A capacity auction method considering flexibility constraints and battery storage

Abstract

The application provides a capacity auction method considering flexibility constraints and battery energy storage, and relates to the technical field of energy systems. In view of the problem that the current capacity market lacks a clearing mechanism for flexible capacity, and consumers bear additional costs on additional flexible resources, the application provides a new capacity auction model considering flexibility constraints and battery energy storage, the model incorporates flexibility requirements into the capacity market model, and a proper mechanism is designed to enhance energy storage, so as to guarantee the benefits of flexible units and have sufficient climbing ability; in this case, the flexible resources including energy storage have their own advantages when competing with other power suppliers. In addition, the model can also analyze the system reliability of peak load and flexibility under the influence of high penetration level of renewable energy. The benefits of flexible units with sufficient climbing ability are maximized.

Description

Technical Field

[0001] This invention relates to the field of energy system technology, and in particular to a capacity auction method that takes into account flexibility constraints and battery energy storage. Background Technology

[0002] With the continuous increase in peak loads in the power system, ensuring sufficient generation capacity has become a key issue for guaranteeing power supply and maintaining power system stability. Under the national framework of building a unified national electricity market, a corresponding market mechanism is needed to ensure the system has sufficient generation capacity. While commodity markets guide investment through price signals, the unique characteristics of electricity complicate this issue in the power industry: 1) Generation resource investment is costly, with long payback periods and high risks from spot market price fluctuations; 2) From a social stability perspective, policymakers or regulatory agencies tend to set price ceilings, but they prohibit issuing correct price signals that threaten system stability; 3) The increasing penetration rate of renewable energy sources such as wind and solar power increases the uncertainty of capacity supply. Therefore, a special market mechanism needs to be designed to provide effective price signals for generation investment.

[0003] Electricity markets around the world are designing different market structures to ensure sufficient generation capacity. For example, in Europe, the combined market design of energy storage power exchanges and flexibility services ensures the reliability of renewable energy-dominated power systems; the United States has adopted a capacity market mechanism, which compensates for generator capacity availability, and many independent system operators (ISOs) have successfully conducted capacity auctions to ensure sufficient generation resources in the medium to long term. Several studies and practical experiences have demonstrated that capacity markets are effective in maintaining sufficient capacity during peak periods.

[0004] The large-scale application of renewable energy has brought new operational challenges. System reliability not only largely depends on capacity adequacy during peak load periods, but system flexibility is also crucial. With high renewable energy levels, power systems require generating resources to provide rapid ramping capabilities to balance fluctuations in wind or solar power. Current capacity market models lack explicit flexibility requirements (FR), and the high construction costs of flexible generation lead to lower investment incentives for flexible resources. Numerous methods have been developed to extend traditional capacity adequacy analysis to flexibility analysis. One known method is loss load expectation probability analysis, which utilizes the speed at which resources respond to changes in load and generation levels, while making important adjustments to loss load expectation probability, effective carrying capacity, and related indices. This illustrates a method using standard loss load expectation probability metrics to represent gradual changes. This method correlates ramping analysis with reliability because a generator's ramping capability depends in part on whether it is under forced shutdown. Although the target value for resource probability under ramping is currently unknown, this method can provide a reference for future research.

[0005] Another challenge facing the current electricity market is designing suitable mechanisms for battery energy storage (ES). Unlike traditional generators, energy storage, as a resource with limited capacity, depends not only on its maximum discharge rate and forced outage rate, but also on various factors such as its maximum energy capacity. The original capacity market struggled to adequately account for these diverse characteristics, and system operators are exploring models for energy storage participation in the capacity market, hoping to pay appropriate fees to reflect its capacity value and thus incentivize subsequent capacity investment. In the early stages of reform, each system operator adopted relatively simple regulations, requiring energy storage to meet a certain continuous discharge time and halving the discharge power for those that could not be met. For example, the California System Operator (CAISO) requires energy storage to discharge continuously for more than 4 hours. A 1MW power storage unit with an energy capacity of 4MW·h that can discharge continuously for 4 hours has a capacity value of 1MW and a capacity factor of 100%; if the energy storage can only discharge continuously for 2 hours, its capacity value is only 0.5MW, and its capacity factor is 50%. Other system operators have adopted similar approaches, but their continuous discharge time requirements differ; for example, PJM requires 10 hours, the Southwest Electricity Market requires 4 hours, and the New England Independent System Operator requires 2 hours. However, these adjustment methods are quite simple and require further research.

[0006] Therefore, it is necessary to provide a capacity auction method that takes into account flexibility constraints and battery energy storage to solve the above-mentioned technical problems. Summary of the Invention

[0007] To address one of the aforementioned technical problems, this patent incorporates flexibility requirements into the capacity market model and designs appropriate mechanisms to enhance energy storage. In this scenario, flexible resources, including energy storage, have an advantage when competing with other power generators. Furthermore, the model can also analyze the system reliability of peak load and flexibility under the influence of high renewable energy penetration levels.

[0008] This invention provides a capacity auction method that considers flexibility constraints and battery energy storage.

[0009] Includes the following steps:

[0010] Step A: Determine a reasonable capacity demand curve and set the key parameters of the demand curve;

[0011] Step B: Construction of the basic auction model; establish an objective function to maximize social benefits, and consider peak load constraints, energy demand constraints, and reliability constraints to establish a basic auction model that considers multiple constraints.

[0012] Step C: Flexibility requirements are defined; considering ramping constraints and embedding capacity markets, the ramping capacity requirements of different generators are calculated.

[0013] Step D: Energy storage model construction; calculate the qualified capacity of each storage technology, optimize the operation of its equipment, and provide strategic support;

[0014] Step E: Design a reasonable settlement mechanism to promote investment and construction in energy storage.

[0015] As a further solution, step A specifically includes the following steps:

[0016] Step A1: Select a sloping curve as the capacity demand curve and construct the sloping demand curve; model the capacity procurement level based on capacity price, if the capacity price is low, be willing to purchase higher capacity; if the capacity price is high, be willing to purchase lower capacity.

[0017] Step A2: Set the key parameters of the demand curve;

[0018] Step A3: Construct a supply curve for capacity supply quotes, and the auction will be settled at a variable and reasonable price.

[0019] As a further solution, step B specifically includes the following steps:

[0020] Step B1: Establish the objective function to maximize social benefits;

[0021] Step B2: Establish peak load constraints;

[0022] Step B3: Establish energy demand constraints;

[0023] Step B4: Establish reliability constraints, using the probability of load loss as an indicator to establish constraint conditions.

[0024] As a further solution, step C specifically includes the following steps:

[0025] Step C1: Define flexibility requirements;

[0026] Step C2: Formulate the probability function for renewable energy fluctuations, collect weather data, generate renewable energy supply data, set data locations and actual power generation ports, and use Gaussian distribution to calculate the cumulative distribution function (CDF) of renewable energy;

[0027] Step C3: Obtain the cumulative distribution function of the absolute ramping of renewable energy, express the absolute ramping capacity rate as the ramping rate, and express the statistical characteristics of ramping demand as the 99th percentile value of the ramping distribution.

[0028] As a further solution, step D specifically includes the following steps:

[0029] Step D1: Express the qualified capacity of energy storage in terms of the non-mandatory capacity UCAP;

[0030] Step D2: Calculate the capacity factor for each type of renewable energy;

[0031] Step D3: Calculate the non-mandatory capacity (UCAP) of energy storage.

[0032] As a further solution, step E specifically includes the following steps:

[0033] Step E1: Set capacity pricing as a primary consideration;

[0034] Step E2: Set the minimum and maximum clearing requirements, refer to the system operator, and provide the quotation parameters;

[0035] Step E3: Clear out the two scenarios, the basic scenario and the scenario with added renewable energy, to verify the contribution of different generators to flexibility and the effectiveness of flexibility constraints.

[0036] Compared with related technologies, the capacity auction method for battery energy storage provided by this invention, which considers flexibility constraints and battery energy storage, has the following advantages:

[0037] 1. This invention provides a new capacity auction mechanism that considers flexibility constraints and battery energy storage. This model takes into account the demand for flexibility ramp-up resources, ensures the benefits of flexible units, and has sufficient ramp-up capability.

[0038] 2. This invention explicitly considers the system flexibility requirements of wind power and solar power generation, thus enabling the formulation of reliability constraints to enhance the current capacity market model;

[0039] 3. This invention designs a special mechanism to represent the capacity value of energy storage; in addition, it conducts case studies on power systems with different renewable energy penetration rates, and the detailed analysis based on the results provides unique insights into the power generation of future new power systems. Attached Figure Description

[0040] Figure 1 A flowchart illustrating the basic steps of a capacity auction method considering flexibility constraints and battery energy storage, provided for an embodiment of the present invention;

[0041] Figure 2 Capacity skew demand curve provided for embodiments of the present invention;

[0042] Figure 3 The cumulative distribution function diagram of absolute wind energy ramp provided in the embodiments of the present invention;

[0043] Figure 4 The cumulative distribution function diagram of absolute solar ramp provided for embodiments of the present invention. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] like Figures 1 to 4 As shown, this embodiment provides a capacity auction method that considers flexibility constraints and battery energy storage.

[0046] Includes the following steps:

[0047] Step A: Determine a reasonable capacity demand curve and set the key parameters of the demand curve;

[0048] Step B: Construction of the basic auction model; establish an objective function to maximize social benefits, and consider peak load constraints, energy demand constraints, and reliability constraints to establish a basic auction model that considers multiple constraints.

[0049] Step C: Flexibility requirements are defined; considering ramping constraints and embedding capacity markets, the ramping capacity requirements of different generators are calculated.

[0050] Step D: Energy storage model construction; calculate the qualified capacity of each storage technology, optimize the operation of its equipment, and provide strategic support;

[0051] Step E: Design a reasonable settlement mechanism to promote investment and construction in energy storage.

[0052] It should be noted that this invention addresses the problem that the current capacity market lacks a clearing mechanism for flexible capacity, leading to consumers bearing additional costs for extra flexible resources. It proposes a new capacity auction mechanism that considers flexibility constraints and battery energy storage. This model takes into account the demand for flexible ramp-up resources, ensures the benefits of flexible units, and has sufficient ramp-up capability; thus maximizing the benefits of flexible units with sufficient ramp-up capability.

[0053] As a further solution, step A specifically includes the following steps:

[0054] Step A1: Select a sloping curve as the capacity demand curve and construct the sloping demand curve; model the capacity procurement level based on capacity price, if the capacity price is low, be willing to purchase higher capacity; if the capacity price is high, be willing to purchase lower capacity.

[0055] Step A2: Set the key parameters of the demand curve;

[0056] Step A3: Construct a supply curve for capacity supply quotes, and the auction will be settled at a variable and reasonable price.

[0057] It should be noted that:

[0058] For step A1: There are two types of capacity demand curves: sloping curves and vertical curves. Sloping curves are reasonable capacity demand curves. This embodiment selects sloping demand curves and models the capacity procurement level based on capacity price. If the capacity price is low, the company is willing to purchase higher capacity; if the capacity price is high, the company is willing to purchase lower capacity.

[0059] For step A2: Set the key parameters of the demand curve. Peak demand is set as the inflection point of the sloping curve, which represents the minimum capacity target of the market auction. The endpoint of the sloping curve is the maximum auction capacity defined by the system reserve ratio. In this case, the clearing price is determined by the reserve ratio. Higher capacity supply will reduce the purchase price, and vice versa.

[0060] For step A3: constructing the supply curve for capacity supply pricing, the auction will be settled at a variable and reasonable price, and the relevant auction curve is as follows: Figure 2 As shown.

[0061] As a further solution, step B specifically includes the following steps:

[0062] Step B1: Establish the objective function to maximize social benefits;

[0063] Step B2: Establish peak load constraints;

[0064] Step B3: Establish energy demand constraints;

[0065] Step B4: Establish reliability constraints, using the probability of load loss as an indicator to establish constraint conditions.

[0066] It should be noted that:

[0067] For step B1: The goal of capacity auction is to purchase sufficient electricity supply at the lowest cost to maintain the stable operation of the power system. The objective function is constructed to maximize social benefits; the specific formula for the objective function is: Obj = max(SW); This formula is then converted into a gradually decreasing curve: the specific formula is:

[0068]

[0069] The PD and ΔPD are respectively the minimum electricity demand target and the settlement part of the electricity demand change; and The division is between price constraints and variable prices; the x m and UG i Both are binary variables; the former is used to determine the clearing capacity of demand, and the latter is used to determine the clearing capacity of bids; This refers to the capacity quoted in the price quote.

[0070] For step B2: Establishing peak load constraints, the clearing capacity bid should be higher than the predicted capacity demand; the specific formula is:

[0071]

[0072] The subscripts {CG, IG, ES} refer to traditional energy units, intermittent units (wind and solar power units), and energy storage, respectively; PD T The specific formula is as follows:

[0073]

[0074] For step B3: establishing energy demand constraints, attention should be paid to energy demand; the specific formula is:

[0075] SER=PD T ·NH·PLF;

[0076] SER represents the system energy demand; SES represents the system energy supply; NH represents the load hours; and PLF represents the load factor (%) of the capacity quotation, the specific formula of which is as follows:

[0077]

[0078] The SES refers to the energy supply from all types of resources, including thermal energy, nuclear energy, and renewable energy. For system operation requirements, the SES should be higher than the SER. The specific formula is SES ≥ SED.

[0079] For step B4: Establish reliability constraints, which are based on the Loss of Load Probability (LOLP). The upper limit of this indicator. Constraints are established for the indicator, using the following formula:

[0080]

[0081] CAP represents capacity, and RM represents system reserve margin (%); a linear approximator is introduced and applied to this model; the specific formula is:

[0082]

[0083]

[0084] C1, C2, and C3 are predetermined constants that must be calculated using the detailed structure and data of the power system, and the constants differ for each jurisdiction.

[0085] As a further solution, step C specifically includes the following steps:

[0086] Step C1: Define flexibility requirements;

[0087] Step C2: Formulate the probability function for renewable energy fluctuations, collect weather data, generate renewable energy supply data, set data locations and actual power generation ports, and use Gaussian distribution to calculate the cumulative distribution function (CDF) of renewable energy;

[0088] Step C3: Obtain the cumulative distribution function of the absolute ramping of renewable energy, express the absolute ramping capacity rate as the ramping rate, and express the statistical characteristics of ramping demand as the 99th percentile value of the ramping distribution.

[0089] It should be noted that:

[0090] For step C1: the system flexibility requirement is defined as follows:

[0091]

[0092] RP represents the flexible ramping capacity supply, and RD represents the ramping capacity demand. Wind power and solar power generation are characterized by fluctuating randomness and require flexibility. In addition to the original changes in load p, the flexible supply should also meet the overall requirements.

[0093] For step C2: Determining the probability function for renewable energy fluctuations, collect weather data, generate renewable energy supply data, set data locations and actual power generation ports, and use Gaussian distribution to calculate the cumulative distribution function (CDF) of renewable energy.

[0094] For step C3: Obtain the cumulative distribution function of the absolute ramp-up of renewable energy, using the ramp-up rate to represent the absolute ramp-up capacity rate, and the 99th percentile value of the ramp-up distribution to represent the statistical characteristics of ramp-up demand; specifically as follows... Figure 3 , Figure 4 As shown.

[0095] As a further solution, step D specifically includes the following steps:

[0096] Step D1: Express the qualified capacity of energy storage in terms of the non-mandatory capacity UCAP;

[0097] Step D2: Calculate the capacity factor for each type of renewable energy;

[0098] Step D3: Calculate the non-mandatory capacity (UCAP) of energy storage.

[0099] It should be noted that:

[0100] For step D1: The qualified capacity of energy storage is represented by the non-mandatory capacity (UCAP). In addition to the energy stored during peak periods, the technical availability of energy storage, the duration of peak load, and the installed capacity are all important factors in calculating the non-mandatory capacity (UCAP).

[0101] For step D2: Calculate the capacity factor for each type of renewable energy; the specific formula is:

[0102]

[0103] The NHP refers to the peak load duration in hours. For step D3: calculating the non-mandatory capacity (UCAP) of energy storage; the specific formula is:

[0104]

[0105] ESP is the peak energy available for supply, ECP is the energy consumption during peak hours, NHP is the duration of peak hours, and FO is the forced outage rate (including maintenance time, etc.). In this way, energy storage can determine how to optimize the operation of its equipment and provide strategic support.

[0106] As a further solution, step E specifically includes the following steps:

[0107] Step E1: Set capacity pricing as a primary consideration;

[0108] Step E2: Set the minimum and maximum clearing requirements, refer to the system operator, and provide the quotation parameters;

[0109] Step E3: Clear out the two scenarios, the basic scenario and the scenario with added renewable energy, to verify the contribution of different generators to flexibility and the effectiveness of flexibility constraints.

[0110] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A capacity auction method considering flexibility constraints and battery energy storage, characterized in that, comprising the following steps: Step A: determining a reasonable capacity demand curve, and setting key parameters of the demand curve; Step B: basic auction model construction; a target function is established to maximize social benefits, and constraint conditions consider peak load constraints, energy demand constraints and reliability constraints, to establish a basic auction model considering multiple constraints; Step C: flexibility requirement formulation; considering ramping constraints, and embedding a capacity market, to calculate the demand for ramping capacity of different generators; Step D: storage model construction; calculating the qualified capacity of each storage technology, optimizing the operation of its equipment, and providing strategic support; said Step D specifically comprises the following steps: Step D1: representing the qualified capacity of the storage by the non-mandatory capacity UCAP of the storage; Step D2: calculating the capacity factor of each type of renewable energy; Step D3: calculating the non-mandatory capacity UCAP of the storage; Step E: designing a reasonable clearing mechanism to promote storage investment and construction.

2. The capacity auction method considering flexibility constraints and battery storage according to claim 1, wherein, said Step A specifically comprises the following steps: Step A1: selecting a tilted curve as the capacity demand curve, and constructing a tilted demand curve; based on the capacity price, modeling the capacity procurement level; Step A2: setting key parameters of the demand curve; Step A3: constructing a supply curve of capacity supply bids, and the auction will be cleared at a variable and reasonable price.

3. The method of capacity auction considering flexibility constraints and battery storage according to claim 1, wherein, said Step B specifically comprises the following steps: Step B1: establishing a target function to maximize social benefits; Step B2: establishing peak load constraints; Step B3: establishing energy demand constraints; Step B4: establishing reliability constraints, and establishing constraint conditions with load loss probability as an index.

4. The method of capacity auction considering flexibility constraints and battery storage according to claim 1, wherein, said Step C specifically comprises the following steps: Step C1: defining flexibility requirements; Step C2: formulating a probability function of renewable energy fluctuations, collecting weather data, generating supply data of renewable energy, setting data locations and actual power generation ports, and using Gaussian distribution to obtain the cumulative distribution function CDF of renewable energy; Step C3: obtaining the cumulative distribution function of renewable energy absolute ramping, expressing the absolute ramping capacity rate by ramping rate, and expressing the statistical characteristics of ramping demand by the 99th percentile value of ramping distribution.

5. The method of capacity auction considering flexibility constraints and battery storage according to claim 1, wherein, said Step E specifically comprises the following steps: Step E1: setting the capacity bid as the main consideration factor; Step E2: setting the minimum clearing demand and the maximum clearing demand, referring to the system operator and giving bid parameters; Step E3: clearing for two cases of basic conditions and renewable energy increase respectively, verifying the contribution size of different generators to flexibility and the effectiveness of flexibility constraints.

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