A joint-clearing market bidding method for aggregators considering user fitness

Abstract

The present invention relates to the field of power systems and their automation management, and provides a method for aggregator joint clearing market bidding considering user fitness, and establishes a three-layer interactive framework between demand response aggregators, end users and system operators, including the following contents: step S1: between system operators and demand response aggregators, a joint clearing model combining wholesale market and reserve market is established; step S2: a joint bidding model for wholesale market and reserve by demand response aggregators; step S3: between demand response aggregators and end users, a user demand side response model is established considering the fitness of users in the retail market.

Description

Technical Field

[0001] The present invention relates to the field of power systems and automation thereof, and in particular to a market bidding method for joint clearing of aggregators taking user adaptability into consideration. Background Art

[0002] In recent years, renewable energy sources with strong uncertainty, such as wind power and photovoltaics, have experienced explosive growth, and the grid's demand for flexible resources has increased dramatically. The traditional reserve capacity margin provided by the power supply side is difficult to adapt to the needs of absorbing a high proportion of new energy and the needs of safe and stable operation of the power grid. There is an urgent need to explore new types of flexible resources.

[0003] The improvement of the electrification level on the user side and the rapid development of communication and control technology provide basic technical support for load-side demand response, and can use price signals based on market-based means to guide users to use electricity in an orderly manner. However, at this stage, users are geographically dispersed, have low maturity, and lack the ability to participate in the market. How to tap the flexibility response potential of end users and incorporate users with many points and wide areas into the market-based control system of the power grid is one of the key difficulties.

[0004] In this context, as an intermediate bridge between wholesale market, reserve market and retail market, demand response aggregator can effectively aggregate the demand-side response resources of distributed users to participate in wholesale market and reserve market to meet its own profit needs, and use efficient incentive model to tap the regulation potential of end users to participate in demand-side response, thereby solving the problem that system operators are difficult to directly dispatch large-scale flexible loads, and further achieve a win-win situation for power grid, demand response aggregator and end users. Under this framework, the research focuses on: 1) How can demand response aggregator formulate reasonable bidding strategies to scientifically participate in wholesale market and reserve market; 2) How can demand response aggregator coordinate flexible loads in the area under its jurisdiction in a reasonable incentive manner based on user response fitness to respond to the regulation needs of the power grid.

[0005] Electricity consumption is growing rapidly, and the peak-to-valley difference of electricity load is widening. As the sensibility and controllability of terminal loads continue to increase, an efficient "source-load" two-way interactive market mechanism is conducive to the optimal dispatch of flexible loads. In the context of a mature electricity market, the number of market players has increased, user market participation has improved, and market transaction demand has increased significantly. Considering that a single user has a small load, small adjustable capacity, and little impact on the system, how to aggregate a large number of small terminal users to participate in the market-oriented regulation of the power grid, and how to provide small and medium-sized distributed loads with a market participation path to participate in the main energy market and the standby market is one of the key problems.

[0006] Existing research 1) rarely considers the impact of user-side demand-side response mechanisms on the equilibrium situation of multi-level markets in the context of multi-level market participation in the main energy market and the reserve market; 2) rarely builds a connection framework for multi-level markets such as wholesale markets, retail markets, and wholesale markets; 3) demand response aggregators lack a reasonable interaction framework to use efficient incentive mechanisms to integrate users' cluster response capacity, thereby participating in the wholesale market and the reserve market to meet their own profit needs. Summary of the invention

[0007] The purpose of the present invention is to provide a method for aggregator joint clearing market bidding that takes user fitness into consideration, and to establish a three-layer model that fully takes into account the operational objectives and interactive behaviors of demand response aggregators, end users, and system operators. Demand response aggregators aggregate user-side demand response resources based on a reasonable standby subsidy mechanism and retail price mechanism to participate in the wholesale market and capacity market.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is: a method for aggregator joint clearing market bidding considering user fitness, establishing a three-layer interactive framework between demand response aggregators, end users and system operators, including the following contents:

[0009] Step S1: A joint clearing model is established between the system operator and the demand response aggregator, combining the wholesale market and the reserve market. The establishment of the joint clearing model includes solving the social welfare maximization problem based on the bidding of conventional units, new energy units and demand response aggregators under the constraint of system safety operation;

[0010] Step S2: a joint bidding model for wholesale market and reserve market by demand response aggregator, wherein the construction of the joint bidding model includes evaluating the response fitness of end users and acting as an agent for end users to participate in bidding in wholesale market and reserve market to achieve profit maximization solution;

[0011] Step S3: Between the demand response aggregator and the end user, a user demand side response model is established by considering the user's fitness in the retail market, wherein the construction of the user demand side response model includes the optimization objective of a single user demand side response model to minimize the utility cost of the user per unit time, including minimizing the cost of purchasing electricity from the demand response aggregator, the fitness cost of participating in the aggregator's regulation, the fitness cost of providing backup capacity to the aggregator, and maximizing the economic benefits of participating in the backup market.

[0012] Through the above technical scheme, the proposed three-layer model framework establishes a joint market clearing model of the wholesale market and the reserve market at the upper layer, establishes a market bidding strategy model for the demand response aggregator facing the joint market at the middle layer, and establishes a demand-side response model considering user fitness at the lower layer. Based on limited parameter transmission, effective game interaction between system operators, demand response aggregators and end users is realized, which fully protects user privacy and meets the profit needs of multiple market players.

[0013] Optimally, the joint clearing model of the wholesale market and the reserve market can be expressed as a social welfare maximization problem based on the bidding of conventional units, new energy units and demand response aggregators under the constraints of system safety operation.

[0014] In the formula, They are the bidding prices of conventional units, demand response aggregators, and system rigid loads in the main energy market; is the bidding price of conventional units, new energy units, and demand response aggregator proxy users in the reserve market; NG, NR, T are the sets of conventional units, renewable energy units, and optimized time respectively; g, r, t are the indicators corresponding to conventional units, renewable energy units, and time points; Provide power for conventional units; and are the system rigid load and the adjustable load aggregated by the demand response aggregator; K g ,K r ,K ret The reserve capacity provided by conventional units g, renewable energy units r and demand response aggregators to the system,

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] In the formula, Output for new energy units; G ,φ R are the reserve rates provided by conventional units and new energy units for the system, is the installed capacity of conventional unit g and renewable energy unit r; G R is the maximum capacity of the demand response aggregator, λ SMP,t ,λ capare the Lagrange multipliers of equations (2) and (3), namely the system marginal price and the reserve capacity clearing price; the Lagrange multipliers corresponding to equations (4) and (6) are

[0021] Through the above technical scheme, the lower-level model and the upper-level model are substituted into the market bidding strategy model of the middle-level demand response aggregator for the joint market as constraints based on the Karush-Kuhn-Tucker (KKT) conditions. The multi-layer nonlinear model is converted into a single-layer model using methods such as approximate linearization and complementary relaxation, thereby ensuring the accuracy and efficiency of the optimization solution.

[0022] Preferably, the demand response aggregator aggregates user demand-side response resources to participate in the wholesale market and the reserve market under the premise of considering user fitness, wherein the optimization goal of the demand-side response model of a single user is to minimize the utility cost of the user per unit time, including minimizing the cost of purchasing electricity from the demand response aggregator, the fitness cost of participating in the aggregator's regulation, the fitness cost of providing reserve capacity to the aggregator, and maximizing the economic benefits of participating in the reserve market.

[0023] Preferably, the revenue of the demand response aggregator includes purchasing electricity from the wholesale market through the retail price λ ret,t Sell ​​electricity to end users to obtain the price difference in the middle, and set the capacity subsidy price λ c,t Aggregate end-user capacity to participate in the capacity market.

[0024] Preferably, the joint clearing model, the joint bidding model and the user demand-side response model are three-layer models based on both parties and are converted into a single-layer linear formula through the KKT condition.

[0025] In summary, the beneficial effects of the present invention are:

[0026] 1. User demand-side response resources can participate in multi-level markets such as wholesale markets and capacity markets based on demand response aggregators. The proposed market framework is conducive to the coupling and connection of wholesale markets, retail markets and capacity markets, and further explores and analyzes the impact of demand-side response resources on the equilibrium price of multi-level markets. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of a method for aggregator-joint clearing market bidding taking into account user fitness according to the present invention. DETAILED DESCRIPTION

[0028] Below in conjunction with the appended Figure 1, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0029] Embodiment 1:

[0030] Upper model - joint clearing model of wholesale market and reserve market:

[0031] The day-ahead clearing optimization model of the wholesale market and the reserve market can be expressed as a social welfare maximization problem based on the bidding of conventional units, new energy units and demand response aggregators under the constraints of system safety operation:

[0032]

[0033] In the formula, They are the bidding prices of conventional units, demand response aggregators, and system rigid loads in the main energy market; It provides bidding prices for conventional units, new energy units and demand response aggregators on behalf of users in the reserve market.

[0034] NG, NR, T are the sets of conventional units, renewable energy units and optimized time respectively; g, r, t are the indicators corresponding to conventional units, renewable energy units and time points; Provide power for conventional units; and are the system rigid load (including the rigid load represented by the demand response aggregator) and the adjustable load aggregated by the demand response aggregator; K g ,K r ,K ret The reserve capacity provided to the system by conventional units g, renewable energy units r and demand response aggregators.

[0035] The physical constraints of the wholesale market and reserve market clearing model are:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] In the formula, Output for new energy units;G ,φ R are the reserve rates provided by conventional units and new energy units for the system, is the installed capacity of conventional unit g and renewable energy unit r; G R is the maximum capacity of the demand response aggregator. SMP,t ,λ cap are the Lagrange multipliers of equations (2) and (3), namely the system marginal price and the reserve capacity clearing price; the Lagrange multipliers corresponding to equations (4) and (6) are

[0042] Lower model - user demand-side response model considering user fitness:

[0043] Demand response aggregators aggregate user demand-side response resources to participate in wholesale markets and reserve markets under the premise of considering user fitness. Among them, the optimization goal of the demand-side response model for a single user is to minimize the utility cost of the user per unit time, including minimizing the cost of purchasing electricity from the demand response aggregator, the fitness cost of participating in the regulation of the aggregator, the fitness cost of providing reserve capacity for the aggregator, and maximizing the economic benefits of participating in the reserve market. Its objective function can be expressed as:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] In the formula, is the adjustable load of user j, is the baseline load of user j; σ min ,σ max is the unit fitness cost of user j when he participates in the demand response aggregator to adjust the power up / down; K is the maximum power limit for user j when the demand response aggregator adjusts power up / down; j The spare capacity provided by user j to the demand response aggregator; π c,t ,λ c,t is the unit fitness cost of the reserve capacity provided by user j to the demand response aggregator and the unit subsidy obtained; is the maximum amount of down- and up-regulation that the user has participated in during this period. The Lagrange multipliers corresponding to equations (10) to (13) are

[0050] Mid-level model - Market bidding model for demand response aggregators towards a joint market

[0051] Demand response aggregators evaluate the response adaptability of end users and participate in the bidding in the wholesale market and reserve market on behalf of end users to maximize profits. The main sources of income for demand response aggregators are: 1) Purchase electricity from the wholesale market at a reasonable retail price λ ret,t Sell ​​electricity to end users; 2) Establish a reasonable capacity subsidy price λ c,t Aggregate end-user capacity to participate in the capacity market, and its objective function can be expressed as:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, λ fix,t The base rate for demand response aggregators to purchase electricity on behalf of users (including transmission and distribution network service costs); The unit subsidy price and minimum subsidy price for capacity response provided by the demand response aggregator to users; ret,t retail prices set by demand response aggregators for end users; are the adjustable demand-side power and capacity aggregated by the demand response aggregator; k j is the scaling factor for enlargement; β ret The expected rate of return of retail price; NJ is the user aggregated by the demand response aggregator. Formula (14)-Formula (16) ensures that the retail price and reserve capacity subsidy price provided by the demand response aggregator to users are within a reasonable range.

[0059] Converting a two-layer model to a single layer

[0060] (1) Wholesale market and reserve market clearing model under KKT transformation

[0061] Considering the upper wholesale market and reserve market clearing optimization problem as a linear problem, it can be transformed into the following constraints based on the KarushKuhn Tucker (KKT) condition:

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[0063]

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[0065]

[0066]

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[0070] Single-layer equivalent model

[0071] The present invention further transforms the proposed three-layer model into an equivalent single-layer problem. Considering that the lower-layer user demand-side response model and the upper-layer wholesale market and reserve market clearing model are both linear models, the lower-layer optimization model equations (1)-(6) and the upper-layer optimization model equations (7)-(11) are replaced by their KKT conditions as additional constraints of the middle-layer demand response aggregator market bidding model (11), then the optimization problem can be transformed into equation (35) as follows:

[0072]

[0073] st formula (12)-(16); formula (17)-(25); formula (26)-(34)

[0074] Linearization process

[0075] In formulas (23)-(25) and (30)-(34), the constraints appear in the mode of 0≤x⊥y≥0, where the complementary relaxation condition is a nonlinear equation. The present invention converts it into a linear constraint expression through the Fortuny-Amat linearization method as follows:

[0076]

[0077] Where M h is a large positive number; u is a 0-1 variable.

[0078] In addition, in the objective function (35), (λ cap φ J K ret ),(λ c,t K j k j) is a bilinear continuous variable, and the binary expansion method is used to discretize it into linear variables. Assume that the bilinear continuous variables in the objective function are x and y, that is, the objective function contains x·y terms.

[0079] st

[0080] 0≤x≤x max ,0≤y (37)

[0081] In the formula, x max is the maximum value of the variable x.

[0082] Further x max Discrete processing is i∈N, For x max At the discrete value of the i scale, a new discrete variable z is added to replace the x·y term in the objective function, and a new 0-1 variable ρ is added i ,α i ,μ, let:

[0083]

[0084] And add the following constraints:

[0085]

[0086]

[0087] 0≤y≤Zμ (41)

[0088]

[0089]

[0090] In the formula, take α i As a 0-1 indicator variable, α i =1 when the x value is Similarly, μ and z i In y ≥ 0 and z i ≥ 0, they are both 1; Z is a very large positive number. Formula (42) ensures that when μ, α i =1, there is ρ i =1; Formula (43) ensures that z i The value range of is within the x, y extreme value constraint range. The present invention converts the nonlinear variables in the objective function into discrete linear variables by adding 0-1 variables and other substitutions, so that the objective function can be directly solved.

[0091] By constructing a three-layer model, in which the lower layer is a user demand-side response model that takes user adaptability into consideration, the upper layer is a market joint clearing model for the wholesale market and the reserve market, and the middle layer is a bidding model for the demand response aggregator facing the joint market, the proposed architecture can realize the coupling and connection of multiple levels of markets, including the wholesale market, capacity market and retail market.

[0092] In the retail market, the interaction between demand response aggregators and end users in the retail market can be equivalent to a Stackelberg game, that is, the demand response aggregator (leader) determines the retail price and incentive price, and the end user (follower) further decides the power load to purchase from the demand response aggregator and the backup capacity to provide to the demand response aggregator based on the price signal of the demand response aggregator to minimize its own energy cost.

[0093] In the wholesale market, the system operator considers the market bidding of all market participants in the wholesale market and the reserve market, and realizes the market clearing of the main energy market and the reserve market based on the principle of maximizing total social welfare. Therefore, the profit of the demand response aggregator is constrained by the clearing results of the lower-level system operator in the wholesale market and the reserve market.

[0094] The present invention substitutes the lower-layer user response model and the upper-layer market clearing model into the bidding model of the middle-layer demand response aggregator facing the joint market based on the Karush-Kuhn-Tucker (KKT) condition as constraints, and uses the approximate linearization and complementary relaxation methods to transform the nonlinear model into a single-layer linear model for solution. The framework is as follows Figure 1 As shown,

[0095] The present invention adopts the IEEE 30-node system as a simulation model. The benefits of each subject when there is a demand-side response project and when there is no demand-side response project are shown in Table 1:

[0096] Table 1 Comparison of the benefits of various market players under demand-side response mode and non-demand-side response mode

[0097]

[0098]

[0099] The case study shows that the proposed demand response aggregator joint clearing market bidding method considering user fitness can effectively aggregate user demand-side response resources, reduce the operating costs of system operators, and provide reasonable benefits for relevant market players.

Claims

1. A method for aggregator joint clearing market bidding considering user fitness, It is characterized in that Establish a three-layer interaction framework between demand response aggregators, end users and system operators, including the following: Step S1: A joint clearing model is established between the system operator and the demand response aggregator, combining the wholesale market and the reserve market. The establishment of the joint clearing model includes solving the social welfare maximization problem based on the bidding of conventional units, new energy units and demand response aggregators under the constraint of system safety operation; Step S2: a joint bidding model for the wholesale market and the reserve market by the demand response aggregator, wherein the construction of the joint bidding model includes evaluating the response fitness of the end users and acting as an agent for the end users to participate in the bidding in the wholesale market and the reserve market to achieve profit maximization; Step S3: between the demand response aggregator and the end user, a user demand side response model is established by considering the user's fitness in the retail market, wherein the establishment of the user demand side response model includes the optimization objective of the single user demand side response model to minimize the utility cost of the user per unit time, including minimizing the cost of purchasing electricity from the demand response aggregator, the fitness cost of participating in the aggregator's regulation, the fitness cost of providing backup capacity to the aggregator, and maximizing the economic benefits of participating in the backup market; The joint clearing model of wholesale market and reserve market can be expressed as the social welfare maximization problem based on the bidding of conventional units, new energy units and demand response aggregators under the constraint of system safety operation. In the formula, They are the bidding prices of conventional units, demand response aggregators, and system rigid loads in the main energy market; is the bidding price of conventional units, new energy units, and demand response aggregator proxy users in the reserve market; NG, NR, T are the sets of conventional units, renewable energy units, and optimized time respectively; g, r, t are the indicators corresponding to conventional units, renewable energy units, and time points; Provide power for conventional units; and are the system rigid load and the adjustable load aggregated by the demand response aggregator; K g ,K r ,K ret The reserve capacity provided by conventional units g, renewable energy units r and demand response aggregators to the system, In the formula, is the output of the new energy unit; φ G , φ R are the reserve rates provided by the conventional unit and the new energy unit for the system respectively, where are the installed capacities of the conventional unit g and the renewable energy unit r; G R is the maximum capacity of the demand response aggregator, λ SMP,t , λ cap are the Lagrange multipliers of equations (2)-(3), namely the system marginal price and the reserve capacity clearing price; the Lagrange multipliers corresponding to equations (4)-(6) are respectively 2. A method for aggregator-joint clearing market bidding considering user fitness according to claim 1, It is characterized in that Demand response aggregators aggregate user demand-side response resources to participate in wholesale markets and reserve markets under the premise of considering user fitness. The optimization goal of a single user's demand-side response model is to minimize the user's utility cost per unit time, including minimizing the cost of purchasing electricity from demand response aggregators, the fitness cost of participating in aggregator regulation, the fitness cost of providing reserve capacity to aggregators, and maximizing the economic benefits of participating in the reserve market.

3. A method for aggregator joint clearing market bidding considering user fitness according to claim 2, It is characterized in that The revenue of the demand response aggregator includes purchasing electricity from the wholesale market through the retail price λ ret,t Sell ​​electricity to end users to obtain the price difference in the middle, and set the capacity subsidy price λ c,t Aggregate end-user capacity to participate in the capacity market.

4. A method for aggregator joint clearing market bidding considering user fitness according to claim 1, It is characterized in that The joint clearing model, joint bidding model and user demand-side response model are three-layer models based on both parties and are converted into a single-layer linear formula through the KKT condition.

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