Distributed resource aggregation and market bidding optimization method based on chino polyhedron

Abstract

The application belongs to the field of power system automation and control technology, and relates to a method for aggregating distributed resources and optimizing market bidding based on chino polyhedron, comprising the following steps: 1, establishing a distributed resource model, wherein the distributed resource model comprises an air conditioning model, a distributed energy storage model, a diesel generator model and a distributed photovoltaic model; 2, performing inner approximation on the feasible region of the distributed resources based on the chino polyhedron; and 3, aggregating the distributed resources to participate in the optimization decision of the power market; by performing inner approximation on the feasible region of the distributed resources based on the chino polyhedron, the application effectively simplifies the aggregation calculation complexity and improves the calculation efficiency, and provides an effective decision method for the distributed resources participating in the power market.

Description

Technical Field

[0001] This invention belongs to the field of power system automation and control technology, specifically relating to a distributed resource aggregation and market bidding optimization method based on the Kino polyhedron. Background Technology

[0002] The efficient aggregation of distributed resources can improve the efficiency of distributed resource market participation, thereby responding more effectively to price and regulatory signals and optimizing their economic benefits. Compared with centralized source-load resources, distributed resources are characterized by small individual capacity, wide geographical distribution, and complex trading behavior. Distributed resources cannot be directly dispatched by the power grid, posing new challenges to the safe, stable operation and economy of the power system. However, virtual power plants, microgrids, and similar systems can aggregate distributed resources on a large scale to participate in grid dispatch and market transactions, fully leveraging the regulatory potential of distributed resources to enhance the overall flexibility of the system and achieve rational and optimal resource allocation.

[0003] Aggregating the feasible regions of distributed resources is essentially a Minkowski sum process. The feasible regions of individual distributed resources are high-dimensional convex polyhedra in geometric space, with dimensions potentially reaching tens of thousands of dimensions. It is nearly impossible to compute exact solutions to the Minkowski sum of such high-dimensional convex polyhedra, thus making the aggregation of feasible regions of distributed resources an NP-hard problem (a nondeterministic polynomial-time problem). Existing research focuses on finding models to approximate the feasible regions of individual distributed resources to simplify the aggregation process. Currently available approximation methods for individual distributed resource feasible regions can be mainly divided into internal approximation and external approximation.

[0004] Methods for approximating the feasible region of distributed resources outside the resource's feasible region mainly include virtual battery models, which describe the feasible region of resources using a set of standard battery model parameters, thereby reducing the computational complexity of aggregated feasible regions of distributed resources. However, although external approximation is more computationally efficient, the expansion of the original feasible region may lead to the infeasibility of actual scheduling instructions.

[0005] Compared to external approximation, internal approximation, while sacrificing some accuracy, guarantees that the approximate aggregate feasible region is feasible. Commonly used internal approximation models include inner box approximation and ellipsoidal approximation. The inner box approximation model maps multi-time-time, high-dimensional, and strongly coupled distributed resource operation constraints, approximating the original feasible region with a "time-decomposed box region" formed by a set of upper and lower trajectories. An ellipsoid can also be used to perform an internal approximation of the distributed resource feasible region, achieving a good internal approximation. However, most existing internal approximation methods are designed for the aggregation of similar resources.

[0006] Therefore, it can be seen that existing methods for approximating the feasible region outside the feasible region of distributed resources often expand the original feasible region, thereby making the actual scheduling instructions infeasible. Existing methods for approximating the feasible region within the feasible region of distributed resources mostly focus on the aggregate representation of similar resources.

[0007] Therefore, an efficient method for aggregating distributed resources is needed to solve the above-mentioned technical problems. Summary of the Invention

[0008] This invention utilizes the Zonotope to achieve internal approximation of distributed resources and verifies the efficiency and accuracy of the aggregation method. First, the feasible region of distributed resources is characterized, and then the feasible region is efficiently aggregated based on the Zonotope model. Next, a two-layer model is used to characterize the strategic bidding process of each distributed resource aggregator in the electricity market, achieving coordinated support between distributed resources and the power system through price signals.

[0009] This invention provides the following technical solution: a distributed resource aggregation and market bidding optimization method based on the Kino polyhedron, comprising the following steps: Step 1: Establish a distributed resource model, which includes: HVAC model, distributed energy storage model, diesel generator model, and distributed photovoltaic model.

[0010] Step 2: Perform an inner approximation of the feasible region of distributed resources based on the Kino polyhedron.

[0011] Step 3: Distributed resource aggregation participates in power market optimization decision-making.

[0012] Preferably, in step 1: The HVAC model is as follows: (1) (2) In the formula, Indicates time Indoor air temperature, Indicates time The outdoor ambient temperature; and These are the lower and upper limits of the preset comfortable temperature range for the room. Indicates time Power consumption of HVAC systems The temperature inertia factor, It is the efficiency coefficient for heat-to-electric energy conversion.

[0013] The distributed energy storage model is as follows: (3) (4) (5) In the formula, Indicates distributed energy storage at time The state of charge, Indicates the self-discharge rate of energy. This indicates the charging / discharging efficiency of distributed energy storage. express The charging power of the energy storage device at any time This represents the time interval between two adjacent moments. and These are the upper and lower limits of the energy state of the stored energy, respectively. and These represent the maximum allowable charging power and maximum discharge power for energy storage, respectively.

[0014] The diesel generator model is as follows: (6) (7) In the formula, Indicates time The active power output of a diesel generator. and These represent the upper and lower limits of the unit's output, respectively. and These are the uphill and downhill limits for diesel generators, respectively.

[0015] The distributed photovoltaic model is as follows: (8) in, Indicates time The active power output of distributed photovoltaic power generation, Indicates time The maximum active power that distributed photovoltaic power can generate.

[0016] Preferably, in step 2, the expression for the feasible region of distributed resources based on the Kino polyhedron is: (20) In the formula, The coordinates of the center point of the Chino polyhedron after distributed resource aggregation. Here, the generator matrix corresponds to different resources, representing the extension direction of the Chino polyhedron. This refers to the extension length of the Chino polyhedron in different directions after the distributed resource aggregation. This refers to the operating power after the aggregation of distributed resources.

[0017] Preferably, step 3 specifically includes: Distributed resources participate in the electricity market as aggregators, and the feasible domain of the distributed resource clusters is used by the aggregators in the bidding decisions of the electricity market.

[0018] A two-layer model is used to characterize the strategic bidding process of each resource aggregator. Each aggregator predicts the bids of other generators and makes a bid based on its own feasible domain aggregated by the Chino polyhedron.

[0019] The power trading center clears the charging and discharging plans of each distributed resource cluster based on the quotations from each aggregator and the segmented quotations from each power generator.

[0020] Among them, the power distribution system operator performs safety verification of the power flow of the power distribution network and implements congestion management for the transaction process.

[0021] More preferably, the upper-layer model of the two-layer model is a distributed resource aggregator bidding decision model; the lower-layer model of the two-layer model is a power market clearing model.

[0022] More preferably, the distributed resource aggregator pricing decision model is as follows: The model objective is to achieve optimal internal economy. The model objective is: (twenty one) In the formula, , , , These are energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. Actual operating power during the time period This is the baseline load for HVAC. For the marginal electricity price at the node, , , , The corresponding resource clusters are located on the nodes. Marginal electricity price at market nodes within a given time period This refers to the charging and discharging loss coefficient of the energy storage cluster. This represents the total operating cost of the diesel generator cluster.

[0023] The objective constraints of the model are: (twenty two) (twenty three) (twenty four) (25) In the formula, , , , These are the coordinates of the center point of the Chino polyhedron after the aggregation of the energy storage resource cluster, HVAC cluster, distributed photovoltaic cluster, and diesel generator cluster. The generator matrix corresponds to different resource clusters. , , , This refers to the extension length of the Chino polyhedron in different directions after the distributed resource aggregation. , , , These are the actual operating power of energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively.

[0024] When aggregators participate in energy trading in the electricity market, their bids must be within the range allowed by the market, that is: (26) (27) (28) (29) In the formula, and These represent the upper and lower limits for the corresponding aggregators' bids in the electricity market.

[0025] More preferably, the lower-level model is a power market clearing model as follows: The model's objective is to maximize social welfare. The model objective is: (30) In the formula, , , , These are the actual operating power of energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. , , , , These represent the upper and lower limits for electricity market bidding by energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. , For the slack variable of the input capacitance. To punish the price.

[0026] The objective constraints of the model are: (31) In the formula, These represent the actual operating power of conventional generator sets, centralized photovoltaic power, and centralized wind power, respectively. The total system load, the power balance constraint corresponds to the value after the colon. The dual variable of this constraint represents the market-cleared price of electricity. (32) (33) (34) (35) (36) In the formula, and These are the upper and lower limits of the output of conventional generating units. and This represents the upper limit of output for centralized photovoltaic and centralized wind power. , , , , , These are the dual variables of the corresponding constraints. For the unit For the line The generator output load transfer factor, For nodes For the line The generator output load transfer factor, Let be the maximum transmission power of branch l; rewrite equation (36) as equation (37) in terms of the power flow factor transfer matrix and the correlation matrix: (37) In the formula, Matrix A represents the unit node association matrix, and matrix B represents the load node association matrix. (36) In the formula, and These are the upper and lower limits of the output of conventional generating units. and This represents the upper limit of output for centralized photovoltaic and centralized wind power. , , , , , These are the dual variables of the corresponding constraints. For the unit For the line The generator output load transfer factor, For nodes For the line The generator output load transfer factor, Let l be the maximum transmission power of branch l; rewrite equation (36) as equation (37) in terms of power flow factor transfer matrix and correlation matrix.

[0027] More preferably, in step 3, the distributed resource aggregator's bidding decision and market clearing constitute a two-level optimization problem. By establishing the Karush-Kuh-Tucker conditions for the market clearing problem and using them as constraints for the distributed resource aggregator's bidding decision model, as shown in equation (39): (39) In the formula, The constraint set corresponding to the KKT system for the lower market clearing problem.

[0028] The beneficial effects of this invention are: This invention uses the Kino polyhedron to approximate the feasible region of distributed resources, effectively simplifying the computational complexity of aggregation and improving computational efficiency. In terms of the optimal decision-making process for distributed resource aggregation in the power market, a two-layer model is used to characterize the strategic bidding process of each resource aggregator. The upper-layer distributed resource aggregator bidding decision model is combined with the lower-layer power market clearing model to construct a two-layer optimization problem. By introducing KKT conditions, it is transformed into a directly solvable mixed integer linear programming problem, providing an effective decision-making method for distributed resources participating in the power market. Attached Figure Description

[0029] Figure 1 This is a schematic diagram illustrating the electricity market interaction of the distributed resource aggregation and market bidding optimization method based on the Kino polyhedron of the present invention. Figure 2 This is a schematic diagram illustrating the Minkowski summation of the feasible region of distributed resources according to the present invention. Figure 3 This is a schematic diagram of the electricity market optimization decision-making model of the present invention; Figure 4 This is a modified IEEE 39-node system topology diagram for the present invention; Figure 5 This is a diagram showing the actual operating power of each resource cluster in this invention; Figure 6 This is the optimal price curve and actual operating power diagram of the diesel generator set cluster of the present invention; Figure 7 This is the optimal price curve and actual operating power diagram of the distributed photovoltaic cluster of the present invention; Figure 8This is the optimal pricing curve and actual operating power diagram of the distributed energy storage cluster of the present invention; Figure 9 This is the optimal price curve and actual operating power diagram of the HVAC load cluster of the present invention. Detailed Implementation

[0030] The related technologies of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0031] like Figures 1-9 As shown, the specific solution of this embodiment is as follows: Step 1: Establish a distributed resource model: A. Heating, ventilation, and air conditioning (HVAC) load Heating, ventilation, and air conditioning (AC) is one of the key flexible load types in power systems. While maintaining user thermal comfort, AC possesses inherent regulatory capabilities for interaction with the power grid. In engineering, a first-order equivalent thermodynamic model is commonly used to characterize its thermal inertia and power regulation characteristics; its mathematical form is: (1) (2) In the formula, Indicates time Indoor air temperature, Indicates time The outdoor ambient temperature; and These are the lower and upper limits of the preset comfortable temperature range for the room, respectively. Indicates time Power consumption of HVAC systems. It is a temperature inertia factor, which is usually related to the building's heat capacity and the thermal insulation / insulation characteristics of the building envelope; It is the thermal-to-electrical energy conversion efficiency coefficient, used to characterize the conversion relationship between electrical energy input and indoor temperature changes in an HVAC system.

[0032] B. Distributed Energy Storage The operational model constraints of distributed energy storage mainly include power constraints and dynamic state of charge constraints, and its mathematical model can be expressed as follows: (3) (4) (5) In the formula, Indicates distributed energy storage at time The state of charge, Indicates the self-discharge rate of energy. This indicates the charging / discharging efficiency of distributed energy storage. express The charging power of the energy storage device at any time This represents the time interval between two adjacent moments. and These are the upper and lower limits of the energy state of the stored energy, respectively. and These represent the maximum allowable charging power and maximum discharge power for energy storage, respectively.

[0033] C. Diesel Generator (DG) The constraints of the diesel generator operation model mainly include upper and lower output limits and gradeability constraints, and their expressions are as follows: (6) (7) in, Indicates time The active power output of a diesel generator. and These represent the upper and lower limits of the unit's output, respectively. and These are the uphill and downhill limits for diesel generators, used to characterize the feasible range of the rate of change in unit output between adjacent time periods.

[0034] D. Distributed photovoltaic The operational model constraints for distributed photovoltaic systems mainly include power constraints, the expression of which is as follows: The operational model constraints for distributed photovoltaic (PV) systems mainly include output (power) constraints, the expressions of which are as follows: (8) in, Indicates time The active power output of distributed photovoltaic power generation, Indicates time The maximum active power that distributed photovoltaic power can generate is determined by meteorological conditions (such as solar irradiance and ambient temperature) and the upper limit of installed capacity.

[0035] The set of power trajectories that satisfy constraints (1)-(8) can all be represented by a high-dimensional convex polyhedron.

[0036] (9) In the formula, the matrix A constant matrix, matrix It is a parameter matrix constructed based on the characteristics of each resource. The parameter matrix of distributed resources with generator-like unit characteristics consists of ramp rate and output power, while the parameter matrix of distributed resources with virtual energy storage characteristics consists of energy state and output power. However, due to the high heterogeneity of distributed resources, its constant matrix... and parameter matrix Not entirely the same, it is almost impossible to calculate the Minkowski sum of the feasible region of distributed resources in detail. Therefore, in practical applications, it is necessary to select appropriate parameters in combination with physical meaning to construct a polyhedral approximation of the original feasible region, thereby simplifying the complexity of the aggregation calculation of the feasible region of distributed resources.

[0037] Step 2: Inner approximation of the feasible region of distributed resources based on the Zonotope: To address the excessive computational complexity of Minkowski sum for distributed resources, this implementation uses Zonotope to approximate the individual feasible regions of distributed resources, thereby achieving efficient aggregation of distributed resources. Zonotope is a polyhedron with a special mathematical expression, which can be represented by its center point c and generator vector g, and its representation is as follows: (10) (11) In the formula, N is the spatial dimension of the feasible region of the distributed resources, and G is a generator matrix consisting of M generator vectors. , indicates the directions in which a polyhedron can be extended from its center point. The extension length of the generator in each direction is limited by the maximum extension length.

[0038] Zonotopes have a concise mathematical expression and possess many excellent geometric and algebraic properties, such as convexity and central symmetry. For Zonotopes with the same generator matrix G, since their extension directions are consistent, their Minkowski sum process is a simple linear superposition process, thus simplifying their computational complexity. (12) (13) (14) In the formula, This refers to the feasible region obtained after aggregating distributed resources. This represents the number of feasible distributed resource domains that need to be aggregated. The coordinates of the center point of the aggregated Zonotope. Let be the coordinates of the center point of the s-th distributed resource. This represents the extension length vector of the aggregated Zonotope in each direction. Let be the extended length vector of the m-th distributed resource.

[0039] The generator G represents the extension direction of the Zonotope. For different feasible regions of distributed resources, the choice of the generator's extension direction is crucial to the accuracy of the final Zonotope approximation. The principles for selecting the generator G for different types of distributed resources are as follows: For distributed resources with virtual energy storage characteristics, such as HVAC loads, electric vehicles, and distributed energy storage, the feasible region after aggregation of such distributed resources can be characterized by power constraints and energy constraints. Therefore, the generator for the feasible region of distributed resources with virtual energy storage characteristics is: (15) (16) In the formula, Equation (15) represents the extension direction corresponding to the power constraint, and Equation (16) represents the extension direction corresponding to the energy constraint. Therefore, the corresponding generator matrix is... .

[0040] For distributed resources with virtual generator characteristics, such as diesel generators and distributed photovoltaics, the feasible region after aggregation of such distributed resources can be characterized by power constraints and ramp constraints. Therefore, the generator for the feasible region of distributed resources with virtual generator characteristics is: (17) (18) Equation (17) represents the extension direction corresponding to the climbing constraint.

[0041] The following demonstrates how to compute the optimal Zonotope for distributed resources using an inner approximation polyhedron. To measure the difference between the Zonotope and the original feasible region, the approximation is measured by calculating the ratio of the projected widths of the Zonotope and the original feasible region along different unit normal directions. The core of the optimization problem is a set of constraints: This constraint ensures that the Zonotope is contained within the polyhedron of the original feasible region. Therefore, the optimal Zonotope can be solved using the following optimization model: (19) In the formula, As a weighting factor, and The Zonotope and the original feasible region are respectively in the first... The width of the projection on a unit normal vector, The number of unit normal vectors, For the extension lengths of the Chino polyhedron in different directions, The average of all extension lengths. The number of extensions. Let A be the coefficient matrix of the high-dimensional convex polyhedron of the original feasible region of the distributed resource, c be the coordinates of the center point of the Chino polyhedron, and G be the extension direction matrix of the Chino polyhedron. According to mathematical derivation, ... , It can be solved The linear programming problems are obtained. The first part of the objective function represents the approximation of the Zonotope to the original feasible region; the second part represents the volatility of the Zonotope's extension length in all directions. Introducing the second part ensures that the Zonotope extends in every dimension. The trade-off between the two parts can be achieved through weighting factors. Different implementations of the value.

[0042] Therefore, the explicit expression for the feasible region based on the Zonotope approximation is obtained as follows: (20) In the formula, The coordinates of the center point of the Chino polyhedron after distributed resource aggregation. Here, the generator matrix corresponds to different resources, representing the extension direction of the Chino polyhedron. This refers to the extension length of the Chino polyhedron in different directions after the distributed resource aggregation. This refers to the operating power after the aggregation of distributed resources.

[0043] Step 3: Distributed resource aggregation participates in electricity market optimization decision-making. Distributed resources participate in the electricity market as aggregators, and the feasible domain of distributed resource clusters is used by aggregators in their bidding decisions. A two-layer model is used to characterize the strategic bidding process of each resource aggregator. Each aggregator predicts the bids of other generators and submits its own bid based on its feasible domain aggregated by Zonotope. The power trading center clears the charging and discharging plans of each distributed resource cluster based on the bids of each aggregator and the segmented bids of each generator. Meanwhile, the distribution system operator performs safety checks on the power flow of the distribution network and implements congestion management in the trading process.

[0044] Upper-level model: Distributed resource aggregator pricing decision model: First, for each distributed resource aggregator, the goal is internal economic optimization, i.e., minimizing electricity costs, as shown in the equation: (twenty one) In the formula, , , , These are energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. Actual operating power during the time period This is the baseline load for HVAC. For the marginal electricity price at the node, , , , The corresponding resource clusters are located on the nodes. Marginal electricity price at market nodes within a given time period This refers to the charging and discharging loss coefficient of the energy storage cluster. This represents the total operating cost of the diesel generator cluster.

[0045] Secondly, the constraints are the feasible regions represented by the above resource clusters after aggregation by Zonotope, namely: (twenty two) (twenty three) (twenty four) (25) When aggregators participate in energy trading in the electricity market, their bids must be within the range allowed by the market, that is: (26) (27) (28) (29) In the formula, and These represent the upper and lower limits for the corresponding aggregators' bids in the electricity market.

[0046] Therefore, equations (21) to (29) constitute the bidding decision model for each resource cluster. The decision variables are the bids of each distributed resource aggregator, and the actual operating power of each resource cluster is determined by market clearing.

[0047] Lower-level model: Electricity market clearing model The goal of market clearing is to maximize social welfare, i.e., minimize system operating costs, as shown in the equation: (30) In the formula, , , , These are the actual operating power of energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. , , , , These represent the upper and lower limits for electricity market bidding by energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. , Let these be the slack variables of the transmission line capacity. If these slack variables are non-zero, it indicates that the transmission line capacity exceeds the limit, and the objective function will be penalized by the price. The penalty, in this embodiment, is set at a price of [amount missing]. In actual electricity market operations, Independent System Operators (ISOs) often introduce slack variables to relax power flow constraints in order to obtain feasible market clearing solutions and thereby gain certain profits.

[0048] The system power balance constraint is: (31) In the formula, These represent the actual operating power of conventional generator sets, centralized photovoltaic power, and centralized wind power, respectively. The total system load, the power balance constraint corresponds to the value after the colon. Let be the dual variable of this constraint, representing the market-cleared price of electrical energy.

[0049] The planned output constraints for conventional units, wind turbines, and photovoltaic units are as follows: (32) (33) (34) The system power flow balance constraints are: (35) (36) In the formula, For the unit For the line The generator output load transfer factor, For nodes For the line The generator output load transfer factor, Let be the maximum transmission power of branch l. Equation (36) can be rewritten as equation (37) in terms of the power flow factor transfer matrix and the correlation matrix: (37) In the formula, Matrix A represents the unit node association matrix, and matrix B represents the load node association matrix. Therefore Indicates the node injected power. This is the power flow factor transition matrix. The maximum transmission power of branch l; , These are the dual variables corresponding to the line power flow constraints.

[0050] Equations (30) to (37) constitute the market clearing model, with the decision variables being the actual operating power clearing amount of each generator and resource aggregator. The dual variables corresponding to each constraint are given after the equation with a colon. Node The nodal marginal electricity price can be obtained by multiplying the system power balance constraint Lagrange multiplier and the transfer factor matrix by the line power flow constraint Lagrange multiplier, as shown in equation (38): (38) Clearly, the bidding decisions of each distributed resource aggregator and market clearing constitute a two-level optimization problem. Therefore, we can establish the Karush-Kuh-Tucker (KKT) conditions for the market clearing problem and use them as constraints for the distributed resource aggregator bidding decision model, as shown in equation (39): (39) In the formula, The set of constraints corresponding to the KKT system for the lower market clearing problem includes equality constraints and complementary slackness constraints.

[0051] According to strong duality theory, the objective function value of the lower-level market clearing problem at the optimal solution can be equivalently rewritten and further substituted into the objective function of the upper-level bidding decision model. At this time, the objective function no longer contains bilinear terms. Therefore, the bi-level optimization problem of resource clusters participating in the power market optimization decision is transformed into a mixed-integer linear programming (MILP) problem, which can be solved directly by commercial solvers.

[0052] Example This embodiment selects the feasible region of 50 air conditioners, 10 energy storage devices, and 5 diesel generators for aggregation. The parameters of the devices are randomly selected within a certain range to simulate the slight differences in characteristics between the devices. Detailed parameter information can be found in [link to example]. This part of the example analyzes the accuracy of the original feasible region based on the Zonotope approximation under different spatial dimensions. The accuracy index comparison results are shown in Table 1 below:

[0053] It can be seen that as the time dimension increases, the accuracy of the feasible region of distributed resources approximated by Zonotope also decreases.

[0054] This embodiment selects a modified IEEE 39-node system to verify the effectiveness of the proposed economic incentive mechanism. To make the test system closer to actual operating scenarios and improve its representativeness, several wind farms and photovoltaic power stations are added to the original test system, thereby constructing a more consistent and realistic simulation system. The actual operating power of each resource cluster in the market model proposed in this embodiment is as follows: Figure 5 As shown, the overall operating power trajectory of various distributed resources after aggregation exhibits system optimization characteristics, and the resource cluster achieves cross-time period coordination and dynamic load balancing. The optimal bid curve and actual operating power of each resource cluster are shown below. Figures 6-9 As shown, for diesel generator clusters, due to their inherent unit power operating cost, their operating power trajectory exhibits a trend of gradually increasing with rising electricity prices, reflecting the cost-driven economic dispatch mechanism of diesel generators. For distributed photovoltaic clusters, since their marginal cost is close to zero, their actual operating power is limited by factors such as weather and sunlight. For distributed energy storage clusters, their bidding decisions and actual operating power trajectory generally exhibit the characteristics of "low charging and high discharging," fully leveraging their energy time-shifting and system support roles, and verifying the effectiveness of the two-layer model's energy storage bidding strategy guided by price signals. For air conditioning load clusters, since there is a certain range of comfortable temperatures within rooms, air conditioning is used as a virtual energy storage device for regulation. During periods of high electricity prices, the load is reduced as much as possible, while during periods of low electricity prices, the comfort of users is maintained, demonstrating the ability to balance and control between flexible load economy and user comfort.

[0055] In summary, the distributed resource aggregation and market optimization method based on the Chino polyhedron proposed in this invention has significant advantages and good application results. By approximating the feasible region of distributed resources using the Chino polyhedron, the computational complexity of distributed resource aggregation is effectively simplified, and computational efficiency is improved. In the participation of distributed resource aggregation in power market optimization decision-making, a two-layer model is used to characterize the strategic bidding process of resource aggregators, reasonably considering the internal economics of each resource aggregator and the maximization of social welfare in market clearing.

[0056] In practical examples, the accuracy analysis of the approximation of the original feasible region by the Chino polyhedron under different spatial dimensions shows that although the accuracy decreases with the increase of the time dimension, it can still approximate the feasible region well within a reasonable range. Verification using a modified IEEE 39-node system demonstrates that the power trajectories of each distributed resource cluster exhibit system optimization characteristics after aggregation, achieving cross-time period coordination and dynamic load balancing. Different types of distributed resource clusters, such as diesel generator clusters, distributed photovoltaic clusters, distributed energy storage clusters, and air conditioning load clusters, can make reasonable bidding decisions and operational adjustments based on their own characteristics and market price signals, fully leveraging their respective advantages and verifying the effectiveness and practicality of the proposed method and model.

[0057] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A distributed resource aggregation and market bidding optimization method based on Kino polyhedra, characterized in that, Includes the following steps: Step 1: Establish a distributed resource model, which includes: HVAC model, distributed energy storage model, diesel generator model, and distributed photovoltaic model; Step 2: Perform an inner approximation of the feasible region of distributed resources based on the Kino polyhedron; Step 3: Distributed resource aggregation participates in power market optimization decision-making.

2. The distributed resource aggregation and market bidding optimization method based on the Kino polyhedron according to claim 1, characterized in that, In step 1: The HVAC model is as follows: （1） （2） In the formula, Indicates time Indoor air temperature, Indicates time The outdoor ambient temperature; and These are the lower and upper limits of the preset comfortable temperature range for the room. Indicates time Power consumption of HVAC systems The temperature inertia factor, The efficiency coefficient for heat-to-electric energy conversion; The distributed energy storage model is as follows: （3） （4） （5） In the formula, Indicates distributed energy storage at time The state of charge, Indicates the self-discharge rate of energy. This indicates the charging / discharging efficiency of distributed energy storage. express The charging power of the energy storage device at any time This represents the time interval between two adjacent moments. and These are the upper and lower limits of the energy state of the stored energy, respectively. and These are the maximum allowable charging power and maximum discharge power for energy storage, respectively. The diesel generator model is as follows: （6） （7） In the formula, Indicates time The active power output of a diesel generator. and These represent the upper and lower limits of the unit's output, respectively. and These are the uphill and downhill limits for diesel generators, respectively. The distributed photovoltaic model is as follows: （8） in, Indicates time The active power output of distributed photovoltaic power generation, Indicates time The maximum active power that distributed photovoltaic power can generate.

3. The distributed resource aggregation and market bidding optimization method based on the Kino polyhedron according to claim 1, characterized in that, In step 2, the expression for the feasible region of distributed resources based on the Chino polyhedron is as follows: （20） In the formula, The coordinates of the center point of the Chino polyhedron after distributed resource aggregation. Here, the generator matrix corresponds to different resources, representing the extension direction of the Chino polyhedron. This refers to the extension length of the Chino polyhedron in different directions after the distributed resource aggregation. This refers to the operating power after the distributed resources are aggregated.

4. The distributed resource aggregation and market bidding optimization method based on the Kino polyhedron according to claim 1, characterized in that, Step 3 specifically includes: Distributed resources participate in the electricity market as aggregators, and the feasible domain of the distributed resource clusters is used by the aggregators in the bidding decision-making of the electricity market. A two-layer model is used to characterize the strategic bidding process of each resource aggregator. Each aggregator predicts the bids of other generators and makes a bid based on its own feasible domain aggregated by the Chino polyhedron. The power trading center clears the charging and discharging plans of each distributed resource cluster based on the quotations of each aggregator and the segmented quotations of each power generator. Among them, the power distribution system operator performs safety verification of the power flow of the power distribution network and implements congestion management for the transaction process.

5. The distributed resource aggregation and market bidding optimization method based on the Kino polyhedron according to claim 4, characterized in that, The upper layer of the two-layer model is a distributed resource aggregator pricing decision model; the lower layer of the two-layer model is a power market clearing model.

6. The distributed resource aggregation and market bidding optimization method based on the Kino polyhedron according to claim 5, characterized in that, The distributed resource aggregator pricing decision model is as follows: The model objective is to achieve optimal internal economy. The model objective is: （21） In the formula, , , , These are energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. Actual operating power during the time period This is the baseline load for HVAC. For the marginal electricity price at the node, , , , The corresponding resource clusters are located on the nodes. Marginal electricity price at market nodes within the time period This refers to the charging and discharging loss coefficient of the energy storage cluster. The total operating cost of the diesel generator cluster; The objective constraints of the model are: （22） （23） （24） （25） In the formula, , , , These are the coordinates of the center point of the Chino polyhedron after the aggregation of the energy storage resource cluster, HVAC cluster, distributed photovoltaic cluster, and diesel generator cluster. The generator matrix corresponds to different resource clusters. , , , This refers to the extension length of the Chino polyhedron in different directions after the distributed resource aggregation. , , , These are the actual operating power of energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. When aggregators participate in electricity trading in the electricity market, their bids must be within the range allowed by the market, that is: （26） （27） （28） （29） In the formula, and These represent the upper and lower limits for the corresponding aggregators' bids in the electricity market.

7. The distributed resource aggregation and market bidding optimization method based on the Kino polyhedron according to claim 5, characterized in that, The lower-level model is the electricity market clearing model: The model's objective is to maximize social welfare. The model objective is: （30） In the formula, , , , These are the actual operating power of energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. , , , , These represent the upper and lower limits for electricity market bidding by energy storage resource aggregators, HVAC aggregators, distributed photovoltaic aggregators, and diesel generator aggregators, respectively. , For the slack variable of the input capacitance. To punish prices; The objective constraints of the model are: （31） In the formula, These represent the actual operating power of conventional generator sets, centralized photovoltaic power, and centralized wind power, respectively. The total system load, the power balance constraint corresponds to the value after the colon. The dual variable of this constraint represents the market-cleared price of electricity. （32） （33） （34） （35） (36) In the formula, and These are the upper and lower limits of the output of conventional generating units. and This represents the upper limit of output for centralized photovoltaic and centralized wind power. , , , , , These are the dual variables of the corresponding constraints. For the unit For the line The generator output load transfer factor, For nodes For the line The generator output load transfer factor, Let be the maximum transmission power of branch l; rewrite equation (36) as equation (37) in terms of the power flow factor transfer matrix and the correlation matrix: （37） In the formula, Matrix A represents the unit node association matrix, and matrix B represents the load node association matrix. Therefore Indicates the node injected power. This is the power flow factor transition matrix. The maximum transmission power of branch l; , These are the dual variables corresponding to the line power flow constraints.

8. The distributed resource aggregation and market bidding optimization method based on the Kino polyhedron according to claim 4, characterized in that, In step 3, the distributed resource aggregator's bidding decision and market clearing constitute a two-level optimization problem. By establishing the Karush-Kuh-Tucker conditions for the market clearing problem and using them as constraints for the distributed resource aggregator's bidding decision model, as shown in equation (39): （39） In the formula, The constraint set corresponding to the KKT system for the lower market clearing problem.

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