Composite distribution method with efficiency optimization as target

Abstract

The invention relates to a combined type distribution method with efficiency optimization as a target, which comprises the following steps of: selecting an optimal energy distribution scheme for each power generation device and load by adopting a combined type distribution algorithm according to real-time data in a power system; according to the optimal energy distribution scheme, generating a corresponding control instruction, and controlling and adjusting each power generation device and load; the step of obtaining the optimal energy distribution scheme comprises the sub-steps of obtaining possible configuration combinations, performing production simulation calculation under each configuration combination based on characteristic description, and generating key indexes according to simulation results; financial measurement and calculation under each configuration combination are carried out based on financial related hypotheses and the simulation results, and financial indexes are obtained; and performing weighted calculation on the key indexes and the financial indexes to obtain a score of each configuration combination, and taking the configuration combination with the highest score as an optimal energy distribution scheme. Compared with the prior art, the method has the advantages of convenience and accuracy in calculation and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of energy allocation, and in particular to a composite allocation method with efficiency optimization as the goal. Background Art

[0002] With the severe challenges of global climate change and the urgent need for energy transformation, new energy (mainly including solar energy, wind energy, hydropower, biomass energy, etc.) as a clean and renewable energy form is gradually becoming an important direction for optimizing the global energy structure. In recent years, driven by technological progress, cost reduction and policy support, new energy power generation has achieved rapid development, with significant growth in installed capacity and power generation, providing strong support for alleviating the contradiction between energy supply and demand, reducing greenhouse gas emissions, and promoting sustainable economic development.

[0003] In the production and operation of alloy submerged arc furnace factories, the stability and efficiency of the power supply system are directly related to the quality and production cost of the products. With the development of new energy technologies, the power supply of existing alloy submerged arc furnace factories usually considers the way of power supply by integrated power generation equipment. This method is an inevitable choice to adapt to changes in the energy market, meet production needs, achieve energy conservation and emission reduction, and achieve sustainable development. How to conveniently and accurately realize the distribution of different power generation equipment and loads is a technical problem that needs to be solved in this field. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a convenient and accurate composite allocation method with the goal of efficiency optimization, which can achieve the coordinated configuration of the load demand of the ore-fired furnace and the new energy power generation capacity.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A composite allocation method with the goal of optimizing efficiency comprises the following steps:

[0007] According to the real-time data in the power system, a composite allocation algorithm is used to select the optimal energy allocation scheme for each power generation equipment and load, wherein the composite allocation algorithm is composed of a combination of multiple optimization strategies;

[0008] According to the optimal energy allocation plan, corresponding control instructions are generated to control and adjust each power generation equipment and load.

[0009] Furthermore, the power generation equipment includes wind power equipment, energy storage equipment, gas turbine equipment and grid power equipment.

[0010] Furthermore, obtaining the optimal energy allocation solution specifically includes:

[0011] Mathematically describe the known load characteristics, energy characteristics and system characteristics and construct characteristic descriptions;

[0012] Obtain possible configuration combinations of various power generation equipment and loads, perform production simulation calculations under each of the configuration combinations based on the characteristic descriptions, and generate key indicators under each of the configuration combinations according to the simulation results;

[0013] Constructing financial related assumptions, and performing financial calculations under each of the configuration combinations based on the financial related assumptions and the simulation results to obtain financial indicators under each of the configuration combinations;

[0014] The key indicators and financial indicators are weightedly calculated to obtain the score of each configuration combination, and the configuration combination with the highest score is used as the optimal energy allocation solution.

[0015] Furthermore, the load characteristics include load climbing rate, rated load, maximum and minimum loads, adjustment time and efficiency curve.

[0016] Furthermore, the energy characteristics include power generation priority, wind power characteristics, energy storage characteristics, gas turbine characteristics and grid power characteristics.

[0017] Furthermore, the power generation priority is determined based on power generation costs of different power generation equipment.

[0018] Furthermore, the financial related assumptions include energy cost, load cost, assumptions related to financing methods and assumptions related to financial models.

[0019] Furthermore, the key indicators include wind curtailment rate, green electricity ratio, LCO alloy, electricity consumption converted into output and purchased electricity.

[0020] Furthermore, the financial indicators include investment profit rate, investment payback period, financial net present value and financial internal rate of return.

[0021] Furthermore, the key indicators and financial indicators are normalized before performing the weighted calculation.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. As an important part of the energy system, the high energy consumption of the submerged arc furnace means high consumption and waste of energy. The introduction of a high proportion of new energy can provide a green and environmentally friendly energy alternative. Through the application of the method of the present invention, the coordinated supply of submerged arc furnace load and new energy power generation can be achieved, the energy utilization efficiency can be optimized, and energy waste can be reduced.

[0024] 2. In the optimization and solution process of the traditional allocation scheme, economic indicators are included in the optimization target, but this makes the calculation very complex, difficult to operate and solve, and wastes computing power. The acquisition process of the optimal energy allocation scheme of the present invention decouples finance and production simulation, is easy to operate, and requires little computing power.

[0025] 3. The financial and production simulations of the present invention are easy to perform sensitivity analysis, which effectively improves the reliability of overall optimization.

[0026] 4. The present invention can be adapted to a variety of production simulation technologies and is easy to control. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The figure is a schematic diagram of the process of obtaining the optimal energy allocation solution of the present invention. DETAILED DESCRIPTION

[0028] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0029] This embodiment provides a composite distribution method with efficiency optimization as the goal, comprising the following steps: based on real-time data in the power system, a composite distribution algorithm is used to select the optimal energy distribution plan for each power generation equipment and load, and the composite distribution algorithm is composed of a combination of multiple optimization strategies; based on the optimal energy distribution plan, corresponding control instructions are generated to control and adjust each power generation equipment and load.

[0030] In this embodiment, the power generation equipment includes wind power equipment, energy storage equipment, gas turbine equipment and grid power equipment, etc., and uses four energy sources, namely wind power, energy storage, gas turbine and grid power, to supply power to an alloy ore-fired furnace factory.

[0031] refer to Figure 1 As shown, the optimal energy allocation scheme is obtained by using a composite allocation algorithm of simulation optimization and accounting optimization, which specifically includes the following steps:

[0032] Step S1: mathematically describe known load characteristics, energy characteristics and system characteristics to construct characteristic descriptions.

[0033] (a) Load characteristics

[0034] The load characteristics considered in this embodiment include load climbing rate, rated load, maximum and minimum loads, adjustment time, efficiency curve, etc. All descriptions of load characteristics are referred to as {L}.

[0035] Load ramp rate: Adjustment frequency: 1hr (baseline); Adjustment range: 10MW (baseline)

[0036] Rated load power: P_L_rated=36

[0037] Maximum load power: P_L_max=P_L_rated*120%

[0038] Minimum load power: P_L_min=P_L_rated*30%

[0039] So: P_L_t represents the power of the load at time t in the operating state

[0040] P_L_min≤P_L_t≤P_L_max

[0041] The efficiency curve is shown in Table 1.

[0042] Table 1 Efficiency curve

[0043] P_L Production efficiency 1X0％-75％ 100％ 70％ 94％ 60％ 91％ 50％ 89％ 40％ 64％ 30％ 38％ 20％ 13％ 15％ 0％

[0044] (b) Energy characteristics

[0045] The energy characteristics considered in this embodiment include power generation priority, wind power characteristics, energy storage characteristics, gas turbine characteristics, and grid power characteristics, and all descriptions of energy characteristics are referred to as {E}.

[0046] Priority: Prioritize generation based on the generation cost of all energy sources in the system.

[0047] Known: LCO_wind, LCO_storage, LCO_fuel, LCO_grid. LCO_i represents the levelized cost of electricity from energy source i.

[0048] The power generation priority is sorted in ascending order of LCO.

[0049] For example: If LCO_wind = 0, LCO_storage = 1, LCO_fuel = 2, LCO_grid = 5, the power generation priority is:

[0050] Wind>Storage>Fuel>Grid

[0051] Wind: Random fluctuations, the output cannot be controlled. The actual annual wind power generation sequence at the project site is used in the calculation.

[0052] ● Storage: Both charging and discharging operations can be performed. The charging and discharging operations are based on the SOC execution strategy of the energy storage battery.

[0053] (1) Energy storage operation status constraints:

[0054]

[0055] (2) Energy storage power constraints:

[0056]

[0057] (3) Energy storage SOC constraints:

[0058]

[0059] SOC min ≤SOC t ≤SOC max

[0060] (4) Constraints on the number of times energy storage is fully charged and discharged per day:

[0061]

[0062] in, is the energy storage charging power (MW), is the energy storage discharge power (MW), is the energy storage rated power (MW), is the rated capacity of energy storage (MWh); SOC t is the SOC state at time t (%), SOC max is the maximum SOC (%), SOC min is the minimum SOC (%); Efficiency is the energy storage operation efficiency (%); Limit Storage_cycles The number of full charges and discharges per day is limited.

[0063] ·Fuel: It changes depending on the capacity of the gas tank. When the SOC of the gas tank is less than a certain value, the gas turbine does not generate electricity. When the SOC of the gas tank is greater than a certain value, the gas tank is forced to generate electricity. When the SOC of the gas tank is between these two thresholds, the power generation of the gas tank can be adjusted arbitrarily between 0 and rated power.

[0064] If SOC_gas <SOC_gas_min:

[0065]

[0066] If SOC_gas>SOC_gas_max:

[0067]

[0068] If SOC_gas_min≤SOC_gas≤SOC_gas_max:

[0069]

[0070] ·Net: A power source that can be used at will. There is an upper limit.

[0071]

[0072] (c) System characteristics

[0073] In this embodiment, the system characteristics represent the characteristics of the operation of the system composed of energy and load, and all descriptions of the system characteristics are called {S}.

[0074] In this embodiment, the system characteristic is mainly power balance, that is, for any time t, there exists:

[0075]

[0076] in, is the wind power, is the engine power, is the grid power, is the energy storage power, and P_L_t represents the power of the load at time t in the operating state.

[0077] Step S2: obtain possible configuration combinations of various power generation equipment and loads, perform production simulation calculations under each of the configuration combinations based on the characteristic description, and generate key indicators under each of the configuration combinations according to the simulation results.

[0078] In this embodiment, a cases table is created to exhaustively list all permutations and combinations of configurations.

[0079] For example, for wind, list all possible situations from 100-200MW, with each interval of 10MW. Assume there are n wind situations.

[0080] For example: Storage: List all permutations and combinations of 22MW-88MW, 1H-4H. Assume there are n storage types.

[0081] Similarly, all permutations and combinations of grid power, gas turbines, and loads are listed separately. It is assumed that there are n_grids, n_gas, and n_loads.

[0082] That is, a total of n_wind*n_storage*n_fuel*n_grid*n_load=N situations can be listed, as shown in Table 2.

[0083] Table 2 Partial configuration combinations

[0084]

[0085] Based on the three types of characteristics and detailed descriptions of {L}, {E}, and {S}, a one-year planning production simulation calculation is performed for all cases to obtain separate simulation results. According to the simulation results, key indicators under each configuration combination are generated, as shown in Table 3.

[0086] Table 3 Production simulation calculation index table

[0087]

[0088] As shown in Table 3, the key indicators generated in this embodiment include wind curtailment rate, green power ratio, LCO alloy, electricity consumption converted to output and purchased electricity, which is called {Q_simulation}. Among them, electricity consumption converted to output means that each case will simulate different outputs based on different configurations, and the output is calculated by the intermediate indicator of load power consumption (a result indicator of production simulation, not shown in the table above). The relationship between output and power consumption is:

[0089] It is known that the comprehensive power consumption level of the alloy industry is: 4050kwh / t. That is, for every ton of silicon manganese produced, 4050kwh of electricity is consumed, so:

[0090] (Annual) output = (Annual) load power consumption / 4050

[0091] Step S3: construct financial related assumptions, and perform financial calculations under each of the configuration combinations based on the financial related assumptions and the simulation results to obtain financial indicators under each of the configuration combinations.

[0092] In this embodiment, the financial related assumptions constructed include energy cost, load cost, assumptions related to the financing method and assumptions related to the financial model. All assumptions about the financial model are {C}, which specifically include:

[0093] i. Wind power: Capex and Opex of different models

[0094] ii. Grid electricity: Opex / price

[0095] iii. Energy storage: Capex, Opex

[0096] iv. Gas turbine: Capex, Opex

[0097] v. Alloy Submerged Arc Furnace: Capex

[0098] vi. Assumptions related to financing methods: such as capital ratio, financing interest rate, etc.

[0099] vii. Assumptions related to the financial model: e.g. how taxes are calculated

[0100] In this embodiment, based on {C} and partial results of production simulation, financial model calculations are performed for all cases respectively to obtain financial calculation results of each case, that is, financial indicators, as shown in Table 4.

[0101] Table 4 Financial indicators

[0102]

[0103] As shown in Table 4, the financial indicators calculated in this embodiment include investment profit rate, investment payback period, financial net present value and financial internal rate of return, which are called {Q_finance}. The financial model calculation will use some results of production simulation, such as: purchased electricity (used to calculate the purchased electricity fee-belonging to the operating cost), actual wind power generation, etc.

[0104] Step S4: weighted calculation is performed on the key indicators and financial indicators to obtain the score of each configuration combination, and the configuration combination with the highest score is used as the optimal energy allocation solution.

[0105] The above simulation results and financial calculation results are combined by case to obtain a comprehensive result table, as shown in Table 5. All indicators {Q} = {Q_simulation} + {Q_finance}.

[0106] Table 5 Comprehensive results

[0107]

[0108] For example, in this embodiment, there are N cases, and each case corresponds to a {Q}, {Q} = {Q_simulation} + {Q_finance} = {Q_wind abandonment rate, Q_green electricity ratio, Q_LCO alloy, Q_electricity consumption converted to output, Q_purchased electricity, Q_investment profit rate, Q_investment payback period, Q_financial net present value, Q_financial internal rate of return} = {Q_1, Q_2, Q_3, Q_4, Q_5, Q_6, Q_7, Q_8, Q_9}. The decision-making method of this embodiment is to weight all indicators and calculate a total score for each case. The process of calculating the total score and obtaining the final decision includes:

[0109] a) Normalize all indicators to ensure uniform dimensions. All normalized indicators are written as: Q_1_fixed, Q_2_fixed, Q_3_fixed, Q_4_fixed, Q_5_fixed, Q_6_fixed, Q_7_fixed, Q_8_fixed, Q_9_fixed}

[0110] b) Define a weight table, as shown in Table 6, to assign weights to all indicators Q_i.

[0111] Table 6 Weight table

[0112]

[0113]

[0114] make sure

[0115] d) Calculate the total score.

[0116] So for each case, the total score can be calculated:

[0117]

[0118] d) The case with the highest total score is decided as the capacity configuration plan for the project.

[0119] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0120] In other embodiments, an electronic device may be provided, comprising one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the composite allocation method with the goal of optimizing efficiency as described above.

[0121] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A composite allocation method with the goal of optimizing efficiency, characterized in that: The following steps are involved: According to the real-time data in the power system, a composite allocation algorithm is used to select the optimal energy allocation scheme for each power generation equipment and load, wherein the composite allocation algorithm is composed of a combination of multiple optimization strategies; According to the optimal energy allocation plan, corresponding control instructions are generated to control and adjust each power generation equipment and load.

2. The composite allocation method with the goal of optimizing efficiency according to claim 1, characterized in that: The power generation equipment includes wind power equipment, energy storage equipment, gas turbine equipment and grid power equipment.

3. The composite allocation method with the goal of optimizing efficiency according to claim 2, characterized in that: The acquisition of the optimal energy allocation scheme specifically includes: Mathematically describe the known load characteristics, energy characteristics and system characteristics and construct characteristic descriptions; Obtain possible configuration combinations of various power generation equipment and loads, perform production simulation calculations under each of the configuration combinations based on the characteristic descriptions, and generate key indicators under each of the configuration combinations according to the simulation results; Constructing financial related assumptions, and performing financial calculations under each of the configuration combinations based on the financial related assumptions and the simulation results to obtain financial indicators under each of the configuration combinations; The key indicators and financial indicators are weightedly calculated to obtain the score of each configuration combination, and the configuration combination with the highest score is used as the optimal energy allocation solution.

4. The composite allocation method with the goal of optimizing efficiency according to claim 3, characterized in that: The load characteristics include load ramp rate, rated load, maximum and minimum loads, adjustment time and efficiency curve.

5. The composite allocation method with the goal of optimizing efficiency according to claim 3, characterized in that: The energy characteristics include power generation priority, wind power characteristics, energy storage characteristics, gas turbine characteristics and grid power characteristics.

6. The composite allocation method with the goal of optimizing efficiency according to claim 5, characterized in that: The power generation priority is determined based on the power generation costs of different power generation equipment.

7. The composite allocation method with the goal of optimizing efficiency according to claim 3, characterized in that: The financial related assumptions include energy cost, load cost, assumptions related to financing methods and assumptions related to financial models.

8. The composite allocation method with the goal of optimizing efficiency according to claim 3, characterized in that: The key indicators include wind curtailment rate, green electricity ratio, LCO alloy, electricity consumption converted into output and purchased electricity.

9. The composite allocation method with the goal of optimizing efficiency according to claim 3, characterized in that: The financial indicators include investment profit rate, investment payback period, financial net present value and financial internal rate of return.

10. The composite allocation method with the goal of optimizing efficiency according to claim 3, characterized in that: The key indicators and financial indicators are normalized before performing the weighted calculation.

Images