Multi-energy coordination and energy management and control evaluation method and system

Abstract

The invention discloses a multi-energy coordination and energy management and control evaluation method and system, and relates to the technical field of multi-energy complementation and coordinated scheduling, and the method comprises the steps: constructing a multi-energy coordination and energy management and control evaluation technology combination evaluation index system; carrying out multi-dimensional weight evaluation; and constructing an electricity market effect evaluation model. According to the method disclosed by the invention, a set of multi-energy coordination and energy management and control evaluation technology combination evaluation index system considering four aspects of section safety, technical indexes, economic benefits and environment evaluation is established. An electric power market effect evaluation model is constructed by applying an FAHP method, an entropy weight method and a TOPSIS method, instance research is performed on Guizhou province, an obtained evaluation result is consistent with an actual policy trend, objective and reasonable evaluation of a multi-energy coordination and energy management and control evaluation technology combination scheme is realized, and the method has important reference significance for optimal dispatching operation of a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy complementarity and coordinated scheduling, and specifically to a multi-energy coordination and energy control evaluation method and system. Background Art

[0002] With the gradual depletion of fossil energy, increasingly serious environmental pollution and low energy conversion efficiency, the global energy crisis is becoming increasingly serious. How to develop new energy, improve energy efficiency and reduce fossil energy consumption and emissions has become one of the focuses of attention around the world. The rapid development of economy and society is driving the revolution in energy production and consumption and building a safe, efficient, clean and low-carbon energy system.

[0003] Different from the traditional energy system construction which is mainly based on the vertical extension of a single system, there is less interaction between energy systems. Multi-source coordination realizes effective scheduling and management among multiple energy sources according to the different characteristics and advantages of different energy sources and energy storage methods, thereby improving the stability and reliability of the power system. The coupled and interconnected multi-energy system develops in a coordinated manner in various links such as source, network, load, and storage, so as to maximize the overall operating efficiency of the energy system. It is a hot topic in the academic community in recent years to conduct energy management and control evaluation and optimize scheduling of multi-energy power systems, reduce losses and improve energy efficiency, while ensuring the reliability and stability of the system.

[0004] From the perspective of macro-strategy, physical carriers and low-carbon technology of energy transformation, it is demonstrated that low-carbon integrated energy system is an effective path for energy transformation, and a research framework for low-carbon integrated energy system is constructed. The emission reduction potential of low-carbon integrated energy system is explored from the links of source-grid-load-storage and carbon capture. The cooperative operation and electricity-carbon coupling transaction of multi-microgrid systems under uncertain environment are studied, and a heat and power cogeneration operation mode coupled with power-to-gas carbon capture system is constructed. A low-carbon operation model of multi-energy microgrid is proposed, and the opportunity constraint and robust optimization methods are used to reduce the impact of uncertainty. The basic structure of hydrogen-containing integrated energy system is studied, and the electricity-carbon-hydrogen coupling transaction mechanism is analyzed based on the research of electricity-carbon and electricity-hydrogen trading markets, and the electricity-carbon-hydrogen coupling trading market mechanism for hydrogen-containing integrated energy system is deeply explored. The IES multi-energy coupling model is constructed, and a comprehensive demand response mechanism for multiple loads is proposed, and an IES optimization scheduling model considering the demand response of multiple loads is established.

[0005] In the evaluation of multi-energy coordination, multi-energy coupling, multi-energy interconnection and their control and regulation in China, the research on its benefit evaluation mainly focuses on the construction of evaluation indicators and weight solution methods: the reliability and economy of different load points connected to the photovoltaic storage microgrid are analyzed from the perspective of capacity and power, and the reliability of the microgrid is evaluated by using parameters such as component failure rate and repair rate. The structure of the green hydrogen and blue hydrogen integrated energy system is constructed based on equipment such as green hydrogen produced by electricity, blue hydrogen produced by gas, hydrogen-blended gas turbines and hydrogen storage tanks, and a low-carbon economic dispatch model with the minimum sum of costs as the optimization goal is established. Finally, the evaluation indicators are fully explored from three aspects: economy, equipment life loss and energy saving and environmental protection, and a comprehensive evaluation index system is constructed. An analytical dynamic evaluation model based on the active regulation capability of the regional integrated energy system (RIES) is established, and the flexibility of the three systems of power system, thermal system and natural gas system is quantitatively evaluated from multiple aspects such as source, energy storage and load, and the impact of load demand response on the adjustable amount of RIES is analyzed. An index evaluation system for the flexibility resources of the integrated energy system is constructed from the system level and the local level, and multiple factors such as economy, environmental protection, and safety are comprehensively considered. The radar chart analysis and fuzzy comprehensive evaluation of the index weights are combined with the dispatching schemes of 8 types of flexibility resources in specific scenarios. The problem of grid absorption capacity evaluation is studied, and a method for evaluating the grid's renewable energy absorption capacity taking into account the synergy of wind, light, water, and fire is proposed.

[0006] Relevant foreign literature also evaluates the development and utilization of multiple energy sources in different scenarios: integrating renewable energy sources such as biomass and photovoltaics to produce hydrogen can form a biomass-photovoltaic-hydrogen integrated energy system (BPH-IES). The comprehensive performance evaluation index system of the system is constructed from the 4E dimension, and a hybrid multi-criteria decision-making (MCDM) framework for the comprehensive performance evaluation of BPH-IES is constructed. The objective weights are determined by the anti-entropy weighting method, and the subjective weights are determined by the decision-making test and evaluation laboratory method improved by the intuitionistic fuzzy set. The low-carbon economic optimization method for the integrated energy system is revised based on life cycle assessment and carbon capture and utilization technology. A complete decision support system is constructed from the four aspects of economy, environment, technology and socio-politics, and the decision information collection and processing method under uncertain environment of triangular fuzzy numbers and hesitant fuzzy language term sets is combined. The schemes of the fuzzy VIKOR method that comprehensively considers the risk preference of decision makers are ranked. Considering that there are few studies on the impact of electric energy substitution on the construction of integrated energy systems (IES) and the unincentive electric energy substitution work in IES. To this end, it proposed a G1 method to construct a consistency matrix to determine the evaluation index weights of the electric energy substitution IES, and constructed a comprehensive energy system evaluation index system including electric energy substitution indicators, low-carbon indicators, technical indicators, economic indicators and their secondary indicators. The comprehensive energy system evaluation method based on the minimum cross entropy model and prospect theory, combined with the positive and negative prospect value matrix, ranks the advantages and disadvantages of the comprehensive energy system schemes, which has a certain reference value.

[0007] Multi-energy system assessment is an important method to ensure the stable operation of multi-energy systems. It can identify risk factors in multi-energy systems, find out weak links, and ensure cost-benefit, thereby improving the overall operation capacity of the entire system. It can serve as a decision-making basis for the planning and configuration of multi-energy systems and safe operation. Although a lot of exploration has been done in academic research and engineering demonstrations on the development of multi-energy systems, there is still a lack of comprehensive evaluation of their operation from all aspects and angles. The development path of multi-energy coordination and energy management and their emission reduction potential need to be explored and excavated, and there is currently a lack of systematic sorting and summary.

[0008] In view of the above research status, in order to facilitate the guidance of efficient consumption and full utilization of new energy, and to ensure the safety and reliability of the operation of multi-energy systems, the present invention proposes a multi-energy coordination and energy management and control evaluation technology based on an electric-carbon coupling support grid, taking into account multiple time scales and operation scenarios. By establishing an energy management and control evaluation index system, a comprehensive evaluation is conducted around the four aspects of section safety, technical indicators, economic benefits and environmental evaluation. The FAHP-entropy weight method is used to calculate the weights of indicators at all levels, and the TOPSIS method is used for model evaluation. Finally, the optimal unit combination scheme is calculated through actual cases, and then the key issues of multi-energy coordination and energy management and control evaluation are analyzed and explained from the aspects of research status, current problems and future research prospects. Summary of the invention

[0009] In view of the above-mentioned problems, the present invention is proposed.

[0010] Therefore, the technical problem solved by the present invention is: the existing power grid dispatching technical methods have low energy conversion efficiency and poor stability, and how to reduce losses and improve energy utilization efficiency while ensuring system reliability and stability.

[0011] To solve the above technical problems, the present invention provides the following technical solutions: a multi-energy coordination and energy management evaluation method, including constructing a multi-energy coordination and energy management evaluation technology combination evaluation index system; performing multi-dimensional weight evaluation; and constructing an electricity market effectiveness evaluation model.

[0012] As a preferred scheme of the multi-energy coordination and energy control evaluation method described in the present invention, wherein: the construction of the multi-energy coordination and energy control evaluation technology combination evaluation index system includes section safety index analysis, including N-1 verification rate and static voltage safety margin index. By analyzing these section safety indicators, the system operation status can be monitored in real time to ensure the safe and efficient operation of each link, prevent potential system risks, and provide a basis for optimizing energy scheduling and control.

[0013] As a preferred scheme of the multi-energy coordination and energy control evaluation method described in the present invention, wherein: the construction of the multi-energy coordination and energy control evaluation technology combination evaluation index system includes economic analysis indicators, including dynamic investment recovery period, internal rate of return, unit fixed cost, and unit variable cost.

[0014] As a preferred scheme of the multi-energy coordination and energy control evaluation method described in the present invention, wherein: the construction of the multi-energy coordination and energy control evaluation technology combination evaluation index system includes environmental analysis indicators, including the degree of environmental impact and the change in renewable energy consumption.

[0015] As a preferred scheme of the multi-energy coordination and energy control evaluation method described in the present invention, wherein: the construction of the multi-energy coordination and energy control evaluation technology combination evaluation index system includes technical performance analysis indicators, including impact time, adjustment accuracy, energy loss rate, service life, load peak-to-valley difference change rate, and section access efficiency.

[0016] As a preferred scheme of the multi-energy coordination and energy control evaluation method described in the present invention, the multi-dimensional weight evaluation includes FAHP weight determination, entropy weight determination, combination of subjective and objective weights, and comprehensive value evaluation.

[0017] As a preferred solution of the multi-energy coordination and energy management and control evaluation method described in the present invention, a power market effectiveness evaluation model is constructed.

[0018] Another object of the present invention is to provide a multi-energy coordination and energy management and evaluation system, which can solve the problems of current traditional energy technology containing less interaction between energy systems and lack of systematic sorting and summary through one of the multi-energy systems.

[0019] As a preferred solution of the multi-energy coordination and energy management and control evaluation system described in the present invention, it includes an evaluation index module, an evaluation method module, and an evaluation model module; the evaluation index module is used to measure the performance of the system in various aspects to ensure the efficient use of energy, stability, flexibility, economy and environmental friendliness. The indicator function of each module is to provide decision makers with a comprehensive system evaluation, help identify the advantages and disadvantages of the technical path, and then optimize the energy structure and improve the overall performance of the system; the evaluation method module is used to evaluate, analyze and optimize various methods and tools of multi-energy systems. It is the technical basis for achieving efficient, reliable, economical and green operation of the system, helping decision makers make reasonable decisions in complex energy systems, and supporting coordination and optimization among different energy sources; the evaluation model module is used for the relationship between each energy source in the system, the operating status and performance evaluation, and supports system design, scheduling decisions and long-term planning.

[0020] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a multi-energy coordination and energy management and control evaluation method.

[0021] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a multi-energy coordination and energy management and control evaluation method.

[0022] Beneficial effects of the present invention: The multi-energy coordination and energy control evaluation method provided by the present invention conducts a comprehensive evaluation through four aspects of section safety, economy, environment, and technical performance, and conducts a comprehensive evaluation under multi-time, multi-dimensional and operation scenarios; then, the FAHP-entropy weight method is combined to calculate the weights of indicators at all levels, and the TOPSIS method is used to evaluate the model, and the optimal unit combination scheme is calculated through actual cases, thereby targeting the key issues of multi-energy coordination and energy control evaluation. The present invention has achieved better results in improving the response speed and regulation accuracy of the power system, low-carbon and environmental protection, high economic benefits, and ensuring system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0024] Figure 1 An overall flow chart of a multi-energy coordination and energy management and evaluation method provided for the first embodiment of the present invention.

[0025] Figure 2 An evaluation index system diagram of a multi-energy coordination and energy management and control evaluation method provided for the second embodiment of the present invention.

[0026] Figure 3 A FAHP weight coefficient diagram of index items of a multi-energy coordination and energy management and control evaluation method provided in the second embodiment of the present invention.

[0027] Figure 4 A weight coefficient diagram of an index item entropy method for a multi-energy coordination and energy management and control evaluation method provided in the second embodiment of the present invention.

[0028] Figure 5 A weight diagram of index item combinations of a multi-energy coordination and energy management and control evaluation method provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0030] Example 1, reference Figure 1-Figure 2, is an embodiment of the present invention, and provides a multi-energy coordination and energy management and control evaluation method, comprising:

[0031] S1: Construct an evaluation index system for the combination of multi-energy coordination and energy management and control assessment technologies.

[0032] Furthermore, it includes section safety analysis indicators, economic analysis indicators, environmental analysis indicators, and technical performance analysis indicators.

[0033] (1) N-1 verification rate

[0034] The power grid is routinely checked for N-1, where 1 refers to the line, transformer, and substation. The N-1 criterion has two meanings: one is to ensure the stability of the power grid; the other is to ensure that users receive continuous power supply that meets quality requirements. During the evaluation process, the substation wiring method and protection action are taken into consideration. The evaluation indicators designed for this purpose include N-1 verification.

[0035] Table 1 Judgment of power grid security situation

[0036] N-1 verification pass status Verification passed Verification failed Power grid security Safety Unsafe

[0037]

[0038] (2) Static voltage safety margin index

[0039] Static safety analysis assumes that the system directly transfers from the steady state before the accident to another steady state after the accident, without considering the intermediate transient process, and is used to verify whether various constraints are met after the accident. Static safety can be characterized by whether various constraints are met under the new operating conditions after the N-1 expected accident occurs in the system, that is, whether the line or transformer is overloaded and whether the bus voltage exceeds the limit.

[0040] With the frequent occurrence of voltage instability and voltage collapse accidents, the voltage stability issue has received more and more attention. In order to prevent the occurrence of voltage instability and voltage collapse accidents, the dispatching and operation personnel are most concerned about how much margin the current power system has and which buses and lines are the weakest. Three types of voltage safety evaluation indicators are constructed: active margin index, voltage margin index, and bus voltage limit.

[0041]

[0042] Among them: P0, V0 and P max 、V crThey are the power and voltage of the load node in the current state and critical state respectively. The reasons for selecting these three are as follows: the active margin reflects the ability of the node to withstand power disturbances, and has a strong correspondence with the ability of the power grid to withstand load growth; while the voltage margin is determined by the power flow equation, that is, the power characteristics of the load node and the network characteristics, and reflects the relative size of the node voltage fluctuation. It does not have a strong correspondence with the ability of the power grid to withstand load growth, and it can better reflect the reactive power reserve capacity of the studied node.

[0043] (1) Dynamic payback period

[0044] The dynamic investment payback period takes into account the time value of an investment and the timing difference between cash inflows and outflows. In simple terms, the dynamic investment payback period is the time it takes for an investment project to recover its entire investment from the beginning. This time may change at different stages of the project because the timing of cash inflows and outflows may vary.

[0045] The dynamic payback period is calculated by adding the project's initial investment to the expected annual cash inflows minus the annual operating costs. This formula gives an expected payback period, which can help investors assess the profitability and risk of a project. The calculation method is:

[0046]

[0047] Where Pt is the dynamic investment payback period; CI n is the cash inflow in the nth year; CO n is the cash outflow in the nth year; BY is the benchmark rate of return.

[0048] (2) Internal rate of return

[0049] The discount rate when the total present value of cash inflows is equal to the total present value of cash outflows is called the Internal Rate of Return (IRR). The internal rate of return is a financial indicator used to evaluate the profitability of an investment project. It is the discount rate that makes the net present value of the project zero, that is, if the IRR of a project is higher than the investor's expected rate of return, then the project is a valuable investment. On the contrary, if the IRR of a project is lower than the investor's expected rate of return, then the project may not be worth investing in. The calculation method is:

[0050]

[0051] (3)Unit fixed cost

[0052] The fixed cost of thermal power, hydropower, wind power and photovoltaic units refers to the cost that needs to be paid during the production process regardless of the output of the equipment, and does not change with the output. Its main components include:

[0053] Purchase cost: including the cost of purchasing machinery and equipment, installation costs, transportation costs, etc.;

[0054] Maintenance costs: including equipment repair and maintenance costs, spare parts replacement costs, spare parts costs, etc.;

[0055] Depreciation cost: Mechanical equipment will gradually lose its value during use. Depreciation cost refers to the cost of asset impairment due to reasons such as gradual aging and wear of the equipment.

[0056]

[0057] Among them, C g , C w , C z are the purchase cost, maintenance cost and depreciation cost respectively, Q h The power generation of the unit.

[0058] Energy storage can participate in the electricity market and the ancillary service market in one cycle. By calculating the unit time cost of energy storage in different schemes in one cycle, and then based on the operation time of energy storage in participating in different ancillary services such as peak shaving, frequency regulation and emergency power support, the total cost of energy storage participating in different ancillary services can be calculated. By allocating the total cost to the total peak shaving electricity or total frequency regulation mileage, the unit fixed cost of energy storage participating in different ancillary services in different schemes can be calculated.

[0059] Fixed cost per unit time in a single cycle:

[0060]

[0061] Among them, C DOD is the fixed cost per unit time in a single cycle of energy storage; is the total fixed cost of the energy storage life cycle; N is the life of the energy storage; n is the annual cycle number of energy storage; T DOD is the cycle time of a single energy storage cycle.

[0062] Fixed cost per peak load:

[0063]

[0064] Among them, C F T is the fixed cost per unit of electricity when energy storage participates in peak load regulation; F The duration of energy storage participating in a single peak load regulation; Q F The amount of electricity consumed when energy storage participates in a single peak regulation.

[0065] Fixed cost per frequency modulation unit:

[0066]

[0067] Among them, C P is the fixed cost per mileage when energy storage participates in frequency regulation; T p P is the duration of energy storage participating in a single frequency regulation; P The mileage consumed by energy storage participating in a single frequency regulation.

[0068] Emergency power support unit fixed cost:

[0069]

[0070] Among them, C JP T is the fixed cost per mileage when energy storage participates in emergency power support; JP P is the duration of energy storage participating in a single emergency power support; JP It is the total mileage consumed when energy storage participates in a single emergency power support.

[0071] (4) Unit variable cost

[0072] Variable costs refer to costs that change with the increase or decrease in business volume, such as raw material costs, manufacturing expenses, sales costs, etc.

[0073] Taking energy storage units as an example, in different schemes, the unit variable cost of energy storage participating in auxiliary services can be calculated based on the variable cost of energy storage and the power or mileage output during the actual operation of energy storage during period t. Therefore, the unit variable cost mc1 of energy storage participating in peak regulation and the unit variable cost mc2 of energy storage participating in frequency regulation and emergency power support can be expressed as:

[0074]

[0075] (1) Environmental impact

[0076] The degree of environmental impact of energy storage mainly evaluates the impact of energy storage facilities on the environment during construction and operation, including water, air, noise, radiation and other pollution. Each energy storage power station will be deducted 5 points for each environmental pollution during the evaluation period. The final score of the indicator is the weighted average of the score of each energy storage facility on the total energy storage installed capacity of the regional power grid. The calculation formula is as follows:

[0077]

[0078] (2) Changes in renewable energy consumption

[0079] The amount of renewable energy consumed per unit installed capacity refers to the amount of renewable energy consumed by the power system per unit installed capacity within a certain period of time. This indicator is used to reflect the power system's ability to accept and digest renewable energy, as well as the efficiency and benefits of renewable energy utilization in the power system.

[0080]

[0081] Among them, C s,e Represents the original renewable energy consumption; C c,e Represents the renewable energy consumption of the new solution.

[0082] The renewable energy absorption capacity of different schemes varies. Renewable energy generation has characteristics such as volatility, intermittency and reverse load characteristics, which will affect its power generation and power supply quality, and thus affect the renewable energy absorption capacity of different schemes. When market demand is strong and the dispatch strategy is reasonable, the absorption of renewable energy will increase accordingly. The changes and optimization of energy storage technology in different schemes can improve the flexibility and dispatchability of the power system, thereby enhancing the absorption capacity of renewable energy.

[0083] Evaluate the power system's ability to accept and digest renewable energy, and provide a scientific basis for power system planning and construction. Reflect the efficiency and benefits of renewable energy in the power system, and provide strong support for the development and application of renewable energy. Guide the operation and management of the power market, and promote the healthy development of the power market.

[0084] (3) Carbon emissions from equipment production

[0085] "Equipment production carbon emissions" refers to the greenhouse gas emissions generated by various equipment throughout its life cycle, especially in the production stage, due to energy consumption, raw material processing, manufacturing process and waste treatment. These greenhouse gases mainly include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), etc., among which CO2 is the most important component.

[0086] This indicator evaluates the environmental friendliness and sustainability of the equipment production process, and provides decision makers with data support on how to reduce the carbon footprint of the production process. By quantifying the carbon emissions of equipment production, high-emission links can be identified and corresponding measures can be taken to reduce emissions, thereby achieving the goals of energy conservation, emission reduction and green production.

[0087] The carbon emissions from the production of computing equipment usually adopt the "Life Cycle Assessment, LCA" method, which considers the environmental impact of the equipment throughout its entire life cycle, from raw material extraction, production and processing, transportation and distribution, use and maintenance to final disposal.

[0088] C=αC d

[0089] Collect energy consumption data, raw material consumption data, waste generation and treatment data, etc. from each link in the equipment production process. Based on the collected data, determine the emission factors of various energy sources and raw materials, that is, the greenhouse gas emissions generated by unit energy consumption or raw material consumption. Calculate the greenhouse gas emissions in the equipment production process using the emission factors and corresponding consumption data. Summarize the emissions generated in each link to obtain the total carbon emissions of equipment production, and analyze and compare them.

[0090] (4)Unit area

[0091] Due to the increasing pressure of environmental protection, it is more difficult to acquire land for power grid construction, and the existing substation sites are tight, so it is more and more important to reduce the land area of ​​power stations. The unit area of ​​a power station is the ratio of the area of ​​the power station to the rated energy, and the calculation is as shown in the formula:

[0092]

[0093] Among them, S is the area occupied by the power station and E is its rated energy.

[0094] (1) Response time

[0095] Response time refers to the time required for a unit or energy storage device to start adjusting its output after receiving an adjustment command. The shorter the response time, the better the response performance. Currently, most auxiliary service markets usually set a standard adjustment time. The performance index of the energy storage power station response time is reflected by the ratio of the actual adjustment time to the standard adjustment time and the inverse processing. The calculation formula is:

[0096]

[0097] Among them, t i is the actual adjustment time, t n Adjust the time to the standard.

[0098] (2) Adjustment accuracy

[0099] Regulation accuracy refers to the difference between the final actual output of the unit or energy storage device and the regulation command value. The smaller the difference, the better the performance. The calculation is shown in the formula:

[0100]

[0101] Among them, ΔP i is the adjustment error; ΔP n is the standard adjustment error.

[0102] (3) Actual discharge capacity ratio

[0103] The actual discharge (generation) power ratio refers to the ratio of the maximum discharge (generation) power in the power equipment to the total storage value or total installed capacity, and is one of the important indicators for evaluating the quality of power equipment.

[0104]

[0105] P is the actual discharge capacity ratio, P max Refers to the maximum discharge (generation) of the power equipment, P a Refers to the total storage value or total installed capacity.

[0106] (4)Energy loss rate:

[0107]

[0108] The conversion efficiency of energy storage equipment refers to the ratio of the actual converted electrical energy to the original electrical energy in the process of converting electrical energy into other forms of energy (such as chemical energy, mechanical energy, etc.) and converting it back to electrical energy. Due to energy loss in the conversion process, the conversion efficiency is usually less than 100%.

[0109] Charging conversion efficiency refers to the ratio of the actual stored electrical energy to the input electrical energy during the charging process of the energy storage device. Different types of energy storage devices have different charging conversion efficiencies. For example, the charging conversion efficiency of lithium-ion batteries is usually between 80% and 90%. Factors such as ambient temperature, charging speed, and charging method will also affect the charging conversion efficiency. Generally speaking, the charging conversion efficiency of energy storage devices is between 60% and 90%.

[0110] Discharge conversion efficiency refers to the ratio of the actual output power of the energy storage device to the power stored inside the device during the discharge process. Similar to the charging conversion efficiency, different types of energy storage devices have different discharge conversion efficiencies. Discharge speed, discharge depth, ambient temperature and other factors will also affect the discharge conversion efficiency. The discharge conversion efficiency of energy storage batteries is generally between 70% and 90%. For certain specific energy storage systems, such as energy storage cabinet systems using lithium iron phosphate batteries, the energy conversion efficiency should not be less than 94%.

[0111] (5) Service life

[0112] The service life indirectly reflects the quality and technical level of the unit or energy storage device. Under the premise of long-term load bearing and short-term fault current impact, high-quality units or energy storage devices have a longer service life.

[0113] (6) Load peak-to-valley difference change rate

[0114]

[0115] Among them, C1 represents the original load peak-valley difference; C2 represents the load peak-valley difference of the new solution.

[0116] (7) Fault group access efficiency

[0117] The section access efficiency reflects the efficiency of connection to the grid after adding new machines or energy storage technology.

[0118]

[0119] K is the section access efficiency, K all is the total installed capacity, K d It is the cross-sectional capacity. Multi-energy coordination and energy control assessment technology combination evaluation index system.

[0120] S2: Conduct multi-dimensional weight assessment.

[0121] Furthermore, the FAHP-entropy weight method combination is carried out by including FAHP method weight determination, entropy weight method weight determination, combination of subjective and objective weights, and comprehensive value evaluation.

[0122] Fuzzy AHP introduces fuzzy theory on the basis of AHP, and verifies matrix consistency by establishing fuzzy consistency judgment matrix and compatibility. In addition, its method of solving fuzzy consistency matrix weight is less computationally intensive and more efficient than the characteristic root method required by AHP. Based on fuzzy theory, the main steps of fuzzy AHP are as follows:

[0123] (1) Establish a judgment matrix. Assume that there is a set of factors X = {x1, x2, ..., xn}, with a total of n indicators. Establish a fuzzy judgment matrix by comparing the factors one by one.

[0124] If factor xj is more important than factor xi, then x ij ∈[0.1,0.5); if factor xi is more important than factor xj, then x ij ∈(0.5,0.9], if the two are equally important, then x ij =0.5. According to the above provisions, the fuzzy judgment matrix X is established based on the importance between the index factors x1, x2, ..., xn, as shown below.

[0125]

[0126] (2) Establish a fuzzy consistency matrix. The AHP method calculates the characteristic roots and then solves the consistency index C1 of the fuzzy judgment matrix. The closer the CI is to 0, the stronger the consistency. In the consistency verification process of the AHP method, the work is cumbersome and the amount of calculation is large, and there are multiple verifications. The fuzzy AHP method reduces the complexity of consistency verification through the fuzzy complementary matrix. The specific theorem [6] is as follows.

[0127] Theorem 1: Fuzzy complementary matrix A = (a ij ) n·n is a necessary and sufficient condition for the fuzzy consistency matrix, any two rows a ik with a jk The difference between corresponding elements is a constant, i.e. ik -a jk =a ij -0.5.

[0128] Theorem 2: Fuzzy complementary matrix A = (a ij ) n·n is a necessary and sufficient condition for the fuzzy consistency matrix, and any specified row a ik and other rows a jk The difference between corresponding elements is a constant, i.e. ik -a jk =r j -r i , r j -r i is a constant.

[0129] Therefore, in the fuzzy judgment matrix A, if the importance of ai compared to a is certain, that is, a is greater than aj. Then there is any ak in A, that is, the relative importance of elements ai and ak is greater than the relative importance of ai and ak, and the relative importance of ai and a is ai=aik-aik+0.5, then A=(a)n is a fuzzy consistency matrix.

[0130] From the above, we can see that the fuzzy consistency matrix A is as follows:

[0131]

[0132] in,

[0133] The attribute weight is deduced. According to the fuzzy consistency matrix, the weight of each element is deduced, and the formula is as follows:

[0134]

[0135] in, n is the fuzzy consistency matrix A, i.e. the number of elements.

[0136] The entropy weight method is a method for determining weights based on the entropy principle. It is an objective weighting method that determines the weights based on the amount of information contained in each evaluation indicator. The smaller the entropy value of the indicator, the more information it provides, the more important the indicator, and the larger the corresponding weight value.

[0137] Assuming there are M evaluation objects and K evaluation indicators, the steps to determine the weights using the entropy weight method are as follows:

[0138] (1) First, the range method is used to standardize each measurement index X in the multi-energy coordination and energy control scheme evaluation system:

[0139]

[0140] Among them, x ij is a positive indicator, x' ij is a negative indicator; i represents the solution, j represents the measurement indicator; X max and X min are the maximum and minimum values ​​of the j-th indicator respectively.

[0141] (2) Determine the evaluation matrix R based on the original data mk ) M×K , r mk is the value of the kth indicator of the mth evaluation object. Where m = 1, 2, ..., M; k = 1, 2, ... K. The matrix form is as follows:

[0142]

[0143] (3) To eliminate the inconsistency of indicators, the evaluation matrix is ​​unified and standardized to obtain the standardized matrix S = (s mk ) M×K The indicator standardization method is as follows:

[0144]

[0145] (4) The entropy of each evaluation index is

[0146]

[0147] When mk = 0, let s mk Ins mk =0.

[0148] (5) Define the difference coefficient of the kth evaluation index as

[0149] λ k =1-E k (k=1,2,…,K)

[0150] (6) The entropy weight of the kth evaluation index is

[0151]

[0152] Entropy Weight V k It reflects the amount of information contained in the indicator. The entropy weight reflects the importance of the indicator. The larger the value, the greater the role of the indicator in the evaluation system.

[0153] Assume that the weight obtained by subjective weighting using fuzzy hierarchical analysis is

[0154] X=[X1,X2,…,X K ] T

[0155] The weight obtained by objectively assigning weights using the entropy weight method is:

[0156] V=[V1,V2,…,V K ] T

[0157] In order to simultaneously consider the subjective experience preference and the authenticity of objective data information and achieve the unity of subjectivity and objectivity, the deviation between the final comprehensive weight and the subjective and objective weights should be as small as possible. To this end, the least squares optimization model is established as shown in the following formula. The established least squares decision model is shown in the following formula.

[0158]

[0159] Where: W k is the comprehensive weight; X k is the subjective weight; V k is the objective weight; mk The index is the index after standardization.

[0160] Based on the determined weights, the comprehensive benefits of energy storage combined with frequency regulation are evaluated. The evaluation calculation is shown in the following formula.

[0161] R z =W1R1+W2R2+W3R3+W4R4

[0162] Among them, R Z To evaluate the comprehensive value of technical solutions for multi-energy coordination and energy control, R1 represents section safety, R2 represents economic benefits, R3 represents environmental benefits, and R4 represents technical performance.

[0163] S3: Construct a power market effectiveness evaluation model.

[0164] Furthermore, TOPSIS model evaluation.

[0165] According to the weighting method, the combined weights corresponding to the evaluation indicators can be determined, and the corresponding weighted values ​​of the indicators can be calculated. However, the comprehensive value score of energy storage participating in power auxiliary services from different angles has not yet been obtained. The relationship between each indicator and the comprehensive score cannot be simply calculated through a simple linear relationship. Therefore, this section uses the improved TOPSIS method to solve the comprehensive score.

[0166] The TOPSIS method, also known as the ranking method of approximating ideal solutions, is often used for multi-objective decision analysis. This method ranks the solutions according to the distance between each solution and the ideal solution. When the solution is closest to the optimal solution and farthest from the worst solution, it is considered the best solution. Traditional TOPSIS calculates the closeness based on the Euclidean distance, and often has problems of insufficient evaluation validity and median line between sample data in certain cases. Therefore, this section improves the traditional TOPSIS method, introduces grey relational analysis (GRA) and combines it with TOPSIS, and proposes an improved TOPSIS comprehensive evaluation method that comprehensively considers grey correlation, which effectively improves the adaptability of the method.

[0167] Since the indicators have been normalized in the process of weight standardization, the maximum value of the indicator is the positive ideal solution, and the minimum value of the indicator is the negative ideal solution.

[0168] The positive ideal solution is:

[0169]

[0170] The negative ideal solution is:

[0171]

[0172] Calculate the grey correlation degree and traditional Euclidean distance of each solution to the positive and negative ideal solutions.

[0173] The calculation of the positive and negative grey correlation coefficients first requires the calculation of the grey correlation degree using the following formula:

[0174]

[0175] Among them, ρ is the resolution coefficient, which affects the positive and negative grey correlation coefficients, and is generally taken as 0.5. The positive and negative grey correlation matrix can be obtained from the above calculation formula, and then the correlation coefficients of each solution with the positive and negative ideal solutions can be obtained:

[0176]

[0177] In addition, according to the Euclidean distance, the distances between each solution and the positive and negative ideal solutions can be obtained:

[0178]

[0179] Solve the relative closeness for comprehensive evaluation, obtain the ranking results of the schemes and analyze the results. First, we can i + 、r i -, The results are dimensionless, and then the pros and cons of the solutions can be judged based on their relative closeness.

[0180] The expression of relative closeness based on Euclidean distance is:

[0181]

[0182] The expression of relative closeness based on grey relational analysis is:

[0183]

[0184] The comprehensive closeness calculated based on the closeness of traditional Euclidean distance and the closeness based on grey correlation analysis is:

[0185] U i =e1D i +e2Y i ,i=1,2,…,m

[0186] When the value of comprehensive closeness is larger, it is closer to the positive ideal solution, which means that the solution is closer to the optimal solution, that is, the comprehensive value is higher. Conversely, it means that the solution is closer to the negative ideal solution and the comprehensive value is worse.

[0187] Example 2, reference Figure 3-Figure 5 , which is an embodiment of the present invention, provides a multi-energy coordination and energy management and control evaluation method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0188] First, when discussing the combination of multi-energy coordination and energy management and evaluation technologies, we need to pay attention not only to the output characteristics and efficiency of a single energy source, but also to fully consider the cross-influence of flexible resources in the combination scheme, as well as the joint output and timing relationship of these resources at different time scales. This comprehensive evaluation is crucial to achieving stable, efficient and reliable operation of multi-energy systems.

[0189] The design of the combination scheme needs to fully consider the interaction between various flexible resources (such as energy storage power stations, pumped storage power stations, thermal power unit transformation, etc.). These resources have different advantages and limitations in providing regulation capabilities, balancing supply and demand fluctuations, and responding to emergencies. Through reasonable configuration and scheduling, the advantages of multiple energy systems can be complemented, and the flexibility and response speed of the overall system can be improved.

[0190] When designing a combination plan, the output characteristics and timing relationships of different energy sources at different time scales must be considered. For example, wind energy and solar energy are intermittent and unstable, and their output levels are significantly affected by weather conditions; while energy storage power stations and pumped storage power stations can provide necessary power support when power demand peaks or when new energy output is insufficient. Through refined scheduling and forecasting, it can be ensured that the combined output of various energy sources in different time periods can meet the actual needs of the power grid, while achieving optimal configuration and efficient use of energy.

[0191] Energy storage technology is a key link in achieving multi-energy complementarity and energy management. In the combination scheme, adding different types of energy storage facilities such as electrochemical energy storage power stations, pumped storage power stations or compressed air energy storage can significantly improve the system's regulation ability and energy storage capacity. These energy storage facilities can absorb excess electricity when new energy output is excessive, and release electricity when new energy output is insufficient, thereby effectively balancing the supply and demand relationship of the power grid and improving the reliability and stability of the system.

[0192] Based on the above considerations, the present invention designs the following five combination schemes:

[0193] Adding electrochemical energy storage power station + pumped storage power station + wind and solar units: This solution combines the rapid response capability of electrochemical energy storage and the large-scale energy storage capacity of pumped storage power stations, complementing wind and solar units, and can effectively cope with the intermittent and instability of new energy output.

[0194] Pumped-storage power station + hydropower unit + wind and solar unit: Utilize the regulation capability of pumped-storage power station and hydropower unit, combine with the clean energy advantage of wind and solar unit, to form a stable and reliable power supply combination, which is suitable for promotion in areas rich in hydropower resources.

[0195] Add electrochemical energy storage power station + compressed air energy storage + wind and solar units: By introducing compressed air energy storage technology, the system's energy storage capacity and regulation capabilities can be further improved, and combined with electrochemical energy storage and wind and solar units to form an efficient energy complementary system.

[0196] Add electrochemical energy storage power stations + transform thermal power units + wind and solar units: Transform the flexibility of thermal power units to improve their peak and frequency regulation capabilities, and together with electrochemical energy storage and wind and solar units, form a flexible and efficient power supply combination, which is suitable for areas that need to balance new energy and traditional energy.

[0197] Adding electrochemical energy storage power stations + hydropower units + wind and solar units: once again emphasizes the complementary advantages of electrochemical energy storage and hydropower units, combines the clean energy characteristics of wind and solar units to form a diversified energy supply system and improve the overall operating efficiency of the system.

[0198] These combination schemes fully consider the cross-influence of flexible resources, the relationship between joint output and timing at multiple time scales, and the key role of energy storage technology in their design, aiming to achieve optimized operation and efficient management of multi-energy systems through scientific and reasonable configuration and scheduling strategies. 2025 was selected as the target node, and the data of actual construction in Guizhou was summarized to evaluate and rank the comprehensive performance of the schemes.

[0199] As can be seen from the table, among the first-level indicators, the weights of the four first-level indicators, namely, section safety, economic benefits, environmental benefits, and technical performance, reached 16.91%, 16.46%, 27.31%, and 39.31%, respectively, indicating that technical performance is the primary factor in the selection of the scheme and is far more influential than other factors. Compared with economic benefits, environmental benefits have a greater impact on the selection of schemes, which shows that it is more important to realize Guizhou's future energy planning and promote the mission of environmental protection and green development.

[0200] Among the secondary indicators, the weights of environmental impact, response time, and service life are ranked in the top three, and the weights of the three indicators are all over 11%, 16.35%, 12.58%, and 11.83% respectively. From the results of all indicators, there are 5 indicators with weights greater than 6.25%. Such indicators play a key role in selecting appropriate multi-energy coordination and energy management technology combination solutions. According to the weight calculation results, compared with the conventional single weighting method, the combined weighting method weakens the larger weights and strengthens the smaller weights, avoiding the problem of some indicators being invalid due to the large differences in the weights of each indicator.

[0201] From the perspective of the weight distribution of primary indicators, technical performance (39.31%) occupies the highest weight ratio, which is much higher than the other three indicators such as section safety (16.91%), economic benefits (16.46%) and environmental benefits (27.31%). This result clearly shows that technical performance is regarded as the primary consideration in the selection of technical combination solutions for multi-energy coordination and energy management. This not only reflects the importance of technological advancement and stability in improving the overall operating efficiency of the energy system, but also reflects that in the construction of multi-energy systems, technological innovation and performance optimization are the core driving forces for promoting high-quality development of the system.

[0202] Further analysis shows that the weight of environmental benefits (27.31%) is higher than that of economic benefits (16.46%), which reflects that in the context of current energy transformation and sustainable development, environmental protection and green development have become important considerations that cannot be ignored. Guizhou's future energy planning not only pursues the improvement of economic benefits, but also shoulders the historical mission of promoting environmental protection and achieving green development. Therefore, in the process of selecting options, the weight of environmental benefits is relatively large, which is in line with the requirements of the times and policy orientation.

[0203] At the secondary indicator level, the weights of environmental impact (16.35%), response time (12.58%) and service life (11.83%) ranked the top three, all exceeding the threshold of 11%. This shows that in the specific technical performance and environmental benefit evaluation, these three indicators play a vital role in selecting the appropriate multi-energy coordination and energy management technology combination scheme.

[0204] The high weight of environmental impact reflects the importance of reducing environmental pollution and promoting the development of clean energy in the construction of multi-energy systems. By evaluating the environmental impact of energy storage facilities during construction and operation, we can provide a scientific basis for the formulation of environmental protection policies and standards and promote the construction of green energy systems.

[0205] As one of the key indicators of technical performance, the high weight of response time reflects the importance of system response speed and regulation ability. Shorter response time means that the system can respond to changes in energy demand more quickly, improving the flexibility and reliability of the system.

[0206] The service life indirectly reflects the quality and technical level of the unit or energy storage device. A longer service life not only reduces the frequency and cost of equipment replacement, but also reduces the environmental impact caused by equipment replacement, which is of great significance for achieving long-term economic and environmental benefits.

[0207] In addition, from the results of all indicators, there are 5 indicators with a weight greater than 6.25%, including N-1 verification rate, static voltage safety margin, internal rate of return, unit floor area and regulation accuracy. These indicators cover multiple aspects such as section safety, economic benefits, environmental benefits and technical performance, and have an important impact on the comprehensive evaluation of the advantages and disadvantages of multi-energy coordination and energy control technology combination solutions.

[0208] Compared with the conventional single weighting method, the FAHP-entropy weight method combined weighting method has significant advantages. This method not only retains the subjective judgment of experts on the importance of indicators by comprehensively considering expert experience and objective data, but also objectively reflects the amount of information in the data itself through the entropy weight method. Therefore, the combined weighting method can more comprehensively reflect the actual impact of each indicator, avoiding the problem of some indicators being invalid due to the large difference in the weights of each indicator. At the same time, this method can also flexibly adjust the weight distribution according to the specific situation, making the evaluation results more scientific and reasonable.

[0209] In summary, the index weight results calculated by the FAHP-entropy weight method combined empowerment method provide strong support for the selection of Guizhou's multi-energy coordination and energy management technology combination scheme. In future energy planning and construction, we should continue to pay attention to the improvement of technical performance and environmental benefits, while taking into account the economic benefits and section safety, and promote the optimized operation and sustainable development of multi-energy systems.

[0210] Table 2 TOPSIS evaluation calculation results

[0211] item Relative proximity C Sorting results Pumped storage + hydropower + wind and solar 0.429 4 Electrochemical energy storage + compressed air energy storage + wind and solar 0.512 2 Electrochemical energy storage + pumped storage + wind and solar 0.514 1 Electrochemical energy storage + thermal power transformation + wind and solar power 0.422 5 Electrochemical energy storage + hydropower + wind and solar 0.437 3

[0212] Based on the above comprehensive evaluation results, for Guizhou Province, the optimal technical combination is "adding electrochemical energy storage power stations + pumped storage power stations + wind and solar units". This solution not only performs well in multiple dimensions such as technical performance, economic benefits, environmental friendliness and section safety, but also highly fits with the current energy policy trend of Guizhou Province, further verifying the reliability and effectiveness of the evaluation index system constructed by this invention.

[0213] Embodiment 3, an embodiment of the present invention provides a multi-energy coordination and energy management and control evaluation system, including an evaluation index module, an evaluation method module, and an evaluation model module;

[0214] The evaluation criteria module is used to measure the performance of the system in various aspects to ensure efficient use of energy, stability, flexibility, economy and environmental friendliness. The role of the indicators in each module is to provide decision makers with a comprehensive system evaluation, help identify the advantages and disadvantages of the technical path, and then optimize the energy structure and improve the overall performance of the system; the evaluation method module is used to evaluate, analyze and optimize various methods and tools for multi-energy systems. It is the technical basis for achieving efficient, reliable, economical and green operation of the system, helping decision makers make reasonable decisions in complex energy systems, and supporting coordination and optimization among different energy sources; the evaluation model module is used for the relationship between each energy source in the system, operating status and performance evaluation, supporting system design, scheduling decisions and long-term planning.

[0215] If the function 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, server, or network device, etc.) to perform all or part of the steps of the methods of 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, etc. Various media that can store program codes.

[0216] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0217] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0218] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0219] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-energy coordination and energy management and evaluation method, characterized in that: include: Construct a combination evaluation index system for multi-energy coordination and energy control assessment technologies; Conduct multi-dimensional weight assessment; Construct an electricity market effectiveness evaluation model.

2. The multi-energy coordination and energy management and evaluation method according to claim 1, characterized in that: The construction of the multi-energy coordination and energy management and control assessment technology combination evaluation index system includes section safety index analysis, including N-1 verification rate and static voltage safety margin index. Through the analysis of these section safety indicators, the operating status of the system can be monitored in real time to ensure the safe and efficient operation of each link, prevent potential system risks, and provide a basis for optimizing energy scheduling and control.

3. The multi-energy coordination and energy management and evaluation method according to claim 2, characterized in that: The multi-energy coordination and energy management and control evaluation technology combination evaluation index system comprises economic analysis indicators, including dynamic investment payback period, internal rate of return, unit fixed cost, and unit variable cost.

4. The multi-energy coordination and energy management and evaluation method according to claim 3, characterized in that: The multi-energy coordination and energy management and control evaluation technology combination evaluation index system comprises environmental analysis indicators, including the degree of environmental impact and the change in renewable energy consumption.

5. The multi-energy coordination and energy management and evaluation method according to claim 4, characterized in that: The multi-energy coordination and energy control evaluation technology combination evaluation index system includes technical performance analysis indicators, including impact time, regulation accuracy, energy loss rate, service life, load peak-to-valley difference change rate, and section access efficiency.

6. The multi-energy coordination and energy management and evaluation method according to claim 5, characterized in that: The multi-dimensional weight evaluation includes FAHP method weight determination, entropy weight method weight determination, combination of subjective and objective weights, and comprehensive value evaluation.

7. The multi-energy coordination and energy management and evaluation method according to claim 6, characterized in that: The electricity market effectiveness evaluation model constructed includes TOPSIS model evaluation, which is used for multi-objective decision analysis, and the schemes are ranked according to the distance between each scheme and the ideal scheme. When the scheme is closest to the optimal scheme and farthest from the worst scheme, the scheme is considered to be the best scheme.

8. A system using the multi-energy coordination and energy management and evaluation method according to any one of claims 1 to 7, characterized in that: Including evaluation index module, evaluation method module and evaluation model module; The evaluation module is used to measure the performance of the system in various aspects to ensure efficient use of energy, stability, flexibility, economy and environmental friendliness. The role of the indicators in each module is to provide decision makers with a comprehensive system evaluation, help identify the advantages and disadvantages of the technical path, and then optimize the energy structure and improve the overall performance of the system; The evaluation method module is used to evaluate, analyze and optimize various methods and tools of multi-energy systems. It is the technical basis for achieving efficient, reliable, economical and green operation of the system, helping decision makers make reasonable decisions in complex energy systems and supporting coordination and optimization among different energy sources; The evaluation model module is used for the mutual relationship, operating status and performance evaluation between various energy sources in the system, and supports system design, scheduling decisions and long-term planning.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-energy coordination and energy management and evaluation method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-energy coordination and energy management and evaluation method described in any one of claims 1 to 7 are implemented.

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