Multi-attribute evaluation method, system and equipment for multi-subject collaborative loss reduction effect

Abstract

The invention relates to a multi-attribute evaluation method, system and equipment for a multi-subject collaborative loss reduction effect, and the method comprises the steps: carrying out the multi-attribute evaluation based on the obtained parameters of a multi-subject loss reduction scheme in combination with a pre-constructed loss reduction effect evaluation index and evaluation standard, and obtaining a q-order orthogonal fuzzy evaluation value of each attribute; calculating by utilizing a q-order orthogonal fuzzy multi-attribute decision analysis method based on the q-order orthogonal fuzzy evaluation value of each attribute to obtain a weight coefficient and an accurate value of each attribute; performing evaluation based on the weight coefficient and the precise value of each attribute to obtain a relative saliency value of the multi-main-body loss reduction scheme; according to the invention, the loss reduction effect evaluation index reflects the interest demands of different subjects, and also considers the influence of multiple subjects on the loss reduction optimization of the power distribution network; the relation between the evaluation result and the economic benefit is discussed by performing multi-attribute evaluation in combination with the evaluation standard; by using the q-order orthogonal fuzzy multi-attribute decision analysis method, the evaluation result is more comprehensive and more accurate.

Description

Technical Field

[0001] The present application relates to a distribution network loss reduction effectiveness evaluation technology, and specifically to a multi-attribute evaluation method, system and equipment for multi-agent collaborative loss reduction effectiveness. Background Art

[0002] The emergence of multiple entities such as integrated energy service providers, energy storage aggregators, load aggregators, distributed power generators, distribution companies and load users has made the dynamic characteristics of distribution network operation more obvious, and also requires comprehensive consideration of different entities in the evaluation of distribution network loss reduction effectiveness. For a distribution network with multiple participants, there are many effective measures to reduce distribution network losses. How to select the best solution from many feasible loss reduction solutions requires not only considering the technical safety factors of the power grid itself, but also considering the demands of different entities. In actual decision-making, these entities that need to be considered are often both contradictory and unified. How to unify them well in a loss reduction solution decision model is worthy of in-depth study.

[0003] At present, there are few studies on the optimization of distribution network loss reduction schemes considering market entities. A similar scheme is the evaluation indicators and evaluation methods for traditional distribution network loss reduction by domestic and foreign scholars. However, the existing indicator system does not reflect the interests of different entities. Moreover, the comprehensive evaluation method of loss reduction effectiveness adopted mainly stays in the loss reduction optimization for a single entity, without considering the influence of multiple entities in the market, and without exploring the relationship between the evaluation results and costs and economic benefits. Summary of the invention

[0004] In order to solve the problem that the existing technology does not consider the impact of multiple entities in the market and does not explore the relationship between evaluation results and costs and economic benefits, this application proposes a multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness, including:

[0005] Based on the obtained parameters of the multi-agent loss reduction scheme and the pre-built loss reduction effectiveness evaluation indicators and evaluation criteria, a multi-attribute evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute;

[0006] The q-order orthogonal fuzzy evaluation value based on each attribute is calculated using the q-order orthogonal fuzzy multi-attribute decision analysis method to obtain the weight coefficient and exact value of each attribute;

[0007] Based on the weight coefficient and exact value of each attribute, the relative significance value of the multi-agent loss reduction scheme is obtained.

[0008] Preferably, the multiple entities include: power distribution companies, load users, integrated energy service providers and distributed power generators.

[0009] Preferably, it also includes a process of constructing a loss reduction effectiveness evaluation index; the construction process includes:

[0010] Based on the parameter calculation of the multi-agent loss reduction scheme, the unit investment benefit, loss rate, line load rate, line or transformer overload rate, bus voltage over-limit percentage and voltage qualification rate are obtained to determine the loss reduction effectiveness evaluation indicators of the distribution company;

[0011] Based on the parameter calculation of the multi-agent loss reduction scheme, the economic degree and comfort level of electricity consumption are obtained, and the loss reduction effectiveness evaluation index of load users is determined;

[0012] Based on the parameter calculation of the multi-agent loss reduction scheme, the net present value, renewable energy consumption and load peak-valley difference are obtained to determine the loss reduction effectiveness evaluation indicators of the integrated energy service provider;

[0013] Based on the parameter calculation of the multi-agent loss reduction plan, the internal rate of return and the amount of renewable energy grid-connected electricity are obtained, and the loss reduction effectiveness evaluation indicators of distributed power producers are determined.

[0014] Preferably, the parameters of the acquired multi-agent loss reduction scheme are combined with pre-constructed loss reduction effectiveness evaluation indicators and evaluation criteria to perform multi-attribute evaluation to obtain q-order orthogonal fuzzy evaluation values ​​of multiple attributes, including:

[0015] The parameters of the multi-agent loss reduction scheme are calculated in combination with the loss reduction effectiveness evaluation index to obtain the calculated values ​​of each attribute;

[0016] Based on the calculated value of each attribute and the evaluation criteria, a fuzzy quantitative evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute.

[0017] Preferably, the q-order orthogonal fuzzy evaluation value based on each attribute is calculated using a q-order orthogonal fuzzy multi-attribute decision analysis method to obtain a weight coefficient and an exact value of each attribute, including:

[0018] The weight coefficient of each attribute is obtained by calculating the q-order orthogonal fuzzy evaluation value of each attribute;

[0019] Based on the obtained multi-agent loss reduction scheme, the membership and non-membership of each attribute are calculated to obtain the exact value of each attribute.

[0020] Preferably, the calculation based on the q-order orthogonal fuzzy evaluation value of each attribute to obtain the weight coefficient of each attribute includes:

[0021] Perform normalization calculation based on the q-order orthogonal fuzzy evaluation value of each attribute to obtain the normalized value of each attribute;

[0022] The correlation coefficients between the attributes are calculated based on the normalized values ​​of the attributes to obtain the correlation coefficients between the attributes;

[0023] The contrast strength and conflict of each attribute are calculated based on the correlation coefficient between the attributes to obtain the contrast strength and conflict of each attribute;

[0024] The weight coefficient is calculated based on the contrast strength and conflict of each attribute to obtain the weight coefficient of each attribute.

[0025] Preferably, the obtained multi-agent loss reduction scheme calculates the membership and non-membership of each attribute to obtain the precise value of each attribute, including:

[0026] Based on the obtained multi-agent loss reduction scheme, the initial decision matrix is ​​constructed with respect to the membership and non-membership of each attribute.

[0027] Based on the initialization decision matrix, the q-ROFRWMSM operator is used to perform fusion calculation to obtain the q-order fuzzy evaluation matrix;

[0028] Based on the q-order fuzzy evaluation matrix, the difference between the q-th power values ​​of the membership degree and the non-membership degree is calculated to obtain the exact value of each attribute and form a new decision matrix.

[0029] Preferably, the evaluation based on the weight coefficient and the exact value of each attribute to obtain the relative significance value of the multi-agent loss reduction scheme includes:

[0030] Perform normalization calculation based on the exact value of each attribute to obtain the normalized value of the exact value of each attribute;

[0031] The normalized value based on the exact value of each attribute is calculated in combination with the weight coefficient of each attribute to obtain the weighted normalized value of each attribute;

[0032] Calculate based on the weighted normalized value of each attribute to obtain the maximum index and the minimum index of each attribute;

[0033] Based on the maximization index and minimization index of each attribute, the relative significance value of the multi-agent loss reduction scheme is obtained.

[0034] Based on the same application concept, this application also proposes a multi-attribute evaluation system for multi-agent collaborative loss reduction effectiveness, including:

[0035] The q-order orthogonal fuzzy evaluation value calculation module is used to perform multi-attribute evaluation based on the acquired parameters of the multi-agent loss reduction scheme combined with the pre-built loss reduction effectiveness evaluation indicators and evaluation criteria to obtain the q-order orthogonal fuzzy evaluation value of each attribute;

[0036] The weight coefficient and precise value calculation module is used to calculate the weight coefficient and precise value of each attribute based on the q-order orthogonal fuzzy evaluation value of each attribute using the q-order orthogonal fuzzy multi-attribute decision analysis method;

[0037] The relative significance value calculation module is used to evaluate based on the weight coefficient and exact value of each attribute to obtain the relative significance value of the multi-agent loss reduction solution.

[0038] Preferably, the multiple entities include: power distribution companies, load users, integrated energy service providers and distributed power generators.

[0039] Preferably, it also includes a construction module of loss reduction effectiveness evaluation index; the construction module of loss reduction effectiveness evaluation index is specifically used for:

[0040] Based on the parameter calculation of the multi-agent loss reduction scheme, the unit investment benefit, loss rate, line load rate, line or transformer overload rate, bus voltage over-limit percentage and voltage qualification rate are obtained to determine the loss reduction effectiveness evaluation indicators of the distribution company;

[0041] Based on the parameter calculation of the multi-agent loss reduction scheme, the economic degree and comfort level of electricity consumption are obtained, and the loss reduction effectiveness evaluation index of load users is determined;

[0042] Based on the parameter calculation of the multi-agent loss reduction scheme, the net present value, renewable energy consumption and load peak-valley difference are obtained to determine the loss reduction effectiveness evaluation indicators of the integrated energy service provider;

[0043] Based on the parameter calculation of the multi-agent loss reduction plan, the internal rate of return and the amount of renewable energy grid-connected electricity are obtained, and the loss reduction effectiveness evaluation indicators of distributed power producers are determined.

[0044] Preferably, the q-order orthogonal fuzzy evaluation value calculation module is specifically used for:

[0045] The parameters of the multi-agent loss reduction scheme are calculated in combination with the loss reduction effectiveness evaluation index to obtain the calculated values ​​of each attribute;

[0046] Based on the calculated value of each attribute and the evaluation criteria, a fuzzy quantitative evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute.

[0047] Preferably, the weight coefficient and precise value calculation module includes:

[0048] The weight coefficient calculation submodule is used to calculate the weight coefficient of each attribute based on the q-order orthogonal fuzzy evaluation value of each attribute;

[0049] The precise value calculation submodule is used to calculate the membership and non-membership of each attribute based on the obtained multi-agent loss reduction solution to obtain the precise value of each attribute.

[0050] Preferably, the weight coefficient calculation submodule is specifically used for:

[0051] Perform normalization calculation based on the q-order orthogonal fuzzy evaluation value of each attribute to obtain the normalized value of each attribute;

[0052] The correlation coefficients between the attributes are calculated based on the normalized values ​​of the attributes to obtain the correlation coefficients between the attributes;

[0053] The contrast strength and conflict of each attribute are calculated based on the correlation coefficient between the attributes to obtain the contrast strength and conflict of each attribute;

[0054] The weight coefficient is calculated based on the contrast strength and conflict of each attribute to obtain the weight coefficient of each attribute.

[0055] Preferably, the precise value calculation submodule is specifically used for:

[0056] Based on the obtained multi-agent loss reduction scheme, the initial decision matrix is ​​constructed with respect to the membership and non-membership of each attribute.

[0057] Based on the initialization decision matrix, the q-ROFRWMSM operator is used to perform fusion calculation to obtain the q-order fuzzy evaluation matrix;

[0058] Based on the q-order fuzzy evaluation matrix, the difference between the q-th power values ​​of the membership degree and the non-membership degree is calculated to obtain the exact value of each attribute and form a new decision matrix.

[0059] Preferably, the relative significance value calculation module is specifically used for:

[0060] Perform normalization calculation based on the exact value of each attribute to obtain the normalized value of the exact value of each attribute;

[0061] The normalized value based on the exact value of each attribute is calculated in combination with the weight coefficient of each attribute to obtain the weighted normalized value of each attribute;

[0062] Calculate based on the weighted normalized value of each attribute to obtain the maximum index and the minimum index of each attribute;

[0063] Based on the maximization index and minimization index of each attribute, the relative significance value of the multi-agent loss reduction scheme is obtained.

[0064] On the other hand, the present application also proposes an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0065] The memory is used to store one or more programs;

[0066] When the one or more programs are executed by the at least one processor, a multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness as described above is implemented.

[0067] On the other hand, the present application also proposes a readable storage medium having an execution program stored thereon, which, when executed, implements the multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness as described above.

[0068] Compared with the prior art, the beneficial effects of this application are:

[0069] A multi-attribute evaluation method, system and device for multi-agent collaborative loss reduction effectiveness, including: performing multi-attribute evaluation based on the acquired parameters of the multi-agent loss reduction scheme in combination with pre-constructed loss reduction effectiveness evaluation indicators and evaluation standards, and obtaining the q-order orthogonal fuzzy evaluation value of each attribute; using the q-order orthogonal fuzzy multi-attribute decision analysis method to calculate based on the q-order orthogonal fuzzy evaluation value of each attribute, and obtaining the weight coefficient and precise value of each attribute; performing evaluation based on the weight coefficient and precise value of each attribute, and obtaining the relative significance value of the multi-agent loss reduction scheme; the loss reduction effectiveness evaluation indicators of the present application reflect the interests of different subjects, and also consider the influence of multiple subjects on the optimization of distribution network loss reduction; the present application combines the evaluation standards to perform multi-attribute evaluation and explores the relationship between the evaluation results and the economic benefits; the present application uses the q-order orthogonal fuzzy multi-attribute decision analysis method to make the evaluation results more comprehensive and accurate;

[0070] This application calculates the weight of each attribute to make the relative significance value of each scheme more precise and the evaluation result more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of a multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness in this application;

[0072] Figure 2 This is a flow chart of the comprehensive evaluation algorithm for loss reduction effectiveness of this application;

[0073] Figure 3 This is a structural diagram of a multi-attribute evaluation system for multi-agent collaborative loss reduction effectiveness in this application;

[0074] Figure 4 A diagram of an electronic device for this application. DETAILED DESCRIPTION

[0075] In order to deal with the uncertain information in multi-attribute decision-making, some uncertain mathematical tools are applied to multi-attribute decision-making, such as intuitionistic fuzzy sets IFSs, Pythagorean fuzzy sets PFSs, etc. Although IFSs and PFSs can effectively describe the complex decision information in multi-attribute decision-making, there are many situations that cannot satisfy IFSs and PFSs. For example, if the sum of the squares of the membership and non-membership degrees is greater than 1, then the decision information cannot be expressed by IFSs or PFSs. This application is based on the idea of ​​multi-attribute decision analysis method and adopts q-order orthogonal fuzzy multi-attribute decision analysis method to solve the multi-attribute decision-making problem.

[0076] This application establishes evaluation indicators from four aspects: distribution companies, load users, distributed power generators, and integrated energy service providers, and determines the quantitative value of each indicator. Combined with the evaluation criteria of the indicators, a multi-attribute evaluation of the loss reduction effect is performed: first, obtaining the information required for decision-making, which includes attribute weights and attribute values; second, selecting the corresponding method for decision-making, by using reasonable and appropriate decision-making methods to aggregate and compare attribute values, and sorting and selecting the best solutions according to certain standards. In order to better understand this application, the content of this application is further explained below in conjunction with the drawings and examples of the specification.

[0077] Embodiment 1:

[0078] A multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness, the specific process is as follows Figure 1 As shown, including:

[0079] Step 1: Based on the acquired parameters of the multi-agent loss reduction scheme and the pre-built loss reduction effectiveness evaluation indicators and evaluation criteria, a multi-attribute evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute;

[0080] Step 2, based on the q-order orthogonal fuzzy evaluation value of each attribute, the q-order orthogonal fuzzy multi-attribute decision analysis method is used to calculate and obtain the weight coefficient and exact value of each attribute;

[0081] Step 3: Based on the weight coefficient and exact value of each attribute, the relative significance value of the multi-agent loss reduction scheme is obtained.

[0082] Before step 1, the process of constructing the loss reduction effectiveness evaluation index is also included, and the construction process specifically includes:

[0083] Evaluation indicators for loss reduction effectiveness of distribution companies:

[0084] For distribution companies, the first prerequisite for reducing grid losses is to ensure grid safety and reliability, and on this basis, to take into account economic and environmental interests. From the perspective of distribution companies, evaluation indicators are established from the aspects of grid loss, reliability, and safety, namely, unit investment income, loss rate, line load rate, line or transformer overload rate, bus voltage over-limit percentage, and voltage qualification rate.

[0085] (1) Unit investment benefit: refers to the ratio of the amount of electricity saved by adopting a loss reduction scheme to the investment, that is, the electricity saving effect per unit investment. The larger the value, the better. It is a profit-oriented indicator. Among them, the amount of electricity saved refers to the difference in the amount of electricity loss before and after adopting a certain loss reduction scheme; investment refers to the funds required to invest in equipment when adopting a certain loss reduction scheme.

[0086] (2) Loss rate: The percentage of electric energy lost in the power grid, i.e., line loss load, to the electric energy supplied to the power grid, i.e., power supply load. The smaller the index, the better.

[0087] (3) Line load rate (%): The load received by each line reflects whether the grid structure is reasonable. The formula is as follows:

[0088]

[0089] (4) Line or transformer overload rate: reflects the degree of line or transformer overload after N-1 expected accidents occur in the distribution network. The smaller the value, the better. It is a non-profit indicator. The formula is as follows:

[0090]

[0091] Where u4 is the overload rate of the line or transformer; a is the set of overloaded lines and transformers.

[0092] (5) Bus voltage over-limit percentage: reflects the degree of bus voltage over-limit after N-1 expected accidents occur in the distribution network. The smaller the value, the better. It is a non-profit indicator. The formula is as follows:

[0093]

[0094] Where, u5 is the percentage of bus voltage over-limit; β is the set of bus voltages over-limit.

[0095] (6) Voltage qualification rate: refers to the percentage of time that the voltage in the power system works normally within the specified range in a certain period of time. It reflects the stability and reliability of the power supply quality of the power system.

[0096] 2. Evaluation indicators of load user loss reduction effectiveness:

[0097] Under the guidance of electricity prices, residential adjustable loads can influence users' electricity consumption habits while saving electricity expenses. Since different users have different sensitivities to the economic efficiency of electricity consumption, in order to prevent the regulation strategy from being too radical, an evaluation index for the user's adjustable load participating in the grid dispatch is established, namely, the user's economy and comfort.

[0098] (1) Electricity economy: Rationally arrange the usage time and power of each user's electrical equipment to improve the economic efficiency of the user's electricity use. The formula is as follows:

[0099]

[0100] In the formula, is the economic degree of electricity use; C(t) is the time-of-use electricity price after loss reduction; is the adjustable load after the loss reduction of the cth household; C f is the fixed electricity price before loss reduction; is the adjustable load of the cth household before loss reduction; Δt is the duration; t represents a certain moment; T is 24 hours a day.

[0101] like If it is less than 1, the user's electricity bill expenditure will be reduced after loss reduction operation, and its economy is better than before loss reduction operation.

[0102] (2) Electricity comfort: refers to the degree of change in the user's electricity consumption behavior under the influence of time-of-use electricity prices. The electricity price in the community is a fixed price, and there is no electricity price guidance. At this time, it can be considered that the residents' electricity comfort is the highest. However, after the dispatch of the residential electricity price IEO, the residents will change their electricity consumption behavior due to the electricity price stimulation, reducing the electricity comfort. The root mean square error RMSE is used to measure the difference between the two adjustable load curves before and after optimization. The electricity comfort is:

[0103]

[0104] In the formula, is the electricity comfort level; t is a certain moment; T is 24 hours a day; P n (t) is the workload status before optimization; Each adjustable workload state after optimization.

[0105] 3. Evaluation indicators for loss reduction effectiveness of integrated energy service providers

[0106] The construction goal of the integrated energy system is to ensure that new energy is more reasonably developed and utilized, and to obtain the maximum benefit possible. Therefore, the integrated energy system is evaluated from the perspective of system performance, and three indicators are set in the evaluation system: the net present value of the integrated energy system, the absorption of renewable energy, and the peak-to-valley difference of load.

[0107] (1) Net present value: refers to the difference between cash inflow and cash outflow during the entire life cycle of the integrated energy system after it is put into use. It is an important indicator for evaluating the feasibility of the operation of the integrated energy system. The formula is as follows:

[0108]

[0109] Where N pv is the net present value; C in (t) is the cash inflow; C out (t) is the cash outflow; t is a certain moment; D is the entire life cycle; α is the discount rate.

[0110] (2) Consumption of renewable energy: The main goal of comprehensive energy is to improve the ability to consume new energy. The formula for the amount of renewable energy consumed per unit cost is as follows:

[0111]

[0112] Where P Renewis the amount of renewable energy consumed per unit cost; g is a typical day; N g is the total number of typical days; is the duration of typical days; C init is the power generation of renewable energy; k is the time; is the new energy consumption capacity on the jth typical day; Δt k is the duration of the new energy consumption capacity at the kth moment.

[0113] (3) Load peak-valley difference: Providing users with flexible and efficient energy services is one of the main purposes of building an integrated energy park. Therefore, it is very meaningful to set demand-side response indicators to evaluate the construction level of the system user side and its contribution to peak shaving and valley filling. Demand-side response refers to the use of reasonable incentive policies to adjust user energy consumption behavior, which can reflect the user's enthusiasm for participating in the park operation and scheduling.

[0114] The load peak-valley difference index reflects the peak-shaving and valley-filling effect of the interaction between the system side and the user side. The smaller the value, the more significant the peak-shaving and valley-filling effect of the integrated energy system and the demand-side response effect. The formula is as follows:

[0115]

[0116] In the formula, v Lpv is the load peak-to-valley difference; ΔE load is the peak-to-valley difference of the system electrical load; is the average value of the system electrical load; ΔE e,l is the peak-to-valley difference of the electric load under scenario l; is the daily average value of the power load under scenario l; l is a certain scenario; L is the total number of scenarios; D l is the duration of scenario l in years.

[0117] 4. Evaluation indicators for loss reduction effectiveness of distributed power generators:

[0118] The indicator evaluation of distributed power generators aims to ensure the efficient operation of distributed power generation systems, optimize resource allocation, and achieve the best economic and environmental benefits. Therefore, from the perspective of system performance and economic benefits, when evaluating distributed power generators, we can set the following two main indicators:

[0119] (1) Internal Rate of Return (%): Internal Rate of Return (IRR) is an indicator used to measure the return on investment of distributed power generators. The higher the IRR, the higher the cash flow generated by the project and the faster the return on investment. The formula is as follows:

[0120]

[0121] In the formula, IRR is the internal rate of return; CI is the cash inflow of the project; CO is the cash outflow of the project; t is a certain moment, and t0 is the total number of moments.

[0122] (2) New energy grid-connected electricity (%): refers to the ratio of new energy grid-connected electricity to actual power generation of distributed power generators over a period of time. The formula is as follows:

[0123]

[0124] Where, P is the amount of electricity connected to the grid from renewable energy sources; is the wind power consumption at time t; is the photovoltaic power consumption at time t; is the actual wind power output at time t; is the actual photovoltaic output at time t; t is a certain moment; T is 24 hours a day; Δt is the duration.

[0125] In step 1, based on the parameters of the obtained multi-agent loss reduction scheme and the pre-built loss reduction effectiveness evaluation indicators and evaluation criteria, a multi-attribute evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute, including:

[0126] The multiple entities include: distribution companies, load users, distributed power generators and integrated energy service providers.

[0127] The parameters of the multi-agent loss reduction scheme are calculated in combination with the loss reduction effectiveness evaluation index to obtain the calculated values ​​of each attribute;

[0128] Based on the calculated values ​​of each attribute combined with the evaluation criteria, fuzzy quantitative evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute. The indicator evaluation criteria are shown in Table 1:

[0129]

[0130]

[0131] Table 1 Index evaluation standard table

[0132] The power system will only be in good operating condition when the indicator value is within a specific threshold range. Combined with the values ​​of some indicators given in the State Grid's "Technical Guidelines for Distribution Network Planning and Design" and other guidelines, and combined with the experience in existing literature, the indicator criteria for the following indicators are finally determined. Indicator criteria usually refer to specific standards or thresholds used to measure and evaluate the performance or status of a specific indicator. Among them, the unit investment benefit, net present value, load peak-to-valley difference, and bus voltage over-limit percentage are determined based on the specific calculations of the project and expert experience, and the remaining indicator evaluation criteria are shown in Table 1.

[0133] In step 2, the q-order orthogonal fuzzy evaluation value of each attribute is calculated using the q-order orthogonal fuzzy multi-attribute decision analysis method to obtain the weight coefficient and exact value of each attribute, specifically including:

[0134] Let A={A1,A2,…,A m} is the solution set, F={F1,F2,…,F n} is an attribute set, where the elements contained in the attribute set are the calculated values ​​of the indicators, and there are three decision experts E = (E1, E2, E3).

[0135] The specific calculation process based on the q-order orthogonal fuzzy multi-attribute group decision-making method is given below:

[0136] In order to eliminate the dimension effect, the following formula is used for normalization calculation:

[0137]

[0138] Where ρ is the normalized value of the attribute; x kj is the score of the jth attribute of the kth solution; max{x j} is the maximum value of attribute j among all solutions; min{x j} is the minimum value of attribute j among all solutions.

[0139] Calculate the correlation coefficient between attributes:

[0140]

[0141] Where r is the correlation coefficient between attribute x and attribute y; k is the normalized value of attribute x of scheme k; is the mean of attribute x in all scenarios; y k is the normalized value of attribute y of scheme k; is the mean of attribute y in all schemes; m is the number of schemes; is the average value of the two attributes in all scenarios.

[0142] Calculate the contrast and conflict between attributes:

[0143]

[0144] In the formula, σ j is the standard deviation of the jth attribute; c j is the conflict of the jth attribute; j is the attribute ranking; n is the total number of attributes; r ij is the correlation coefficient between the i-th attribute and the j-th attribute.

[0145] Calculate the weight coefficient of each attribute.

[0146]

[0147] In the formula, ω j is the weight coefficient of the jth attribute; σ j is the contrast strength of the jth attribute; c j is the conflict of the jth attribute; j is the attribute ranking; n is the total number of attributes.

[0148] The decision maker gives the q-order orthogonal fuzzy evaluation value of the scheme on the attribute to form three q-order orthogonal fuzzy initial decision evaluation matrices Don't give option A to decision maker t i About Attribute F j Membership and non-membership

[0149] Given the decision maker weight vector (0.25, 0.35, 0.4), the q-ROFRWMSM operator is used to fuse the information in the initial decision matrices of the three decision makers to form a decision matrix, and finally the q-order orthogonal fuzzy decision matrix is ​​obtained.

[0150] The result after aggregation by the q-ROFWMSM operator is still a generalized orthogonal fuzzy number, and the q-ROFWMSM operator is expressed as:

[0151]

[0152] In the formula, Φ k =(μ k ,v k )(k=1,2,…,s) is a set of generalized orthogonal fuzzy numbers and p=1,2,…,s; ω k Φ k The weight vector of k is the number of options; s is the total number of initial decision matrices; k p is the number of initial decision matrices; j represents the attribute; q is the order; μ k is the membership degree of scheme k; v k is the non-membership degree of solution k; i p is the number of matrices.

[0153] According to the fused decision matrix, the difference between the membership and non-membership q-th power values ​​is calculated, and the q-order orthogonal fuzzy evaluation value is converted into an accurate value. The new decision matrix is ​​formed as follows:

[0154]

[0155] In the formula, r 11 is the decision matrix element of the first solution relative to the first attribute; r 1j is the decision matrix element of the first solution relative to the jth attribute; r1n is the decision matrix element of the first solution relative to the nth attribute; r k1 is the decision matrix element of the kth solution relative to the first attribute; r kj is the decision matrix element of the kth solution relative to the jth attribute; r kn is the decision matrix element of the kth solution relative to the nth attribute; r m1 is the decision matrix element of the mth solution relative to the first attribute; r mj is the decision matrix element of the mth solution relative to the jth attribute; r mn is the decision matrix element of the mth option relative to the nth attribute; R is a matrix.

[0156] In step 3, the relative significance value of the multi-agent loss reduction scheme is obtained based on the weight coefficient and exact value of each attribute, including:

[0157] Normalization calculation based on the exact values ​​in the new decision matrix:

[0158]

[0159] In the formula, is the normalized value of the k-th solution relative to the j-th attribute; r kj is the decision matrix element of the kth solution relative to the jth attribute; k is the solution ranking; m is the total number of solutions.

[0160] Calculation is based on the exact value combined with the weight coefficient of each attribute:

[0161]

[0162] In the formula, is the weighted normalized value of the jth attribute of the kth solution phase; is the normalized value of the k-th solution relative to the j-th attribute; ω j is the weight coefficient of the jth attribute.

[0163] Compute the maximized and minimized indices:

[0164]

[0165] In the formula, I +k To maximize the index; is the weighted normalized value of the jth attribute of the kth solution phase; I -k is the minimization index; j is the attribute; g is the number of benefit attributes; μ is the number of cost attributes.

[0166] Calculate the relative significance value of each solution:

[0167]

[0168] In the formula, Q k is the relative significance value of the kth solution; I -k is the minimization index in the kth solution; I +k is the maximization index in the kth solution; M is the total number of indicators.

[0169] Sort the alternatives according to their relative significance values.

[0170] The following is an example of the loss reduction effectiveness evaluation indicators for integrated energy service providers. The process of the comprehensive evaluation technology for loss reduction effectiveness based on a multi-subject market environment is as follows: Figure 2 shown.

[0171] After preliminary screening by experts, there are 4 options (A1, A2, A3, A4) as alternative options that need to be further selected. Among them, the following 5 indicators need to be considered mainly for their impact on loss reduction effectiveness: cooling, heating and electricity coordination cost (F1), system loss (F2), energy supply shortage rate (F3), electricity sales revenue (F4), system pollutant emission cost (F5). After determining the weight of each attribute through the evaluation method, the expert gives the q-order fuzzy evaluation value of the scheme relative to the attribute, and uses the new method proposed in this application to sort the schemes. The calculated values ​​are the basis, and they provide actual data support; the q-order fuzzy evaluation value is based on the calculated value, combined with the expert's professional knowledge and experience, to conduct a fuzzy quantitative evaluation of the performance of the scheme in various indicators. This fuzzy evaluation takes into account both the actual data support and the subjective judgment and experience of experts, making the evaluation results more comprehensive and accurate. The initial data are shown in Table 2:

[0172] F1 F2 F3 F4 F5 A1 4.3 0.57 0.72 0.74 0.21 A2 5.9 0.78 0.36 1.28 0.38 A3 5.1 0.42 0.64 1.47 0.46 A4 4.8 0.23 0.52 1.36 0.84

[0173] Table 2 Initial data table

[0174] After normalizing the attributes, as shown in Table 3:

[0175] F1 F2 F3 F4 F5 A1 1.0000 0.3818 0.0000 0.0000 1.0000 A2 0.0000 0.0000 1.0000 0.7397 0.7302 A3 0.5000 0.6545 0.2222 1.0000 0.6032 A4 0.6875 1.0000 0.5556 0.8493 0.0000

[0176] Table 3 Normalization results of each attribute The correlation coefficients between the calculated attributes are shown in Table 4:

[0177] F1 F2 F3 F4 F5 A1 1.0000 0.5320 -0.8764 -0.5943 0.0481 A2 0.5320 1.0000 -0.3693 0.3292 -0.7663 A3 -0.8764 -0.3693 1.0000 0.4880 -0.3133 A4 -0.5943 0.3292 0.4880 1.0000 -0.6512

[0178] Table 4 Correlation coefficient table between attributes The attribute contrast strength σ and the attribute conflict c are calculated as shown in Table 5:

[0179] σ1 σ2 σ3 σ4 σ5 0.8951 0.5690 0.8422 0.6648 0.3669 c1 c2 c3 c4 c5 3.9388 2.5082 3.7578 2.7772 5.6827

[0180] Table 5 Attribute comparison strength and attribute conflict table The weight coefficient ω of each attribute is calculated as shown in Table 6:

[0181] ω1 ω2 ω3 ω4 ω5 0.2926 0.1185 0.2627 0.1532 0.1730

[0182] Table 6 Weight coefficient table of each attribute

[0183] The decision maker gives the initial decision matrix of the scheme on the attributes, where u is the membership degree and v is the non-membership degree;

[0184] As shown in Table 7, Table 8 and Table 9:

[0185]

[0186] Table 7 Initial decision matrix of decision maker E1

[0187]

[0188] Table 8 Initial decision matrix of decision maker E2

[0189]

[0190] Table 9: Initial decision matrix of decision maker E3

[0191] The q-ROFRWMSM operator is used to fuse the fuzzy evaluation matrices of the three decision makers to form a q-order orthogonal fuzzy decision matrix as shown in Table 10:

[0192]

[0193] Table 10 The q-order fuzzy evaluation matrix table converts the q-order orthogonal fuzzy evaluation value into the precise value of the index, as shown in Table 11:

[0194] F1 F2 F3 F4 F5 A1 -0.0056 0.1614 -0.0056 0.2166 0.0906 A2 0.1468 0.2546 -0.0186 0.2747 0.1402 A3 -0.1117 0.1985 0.0812 0.0276 -0.0043 A4 0.0438 0.2358 -0.0950 0.0462 0.0301

[0195] Table 11 Score function table The normalized decision matrix is ​​calculated as shown in Table 12:

[0196] F1 F2 F3 F4 F5 A1 -0.0761 0.1898 0.1465 0.3833 0.3532 A2 2.0045 0.2995 0.4904 0.4861 0.5465 A3 -1.5261 0.2334 -2.1347 0.0488 -0.0169 A4 0.5977 0.2773 2.4978 0.0818 0.1172

[0197] Table 12 Normalized decision matrix table calculation weighted normalized decision matrix is ​​shown in Table 13:

[0198] F1 F2 F3 F4 F5 A1 -0.0223 0.0225 0.0385 0.0587 0.0611 A2 0.5865 0.0355 0.1288 0.0745 0.0946 A3 -0.4465 0.0276 -0.5607 0.0075 -0.0029 A4 0.1749 0.0328 0.6561 0.0125 0.0203

[0199] Table 13 Weighted normalized decision matrix table calculates the maximum index I+ and the minimum index I- as shown in Table 14:

[0200] I1+ I2+ I3+ I4+ 0.0587 0.0745 0.0075 0.0125 I1- I2- I3- I4- 0.0998 0.8454 -0.9825 0.8841

[0201] Table 14: The relative significant values ​​calculated for the maximization and minimization index tables are shown in Table 15:

[0202] Q1 Q2 Q3 Q4 0.8085 0.1630 -0.0687 0.0972

[0203] Table 15 Relative significance value table

[0204] Sort the alternatives according to their relative significance values:

[0205] Q1>Q2>Q4>Q3

[0206] In the formula, Q1 is the relative significance value of scheme A1; Q2 is the relative significance value of scheme A2; Q4 is the relative significance value of scheme A4; Q3 is the relative significance value of scheme A3.

[0207] In response to the problem that the current comprehensive evaluation research on loss reduction effectiveness does not take into account the interests of different entities, this application establishes a multi-level and multi-dimensional comprehensive evaluation index for loss reduction effectiveness by considering the economy, technology, environmental protection, reliability and user satisfaction of different market entities. It not only reflects the interests of different entities, but also considers the impact of multiple entities in the market on the optimization of distribution network loss reduction. It combines the evaluation standards and indicator criteria of the indicators to conduct a multi-attribute evaluation of the loss reduction effectiveness, and explores the relationship between the evaluation results and economic benefits.

[0208] Embodiment 2:

[0209] A multi-attribute evaluation system for multi-agent collaborative loss reduction effectiveness, with the following structure: Figure 3 As shown, including:

[0210] The q-order orthogonal fuzzy evaluation value calculation module is used to perform multi-attribute evaluation based on the acquired parameters of the multi-agent loss reduction scheme combined with the pre-built loss reduction effectiveness evaluation indicators and evaluation criteria to obtain the q-order orthogonal fuzzy evaluation value of each attribute;

[0211] The weight coefficient and precise value calculation module is used to calculate the weight coefficient and precise value of each attribute based on the q-order orthogonal fuzzy evaluation value of each attribute using the q-order orthogonal fuzzy multi-attribute decision analysis method;

[0212] The relative significance value calculation module is used to evaluate based on the weight coefficient and exact value of each attribute to obtain the relative significance value of the multi-agent loss reduction solution.

[0213] The multiple entities include: power distribution companies, load users, integrated energy service providers and distributed power generators.

[0214] It also includes a construction module of loss reduction effectiveness evaluation indicators; the construction module of loss reduction effectiveness evaluation indicators is specifically used for:

[0215] Based on the parameter calculation of the multi-agent loss reduction scheme, the unit investment benefit, loss rate, line load rate, line or transformer overload rate, bus voltage over-limit percentage and voltage qualification rate are obtained to determine the loss reduction effectiveness evaluation indicators of the distribution company;

[0216] Based on the parameter calculation of the multi-agent loss reduction scheme, the economic degree and comfort level of electricity consumption are obtained, and the loss reduction effectiveness evaluation index of load users is determined;

[0217] Based on the parameter calculation of the multi-agent loss reduction scheme, the net present value, renewable energy consumption and load peak-valley difference are obtained to determine the loss reduction effectiveness evaluation indicators of the integrated energy service provider;

[0218] Based on the parameter calculation of the multi-agent loss reduction plan, the internal rate of return and the amount of renewable energy grid-connected electricity are obtained, and the loss reduction effectiveness evaluation indicators of distributed power producers are determined.

[0219] The q-order orthogonal fuzzy evaluation value calculation module is specifically used for:

[0220] The parameters of the multi-agent loss reduction scheme are calculated in combination with the loss reduction effectiveness evaluation index to obtain the calculated values ​​of each attribute;

[0221] Based on the calculated value of each attribute and the evaluation criteria, a fuzzy quantitative evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute.

[0222] The weight coefficient and precise value calculation module includes:

[0223] The weight coefficient calculation submodule is used to calculate the weight coefficient of each attribute based on the q-order orthogonal fuzzy evaluation value of each attribute;

[0224] The precise value calculation submodule is used to calculate the membership and non-membership of each attribute based on the obtained multi-agent loss reduction solution to obtain the precise value of each attribute.

[0225] The weight coefficient calculation submodule is specifically used for:

[0226] Perform normalization calculation based on the q-order orthogonal fuzzy evaluation value of each attribute to obtain the normalized value of each attribute;

[0227] The correlation coefficients between the attributes are calculated based on the normalized values ​​of the attributes to obtain the correlation coefficients between the attributes;

[0228] The contrast strength and conflict of each attribute are calculated based on the correlation coefficient between the attributes to obtain the contrast strength and conflict of each attribute;

[0229] The weight coefficient is calculated based on the contrast strength and conflict of each attribute to obtain the weight coefficient of each attribute.

[0230] The precise value calculation submodule is specifically used for:

[0231] Based on the obtained multi-agent loss reduction scheme, the initial decision matrix is ​​constructed with respect to the membership and non-membership of each attribute.

[0232] Based on the initialization decision matrix, the q-ROFRWMSM operator is used to perform fusion calculation to obtain the q-order fuzzy evaluation matrix;

[0233] Based on the q-order fuzzy evaluation matrix, the difference between the q-th power values ​​of the membership degree and the non-membership degree is calculated to obtain the exact value of each attribute and form a new decision matrix.

[0234] The relative significance value calculation module is specifically used for:

[0235] Perform normalization calculation based on the exact value of each attribute to obtain the normalized value of the exact value of each attribute;

[0236] The normalized value based on the exact value of each attribute is calculated in combination with the weight coefficient of each attribute to obtain the weighted normalized value of each attribute;

[0237] Calculate based on the weighted normalized value of each attribute to obtain the maximum index and the minimum index of each attribute;

[0238] Based on the maximization index and minimization index of each attribute, the relative significance value of the multi-agent loss reduction scheme is obtained.

[0239] Embodiment 3:

[0240] like Figure 4 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0241] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of a multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness in the above-mentioned embodiment.

[0242] Example 4

[0243] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a multi-attribute evaluation method for the effectiveness of multi-agent collaborative loss reduction in the above embodiment.

[0244] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0245] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0246] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0248] The above are merely embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application are included in the scope of the claims of the present application to be approved.

Claims

1. A multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness, characterized in that: include: Based on the obtained parameters of the multi-agent loss reduction scheme and the pre-built loss reduction effectiveness evaluation indicators and evaluation criteria, a multi-attribute evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute; The q-order orthogonal fuzzy evaluation value based on each attribute is calculated using the q-order orthogonal fuzzy multi-attribute decision analysis method to obtain the weight coefficient and exact value of each attribute; Based on the weight coefficient and exact value of each attribute, the relative significance value of the multi-agent loss reduction scheme is obtained.

2. The method according to claim 1, characterized in that The multiple entities include: power distribution companies, load users, integrated energy service providers and distributed power generators.

3. The method according to claim 2, characterized in that It also includes the process of constructing indicators for evaluating the effectiveness of loss reduction; The construction process includes: Based on the parameter calculation of the multi-agent loss reduction scheme, the unit investment benefit, loss rate, line load rate, line or transformer overload rate, bus voltage over-limit percentage and voltage qualification rate are obtained to determine the loss reduction effectiveness evaluation indicators of the distribution company; Based on the parameter calculation of the multi-agent loss reduction scheme, the economic degree and comfort level of electricity consumption are obtained, and the loss reduction effectiveness evaluation index of load users is determined; Based on the parameter calculation of the multi-agent loss reduction scheme, the net present value, renewable energy consumption and load peak-valley difference are obtained to determine the loss reduction effectiveness evaluation indicators of the integrated energy service provider; Based on the parameter calculation of the multi-agent loss reduction plan, the internal rate of return and the amount of renewable energy grid-connected electricity are obtained, and the loss reduction effectiveness evaluation indicators of distributed power producers are determined.

4. The method according to claim 1, characterized in that: The parameters of the multi-agent loss reduction scheme obtained are combined with the pre-constructed loss reduction effectiveness evaluation indicators and evaluation criteria to perform multi-attribute evaluation, and obtain q-order orthogonal fuzzy evaluation values ​​of the multiple attributes, including: The parameters of the multi-agent loss reduction scheme are calculated in combination with the loss reduction effectiveness evaluation index to obtain the calculated values ​​of each attribute; Based on the calculated value of each attribute and the evaluation criteria, a fuzzy quantitative evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute.

5. The method according to claim 1, characterized in that The q-order orthogonal fuzzy evaluation value based on each attribute is calculated using a q-order orthogonal fuzzy multi-attribute decision analysis method to obtain a weight coefficient and an exact value of each attribute, including: The weight coefficient of each attribute is obtained by calculating the q-order orthogonal fuzzy evaluation value of each attribute; Based on the obtained multi-agent loss reduction scheme, the membership and non-membership of each attribute are calculated to obtain the exact value of each attribute.

6. The method according to claim 5, characterized in that The calculation is performed based on the q-order orthogonal fuzzy evaluation value of each attribute to obtain the weight coefficient of each attribute, including: Perform normalization calculation based on the q-order orthogonal fuzzy evaluation value of each attribute to obtain the normalized value of each attribute; The correlation coefficients between the attributes are calculated based on the normalized values ​​of the attributes to obtain the correlation coefficients between the attributes; The contrast strength and conflict of each attribute are calculated based on the correlation coefficient between the attributes to obtain the contrast strength and conflict of each attribute; The weight coefficient is calculated based on the contrast strength and conflict of each attribute to obtain the weight coefficient of each attribute.

7. The method according to claim 5, characterized in that The obtained multi-agent loss reduction scheme calculates the membership and non-membership of each attribute to obtain the precise value of each attribute, including: Based on the obtained multi-agent loss reduction scheme, the initial decision matrix is ​​constructed with respect to the membership and non-membership of each attribute. Based on the initialization decision matrix, the q-ROFRWMSM operator is used to perform fusion calculation to obtain the q-order fuzzy evaluation matrix; Based on the q-order fuzzy evaluation matrix, the difference between the q-th power values ​​of the membership degree and the non-membership degree is calculated to obtain the exact value of each attribute and form a new decision matrix.

8. The method according to claim 1, characterized in that The evaluation is performed based on the weight coefficient and the exact value of each attribute to obtain the relative significance value of the multi-agent loss reduction scheme, including: Perform normalization calculation based on the exact value of each attribute to obtain the normalized value of the exact value of each attribute; The normalized value based on the exact value of each attribute is calculated in combination with the weight coefficient of each attribute to obtain the weighted normalized value of each attribute; Calculate based on the weighted normalized value of each attribute to obtain the maximum index and the minimum index of each attribute; Based on the maximization index and minimization index of each attribute, the relative significance value of the multi-agent loss reduction scheme is obtained.

9. A multi-attribute evaluation system for multi-agent collaborative loss reduction effectiveness, characterized in that: include: The q-order orthogonal fuzzy evaluation value calculation module is used to perform multi-attribute evaluation based on the acquired parameters of the multi-agent loss reduction scheme combined with the pre-built loss reduction effectiveness evaluation indicators and evaluation criteria to obtain the q-order orthogonal fuzzy evaluation value of each attribute; The weight coefficient and precise value calculation module is used to calculate the weight coefficient and precise value of each attribute based on the q-order orthogonal fuzzy evaluation value of each attribute using the q-order orthogonal fuzzy multi-attribute decision analysis method; The relative significance value calculation module is used to evaluate based on the weight coefficient and exact value of each attribute to obtain the relative significance value of the multi-agent loss reduction solution.

10. The system according to claim 9, characterized in that The multiple entities include: power distribution companies, load users, integrated energy service providers and distributed power generators.

11. The system according to claim 10, characterized in that It also includes a construction module of loss reduction effectiveness evaluation indicators; the construction module of loss reduction effectiveness evaluation indicators is specifically used for: Based on the parameter calculation of the multi-agent loss reduction scheme, the unit investment benefit, loss rate, line load rate, line or transformer overload rate, bus voltage over-limit percentage and voltage qualification rate are obtained to determine the loss reduction effectiveness evaluation indicators of the distribution company; Based on the parameter calculation of the multi-agent loss reduction scheme, the economic degree and comfort level of electricity consumption are obtained, and the loss reduction effectiveness evaluation index of load users is determined; Based on the parameter calculation of the multi-agent loss reduction scheme, the net present value, renewable energy consumption and load peak-valley difference are obtained to determine the loss reduction effectiveness evaluation indicators of the integrated energy service provider; Based on the parameter calculation of the multi-agent loss reduction plan, the internal rate of return and the amount of renewable energy grid-connected electricity are obtained, and the loss reduction effectiveness evaluation indicators of distributed power producers are determined.

12. The system according to claim 9, characterized in that: The q-order orthogonal fuzzy evaluation value calculation module is specifically used for: The parameters of the multi-agent loss reduction scheme are calculated in combination with the loss reduction effectiveness evaluation index to obtain the calculated values ​​of each attribute; Based on the calculated value of each attribute and the evaluation criteria, a fuzzy quantitative evaluation is performed to obtain the q-order orthogonal fuzzy evaluation value of each attribute.

13. The system according to claim 9, characterized in that The weight coefficient and precise value calculation module includes: The weight coefficient calculation submodule is used to calculate the weight coefficient of each attribute based on the q-order orthogonal fuzzy evaluation value of each attribute; The precise value calculation submodule is used to calculate the membership and non-membership of each attribute based on the obtained multi-agent loss reduction solution to obtain the precise value of each attribute.

14. The system according to claim 13, characterized in that The weight coefficient calculation submodule is specifically used for: Perform normalization calculation based on the q-order orthogonal fuzzy evaluation value of each attribute to obtain the normalized value of each attribute; The correlation coefficients between the attributes are calculated based on the normalized values ​​of the attributes to obtain the correlation coefficients between the attributes; The contrast strength and conflict of each attribute are calculated based on the correlation coefficient between the attributes to obtain the contrast strength and conflict of each attribute; The weight coefficient is calculated based on the contrast strength and conflict of each attribute to obtain the weight coefficient of each attribute.

15. The system according to claim 13, characterized in that The precise value calculation submodule is specifically used for: Based on the obtained multi-agent loss reduction scheme, the initial decision matrix is ​​constructed with respect to the membership and non-membership of each attribute. Based on the initialization decision matrix, the q-ROFRWMSM operator is used to perform fusion calculation to obtain the q-order fuzzy evaluation matrix; Based on the q-order fuzzy evaluation matrix, the difference between the q-th power values ​​of the membership degree and the non-membership degree is calculated to obtain the exact value of each attribute and form a new decision matrix.

16. The system according to claim 9, characterized in that The relative significance value calculation module is specifically used for: Perform normalization calculation based on the exact value of each attribute to obtain the normalized value of the exact value of each attribute; The normalized value based on the exact value of each attribute is calculated in combination with the weight coefficient of each attribute to obtain the weighted normalized value of each attribute; Calculate based on the weighted normalized value of each attribute to obtain the maximum index and the minimum index of each attribute; Based on the maximization index and minimization index of each attribute, the relative significance value of the multi-agent loss reduction scheme is obtained.

17. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness as described in any one of claims 1 to 8 is implemented.

18. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a multi-attribute evaluation method for multi-agent collaborative loss reduction effectiveness as described in any one of claims 1 to 8 is implemented.

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