A quantitative analysis method for new energy utilization of power market and policy service

Abstract

The application relates to a quantitative analysis method of power market and policy service new energy utilization, belongs to the field of power market, and is used for quantitatively analyzing Chinese power market and policy service new energy. The method adopts a quantitative analysis model considering three types of elements of source network load physical boundary, power market and policy, quantitatively calculates and analyzes indexes of power market and policy service new energy utilization, can provide decision reference for guiding long-acting consumption of a high-proportion new energy system in a power market environment, and provides theoretical support for accelerating the formation of a Chinese power market mechanism and policy favorable to new energy consumption.

Description

Technical Field

[0001] This disclosure relates to the field of electricity market transactions, and in particular to a quantitative analysis method for electricity market and policy services for the utilization of new energy, which is used to model and provide quantitative guidance for the quantitative analysis of China's electricity market and policy services for the utilization of new energy. Background Technology

[0002] Since the launch of the new round of power system reform, China has issued a number of guiding opinions to guide the revolution in energy production and consumption, promote the adjustment of the energy industry structure, and promote the consumption of renewable energy. Adhering to the principle of "clean, low-carbon, safe, and efficient," a new mechanism for renewable energy consumption has been formed, characterized by government guidance, enterprise implementation, market promotion, and public participation. Currently, further research on the impact of the electricity market and policies on the utilization of new energy is a key issue that urgently needs to be addressed.

[0003] From the perspective of the physical boundaries of power generation, grid, and load, the randomness and volatility of renewable energy generation have a significant impact on the stability of the power system. Basic physical boundary conditions are the primary conditions that the power grid must meet to ensure safe and reliable operation. Therefore, dispatching and management agencies need to scientifically and rationally arrange operating modes to meet the demand for large-scale grid connection of renewable energy and avoid wind and solar power curtailment. From a market perspective, China's electricity trading is still in the exploratory stage of market-based trading, with imperfect market mechanisms and a low degree of marketization. From the perspective of renewable energy development policies, with the development of the electricity system marketization, carbon emission trading markets, quota systems, and green certificate systems are becoming increasingly sophisticated. Studying the impact of these electricity market mechanisms and policies on renewable energy utilization and optimizing policies to promote renewable energy consumption has become a key research issue.

[0004] However, there is currently no quantitative assessment model that comprehensively considers the impact of three key factors on the utilization of new energy sources: the physical boundary between power generation, grid and load, the electricity market, and policies. The effectiveness of these three key factors in serving the utilization of new energy sources is difficult to quantify and measure, and even more difficult to provide decision support for improving the electricity market and policies. Summary of the Invention

[0005] To overcome the aforementioned problems, the present invention aims to provide a quantitative analysis method for electricity market and policy services supporting the utilization of renewable energy. This method establishes a quantitative analysis model considering three key elements: the physical boundaries of power generation, grid, and load; the electricity market; and policy. It quantitatively measures and analyzes indicators related to electricity market and policy services supporting renewable energy utilization, providing new decision-making support for formulating electricity market mechanisms and policies that ensure grid economics while improving renewable energy absorption capacity. To achieve the above objective, the present invention adopts the following technical solution.

[0006] This invention proposes a quantitative analysis method for the utilization of new energy under the power market and policy services. The method adopts a quantitative analysis model with three types of constraints: source-grid-load physical boundary, power market, and policy, to quantitatively measure and analyze the utilization indicators of new energy under the power market and policy services.

[0007] The quantitative analysis model includes an objective function and constraints;

[0008] The objective function is as follows:

[0009]

[0010] Where m is the region index, M is the region set, and π is the total operating cost of the power system. Let m be the generator operating cost in region m. Let m be the deep peak-shaving cost of the generator sets in region m. The transaction cost of green certificates in region m. The cost of carbon emission trading in region m. Let m be the cost of wind and solar curtailment in region m;

[0011] Generator operating costs The calculation is as follows:

[0012]

[0013] Deep peak shaving cost of generator sets The calculation is as follows:

[0014]

[0015] Green certificate transaction costs The calculation is as follows:

[0016]

[0017] Carbon emission trading costs The calculation is as follows:

[0018]

[0019] Costs of wind and solar power curtailment The calculation is as follows:

[0020]

[0021] In the formula: n is the index of thermal power unit, t is the time index, r is the index of renewable energy unit, and N m Let m be the set of thermal power units within region m, and T be the total number of scheduling periods. Let u be the starting cost of the nth thermal power unit in region m. m，n，tLet be the start-up signal of the nth thermal power unit in region m at time t. Let v be the shutdown cost of the nth thermal power unit in region m. m，n，t Let a be the shutdown signal of the nth thermal power unit in region m at time t. m，n Let P be the coefficient of the quadratic term of the cost function for the nth thermal power unit in region m. m，n，t Let b be the output of the nth thermal power unit in region m at time t. m，n Let d be the coefficient of the first term in the cost function of the nth thermal power unit in region m. m，n Let p be the constant term of the cost function of the nth thermal power unit in region m. p Y is the peak-shaving cost coefficient. m，n，t Let be the peak shaving amount of the nth thermal power unit in region m at time t. p represents the number of green certificates purchased within region m. g To unify the price of green certificates in the national green certificate market, p c C is the unit price of carbon emission rights. m，n For the additional carbon emission rights purchased by the nth thermal power unit in region m within a dispatch cycle, p r The cost of penalties for abandoning wind and solar power, To predict the output of the r-th renewable energy unit within region m at time t, This represents the actual power generation of the r-th renewable energy unit within region m.

[0022] The constraints include physical boundary constraints of power generation, grid, and load; electricity market constraints; and policy constraints.

[0023] The source-grid-load physical boundary constraints consist of system constraints and unit constraints; wherein: system constraints include power balance constraints, reserve constraints, and line power flow constraints; unit constraints include unit status constraints, minimum start-up and shutdown time constraints, unit output constraints, maximum ramp constraints, and unit output constraints with maximum ramp constraints.

[0024] The power balance constraints are as follows:

[0025]

[0026]

[0027] The alternative constraints are as follows:

[0028]

[0029] The power flow constraints for the line are as follows:

[0030]

[0031]

[0032] The unit's state constraints are as follows:

[0033]

[0034]

[0035] The minimum start-stop time constraints are as follows:

[0036]

[0037]

[0038] The unit output constraints are as follows:

[0039]

[0040]

[0041] Maximum gradeability constraint:

[0042]

[0043] The electricity market constraints include constraints on power generation rights trading, constraints on direct power purchase by large users, and constraints on deep peak shaving.

[0044] The constraints on power generation rights trading are as follows:

[0045] During operation, the power plant units will generate electricity according to the contracted power volume plan to meet the following requirements:

[0046]

[0047] After the power generation rights are swapped, the actual power generation of thermal power units is equal to the difference between the contracted power generation and the swapped power generation rights.

[0048]

[0049] Actual power generation of new energy units:

[0050]

[0051]

[0052] The quantity relationship of power generation rights transactions is as follows:

[0053]

[0054] The constraints for direct power purchase transactions by large users are (18)-(19) or (20)-(21):

[0055]

[0056]

[0057]

[0058]

[0059] The deep peak-shaving constraints are as follows:

[0060]

[0061] The policy constraints include renewable energy quota and green certificate policy constraints, and carbon emission trading policy constraints.

[0062] The constraints of the renewable energy quota system and green certificate policy are as follows:

[0063]

[0064] The carbon emissions trading policy is subject to the following constraints:

[0065]

[0066]

[0067] In equations (1)-(25):

[0068] b is the node index, B m Let B be the set of power system busbars within region m. m，t This is for the hot standby of region m at time t. To pre-allocate carbon emission allowances for the nth thermal power unit in region m within one scheduling cycle, C m，n The additional carbon emission rights purchased for the nth thermal power unit in region m within one dispatch cycle. Let D be the maximum total carbon emissions of region m within one scheduling cycle. m，b，t Let the load of the b-th bus in region m at time t be... The contracted power volume for the nth thermal power unit in region m within one dispatch cycle. The total contracted electricity volume in region m participating in direct purchase transactions with large users. To limit the maximum transmission capacity of the l-th transmission line in region m, The maximum transmission capacity limit of the tie line between regions m and m' is... The branch-thermal power unit transfer distribution factor matrix, For the branch-new energy unit transfer distribution factor matrix, H m，l，b Let L be the branch-node transfer distribution factor matrix. m Let N be the set of transmission lines within region m. mLet P be the set of thermal power units within region m. m，n，t Let P be the output of the nth thermal power unit in region m at time t. m，n，t-1 Let n be the output of the nth thermal power unit in region m at time t-1. To determine the maximum output of the nth thermal power unit in region m, To determine the minimum output of the nth thermal power unit in region m, The amount of electricity transferred from region m to region m′ at time t. The amount of electricity transferred from region m′ to region m at time t is... Let n be the actual power generation of the nth thermal power unit in region m. For the direct purchase of electricity by the nth thermal power unit in region m, ΔP m，n，t Let R be the power generation rights trading volume of the nth thermal power unit in region m at time t. m For the collection of renewable energy units within region m, RD m，n Let t be the maximum downhill slope at time t for the nth thermal power unit in region m. To determine the maximum downhill gradient of the nth thermal power unit in region m at the time of shutdown, RU m，n Let be the maximum uphill climb of the nth thermal power unit in region m at time t. Let m be the maximum uphill gradient of the nth thermal power unit in region m at startup. Let n be the minimum continuous operating time of the nth thermal power unit in region m. u is the minimum continuous shutdown time of the nth thermal power unit in region m. m，n，t Let u be the start-up signal of the nth thermal power unit in region m at time t. m，n，τ Let v be the start-up signal of the nth thermal power unit in region m at time τ. m，n，t Let v be the shutdown signal of the nth thermal power unit in region m at time t. m，n，τ W represents the shutdown signal of the nth thermal power unit in region m at time τ. m，r，t For the output of the r-th renewable energy unit in region m at time t, To predict the output of the r-th renewable energy unit in region m at time t, Let r be the actual power generation of the r-th renewable energy unit within region m. For the direct purchase of electricity by the rth renewable energy unit in region m, The number of green certificates purchased for region m, ΔW m，r，t Let x be the power generation rights trading volume of the r-th renewable energy unit in region m at time t. m，n，t Let x be the start-up / shutdown state of the nth thermal power unit in region m at time t. m，n，t-1 Let x be the start-up and shutdown state of the nth thermal power unit in region m at time t-1. m，n，τLet Y be the start-up and shutdown state of the nth thermal power unit in region m at time τ. m，n，t Let α be the peak load of the nth thermal power unit in region m at time t. m Let m be the quota requirement for region m, m′ be the region index, and Λ be the set of interconnected regions, Λ = {(m, m′)}. m τ is the set of interconnected regions that are interconnected with region m. P and τ W These represent the proportions of thermal power and new energy in the bundled transactions, η m Let be the carbon-to-electric conversion coefficient of the thermal power unit in region m.

[0069] The aforementioned technical solution addresses the challenges of quantitatively evaluating indicators related to the expanding scale of renewable energy market transactions and the gradual improvement of policies. It overcomes the difficulty in quantifying and evaluating the role of various market mechanisms and policies in serving renewable energy utilization, and solves the problem of quantitatively analyzing "the extent to which the power market and policies play a role." The adopted quantitative analysis model integrates the impact of the physical boundaries of power generation, grid, and load, the power market, and policies on renewable energy utilization. It is applied to the quantitative calculation of indicators related to the power market and policies serving renewable energy utilization, providing decision-making references for guiding the long-term absorption of high-proportion renewable energy systems in the power market environment, and providing theoretical support for accelerating the formation of a Chinese power market mechanism and policies conducive to renewable energy absorption. Attached Figure Description

[0070] Figure 1 1 is a schematic diagram of a quantitative analysis model in one embodiment;

[0071] Figure 2-1 Here is a schematic diagram of the load curve and output curve of the sending-end power grid in one embodiment;

[0072] Figure 2-2 Here is a schematic diagram of the load curve and output curve of the receiving-end power grid in one embodiment;

[0073] Figure 3-1 Here is a schematic diagram of the renewable energy utilization rate of the sending-end power grid in one embodiment;

[0074] Figure 3-2 Here is a schematic diagram of the renewable energy utilization rate of the receiving-end power grid in one embodiment;

[0075] Figure 4-1 Here is a schematic diagram illustrating the proportion of renewable energy generation in the sending-end power grid in one embodiment;

[0076] Figure 4-2 Here is a schematic diagram illustrating the proportion of renewable energy generation in the receiving-end power grid in one embodiment;

[0077] Figure 5-1 This is a schematic diagram of the price quotation curve for thermal power units in the sending-end power grid in one embodiment;

[0078] Figure 5-2 This is a schematic diagram of the price quotation curve for thermal power units in the receiving-end power grid in one embodiment;

[0079] Figure 6-1 Here is a schematic diagram of the electricity price curve for the sending-end grid node in one embodiment;

[0080] Figure 6-2 Here is a schematic diagram of the electricity price curve for the receiving-end grid node in one embodiment;

[0081] Figure 7-1 Here is a schematic diagram of the total surplus in the sending-end power grid market in one embodiment;

[0082] Figure 7-2 This is a schematic diagram of the total surplus in the receiving-end power grid market in one embodiment;

[0083] Figure 8-1 This is a schematic diagram illustrating the average generation cost of the sending-end power grid in one embodiment;

[0084] Figure 8-2 The diagram below illustrates the average generation cost of the receiving-end power grid in one embodiment. Detailed Implementation

[0085] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0086] In one embodiment, the quantitative analysis method for renewable energy utilization in the electricity market and policy services of this invention is implemented through the following steps:

[0087] (1) Analysis of key elements of electricity market services for renewable energy utilization

[0088] 1) Source-grid-load physical boundary elements

[0089] The source side represents the physical boundaries of generator unit operation, primarily including safe operating boundaries, maximum ramp rate boundaries, and minimum start-up / shutdown time boundaries. Firstly, the safe operating boundary refers to the requirement that the unit must meet the corresponding upper and lower output limits and cannot operate outside the output range. The maximum output limit depends on the rated capacity designed and manufactured by the unit, which is the maximum capacity that can be continuously output throughout its specified normal service life. The minimum output limit refers to the minimum output required for the boiler to burn stably and the generator unit to operate continuously and stably under technical constraints. Due to the random and intermittent nature of photovoltaic and wind power output, large-scale grid connection and absorption of photovoltaic and wind power have increased the peak-valley difference in load. Conventional peak shaving by thermal power units alone is insufficient to meet the output fluctuations of the existing installed capacity of wind and photovoltaic power. This can be alleviated by modifying thermal power units to enable flexible deep peak shaving operation. Secondly, the maximum ramp rate boundary refers to the requirement that the output change between two consecutive adjacent time periods cannot exceed a certain threshold during continuous operation. Typically, the ramp rate of thermal power units is 10%–20% of rated power per hour, while gas-fired and oil-fired units have a faster ramp rate, reaching 50%–100% of rated power per hour. The maximum ramp rate boundary impacts renewable energy consumption by flexibly responding to drastic fluctuations in renewable energy output. Thirdly, the minimum start-up / shutdown time boundary refers to the minimum continuous start / stop time of the unit. This boundary also impacts renewable energy consumption by flexibly responding to drastic fluctuations in renewable energy output. When renewable energy fluctuates drastically, it is necessary to start or shut down the unit. If the minimum start-up / shutdown time of the system units is long, and physical limitations prevent starting or stopping, then renewable energy power must be discarded. If the minimum start-up / shutdown time of the system units is short, allowing for immediate start-up or shutdown, then renewable energy power can be supplied to the system.

[0090] The power grid is a power transmission network, and its physical boundary is the maximum transmission capacity boundary of the lines. The impact on renewable energy consumption mainly lies in insufficient maximum transmission capacity, leading to line congestion. The maximum transmission capacity boundary of a line refers to the power flowing through the line not exceeding a certain threshold, typically modeled using DC power flow. When studying renewable energy consumption within a province, changes in inter-provincial transmission channels, resulting in an increase or decrease in exported power, can be equivalent to a decrease or increase in intra-provincial load. By adding new inter-regional AC / DC transmission channels or improving the transmission capacity of key sections, the overall transmission capacity of the power grid can be enhanced, thereby improving the grid's renewable energy consumption capacity.

[0091] Power system load refers to the total power consumed by all electrical equipment in the system, including rotating and non-rotating loads. In a power system, a balance between generation and load must be maintained at all times. Power balance refers to the balance between power supply and demand, requiring the total generation and total load of the power system to remain balanced at all times. Imbalances in active power lead to changes in system frequency; insufficient active power sources cause frequency drops, and vice versa. Imbalances in reactive power lead to changes in system voltage; insufficient reactive power sources cause voltage drops, and vice versa. Traditionally, generation should be dispatched to meet load demand. However, with the increasing proportion of renewable energy capacity and the decreasing proportion of controllable thermal power, the power system's adjustability and ability to cope with renewable energy fluctuations have declined. Therefore, it is necessary to start from the load side, adjusting the load curve to reduce the adjustment pressure on the generation side. Demand-side response and energy storage methods can be introduced.

[0092] 2) Market Factors

[0093] Market elements include power generation rights trading, deep peak-shaving ancillary services, and direct power purchase transactions by large users. Power generation rights trading refers to the transfer of power generation shares held by power generation companies to other power generation companies through market trading platforms via bilateral negotiations, centralized bidding, or listing. This involves the paid transfer of power generation shares to other power generation companies, which then replace their existing power generation. Traditional power generation rights trading follows a high-low matching rule, matching and clearing based on the bids of conventional power generators from high to low and the bids of renewable energy power generators from low to high. This clearing rule fails to consider the positive environmental externalities of renewable energy generation, thus failing to fully motivate conventional energy power generators to participate in power generation rights trading. In a combined electricity and carbon market environment, power generation rights trading aims to minimize power generation costs through joint clearing of renewable and conventional power generation units. As carbon cost transmission leads to increased power generation costs for conventional units, renewable energy power generators increase their power generation, thereby increasing their revenue. Meanwhile, renewable energy generators can realize the consumption of curtailed wind and solar power by purchasing power generation rights. The revenue of renewable energy generators is the difference between the local benchmark electricity price and renewable energy subsidies, minus the difference between variable costs and fixed costs. Since the variable costs of renewable energy power generation are relatively low, there is a large room for electricity price in the power generation rights trading market.

[0094] The peak-shaving capacity of thermal power units refers to their ability to track changes in system load. Based on the unit's stable combustion state and combustion medium, the peak-shaving process of thermal power units can be divided into conventional peak-shaving and deep peak-shaving. Deep peak-shaving ancillary services require that the sum of the actual output of the thermal power unit and the deep peak-shaving amount be greater than or equal to the minimum output of the thermal power unit. China's power structure, dominated by coal, necessitates the development of peak-shaving ancillary service trading. With the increasing proportion of renewable energy, incentivizing thermal power to tap into its flexible adjustment capabilities through peak-shaving, thereby enhancing system flexibility and promoting renewable energy consumption, will become the norm. Segmented price limits based on thermal power adjustment capabilities are beneficial for improving market efficiency and meeting the physical conditions for flexible peak-shaving by thermal power.

[0095] Direct power purchase transactions by large users refer to transactions between qualified power users and power generation companies, conducted on a voluntary basis through bilateral negotiation or centralized bidding, with the power grid company responsible for providing power transmission services. This transaction method directly liberalizes the power purchase and sale rights of power generation companies and power users, helping the market play a decisive role in the optimal allocation of resources, forming a situation with multiple sellers and buyers, and improving the electricity price formation mechanism and the formation of a competitive market. Participation of large users in renewable energy consumption refers to direct transactions between power users and power generation companies. Economic measures incentivize large power users to adjust their production shifts and electricity demand, actively participating in renewable energy consumption. Renewable energy has low marginal costs and a price advantage, allowing for preferential electricity prices to attract large-scale users, thus ensuring better consumption of clean energy and benefiting energy-intensive enterprises. Furthermore, new energy units can participate in direct purchase transactions together with thermal power units, implementing a transaction method where new energy power and thermal power are sold in a bundled manner according to a certain ratio. Because large-scale participation of renewable energy in inter-provincial direct power purchase can impact the security and stability of the power grid, bundling renewable energy for inter-provincial direct power purchase can not only achieve peak shaving and valley filling by leveraging the output characteristics of different generating units to ensure system security, but also fully utilize the advantage of low marginal generation costs of renewable energy. This approach promotes the diversification of renewable energy consumption pathways, guarantees basic renewable energy generation, encourages the development of the renewable energy industry, and is conducive to expanding competition on the generation side.

[0096] 3) Policy elements

[0097] Policy elements include renewable energy quota systems, green certificate policies, and carbon emission trading policies. Renewable energy electricity quotas refer to the minimum renewable energy electricity consumption ratios stipulated for each provincial-level administrative region's total electricity consumption, based on national renewable energy development goals and energy development plans. Under the quota system, various market players will seek the lowest cost (or maximum benefit), highest efficiency, and most flexible methods to meet quota requirements through the market. The implementation of the renewable energy quota system is conducive to promoting the development of the renewable energy market and building a new and effective electricity market system. The quota system is a quantity- and market-based system. Its impact on serving the utilization of new energy is mainly reflected in the following aspects: First, the quota system reflects the external value of renewable energy electricity, reasonably solving the problem of price difference sharing between renewable and conventional electricity. That is, it uses legal means to distribute environmental benefits and costs among all electricity products, mobilizing the initiative and enthusiasm of market participants and creating conditions for fair competition of renewable energy electricity in the entire energy market. Secondly, clear quota development targets not only guarantee the quantitative development goals of renewable energy over a long period but also ensure market demand for renewable energy power generation, enhancing the confidence of investors, developers, and equipment providers, stimulating the investment enthusiasm of power generators, improving production technology, increasing production efficiency, reducing development costs, and ultimately achieving optimal resource allocation. Thirdly, the green certificate trading mechanism, as a supporting mechanism to the quota system, makes the trading of renewable energy power more flexible and liquid, facilitating the formation of a unified market, promoting the exchange of funds and resources between regions, and optimizing the development and utilization of renewable energy resources on a larger scale. Fourthly, the policy framework is stable, with low government regulatory costs. Achieving renewable energy targets through minimal government administrative participation helps reduce government management costs.

[0098] Green certificates are electronic certificates with unique identification codes issued by the state to power generation enterprises for each megawatt-hour of non-hydropower renewable energy electricity fed into the grid. They serve as confirmation and proof of the attributes of non-hydropower renewable energy generation and are the sole credential for consuming green electricity. The renewable energy green electricity certificate trading system effectively complements and supplements the quota system. All electricity produced by renewable energy power generation enterprises will receive green certificates, which can be freely traded on the market. Therefore, power purchasers have multiple ways to meet quota requirements: they can directly purchase electricity generated by renewable energy power generation enterprises; when there are problems with power transmission, they can purchase green certificates from renewable energy power generation enterprises to meet quota requirements. This system gives renewable energy electricity two commodity attributes: one is normal electricity, sold on the electricity market like thermal power; the other is green certificates, which represent its environmental attributes and generate revenue through market sales. For the entire renewable energy power generation industry, the price of a green certificate includes the difference between its cost and that of non-renewable energy generation. The trading of green certificates spreads this cost difference throughout the power generation industry, transferring the inherent cost disadvantage of using renewable energy power generation to other power plants with cost advantages due to their energy types and technological advantages.

[0099] Carbon emissions trading is a market-based environmental regulation. According to the "Administrative Measures for Carbon Emission Trading (Trial)" and the "Interim Measures for the Administration of Voluntary Greenhouse Gas Emission Reduction Trading," power companies included in the carbon trading system can trade both carbon emission allowances and certified emission reductions from voluntary emission reduction projects, using various trading methods such as public trading and negotiated transfers. An offsetting mechanism is established, allowing power companies subject to emission controls to use a certain proportion of certified voluntary emission reductions to fulfill their carbon trading compliance targets. For the power industry, carbon emission reduction tasks will encourage the industry to accelerate technological research and development in clean power generation, transmission, distribution, and consumption. To meet carbon emission constraints, power generation companies will reduce their own carbon emissions through technological upgrades, strengthened energy efficiency management, optimized operation, adoption of clean energy power generation technologies, carbon capture technologies, etc., or by purchasing sufficient carbon emission allowances from the market. Under the carbon emissions trading system, the establishment of the carbon trading market will improve the cleanliness and power generation efficiency of thermal power, increase the proportion of clean energy power generation in total power generation, and the power industry will gradually shift to a low-carbon development model. For new energy companies, since new energy power generation produces almost no carbon emissions... Carbon dioxide emissions are projected to peak in 2030, meaning that continued increases in carbon emissions will require more carbon allowances. This demand exceeding supply will drive up carbon prices, increasing costs for thermal power plants. Renewable energy plants, however, do not incur this cost, which will allow them to maintain a sustained competitive advantage in the market.

[0100] The integration of the carbon market and the electricity market will promote the utilization and development of new energy sources. With the establishment of a national carbon market, under the constraint of total carbon emissions, power companies will be compelled to conserve energy and reduce emissions, develop and utilize low-carbon and energy-saving technologies, and vigorously promote the development of low-carbon energy sources such as wind power and photovoltaic power. Simultaneously, carbon prices will be incorporated into the costs of thermal power plants, reducing their market competitiveness and providing development space for renewable energy. On the other hand, while my country has abundant renewable energy resources such as wind and solar power, most are located far from load centers. By accelerating the establishment of a unified national electricity market that can provide flexible trading such as real-time balancing and ancillary services, and combining the electricity market with the carbon market, China will be encouraged to optimize resource allocation through inter-provincial power transmission. Clean energy sources such as hydropower in the Northwest and Southwest will be consumed in a wider area, promoting the optimized allocation of clean energy on a larger scale and facilitating investment, construction, and utilization of clean energy.

[0101] (2) Construction of a quantitative assessment model for the impact of electricity market and policies on renewable energy utilization

[0102] like Figure 1 As shown, the method of the present invention adopts a quantitative analysis model that considers three types of constraints: the physical boundary of the source, grid, and load, the electricity market, and policies.

[0103] 1) Model symbol set

[0104]

[0105]

[0106]

[0107] 2) Modeling of physical boundary constraints of source network and load

[0108] The physical boundary elements of the source-grid-load system are modeled as system constraints and unit constraints. System constraints include power balance constraints, reserve constraints, and line power flow constraints. Unit constraints include unit status constraints, minimum start-up and shutdown time constraints, unit output constraints, and maximum ramp constraints.

[0109] First is the power balance constraint. Power balance in a power system refers to the balance between power supply and demand, requiring that the total power generation and total load of the power system be kept in balance at all times.

[0110]

[0111]

[0112] Constraint (1) represents the power balance of region m, that is, the total power generation in the region is equal to the sum of the total load power and the total power transmitted to other regions. Constraint (2) represents the logical relationship of the interconnection power between regions, ensuring the uniqueness of the transmitted power. This indicates taking an integer within a given range.

[0113] Secondly, there is the backup constraint. Constraint (3) considers using both thermal power units and new energy units as backup units.

[0114]

[0115] in, This indicates the maximum power generation capacity that the system can provide; This represents the load power used by the system, i.e., the load within the area and the power transmitted to other areas. The difference between the two is the spinning reserve provided by the system.

[0116] The third is line power flow constraint. The maximum transmission capacity boundary of a line refers to the power flowing through the line not exceeding a certain threshold, which is usually modeled using DC power flow as (4)-(5).

[0117]

[0118]

[0119] Among them, constraint (4) represents the line power flow constraint that should be satisfied within the region. Constraint (18) represents the line power flow constraint that should be satisfied between regions, which limits the upper limit of power exchange between any two regions m and m'.

[0120] The fourth is the unit state constraint. The unit state constraint refers to using three integer variables to constrain the start-up, shutdown and operation states of the unit, which are modeled as (6)-(7).

[0121]

[0122]

[0123] Constraint (6) indicates that the unit can only issue one of the two signals, namely, the start-up signal or the shutdown signal, at the same time. Constraint (7) describes the logical relationship between the start-up signal, the shutdown signal and the unit's operating status.

[0124] The fifth constraint is the minimum start-up and shutdown time. The minimum start-up and shutdown time boundary refers to the requirement that the unit should not be in a continuous start-up / shutdown state for less than a certain time limit, which is modeled as (8)-(9).

[0125]

[0126]

[0127] The sixth constraint is the unit output constraint. It is modeled as (10)-(11).

[0128]

[0129]

[0130] Constraints (10) and (11) represent the safe operating boundaries of thermal power generating units and new energy generating units, respectively. The unit operation should meet the corresponding upper and lower limits of output and cannot operate outside the output range.

[0131] The seventh constraint is the maximum ramp constraint. When the unit is running continuously, the output change between two consecutive adjacent time periods cannot exceed a certain threshold, which is modeled as (12).

[0132]

[0133] 3) Market constraint modeling

[0134] Market factors are modeled as constraints on power generation rights trading, direct power purchase trading, and deep peak shaving.

[0135] First, there are constraints on power generation rights trading. Power generation rights trading involves power generation companies transferring contracted electricity volume to other power generation companies through market trading platforms via bilateral negotiations, centralized bidding, or listing, thereby enabling clean energy power generation units to replace inefficient and high-polluting thermal power units.

[0136] Considering medium- to long-term electricity contracts, power plant units will generate electricity according to the contracted electricity volume plan during operation.

[0137]

[0138] After the power generation rights trading and electricity swap, the actual power generation of thermal power units is as follows:

[0139]

[0140] This constraint means that the actual power generation of a thermal power unit is equal to the difference between the contracted power generation and the power generation rights exchanged.

[0141] Actual power generation of new energy units:

[0142]

[0143]

[0144] The quantity relationship of power generation rights transactions is as follows:

[0145]

[0146] Secondly, there are constraints on direct power purchase transactions for large users. Direct power purchase by large users refers to a situation where, when a user's electricity consumption reaches a certain scale, the user purchases electricity directly from the power generation company without going through third-party restrictions. The electricity purchased by large users is transferred through the main power grid or through dedicated lines built by both parties. Two modeling methods can be considered for direct power purchase transactions by large users. The first is for new energy units and thermal power units to participate in intra-provincial direct power purchase transactions, where the actual power generation of each unit should exceed the amount of direct power purchase transactions it has signed.

[0147]

[0148]

[0149] The second modeling approach for large-user direct power purchase transactions considers bundling renewable energy units and thermal power units proportionally for participation in inter-provincial direct power purchase transactions. Since large-scale participation of renewable energy in inter-provincial direct power purchase transactions can impact grid security and stability, bundling renewable energy units not only achieves peak shaving and valley filling through the output characteristics of different units, ensuring system security, but also fully leverages the low marginal generation cost of renewable energy. In this case, the actual external power transmission volume should exceed the contracted direct power purchase transaction volume, where τ... P and τ W These represent the proportions of thermal power and new energy in bundled transactions.

[0150]

[0151]

[0152] Thirdly, there is the constraint of deep peak-shaving ancillary services. As the pressure to absorb new energy sources increases, there is a further requirement for thermal power units to provide peak-shaving services to free up power generation space.

[0153]

[0154] Constraint (22) indicates that the output of the thermal power unit can be lower than the minimum output of the unit. At this time, the unit can provide more regulation capacity, which is conducive to the consumption of renewable energy. Deep peak shaving usually requires oil injection operation, so its cost is higher and should be included in the operating cost.

[0155] 4) Policy constraint modeling

[0156] The policy elements are modeled as quota constraints and carbon emission trading constraints.

[0157] First, there are renewable energy quota constraints. The quota system sets renewable energy power consumption responsibility weights for electricity consumption based on provincial administrative regions. Market entities can fulfill quota requirements in two ways: one is by actually consuming renewable energy power, and the other is by purchasing green certificates.

[0158]

[0159] Constraint (23) means that entities participating in the renewable energy quota system must purchase or sell green certificates in a unified market.

[0160] Secondly, there are constraints on carbon emission trading. Carbon emission trading involves government agencies assessing the maximum carbon emissions within a given region that meet environmental capacity. The government assigns an initial share to each key emitting entity, and carbon emission rights can be bought and sold through the national carbon emission trading system. The main trading entities are thermal power plants, and currently only thermal power units have carbon emission quotas. In addition, carbon emission rights can be bought or sold on the secondary market. Considering that each region must meet its own quotas, the model is (24)-(25).

[0161]

[0162]

[0163] Constraint (24) states that the carbon emissions of each region shall not exceed the sum of the initially allocated carbon emission credits and the purchased carbon emission credits, while constraint (25) limits the number of carbon emission credits that can be purchased, i.e. the sum of carbon emission credits purchased by all entities in a region shall meet the upper limit.

[0164] 5) Objective function

[0165] This invention, while simultaneously considering economic, environmental, and renewable energy consumption benefits, takes as its objective function the minimization of system operating costs, the maximization of renewable energy consumption, and the optimization of the carbon economy, and comprehensively considers the linkage between the electricity market, the green certificate market, and the carbon emission market. The objective function consists of five parts of system operation, including the fuel costs of traditional thermal power units and the additional costs and subsidies brought by clean energy electricity. The additional costs mainly include: the deep peak-shaving costs of thermal power units to absorb more renewable energy, and the costs of coordinated markets, including carbon emission trading costs and green certificate costs under the quota system.

[0166]

[0167] st:

[0168] (1) Generator set operating costs:

[0169]

[0170] (2) Cost of deep peak shaving by generator sets:

[0171]

[0172] (3) Green certificate transaction costs:

[0173]

[0174] (4) Costs of carbon emission trading:

[0175]

[0176] (5) Costs of wind and solar power curtailment:

[0177]

[0178] (II) Case Analysis

[0179] The quantitative analysis method for renewable energy utilization in the power market and policy services designed in this invention was applied to a typical scenario of a typical power grid to conduct a case study, quantitatively measuring the indicators of renewable energy utilization in the power market and policy services. Basic data are shown in Table I.

[0180] Table I

[0181]

[0182]

[0183] Figure 2-1 and Figure 2-2 The figures show the load curves, thermal power output curves, and renewable energy output curves for the receiving and sending power grids in August. Specifically, for the sending power grid, renewable energy generation accounted for 37.13%, with a renewable energy utilization rate of 98.01%; for the receiving power grid, renewable energy generation accounted for 10.00%, with a renewable energy utilization rate of 99.89%.

[0184] First, physical indicators are calculated based on a quantitative analysis model, including the utilization rate of new energy sources and the proportion of new energy power generation. Figure 3-1 and Figure 3-2 This represents the renewable energy utilization rate of the sending and receiving power grids during August in this embodiment. The renewable energy utilization rate of the sending power grid varied from 75.94% to 100%; the renewable energy utilization rate of the receiving power grid varied from 74.10% to 100%. Figure 4-1 and Figure 4-2 The figures represent the proportion of renewable energy generation in the sending and receiving power grids during August in this embodiment. The proportion of renewable energy generation in the total load of the sending power grid varied from 0.31% to 97.94%, while the proportion of renewable energy generation in the total load of the receiving power grid varied from 0.21% to 38.42%.

[0185] Secondly, market indicators are calculated based on quantitative analysis models, including thermal power unit price curves, nodal electricity prices, market surplus, and average generation costs. Figure 5-1 and Figure 5-2This is the price quote curve for thermal power units in the sending and receiving power grids in this embodiment. As the declared output of the units increases, the total price quote for thermal power units in the sending and receiving power grids increases, with the price range being RMB 182.60 / MWh to RMB 477.8 / MWh. Figure 6-1 and Figure 6-2 The figures show the electricity price curves at the sending and receiving ends of the power grid in this embodiment. The electricity price at the sending end of the power grid fluctuates between RMB 136.28 / MWh and RMB 359.55 / MWh; the electricity price at the receiving end of the power grid fluctuates between RMB 177.33 / MWh and RMB 259.96 / MWh. Figure 7-1 and Figure 7-2 This represents the total market surplus of the sending and receiving power grids in this embodiment. The total market surplus is expressed as the difference between the transaction price and the declared price multiplied by the transaction volume. The market surplus of the sending power grid fluctuates between RMB 2.5652 million and RMB 1.4828 million; the market surplus of the receiving power grid fluctuates between RMB 79,200 and RMB 1.356 million. Figure 8-1 and Figure 8-2 This represents the average generation cost of the sending and receiving power grids in this embodiment. The average generation cost is expressed as the bid price of each generating unit multiplied by the transaction volume of each generating unit divided by the total electricity volume of all generating units. The average generation cost of thermal power units in the sending power grid fluctuates between RMB 184.47 / MWh and RMB 254.25 / MWh; the average generation cost of the receiving power grid fluctuates between RMB 189.75 / MWh and RMB 260.03 / MWh.

[0186] Finally, policy indicators, including the weight of grid absorption responsibility and carbon allowances, were calculated based on a quantitative analysis model. Table II shows the completion status of the grid absorption responsibility weight indicators for both the sending and receiving ends in this embodiment. Table III shows the carbon emissions and carbon allowance completion status of the sending and receiving ends in this embodiment.

[0187] Table II

[0188] Typical power grid indicators Provincial power grid A Provincial receiving-end power grid B Green electricity percentage (excluding power transmission) 37.13％ 10.00％ Minimum Responsibility Weight 20％ 13％ Incentive-based consumption responsibility weight 22％ 14.3％ Did the quota meet this month? yes no Green Certificate Sales / Purchase Volume 1666659MWh 459522MWh Green Certificate Price Setting 150 yuan / MWh 150 yuan / MWh Green Certificate Total Income / Expenditure 250 million yuan 68.93 million yuan

[0189] Table III

[0190] Typical power grid indicators Provincial power grid A Provincial receiving-end power grid B Unit carbon quota settings Both were 168,341 tons. Both were 179,082 tons. Total carbon emissions of all units 7,064,319 tons 8,564,796 tons Number of units that have not met carbon quotas 17 27 Percentage of units that have not met carbon quotas 36.2％ 61.4％ Carbon emission rights sold / purchased 847,718 tons sold Purchased 685,184 tons Carbon trading price setting 52.78 yuan / ton 52.78 yuan / ton Total revenue / expenditure from carbon trading 44.74 million yuan 36.16 million yuan

[0191] Through the above description of the embodiments, those skilled in the art can clearly understand that this disclosure can be implemented using software plus necessary general-purpose hardware, or it can be implemented using dedicated hardware including dedicated integrated circuits, dedicated CPUs, dedicated memory, dedicated components, etc. Generally, any function performed by a computer program can be easily implemented using corresponding hardware, and the specific hardware structure used to implement the same function can be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for this disclosure, software implementation is more often a preferred implementation method.

[0192] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A quantitative analysis method for electricity market and policy services for renewable energy utilization, characterized in that, The method employs a quantitative analysis model with three types of constraints: source grid-load physical boundary, electricity market, and policy, to quantitatively measure and analyze the utilization indicators of new energy under the services of the electricity market and policies. The quantitative analysis model includes an objective function and constraints; The objective function is as follows: Where m is the region index, M is the region set, and π is the total operating cost of the power system. Let m be the generator operating cost in region m. Let m be the deep peak-shaving cost of the generator sets in region m. The transaction cost of green certificates in region m. The cost of carbon emission trading in region m. Let m be the cost of wind and solar curtailment in region m; Generator operating costs The calculation is as follows: Deep peak shaving cost of generator sets The calculation is as follows: Green certificate transaction costs The calculation is as follows: Carbon emission trading costs The calculation is as follows: Costs of wind and solar power curtailment The calculation is as follows: In the formula: n is the index of thermal power unit, t is the time index, r is the index of renewable energy unit, and N m Let m be the set of thermal power units within region m, and T be the total number of scheduling periods. Let u be the starting cost of the nth thermal power unit in region m. m，n，t Let be the start-up signal of the nth thermal power unit in region m at time t. Let v be the shutdown cost of the nth thermal power unit in region m. m，n，t Let a be the shutdown signal of the nth thermal power unit in region m at time t. m，n Let P be the coefficient of the quadratic term of the cost function for the nth thermal power unit in region m. m，n，t Let b be the output of the nth thermal power unit in region m at time t. m，n Let d be the coefficient of the first term in the cost function of the nth thermal power unit in region m. m，n Let p be the constant term of the cost function of the nth thermal power unit in region m. p Y is the peak-shaving cost coefficient. m，n，t Let be the peak shaving amount of the nth thermal power unit in region m at time t. p represents the number of green certificates purchased within region m. g To unify the price of green certificates in the national green certificate market, p c C is the unit price of carbon emission rights. m，n For the additional carbon emission rights purchased by the nth thermal power unit in region m within a dispatch cycle, p r The cost of penalties for abandoning wind and solar power, To predict the output of the r-th renewable energy unit within region m at time t, This represents the actual power generation of the r-th renewable energy unit within region m. The constraints include physical boundary constraints of power generation, grid, and load; electricity market constraints; and policy constraints. The source-grid-load physical boundary constraints consist of system constraints and unit constraints; wherein: system constraints include power balance constraints, reserve constraints, and line power flow constraints; unit constraints include unit status constraints, minimum start-up and shutdown time constraints, unit output constraints, maximum ramp constraints, and unit output constraints with maximum ramp constraints. The power balance constraints are as follows: The alternative constraints are as follows: The power flow constraints for the line are as follows: The unit's state constraints are as follows: The minimum start-stop time constraints are as follows: The unit output constraints are as follows: Maximum gradeability constraint: The electricity market constraints include constraints on power generation rights trading, constraints on direct power purchase by large users, and constraints on deep peak shaving. The constraints on power generation rights trading are as follows: During operation, the power plant units will generate electricity according to the contracted power volume plan to meet the following requirements: After the power generation rights are swapped, the actual power generation of thermal power units is equal to the difference between the contracted power generation and the swapped power generation rights. Actual power generation of new energy units: The quantity relationship of power generation rights transactions is as follows: The constraints for direct power purchase transactions by large users are (18)-(19) or (20)-(21): The deep peak-shaving constraints are as follows: The policy constraints include renewable energy quota and green certificate policy constraints, and carbon emission trading policy constraints. The constraints of the renewable energy quota system and green certificate policy are as follows: The carbon emissions trading policy is subject to the following constraints: In equations (1)-(25): b is the node index, B m Let B be the set of power system busbars within region m. m，t This is for the hot standby of region m at time t. To pre-allocate carbon emission allowances for the nth thermal power unit in region m within one scheduling cycle, C m，n The additional carbon emission rights purchased for the nth thermal power unit in region m within one dispatch cycle. Let D be the maximum total carbon emissions of region m within one scheduling cycle. m，b，t Let the load of the b-th bus in region m at time t be... The contracted power volume for the nth thermal power unit in region m within one dispatch cycle. The total contracted electricity volume in region m participating in direct purchase transactions with large users. To limit the maximum transmission capacity of the l-th transmission line in region m, The maximum transmission capacity limit of the tie line between regions m and m' is... The branch-thermal power unit transfer distribution factor matrix, For the branch-new energy unit transfer distribution factor matrix, H m，l，b Let L be the branch-node transfer distribution factor matrix. m Let N be the set of transmission lines within region m. m Let P be the set of thermal power units within region m. m，n，t Let P be the output of the nth thermal power unit in region m at time t. m，n，t-1 Let n be the output of the nth thermal power unit in region m at time t-1. To determine the maximum output of the nth thermal power unit in region m, To determine the minimum output of the nth thermal power unit in region m, The amount of electricity transferred from region m to region m′ at time t. The amount of electricity transferred from region m′ to region m at time t is... Let n be the actual power generation of the nth thermal power unit in region m. For the direct purchase of electricity by the nth thermal power unit in region m, ΔP m，n，t Let R be the power generation rights trading volume of the nth thermal power unit in region m at time t. m For the collection of renewable energy units within region m, RD m，n Let t be the maximum downhill slope at time t for the nth thermal power unit in region m. To determine the maximum downhill gradient of the nth thermal power unit in region m at the time of shutdown, RU m，n Let be the maximum uphill climb of the nth thermal power unit in region m at time t. Let m be the maximum uphill gradient of the nth thermal power unit in region m at startup. Let n be the minimum continuous operating time of the nth thermal power unit in region m. u is the minimum continuous shutdown time of the nth thermal power unit in region m. m，n，t Let u be the start-up signal of the nth thermal power unit in region m at time t. m，n，τ Let v be the start-up signal of the nth thermal power unit in region m at time τ. m，n，t Let v be the shutdown signal of the nth thermal power unit in region m at time t. m，n，τ W represents the shutdown signal of the nth thermal power unit in region m at time τ. m，r，t For the output of the r-th renewable energy unit in region m at time t, To predict the output of the r-th renewable energy unit in region m at time t, Let r be the actual power generation of the r-th renewable energy unit within region m. For the direct purchase of electricity by the rth renewable energy unit in region m, The number of green certificates purchased for region m, ΔW m，r，t Let x be the power generation rights trading volume of the r-th renewable energy unit in region m at time t. m，n，t Let x be the start-up and shutdown state of the nth thermal power unit in region m at time t. m，n，t-1 Let x be the start-up and shutdown state of the nth thermal power unit in region m at time t-1. m，n，τ Let Y be the start-up and shutdown state of the nth thermal power unit in region m at time τ. m，n，t Let α be the peak load of the nth thermal power unit in region m at time t. m Let m be the quota requirement for region m, m′ be the region index, and Λ be the set of interconnected regions, Λ = {(m, m′)}. m τ is the set of interconnected regions that are interconnected with region m. P and τ W These represent the proportions of thermal power and new energy in the bundled transactions, η m Let be the carbon-to-electric conversion coefficient of the thermal power unit in region m.

2. The method according to claim 1, characterized in that, The indicators for the utilization of new energy sources include physical indicators, market indicators, and policy indicators. Physical indicators include the utilization rate of new energy sources and the proportion of new energy power generation. Market-related indicators include thermal power unit price curves, nodal tariffs, market surplus, and average generation cost; Policy-related indicators include the weight of the responsibility for carbon absorption and carbon allowances.

3. The method according to claim 2, characterized in that, The consumption responsibility weights include: green electricity ratio, minimum consumption responsibility weight, incentive consumption responsibility weight, whether the quota is met in the current month, and the amount of green certificates sold / purchased.

4. The method according to claim 2, characterized in that, The carbon quotas include the carbon quota settings for generating units, the total carbon emissions of all generating units, the number of generating units that have not met their carbon quotas, the percentage of generating units that have not met their carbon quotas, and the amount of carbon emission rights sold / purchased.

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