Based on the optimization result, controlling relevant parameters of the target resource aggregate in actual operation; The resource optimization index is expressed as: ， in, , They are the participants in the cooperative operation model. The initial resource optimization index and the comprehensive resource optimization index considering the marginal participation contribution and the risk factor of adjustment, For the participants The historical benefits Indicates the cooperative operation model, for The marginal contribution value of participation is is the risk factor for adjustment.

6. The power resource aggregation operation control system according to claim 5, characterized in that: The first building block is used to: Determining a cooperation goal according to the output model and the load model, wherein the cooperation goal includes maximizing energy utilization; Based on the physical characteristics of each participant in the target resource aggregate, corresponding responsibilities are assigned to each participant, wherein the uncontrollable distributed unit provides renewable energy as an energy supplier, the controllable distributed unit balances the supply and demand relationship as an energy regulator, and the adjustable load supports the stable operation of the power system as a demand-side response; Generate a cooperation mechanism according to the cooperation goal and the corresponding responsibilities of each of the participants, wherein the cooperation mechanism includes information sharing, coordinated scheduling and loss compensation; A cooperative operation model of the target resource aggregate is constructed based on the cooperation target and the cooperation mechanism.

7. The power resource aggregation operation control system according to claim 6, characterized in that: The first building block is further used for: Acquire historical operation data of each of the participants in the target resource aggregate; Determine the objective function according to the cooperation goal, determine the constraint conditions according to the cooperation mechanism, and construct a mathematical model of the target resource aggregate; The mathematical model is optimized according to the historical operation data to obtain the cooperative operation model.

8. The power resource aggregation operation control system according to claim 5, characterized in that: The uncontrollable distributed units include distributed wind power and distributed photovoltaics, the controllable distributed units include gas units and energy storage systems, and the adjustable loads include transferable loads and interruptible loads; The first building block is further used for: Acquire historical operation data of the distributed wind power, the distributed photovoltaic, the gas generator set, the energy storage system, the transferable load and the interruptible load; Based on the historical operation data, a distributed wind power output model, the distributed photovoltaic model, a gas unit model, an energy storage system model, a transferable load model and an interruptible load model are respectively constructed.

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