Assessment and analysis method for coal-electricity blending combustion cost reduction in large-base joint mode

Abstract

The invention relates to the technical field of power production strategy optimization, in particular to a coal-electricity blending combustion cost reduction evaluation and analysis method in a large-base joint operation mode, which comprises the following steps of: establishing a coal-fired power generation cost model considering coal blending combustion, and considering wind power and photovoltaic power abandoning cost and energy storage operation cost; aiming at the uncertainty of new energy output, constructing and clustering to generate a wind and light typical scene set; and on the basis, a clean energy large-base operation optimization model is constructed by taking the minimum expected value of the total operation cost of the system as a target. By inputting different thermal power and coal blending combustion transformation capacities, the model is used for solving the optimal operation cost of the system under each scheme, and then the corresponding cost reduction amount is calculated and sorted, so that the optimal transformation capacity is optimized. According to the method, the problem that the real economic benefit of coal blending combustion in a complex environment cannot be accurately quantified by a traditional evaluation method is solved, and an accurate and reliable decision basis can be provided for coal-electricity blending combustion transformation in a joint operation mode.

Description

Technical Field

[0001] This invention relates to the field of power production strategy optimization technology, specifically to a method for assessing and analyzing cost reduction through coal-fired power co-firing under a large-scale joint venture model. Background Technology

[0002] Large-scale new energy bases, by integrating renewable energy sources such as wind and solar power and relying on the flexible adjustment capabilities of thermal power and energy storage, have become important carriers for energy transformation. Against this backdrop, the functional positioning of coal-fired power units is shifting towards peak shaving and flexible support to mitigate the intermittency and instability of new energy power generation. With the deepening of power market reforms, the large-scale base joint operation model has emerged, its core being the joint and optimized operation of various internal power sources. Simultaneously, influenced by multiple factors such as supply and demand, international situation, and transportation costs, coal prices are fluctuating more sharply, posing a severe challenge to the fuel cost control and operating efficiency of coal-fired power plants. Driven by policy guidance, technological demands, and market risks, coal blending technology—that is, scientifically mixing different quality coal types to partially replace high-priced market coal with economical coal types—has become a key means for thermal power plants to reduce costs and increase efficiency while achieving safe, stable, and efficient operation.

[0003] However, under the large-scale joint venture model, the traditional economic evaluation method for coal-fired power plant co-firing has the following inherent defects, which prevent it from providing a scientific and reliable basis for investment in technological transformation:

[0004] I. The problem of efficient dimensionality reduction and representativeness preservation in scenarios with massive uncertainty.

[0005] In large-scale integrated systems, historical data on wind and solar power output typically covers an annual timescale of 8760 hours or even longer, exhibiting complex and varied fluctuation patterns. Existing assessment methods either directly enumerate all original scenarios, leading to an explosion in the scale of operational optimization models and rendering calculations infeasible; or subjectively select a few typical days for simplification, but such simple sampling easily loses key fluctuation characteristics of renewable energy output (such as extreme conditions like high winds and low solar power, high solar power and low winds, and extremely low wind and solar power output), causing assessment results to deviate significantly from actual operating conditions. How to systematically retain the main statistical characteristics and typical fluctuation patterns of wind and solar power output while drastically reducing the number of scenarios is the primary technical bottleneck restricting the accurate quantification of co-firing benefits.

[0006] II. The issue of accurately quantifying the cost-saving effect of admixture modification under the coordinated operation of the system.

[0007] Traditional methods for assessing the economic benefits of co-firing are based on the assumption of independent unit operation, treating the fuel cost savings from co-firing as a fixed value independent of system operating status, which can be directly summed to obtain the total benefit. However, under the large-scale joint operation model, coal-fired power units have transformed from basic power sources to deep peak-shaving resources. Their output plans, load distribution, and start-up and shutdown methods are all determined by the random fluctuations of new energy sources, energy storage scheduling strategies, and system balance requirements. Co-firing retrofits not only change the fuel cost curve of the unit itself but also reshape the coordinated scheduling scheme of various power sources within the system by affecting the unit's economic output range. Existing technologies are usually based on the assumption of independent unit operation and use static, deterministic cost accounting methods, which are difficult to account for the multi-scenario probabilistic characteristics of wind and solar power output, the coordinated scheduling of energy storage, and the profound impact of system physical operating constraints on coal-fired power plant operation. Therefore, under the large-scale joint operation model, the cost savings brought by co-firing retrofits—especially the actual contribution under typical operating conditions such as large-scale new energy generation and long-term low-load operation of thermal power—cannot be accurately separated and quantified at the system level, resulting in a serious distortion in the economic assessment of technical retrofit investments and making it difficult to support scientific decision-making.

[0008] III. Quantitative optimization of the optimal renovation capacity and its support for investment decisions.

[0009] Coal-fired power plant co-firing retrofitting is a high-capital-expenditure technological upgrading project. If the scale of the upgrade is too small, the cost-reduction potential will not be fully realized; if the scale is too large, it faces the dual risks of sunk fixed costs and increased efficiency losses under low-load conditions. However, existing assessment methods can only perform isolated calculations for a few pre-set upgrade schemes. They cannot dynamically depict the trend of comprehensive operating cost changes under different upgrade intensities at the system level, nor can they quantitatively analyze the correlation between the scale of the upgrade and the economic efficiency of system operation under given boundary conditions such as wind and solar resources, load characteristics, energy storage configuration, and fuel market. Investment decisions have long been trapped at the level of "empirical estimation" and "qualitative comparison," making it difficult to achieve quantitative optimization of capital investment and risk avoidance.

[0010] In summary, there is currently a lack of a systematic evaluation and analysis method for reducing co-firing costs that can comprehensively account for the dimensionality reduction of wind and solar uncertainties, the coupling mechanism of multi-energy collaborative operation, and the optimization of marginal benefits of retrofitted capacity. This technological gap not only prevents the economic viability of huge technological upgrade investments from being scientifically verified at the joint venture level, but also hinders the pace of transformation and upgrading of coal-fired power enterprises and restricts the maximization of overall energy economy in large-scale power bases. Summary of the Invention

[0011] The purpose of this invention is to provide a method for assessing and analyzing the cost reduction of coal-fired power co-firing under a large-scale joint operation model. This method solves the problems of existing technologies, which cannot efficiently reduce the dimensionality of uncertain scenarios while retaining the key fluctuation characteristics of wind and solar power output, cannot accurately quantify the real marginal benefits of co-firing transformation at the system level that takes into account the coordinated operation of multiple energy sources and the probability weighting of multiple scenarios, and cannot achieve quantitative optimization and scientific decision-making for the scale of technological transformation investment.

[0012] To achieve the above objectives, a method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model is provided, including:

[0013] Step 1: Establish cost models for coal-fired power generation, wind power curtailment, photovoltaic power curtailment, and energy storage operation under the large-scale joint operation model, taking into account coal blending and combustion.

[0014] Step 2: Obtain historical power output data for wind and solar power under the large-scale joint operation model. Based on the historical power output data, construct an initial scenario set S that can characterize the uncertainty of wind and solar power output. Perform scenario dimensionality reduction processing on the initial scenario set to generate a typical scenario set containing several typical scenarios and their corresponding probabilities of occurrence. ;

[0015] Step 3: Using the aforementioned set of typical scenarios Based on the probability of occurrence of the coal-fired power generation cost model, wind power curtailment cost model, photovoltaic curtailment cost model and energy storage operation cost model in step S1, the total cost is minimized, and the operation constraints of the large base consortium are taken into account, to construct a clean energy large base operation optimization model.

[0016] Step 4: Obtain different coal-fired power plant co-firing and retrofit capacity schemes. Each scheme corresponds to a coal-fired power plant co-firing and retrofit capacity value. Use the retrofit capacity value as the new boundary condition for the coal-fired power system co-firing capacity in the Clean Energy Base Operation Optimization Model. Then call the Clean Energy Base Operation Optimization Model to solve the problem and obtain the expected total operating cost of the system corresponding to each scheme.

[0017] Step 5: Using the unmodified scheme as a benchmark, calculate the cost reduction of each scheme; sort the schemes and select the modification capacity value corresponding to the scheme with the largest cost reduction as the optimal coal blending modification capacity for thermal power.

[0018] Furthermore, the scene dimensionality reduction process employs a clustering algorithm to cluster the initial scene set, generating representative typical scenes and their probabilities.

[0019] Furthermore, the set of typical scenarios includes scenarios with combined wind and solar power output, and the probability of occurrence of each scenario is determined based on the proportion of the sample size represented by each typical scenario to the total sample size.

[0020] Furthermore, the clustering algorithm is the K-means clustering algorithm, and the number K of typical scenarios is determined by the elbow rule.

[0021] Furthermore, in step S3, the objective function of the clean energy large-scale base operation optimization model is to minimize the expected value of the total system operating cost, as expressed below:

[0022]

[0023] in: This represents a typical scenario index. Typical scenarios The probability, Indicates in the scene The total operating cost of the system includes the operating cost of coal-fired power plants that have not undergone coal blending and combustion modification, the operating cost of coal-fired power plants that have undergone coal blending and combustion modification, the cost of wind power curtailment, the cost of photovoltaic power curtailment, and the operating cost of energy storage.

[0024] Furthermore, the formula for calculating the total operating cost of the system is as follows:

[0025]

[0026] in, Typical scenarios Operating costs of coal-fired power plants that did not undergo coal blending and combustion modification in the next t period; Typical scenarios Operating costs of coal-fired power plants that have undergone coal blending and combustion modification in the next time period (t); Typical scenarios Cost of wind power curtailment in the next time period t; Typical scenarios Cost of solar power curtailment in the next time period t; Typical scenarios Operating costs of energy storage during the next time period t.

[0027] Furthermore, the physical constraints on system operation corresponding to minimizing the expected total operating cost of the system include power balance constraints, upper and lower limits of output of various power sources, ramping constraints, and operating constraints of the energy storage system.

[0028] Furthermore, in step S5, the cost reduction amount The calculation formula is:

[0029]

[0030] in, For the capacity of coal blending and combustion modification of thermal power plants, The baseline operating cost of the system when the capacity is 0. For the capacity to be upgraded The expected total operating cost of the system under multiple typical scenarios and probabilistic influences;

[0031] when When this occurs, it indicates that the system expects its total operating cost to decrease.

[0032] Furthermore, in step S1, the coal-fired power generation cost model considering coal blending is as follows:

[0033]

[0034] in, The output power of the coal-fired power plant is given by r, where r is the blending ratio; cost coefficient. Both are functions of r, used to characterize the combined impact of blending low-quality coal on fuel cost, efficiency, and fixed cost.

[0035] Furthermore, the blending ratio r is a continuous variable, and the cost coefficient... , , Calibration is performed using historical operating data or field tests to reflect the combined impact of different blending ratios on fuel costs, operating efficiency, and fixed costs.

[0036] Principles and advantages:

[0037] I. Addressing the issue of "efficient dimensionality reduction and representativeness preservation in scenarios with massive uncertainty"

[0038] This invention employs scene dimensionality reduction technology to compress the original massive uncertainties in wind and solar power output into several typical scenes and their corresponding probabilities of occurrence. This technology brings the following technical effects:

[0039] The unity of computational feasibility and statistical completeness: Compared with the "curse of dimensionality" caused by directly enumerating all original scenarios, this invention compresses the size of the optimization model to a solvable range through dimensionality reduction; Compared with the "feature loss" caused by subjectively selecting a single typical day, the typical scenario set generated by this invention retains the main statistical characteristics and typical fluctuation patterns of wind power and photovoltaic output in the original dataset (such as strong wind and weak solar power, strong solar power and weak wind, extremely low wind and solar power, and general operating conditions).

[0040] The fundamental improvement in robustness assessment lies in the fact that the optimization objective is to minimize the expected total cost. The decision results naturally cover various typical scenarios and their probability weights, rather than relying on a fixed prediction trajectory. This allows the assessment conclusions to robustly handle multiple uncertain implementation paths of wind and solar power output, fundamentally reducing the decision-making risk under a single scenario assumption.

[0041] II. Addressing the issue of "precisely quantifying the cost-saving effect of admixture modification under system collaborative operation"

[0042] This invention constructs an operational optimization model for large-scale clean energy bases with the objective of minimizing the expected total cost, and uses the capacity for coal blending retrofitting of thermal power plants as the input boundary conditions of the model (steps 3 and 4). This technical feature brings the following technical effects:

[0043] A paradigm shift from "independent accounting" to "system collaboration": Traditional methods treat fuel cost savings from co-firing as a fixed value independent of system operating status, a theoretical premise that no longer holds true under the joint venture model. This invention embeds the retrofitted capacity as an independent variable into the system optimization model. During the solution process, the model adaptively optimizes all decision variables, including coal-fired power output, wind and solar power consumption, and energy storage scheduling. The final output system operating cost incorporates a complete causal chain: "changes in retrofitted capacity → changes in the economic output range of coal-fired power → changes in multi-source collaborative scheduling schemes → changes in total cost."

[0044] Precise isolation and measurement of marginal benefits: By setting an unmodified scheme (modification capacity of 0) as a comparison benchmark, the cost difference under the same optimization framework is calculated. The resulting cost reduction purely reflects the net saving effect brought about by the change in modification capacity after global system optimization. This technical feature achieves for the first time the scientific isolation and quantitative verification of the marginal economic benefits of co-firing technology modification in a complex environment of multi-energy complementarity in large-scale bases.

[0045] III. Addressing the issue of "quantitative optimization of optimal renovation capacity and support for investment decisions"

[0046] This invention transforms technological upgrading investment decisions into a quantifiable and optimal engineering problem by iterating through different capacity modification schemes, calculating the cost reduction of each scheme, sorting them, and automatically selecting the optimal value (step 5). This technical feature brings the following technical effects:

[0047] An upgrade from "scheme comparison" to "marginal optimization": Existing technologies can only perform isolated calculations for a few preset discrete schemes, failing to reveal the continuous variation between "renovation capacity and cost reduction." This invention, through systematic enumeration and optimization, fully outputs the marginal benefit curves under different technological renovation intensities, enabling decision-makers to clearly identify the critical points of increasing, decreasing, or even negative returns.

[0048] Scientific Investment Decision-Making and Risk Aversion: When the capacity for upgrades is too small, the cost reduction is not yet saturated; when the capacity for upgrades is too large, the sunk costs of fixed costs and the losses due to low-load efficiency are aggravated, and marginal benefits decline or even turn negative. This invention provides investors with a clear and reproducible quantitative basis for decision-making by automatically identifying the optimal upgrade capacity that minimizes the expected total operating cost of the system. This effectively avoids the dual risks of "insufficient upgrades" and "over-investment," achieving Pareto optimality in capital allocation. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating an assessment and analysis method for reducing coal-fired power generation costs under a large-scale joint venture model, as described in an embodiment of the present invention.

[0050] Figure 2 The diagram shows power curve simulations for four typical scenarios. Detailed Implementation

[0051] The following detailed description illustrates the specific implementation method:

[0052] Example

[0053] A method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model, basically as follows: Figure 1 As shown, it includes:

[0054] Step 1: Establish cost models for coal-fired power generation, wind power curtailment, photovoltaic power curtailment, and energy storage operation under the large-scale joint operation model, considering coal blending. Calculate the coal-fired power generation cost based on the coal-fired power generation cost model. Analyze the wind power curtailment cost, photovoltaic power curtailment cost, and energy storage operation cost under the large-scale joint operation model using the wind power curtailment cost model, photovoltaic power curtailment cost model, and energy storage operation cost model, respectively. Existing models can be used for the wind power curtailment cost model, photovoltaic power curtailment cost model, and energy storage operation cost model, or relevant data can be obtained directly from historical data of wind power, photovoltaic, and energy storage. However, the core is the coal-fired power generation cost model, so it will not be elaborated further. To construct a cost model that reflects the principle of coal blending cost changes, it is necessary to first comprehensively analyze, quantify, and model each component affecting the total cost; second, construct an overall cost model by integrating various cost characteristics; and finally, establish a coal-fired power generation cost model considering coal blending based on the costs and impacts required for coal blending.

[0055] (1) Cost component analysis

[0056] The cost of coal-fired power units before the renovation can be further divided from a principle perspective, mainly considering fuel costs, environmental protection costs, operation and maintenance costs, and capital depreciation costs.

[0057] 1) Fuel cost: Fuel cost is the most significant variable cost of a coal-fired power unit, typically accounting for 60% to 70% of the total cost. Its relationship with power generation output is usually a quadratic function.

[0058]

[0059] in, This indicates the output power of the coal-fired power unit. The fuel cost characteristic coefficient of a coal-fired power unit can be obtained by fitting historical fuel consumption rate data under different loads.

[0060] 2) Environmental Costs: Under environmental regulations, coal-fired power units need to perform desulfurization, denitrification, and dust removal during power generation. The environmental cost function is as follows:

[0061]

[0062] in, This represents the cost characteristic coefficient of harmful gas emissions into the environment.

[0063] 3) Operation and maintenance costs: Operation and maintenance costs include fixed and variable components. Fixed components include employee salaries, daily management expenses, routine inspections, training, etc., which are unrelated to the unit's output. Variable components include the cost of consumables such as equipment wear and tear, lubricants, and chemicals. This part of the cost has a positive linear relationship with the unit's output.

[0064]

[0065] in, This represents the cost coefficient for the variable portion of linear operation and maintenance costs. This represents the coefficient for the fixed portion of operation and maintenance costs.

[0066] 4) Capital depreciation cost: Capital depreciation represents the amortization of the huge initial investment in the construction of the power plant over its service life.

[0067] Hourly fixed costs:

[0068]

[0069] in, This represents the hourly fixed cost, which is the fixed cost generated per hour when the unit is running, expressed in yuan / hour. This represents the annual depreciation cost, expressed in yuan per year. This represents the annual fixed operating and maintenance cost, which mainly includes the cost of regular maintenance, and is expressed in yuan / year. This indicates the planned operating hours for the year, which can be estimated using historical data, and is expressed in hours per year.

[0070]

[0071] in, Indicates initial investment; Indicates the discount rate; Indicates service life.

[0072] (2) Construction of the total cost function of the unit

[0073] The relationship between power generation cost and power output of coal-fired power units before coal blending and combustion modification is expressed by a classic quadratic polynomial function:

[0074]

[0075]

[0076]

[0077]

[0078] in, This indicates the output power of a coal-fired power plant; This represents the coefficient of the quadratic term, typically indicating a non-linear increase in fuel consumption costs. As output power increases, fuel consumption and other costs may increase at an accelerated rate. This represents the coefficient of the first-order term, which is affected by factors related to the linear power term, such as operating and maintenance costs and fuel costs. This refers to fixed costs unrelated to power, such as equipment depreciation and basic maintenance costs.

[0079] (3) The cost change after blending and burning coal is calculated using the coal-fired power generation cost model:

[0080] The benefits of coal blending are not simply "price difference gains." The addition of low-quality coal can lead to decreased boiler efficiency, increased equipment wear and tear, higher pollutant control costs, and even serious safety accidents such as coking and fire extinguishing. Therefore, the operating cost function of a coal-fired power plant after coal blending retrofitting can be expressed as a quadratic function, with each coefficient representing the blending ratio. Functions:

[0081]

[0082]

[0083]

[0084]

[0085] in, Represents a nonlinear cost coefficient function The first-order coefficient reflects the systematic impact of blending low-quality coal on the nonlinear part of the unit's consumption characteristic curve. Because it is a negative impact, It is usually a positive value; Represents the linear cost coefficient function The first-order coefficient is mainly determined by the difference in fuel prices; since low-quality coal has a low price, it is usually a negative value. Represents the linear cost coefficient function The quadratic coefficient is mainly determined by the nonlinear superposition effect of negative factors such as efficiency deterioration and increased auxiliary power consumption, and is usually a positive value; It is a fixed cost coefficient function The coefficient of the first term represents the additional fixed cost increase brought about by the unit blending ratio, and is usually a positive value.

[0086] Regarding energy storage operating costs:

[0087] The operating depreciation cost of the energy storage device at each time period t can be expressed as:

[0088]

[0089] in, The unit depreciation cost of energy storage equipment is expressed in yuan / kWh. The charging power of the energy storage device during time period t; Let t be the discharge power of the energy storage device during time period t. Here, it is assumed that charging and discharging have an equivalent impact on the device's lifespan.

[0090] 2) Costs of wind power curtailment and costs of solar power curtailment

[0091] Wind power curtailment cost and solar power curtailment cost , This is to guide the optimization model to prioritize the use of clean energy. It can generally be defined as:

[0092]

[0093]

[0094] in, This represents the unit cost coefficient for wind curtailment. It can be understood as an economic penalty for curtailed wind power, reflecting the principle of prioritizing clean energy. This represents the unit cost coefficient for curtailed solar power. Similarly, it is set to a value to encourage the priority use of photovoltaic power. This represents a typical scenario: a wind farm. The predicted maximum power output for time period t. This represents the predicted maximum power generation of a typical photovoltaic power station s during time period t. , This represents a typical scenario: a wind farm. The actual power generation of the photovoltaic power station s during time period t.

[0095] By increasing the cost coefficients for wind and solar curtailment, the model will tend to reduce the amount of wind and solar curtailment, thus encouraging wind and solar power to be prioritized.

[0096] Step 2: Obtain historical power output data for wind and solar power under the large-scale joint operation model. Based on the historical power output data, construct an initial scenario set S that can characterize the uncertainty of wind and solar power output. Perform scenario dimensionality reduction processing on the initial scenario set to generate a typical scenario set containing several typical scenarios and their corresponding probabilities of occurrence. This embodiment includes the following two steps:

[0097] Step 201: Obtain historical power output data for wind and solar power under the large-scale joint operation model (i.e., statistically analyze the power output of wind and solar power within a certain time period, such as high wind and low solar power, high solar power and low wind, extremely low wind and solar power, and normal operating conditions), filter historical uncertainty data of wind and solar power output, and construct an initial scenario set S containing multiple uncertainty scenarios; the initial scenario set S is represented as:

[0098]

[0099]

[0100] in, For a subset of uncertainties in wind power scenarios and This is a subset of uncertain scenarios in the photovoltaic industry. Indicates the number of uncertain scenarios. For the number of wind farms, Where T represents the number of photovoltaic power stations and T is the evaluation period.

[0101]

[0102]

[0103] in, and Representing the first and second generations of wind power and photovoltaic power respectively One scenario, and They represent the first In the scenario, the first The maximum generating power of the first wind farm and the s-th photovoltaic farm during the t-th time period.

[0104] Step 202: Perform cluster analysis on the set of uncertain scenarios S from Step 201 using the k-means method to generate a set of typical scenarios containing K typical scenarios and their corresponding probabilities. To improve computational efficiency; in this step, the typical scenario set It can be represented as:

[0105]

[0106] in, After integrating and clustering wind power and photovoltaics, the representative of the first Cluster centers for a typical scenario This represents the probability of this typical scenario occurring. , For belonging to the first The first typical scenario represents the The number of uncertain scenarios corresponding to the cluster sample set. The typical scenario set. Including wind power and photovoltaics A typical scenario is detailed below:

[0107]

[0108]

[0109]

[0110] in, and Representing the first and second parts of wind power and photovoltaic power respectively A typical scenario This indicates the first [unit / item] after the integration of wind power and photovoltaics. A typical scenario Indicates the first Cluster sample set, Indicates the first The number of samples in the cluster sample set (i.e., the number of uncertain scenarios); To determine the optimal number of scene clusters based on the elbow rule, and using the cluster center of each cluster as the typical sample of that cluster; the first... Probability of a typical scenario Number of samples within the cluster With the total number of samples The ratio:

[0111] .

[0112] Step 3: Using the aforementioned set of typical scenarios Based on the corresponding probabilities of occurrence, and with the goal of minimizing the total cost comprised of the coal-fired power generation cost model, wind power curtailment cost model, photovoltaic power curtailment cost model, and energy storage operation cost model from step S1, and taking into account the operational constraints of the large-scale energy base consortium, a clean energy large-scale base operation optimization model is constructed. The calculation formula for the clean energy large-scale base operation optimization model with the goal of minimizing costs is as follows:

[0113]

[0114] in: An index representing typical scenarios indicates different scenarios of wind and light forecasting errors. Typical scenarios The probability, In typical scenarios The total operating cost of the system is as follows;

[0115]

[0116] in, Typical scenarios Operating costs of coal-fired power plants that did not undergo coal blending and combustion modification in the next t period; Typical scenarios Operating costs of coal-fired power plants that have undergone coal blending and combustion modification in the next time period (t); Typical scenarios Cost of wind power curtailment in the next time period t; Typical scenarios Cost of solar power curtailment in the next time period t; Typical scenarios Energy storage operating costs during the next time period (t);

[0117] The clean energy base operation optimization model addresses each typical scenario. For each time period t, minimize the expected total system operating cost and the corresponding physical constraints of system operation:

[0118] Constraints include power balance, coal power output, coal power ramping, wind power output, photovoltaic power output, energy storage charging power, energy storage discharging power, dynamic update constraints of energy storage capacity, and upper and lower limits of energy storage capacity.

[0119] The power balance constraints are as follows:

[0120]

[0121] in, Typical scenarios The output power of coal-fired power plants that have not undergone coal blending and combustion modification in the next time period t. Typical scenarios The coal-fired power output of the next time period t that has undergone coal blending and combustion modification, in MW; , Typical scenarios Wind farm station The actual power generation of the photovoltaic power station s during time period t, in MW; Typical scenarios The energy storage discharge power in the next time period t, in MW; Representing a scene The energy storage charging power in the next time period t, in MW; The curve representing the long-term contract of a large-scale consortium during time period t represents the system's electricity demand.

[0122] The coal-fired power output constraints:

[0123]

[0124]

[0125]

[0126] in, Typical scenarios The output power of coal-fired power plants that have not undergone coal blending and combustion modification in the next time period t. Typical scenarios The coal-fired power output of the next time period t that has undergone coal blending and combustion modification, in MW; This represents the maximum output power of coal-fired power plants that have not undergone coal blending and combustion modification during time period t. This represents the maximum output power of coal-fired power plants that have undergone coal blending and combustion modification during time period t. This represents the maximum output power of all coal-fired power units during time period t, in MW.

[0127] The coal-fired power plant ramping constraint:

[0128]

[0129] in, Representing a scene The coal-fired power output in the next time period t, in MW; Representing a scene The coal-fired power output in the next time period t-1, in MW; This indicates the maximum range of power variation of a thermal power unit per unit time.

[0130] The wind power output constraint:

[0131]

[0132] in, Typical scenarios Wind farm station The actual power generation in time period t, in MW; Typical scenarios Downwind farm station The predicted maximum available power output within time period t, in MW;

[0133] The photovoltaic output constraint:

[0134]

[0135] in, Typical scenarios Photovoltaic power station The actual power generation in time period t, in MW; Typical scenarios The predicted maximum available power output of the photovoltaic power station within time period t (s), in MW.

[0136] The energy storage charging power constraint:

[0137]

[0138] in, Representing a scene The energy storage charging power in the next time period t, in MW; This indicates the maximum charging power of the energy storage system, measured in MW.

[0139] The energy storage discharge power constraint:

[0140]

[0141] in, Representing a scene The energy storage discharge power in the next time period t, in MW; This indicates the maximum discharge power of the energy storage system, measured in MW.

[0142] The dynamic update constraints for the energy storage capacity are as follows:

[0143]

[0144] in, Typical scenarios The energy storage status in the next time period t; Typical scenarios Energy storage charging power in the next time period t; Indicates energy storage charging efficiency; Typical scenarios The energy storage discharge power in the next time period t, in MW; Indicates the energy storage discharge efficiency; Typical scenarios The energy storage status in the next time period t+1;

[0145] The upper and lower limits of the energy storage capacity are constrained as follows:

[0146]

[0147] in, Typical scenarios The energy storage status in the next time period t; Indicates the minimum allowable energy storage capacity; This indicates the maximum allowable amount of energy stored.

[0148] Step 4: Obtain different coal-fired power plant co-firing and retrofitting capacity schemes. Each scheme corresponds to a coal-fired power plant co-firing and retrofitting capacity value. Use the retrofitting capacity value as the new boundary condition for the coal-fired power system co-firing capacity in the Clean Energy Base Operation Optimization Model, and call the Clean Energy Base Operation Optimization Model to solve it, so as to obtain the expected total operating cost of the system corresponding to each scheme. That is, by inputting different coal-fired power plant co-firing and retrofitting capacities, the expected total operating cost of the system under different coal-fired power plant co-firing and retrofitting capacities is calculated by the Clean Energy Base Operation Optimization Model.

[0149] Step 5: Calculate the cost reduction based on the expected total operating cost of different schemes, and evaluate and analyze the system operating cost under the current coal-fired power plant co-firing retrofit capacity based on the cost reduction; then select the optimal coal-fired power plant co-firing retrofit capacity based on the ranking of cost reductions. The formula for calculating the cost reduction in Step 5 is as follows:

[0150]

[0151] in, For the capacity of coal blending and combustion modification of thermal power plants, The baseline operating cost of the system when the capacity is 0. For the capacity to be upgraded The expected total operating cost of the system under multiple typical scenarios and probabilistic influences;

[0152] The calculation formula is as follows:

[0153]

[0154] in, This indicates that the capacity for coal blending and combustion retrofitting of thermal power plants has been completed. At that time, the system operating costs that meet the medium and long-term contracts mainly include the operating costs of coal-fired power and energy storage, while the operating costs of wind power and photovoltaic power are negligible. This indicates that the capacity for coal blending and combustion retrofitting of thermal power plants has been completed. Cost reduction after renovation; capacity improvement As a new boundary condition for the co-firing capacity of the coal-fired power system in the aforementioned clean energy large-scale base operation optimization model, it mainly addresses... It has an impact.

[0155] when This indicates that the system operating cost under the current upgraded capacity has increased;

[0156] when This indicates that the system operating cost under the current upgraded capacity has decreased.

[0157] In step 5, adjustments are made multiple times based on the number and capacity of coal-fired power units. The parameters were used to calculate the cost reduction for different retrofit capacities, and then the optimal coal blending retrofit capacity for thermal power plants was selected based on the ranking of cost reductions. .

[0158] This solution can rank the system cost reductions for different co-firing retrofit capacity schemes, thereby identifying the optimal or economically optimal retrofit capacity under given boundary conditions. This directly provides power plant or base operators with crucial investment decision-making support, helping to avoid under-retrofitting or over-investment and optimize capital allocation.

[0159] This embodiment uses a typical large-scale multi-energy complementary base of wind, solar, thermal, and energy storage as an example to verify the effectiveness of the method described in this invention. The base includes five 200MW coal-fired power generating units, a 500MW wind farm, a 300MW photovoltaic power station, and a 100MW / 200MWh electrochemical energy storage system. The evaluation objective is to determine the optimal capacity for coal-fired power generating units to undergo coal blending retrofitting.

[0160] Corresponding to step 1: Constructing the cost model and initializing parameters

[0161] First, establish a coal-fired power generation cost model that takes into account coal blending and combustion.

[0162] Basic parameter settings: Before the modification, the coal-fired power unit uses the standard design coal type, and its cost function coefficient is set as follows: quadratic term coefficient Yuan / coefficient of the first term Yuan / Fixed cost coefficient Yuan / h.

[0163] Blending parameter correction: Set the preset blending ratio of low-quality coal to... According to the model described in this invention, the correction coefficients for each term are defined as follows:

[0164] (First-order coefficient of non-linear cost coefficient): Values ​​are... This reflects the systematic impact of blending low-quality coal on the nonlinear portion of the unit's consumption characteristic curve.

[0165] (The first-order coefficient of the linear cost factor): Values ​​are... The value is mainly determined by the difference in fuel prices, with negative values ​​representing the price advantage brought by low-quality coal.

[0166] (The quadratic coefficient of the linear cost factor): Values ​​are... The nonlinear superposition effect of negative factors such as deteriorating efficiency and increased auxiliary power consumption on the coefficient of the primary term is reflected. Corrections.

[0167] (First-order coefficient of fixed cost): Values ​​are... This represents the increase in additional fixed maintenance costs resulting from the proportion of blending in the unit.

[0168] For step 2: Uncertainty scenario generation and clustering

[0169] This embodiment uses the K-means algorithm to cluster historical output data (8760 hours per year) into four typical scenarios. The wind power, solar power, and load power curves for various scenarios are as follows: Figure 2 As shown. The horizontal axis represents 24 hours in a day, and the vertical axis represents power (unit: MW).

[0170] Typical Scenario 1 ( Figure 2 Scenario 1 (High wind, low solar power, probability p1=0.25): This scenario is characterized by high wind power output at night (0:00-6:00 and 20:00-24:00), while solar power output is weak during the day. During this period, the load is at its lowest, and the high wind power output puts significant pressure on the system's peak-shaving capacity, making it a scenario with a high risk of wind curtailment.

[0171] Typical Scenario 2 ( Figure 2 Scenario 2 (High solar power, low wind power, probability p2=0.30): This scenario is characterized by peak solar power output at midday (10:00-15:00), while wind power output remains at a low level throughout the day. The system operates primarily to absorb peak solar power output at midday.

[0172] Typical Scenario 3 ( Figure 2 Scenario 3 (Extremely low wind and solar power output, probability p3=0.20): In this scenario, the output of wind and solar power is at an extremely low level throughout the day. The ability of new energy sources to support the load is the weakest, and coal-fired power units need to bear the majority of the load demand. This is an extreme scenario that tests the coal power supply guarantee capability.

[0173] Typical Scenario 4 Figure 2 Scenario 4 (normal operating condition, probability p4=0.25): In this scenario, the wind power and photovoltaic output curves fluctuate smoothly, close to the annual average level, representing the most common operating condition of the system.

[0174] By selecting these four typical scenarios with significantly different characteristics for subsequent operational simulation and optimization, we can comprehensively cover the main operational boundary conditions faced by the large-scale joint venture system, ensuring the robustness and representativeness of the evaluation results.

[0175] For step 3: Run the optimization simulation

[0176] A clean energy large-scale base operation optimization model is constructed, and the system needs to meet the medium- and long-term contract load curve (average value of 800MW). Calculations are performed using Scenario 1 (high wind period) as an example, inputting the predicted wind power output curve and load demand curve for the period. Figure 2 Scenario 1).

[0177] For steps 4 and 5: Scheme evaluation and selection

[0178] In this embodiment, the total installed capacity of the coal-fired power units is 1000 MW, of which the maximum output capacity of the portion without coal blending modification is denoted as... The maximum output capacity of the section that has undergone coal blending and combustion modification is denoted as: And satisfy:

[0179]

[0180] To evaluate the system's economic benefits under different investment intensities for coal blending and combustion retrofitting, the following discrete retrofitting capacity schemes are proposed:

[0181] Option 1 (Baseline Option): Upgrade Capacity =0 MW, meaning that none of the coal-fired power units have been upgraded;

[0182] Option 2: Capacity Upgrade =400 MW;

[0183] Option 3: Capacity Upgrade =600 MW;

[0184] Option 4: Capacity Upgrade =800 MW;

[0185] Option 5: Capacity Upgrade =1000 MW;

[0186] (1) The method of embedding the modified capacity as a boundary condition.

[0187] For each capacity modification scheme, its value is used as a new boundary condition for the maximum output capacity of the modified coal-fired power units in the clean energy base operation optimization model, that is, directly setting... (The time period t is the same for all time periods), correspondingly, the maximum output capacity of the unmodified portion is This boundary condition will directly affect the coal-fired power output constraints in the model, and then be transmitted to energy storage scheduling, new energy consumption and overall system operation strategies through power balance constraints, ramp-up constraints and other factors.

[0188] (2) Solution of the expected total operating cost of the system under each scheme

[0189] For each capacity upgrade plan, the clean energy large-scale base operation optimization model constructed in step 4 is used as the core, and the corresponding values ​​of the plan are substituted into the model. and The values ​​remain unchanged; meanwhile, the model still fully incorporates the four typical scenarios generated in step 3 and their probabilities (p1=0.25, p2=0.30, p3=0.20, p4=0.25), the various cost models established in step 1, and all system operation physical constraints. The optimization solver is called to solve the model, obtaining the optimal scheduling scheme and operating cost for each typical scenario and time period, respectively. Then, the expected total operating cost of the system under this capacity modification scheme is calculated using the following formula:

[0190]

[0191] (3) Calculation of cost reduction and selection of optimal solution

[0192] The expected total operating cost C of the system based on the baseline solution (with zero capacity to be upgraded) op Based on (0), calculate the cost reduction for each capacity renovation scheme:

[0193] when This indicates that the capacity upgrade plan achieved a net saving in system operating costs compared to the unupgraded state.

[0194] The daily and annual cost reductions (calculated over 365 days) for each scheme are calculated in this embodiment.

[0195] The cost reductions mentioned above are sorted in descending order. Option 3 (600 MW capacity) has the largest annual cost reduction, amounting to RMB 222,463,850 per year. Therefore, under the boundary conditions given in this embodiment (wind and solar resource characteristics, load curve, energy storage configuration, expected coal prices, etc.), the optimal coal-fired power plant capacity for retrofitting is determined to be 600 MW.

[0196] Results analysis:

[0197] When the capacity of the upgraded units increases to 600MW, the fuel benefits from low-quality coal exceed the maintenance and efficiency costs, and the annual cost reduction reaches its peak. However, at this point, the marginal benefits gradually narrow. If the upgrade is excessively carried out to 800MW or more, the high fixed costs and efficiency penalties will outweigh the fuel benefits, leading to a sharp decline in overall benefits.

[0198] Traditional methods neglect the reshaping effect of renewable energy coupling on thermal power plant operation, often blindly recommending full-capacity retrofitting when fuel price differences are favorable. This method, by introducing renewable energy joint operation constraints, accurately captures the characteristic of thermal power plants operating at 'long-term low load' as a regulatory resource. Numerical examples show that in large-scale power plant scenarios with frequent low-load conditions, full-capacity retrofitting faces the risk of diminishing marginal returns or even negative returns (the mismatch between high fixed costs and low utilization rates). Selecting the optimal partial retrofit capacity, however, can minimize the overall system cost while ensuring flexibility.

[0199] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, based on the guidance provided in this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model, characterized in that... include: Step 1: Establish cost models for coal-fired power generation, wind power curtailment, photovoltaic power curtailment, and energy storage operation under the large-scale joint operation model, taking into account coal blending and combustion. Step 2: Obtain historical power output data for wind and solar power under the large-scale joint operation model. Based on the historical power output data, construct an initial scenario set S that can characterize the uncertainty of wind and solar power output. Perform scenario dimensionality reduction processing on the initial scenario set to generate a typical scenario set containing several typical scenarios and their corresponding probabilities of occurrence. ; Step 3: Using the aforementioned set of typical scenarios Based on the probability of occurrence of the coal-fired power generation cost model, wind power curtailment cost model, photovoltaic curtailment cost model and energy storage operation cost model in step S1, the total cost is minimized, and the operation constraints of the large base consortium are taken into account, to construct a clean energy large base operation optimization model. Step 4: Obtain different coal-fired power plant co-firing and retrofit capacity schemes. Each scheme corresponds to a coal-fired power plant co-firing and retrofit capacity value. Use the retrofit capacity value as the new boundary condition for the coal-fired power system co-firing capacity in the Clean Energy Base Operation Optimization Model. Then call the Clean Energy Base Operation Optimization Model to solve the problem and obtain the expected total operating cost of the system corresponding to each scheme. Step 5: Using the unmodified scheme as a benchmark, calculate the cost reduction of each scheme; sort the schemes and select the modification capacity value corresponding to the scheme with the largest cost reduction as the optimal coal blending modification capacity for thermal power.

2. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 1, characterized in that: The scene dimensionality reduction process uses a clustering algorithm to cluster the initial scene set, generating representative typical scenes and their probabilities.

3. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 2, characterized in that: The set of typical scenarios includes scenarios with combined wind and solar power output, and the probability of occurrence of each scenario is determined based on the proportion of the sample size represented by each typical scenario to the total sample size.

4. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 2, characterized in that: The clustering algorithm is the K-means clustering algorithm, and the number of typical scenarios K is determined by the elbow rule.

5. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 3, characterized in that: In step S3, the objective function of the clean energy large-scale base operation optimization model is to minimize the expected value of the total system operating cost, as expressed below: in: This represents a typical scenario index. Representing typical scenarios The probability, Indicates in the scene The total operating cost of the system includes the operating cost of coal-fired power plants that have not undergone coal blending and combustion modification, the operating cost of coal-fired power plants that have undergone coal blending and combustion modification, the cost of wind power curtailment, the cost of photovoltaic power curtailment, and the operating cost of energy storage.

6. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 5, characterized in that: The formula for calculating the total operating cost of the system is as follows: in, Representing typical scenarios Operating costs of coal-fired power plants that did not undergo coal blending and combustion modification in the next t period; Representing typical scenarios Operating costs of coal-fired power plants that have undergone coal blending and combustion modification in the next time period (t); Representing typical scenarios Cost of wind power curtailment in the next time period t; Representing typical scenarios Cost of solar power curtailment in the next time period t; Representing typical scenarios Operating costs of energy storage during the next time period t.

7. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 5, characterized in that: The physical constraints on system operation corresponding to the expected value of minimizing the total operating cost of the system include power balance constraints, upper and lower limits of output of various power sources, ramping constraints, and operating constraints of energy storage systems.

8. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 5, characterized in that: In step S5, the cost reduction amount The calculation formula is: in, For the capacity of coal blending and combustion modification of thermal power plants, The baseline operating cost of the system when the capacity is 0. For the capacity to be upgraded The expected total operating cost of the system under multiple typical scenarios and probabilistic influences; when When this occurs, it indicates that the system expects its total operating cost to decrease.

9. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 1, characterized in that: In step S1, the coal-fired power generation cost model considering coal blending is as follows: in, The output power of the coal-fired power plant is given by r, where r is the blending ratio; cost coefficient. Both are functions of r, used to characterize the combined impact of blending low-quality coal on fuel cost, efficiency, and fixed cost.

10. The method for assessing and analyzing cost reduction through coal-fired power generation under a large-scale joint venture model as described in claim 9, characterized in that: The blending ratio r is a continuous variable, and the cost coefficient , , Calibration is performed using historical operating data or field tests.

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