Method for participating in frequency modulation auxiliary service market based on user side energy storage

Abstract

The invention provides a method for participating in a frequency modulation auxiliary service market based on user side energy storage, and belongs to the technical field of power system automation, and the method comprises the steps: constructing an energy storage system monomer operation model which comprises a charging and discharging feasible region model, an energy storage system operation model, and an energy storage system boundary aging cost model; constructing an aggregator response potential model, and constructing an aggregator aggregation model by aggregating and controlling large-scale energy storage resources in a certain area range; constructing a frequency modulation auxiliary service market settlement mechanism model; an aggregator bidding decision-making model based on the minimum and maximum regret values is constructed, and user side energy storage participates in the frequency modulation auxiliary service market, so that a new technical means is provided for stable operation of a power system, and important support is provided for realizing high-proportion renewable energy access and clean energy development targets.

Description

Technical Field

[0001] The present invention relates to the field of power system automation technology, and in particular to a method for participating in a frequency regulation auxiliary service market based on user-side energy storage. Background Art

[0002] With the increasing penetration of renewable energy in the power system, power system stability and frequency regulation requirements face unprecedented challenges. The intermittent and fluctuating nature of renewable energy sources such as wind and solar power makes the balance of power supply and demand more complex and unpredictable. Traditional frequency regulation methods rely primarily on the regulation of large generators, but these units typically have high inertia and long response times, making them unable to quickly respond to short-term fluctuations in the power system. Furthermore, large generators have limited frequency regulation capabilities, and their flexibility and regulation capabilities are particularly insufficient when a high proportion of renewable energy is integrated. With the advancement of energy storage technology and the reduction in costs, behind-the-meter energy storage has become a promising solution for participating in the power system frequency regulation ancillary services market. Energy storage systems offer advantages such as fast response, high flexibility, and controllability, enabling them to provide or absorb power in a short period of time, thereby rapidly balancing fluctuations in power supply and demand. Behind-the-meter energy storage can not only participate in the market as an independent frequency regulation resource, but can also form virtual power plants by aggregating and coordinating multiple distributed energy storage units, providing larger-scale and more efficient frequency regulation services. Summary of the Invention

[0003] The main purpose of the embodiments disclosed herein is to develop a method for user-side energy storage to participate in the frequency regulation ancillary service market, so as to improve the stability and operational efficiency of the power system. By constructing a single-unit operation model of the energy storage system, an aggregator response potential model, a frequency regulation ancillary service market settlement mechanism model, and an adjustable capacity uncertainty and output deviation risk model, this study aims to achieve effective integration and intelligent regulation of distributed energy storage resources, ensuring their efficient operation in the frequency regulation ancillary service market. At the same time, through the aggregator bidding decision model based on minimum and maximum regret values, this study also strives to optimize the aggregator's decision-making process, reduce the risks caused by uncertainty, and enhance the competitiveness and benefits of energy storage resources in the market.

[0004] To achieve the above objectives, embodiments of the present disclosure provide a method for participating in the frequency regulation ancillary service market based on user-side energy storage. The method is characterized by being applied to an energy storage aggregator system including a battery energy storage system. The method comprises:

[0005] Construct a single-unit operation model of the energy storage system;

[0006] Constructing a model of aggregator response potential;

[0007] Construct a frequency regulation ancillary services market settlement mechanism model;

[0008] Construct an aggregator bidding decision model based on minimum maximum regret value;

[0009] Based on the construction of the energy storage system single-unit operation model, the construction of the aggregator response potential model, the construction of the frequency regulation auxiliary service market settlement mechanism model, and the construction of the aggregator bidding decision model based on minimum and maximum regret values, a method and system for user-side energy storage to participate in the frequency regulation auxiliary service market are constructed.

[0010] In some embodiments, constructing a single energy storage system operation model includes:

[0011] Construct a charging and discharging feasible domain model;

[0012] Construct an energy storage system operation constraint model;

[0013] Construct a boundary aging cost model for energy storage systems;

[0014] According to the construction of the charging and discharging feasible domain model, the construction of the energy storage system operation constraint model, and the construction of the energy storage system boundary aging cost model, a single energy storage system operation model is constructed.

[0015] In some embodiments, constructing the aggregator response potential model includes:

[0016] Constructing a power boundary model that reflects the aggregator's responsiveness;

[0017] Constructing a power boundary model of the responsiveness of reactive aggregators;

[0018] According to the electricity boundary model for constructing the response capability of the reaction aggregator and the power boundary model for constructing the response capability of the reaction aggregator, an aggregator response potential model is constructed.

[0019] In some embodiments, constructing a frequency regulation ancillary service market settlement mechanism model includes:

[0020] Construct FM signal model;

[0021] Build a model of up and down frequency modulation capacity provided by aggregators;

[0022] Construct frequency modulation mileage model;

[0023] Constructing a revenue model for aggregators participating in the frequency regulation ancillary services market;

[0024] A frequency modulation auxiliary service market settlement mechanism model is constructed based on the frequency modulation signal model, the up and down frequency modulation capacity model provided by the aggregator, the frequency modulation mileage model, and the revenue model of the aggregator participating in the frequency modulation auxiliary service market.

[0025] In some embodiments, constructing the minimum-maximum-regret-value-based aggregator bidding decision model includes:

[0026] Construct a deterministic optimization scheduling problem model based on complete information;

[0027] Construct a minimum-maximum-regret aggregator bidding decision model based on uncertainty electricity prices;

[0028] According to the construction of the deterministic optimization scheduling problem model based on complete information and the construction of the minimum-maximum-regret aggregator bidding decision model based on uncertain electricity prices, an aggregator bidding decision model based on minimum-maximum-regret is constructed.

[0029] In some embodiments, constructing a deterministic optimization scheduling problem model based on complete information includes:

[0030] Construct the objective function of the deterministic optimization scheduling problem based on complete information;

[0031] Construct the constraints of deterministic optimization scheduling problem under complete information;

[0032] According to the said construction of the deterministic optimization scheduling problem objective function based on complete information and the said construction of the deterministic optimization scheduling problem constraint conditions based on complete information, a deterministic optimization scheduling problem model based on complete information is constructed.

[0033] In some embodiments, constructing a two-stage minimum-maximum-regret optimization scheduling problem based on uncertainty factors includes:

[0034] Construct a two-stage minimum-maximum regret optimization scheduling problem objective function;

[0035] Construct constraints for a two-stage minimum-maximum regret optimization scheduling problem;

[0036] According to the objective function of constructing the two-stage minimum-maximum regret optimization scheduling problem and the constraint conditions of constructing the two-stage minimum-maximum regret optimization scheduling problem, a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors is constructed. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flowchart of a method for user-side energy storage to participate in the frequency regulation auxiliary service market, as provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0039] With the increasing penetration of renewable energy in the power system, power system stability and frequency regulation requirements face unprecedented challenges. The intermittent and fluctuating nature of renewable energy sources such as wind and solar power makes the balance of power supply and demand more complex and unpredictable. Traditional frequency regulation methods rely primarily on the regulation of large generators, but these units typically have high inertia and long response times, making them unable to quickly respond to short-term fluctuations in the power system. Furthermore, large generators have limited frequency regulation capabilities, and their flexibility and regulation capabilities are particularly insufficient when a high proportion of renewable energy is integrated. With the advancement of energy storage technology and the reduction in costs, behind-the-meter energy storage has become a promising solution for participating in the power system frequency regulation ancillary services market. Energy storage systems offer advantages such as fast response, high flexibility, and controllability, enabling them to provide or absorb power in a short period of time, thereby rapidly balancing fluctuations in power supply and demand. Behind-the-meter energy storage can not only participate in the market as an independent frequency regulation resource, but can also form virtual power plants by aggregating and coordinating multiple distributed energy storage units, providing larger-scale and more efficient frequency regulation services.

[0040] Based on this, the main purpose of the embodiments of the present disclosure is to propose a method for participating in the frequency regulation auxiliary service market based on user-side energy storage, which is specifically described through the following embodiments.

[0041] Reference Figure 1 According to the energy scheduling method of the embodiment of the present disclosure, it is applied to the energy storage aggregator system, and the energy storage aggregator system includes a battery energy storage system. The method of participating in the frequency regulation auxiliary service market based on user-side energy storage includes but is not limited to steps S110 to S140.

[0042] S110, constructing a single-unit operation model of the energy storage system;

[0043] S120, constructing a model of aggregator response potential;

[0044] S130, building a frequency regulation ancillary services market settlement mechanism model;

[0045] S140, constructing an aggregator bidding decision model based on minimum maximum regret value.

[0046] In step S110, a single-unit operation model of the energy storage system is constructed, including a charge and discharge feasible domain model, an energy storage system operation model, and an energy storage system boundary aging cost model;

[0047] Charge and discharge feasible domain model: minimum reserve power of the system It is expressed as follows:

[0048]

[0049] Where i represents the i-th energy storage system; P i,BS (t) is the basic power load of the i-th system; Indicates the upper limit value of the energy storage system process; η d Indicates the discharge efficiency of the energy storage system; and They represent the upper and lower boundaries of the energy storage system's power at time t.

[0050] Energy storage system operation model:

[0051]

[0052] in, and Represent the charging power and discharging power of the energy storage system respectively; P i bs,max Represents the maximum charging power of the energy storage system; η c Represents the charging efficiency of the energy storage system; e i,bs (t) represents the amount of electricity in the energy storage system at time t; P i bs,+ (t) and P i bs,- (t) represent the upper and lower power boundaries of the energy storage system at time t.

[0053] Energy storage system boundary aging cost model. The cyclic aging of the energy storage system is the main factor that needs to be considered. The cycle depth stress function of the energy storage system is defined as follows:

[0054] φ(δ(t))=αδ 1+β (t),δ(t)∈[0,1] (3)

[0055] Where φ represents the cycle life loss; δ(t) represents the depth of discharge; α and β represent the shape of the stress function.

[0056] Therefore, the marginal degradation can be obtained by differentiating (3) with respect to the discharge power:

[0057]

[0058] Among them, C S represents the segmented cost function; R represents the replacement cost of the equipment; E bat Indicates energy storage capacity;

[0059] Define the marginal degradation cost as a function of the current cycle depth level:

[0060]

[0061] In step S120, an aggregator response potential model is constructed. The aggregator aggregation model is constructed by aggregating and controlling large-scale energy storage resources within a certain area. The power boundary of the aggregator is as follows:

[0062]

[0063] Where N represents the number of aggregated energy storage systems.

[0064] The power boundaries for the aggregator are as follows:

[0065]

[0066] In step S130, a frequency regulation auxiliary service market settlement mechanism model is constructed;

[0067] The frequency modulation signal q(t,l)∈[-1,1] is the lth frequency modulation signal in the tth time period, l=1,…,N l , which represents the proportion of each frequency modulation instruction to the frequency modulation service capacity of the period. The frequency modulation capacities provided by the aggregator in the tth period are R up (t) and R down (t). When q(t,l)>0, it means that the aggregator needs to provide upstream frequency regulation capacity, that is, the power of interaction between the aggregator and the power grid decreases q(t,l)R up (t); When q(t,l)<0, it means that the aggregator needs to provide downlink frequency regulation capacity, that is, the power of interaction between the aggregator and the grid increases by q(t,l)R down (t).

[0068] The expression of frequency modulation mileage M(t) is as follows:

[0069]

[0070] Where P(t,l) represents the response power of the lth aggregator at time t.

[0071] The revenue f(t) of the aggregator participating in the frequency regulation ancillary services market is as follows:

[0072]

[0073] Among them, λ down (t) and λ up (t) represents the upper frequency regulation capacity price and the lower frequency regulation capacity price; λ mile (t) represents the frequency modulation mileage price; ν(t) represents the frequency modulation accuracy.

[0074] In step S140, a bidding decision model for an aggregator based on minimum and maximum regret values is constructed:

[0075] First, the deterministic optimization scheduling problem model under complete information is as follows. The objective function includes the benefits of participating in the frequency regulation auxiliary service market, the energy storage aging cost, and the frequency regulation electricity cost C f (t) and day-ahead electricity cost C DA (t).

[0076]

[0077] in, and represents the day-ahead and real-time energy prices at time t; P(t) represents the energy of the system participating in the day-ahead market.

[0078] Construct a minimum-maximum regret optimization scheduling problem based on uncertainty factors, and define the uncertainty of electricity price as Defining uncertainty factors In the minimum-maximum regret problem, the regret value Reg is defined as follows:

[0079]

[0080] The minimum maximum regret optimization problem is as follows:

[0081]

[0082] Reconstruct the two-stage minimum-maximum regret optimization scheduling problem and define Y(P(t),R up (t),R down (t)) and X(ΔP(t)) are the first-stage decision variables and the second-stage decision variables of the minimum-maximum regret problem. Reg(Y) is expressed as follows:

[0083]

[0084] Among them, f(Y,s),g(Y),h(X),l(Y) corresponds to each term of the objective function in (6).

[0085] Therefore, the minimum maximum regret optimization scheduling problem can be expressed as follows:

[0086]

[0087] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the content disclosed in the present invention should be included in the protection scope recorded in the claims.

Claims

1. A method for participating in the frequency regulation auxiliary service market based on user-side energy storage, characterized in that: Applied to an energy storage aggregator system, the energy storage aggregator system including a battery energy storage system, the method comprising: Construct a single-unit operation model of the energy storage system; Constructing a model of aggregator response potential; Construct a frequency regulation ancillary services market settlement mechanism model; Construct an aggregator bidding decision model based on minimum maximum regret value; Based on the construction of the energy storage system single-unit operation model, the construction of the aggregator response potential model, the construction of the frequency regulation auxiliary service market settlement mechanism model, and the construction of the aggregator bidding decision model based on minimum and maximum regret values, a method and system for user-side energy storage to participate in the frequency regulation auxiliary service market are constructed.

2. The method for participating in the frequency regulation auxiliary service market based on user-side energy storage according to claim 1, characterized in that: The construction of a single energy storage system operation model includes: Construct a charging and discharging feasible domain model; Construct an energy storage system operation constraint model; Construct a boundary aging cost model for energy storage systems; According to the construction of the charging and discharging feasible domain model, the construction of the energy storage system operation constraint model, and the construction of the energy storage system boundary aging cost model, a single energy storage system operation model is constructed.

3. The method for participating in the frequency regulation auxiliary service market based on user-side energy storage according to claim 1, characterized in that: The constructing of the aggregator response potential model includes: Constructing a power boundary model that reflects the aggregator's responsiveness; Constructing a power boundary model of the responsiveness of reactive aggregators; According to the electricity boundary model for constructing the response capability of the reaction aggregator and the power boundary model for constructing the response capability of the reaction aggregator, an aggregator response potential model is constructed.

4. The method for participating in the frequency regulation auxiliary service market based on user-side energy storage according to claim 1, characterized in that: The construction of the frequency regulation ancillary service market settlement mechanism model includes: Construct FM signal model; Build a model of up and down frequency modulation capacity provided by aggregators; Construct frequency modulation mileage model; Constructing a revenue model for aggregators participating in the frequency regulation ancillary services market; A frequency modulation auxiliary service market settlement mechanism model is constructed based on the frequency modulation signal model, the up and down frequency modulation capacity model provided by the aggregator, the frequency modulation mileage model, and the revenue model of the aggregator participating in the frequency modulation auxiliary service market.

5. The method for participating in the frequency regulation auxiliary service market based on user-side energy storage according to claim 1, characterized in that: The construction of the aggregator bidding decision model based on minimum maximum regret value includes: Construct a deterministic optimization scheduling problem model based on complete information; Construct a minimum-maximum-regret aggregator bidding decision model based on uncertainty electricity prices; According to the construction of the deterministic optimization scheduling problem model based on complete information and the construction of the minimum-maximum-regret aggregator bidding decision model based on uncertain electricity prices, an aggregator bidding decision model based on minimum-maximum-regret is constructed.

6. The method for participating in the frequency regulation auxiliary service market based on user-side energy storage according to claim 5, characterized in that: The construction of a deterministic optimization scheduling problem model based on complete information includes: Construct the objective function of the deterministic optimization scheduling problem based on complete information; Construct the constraints of deterministic optimization scheduling problem under complete information; According to the said construction of the deterministic optimization scheduling problem objective function based on complete information and the said construction of the deterministic optimization scheduling problem constraint conditions based on complete information, a deterministic optimization scheduling problem model based on complete information is constructed.

7. The method for participating in the frequency regulation auxiliary service market based on user-side energy storage according to claim 6, characterized in that: The two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors is constructed, including: Construct a two-stage minimum-maximum regret optimization scheduling problem objective function; Construct constraints for a two-stage minimum-maximum regret optimization scheduling problem; According to the objective function of constructing the two-stage minimum-maximum regret optimization scheduling problem and the constraint conditions of constructing the two-stage minimum-maximum regret optimization scheduling problem, a two-stage minimum-maximum regret optimization scheduling problem based on uncertainty factors is constructed.

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