Demand side response resource value evaluation method based on rough set theory
A technology of demand-side response and rough set theory, which is applied in the field of demand-side response resource value evaluation based on rough set theory, can solve problems such as poor integrity and incompleteness, and achieve a comprehensive, strong integrity, and simple concept of value evaluation model. Effect
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Embodiment 1
[0192] Table 1 shows the initial evaluation index value and expected index value of demand-side response resource value in a certain region. The specific index data is taken from the demand-side response measures implemented by a provincial power grid company in 2007, 2008, and 2009. According to the index system formulated above, the data of each index is obtained. The following section will calculate this data Example analysis.
[0193] As shown in Table 1, this model will evaluate the value of demand-side response resources for the power generation side, power grid side, large users, residents, and the whole society, analyze the benefits brought to each participant in demand-side response, and find out the gap link.
[0194] Table 1 The initial evaluation index value and expected index value of a region
[0195] Table 1 Initial evaluation of the value and expectation of regional indexes
[0196]
[0197]
[0198] It can be known from Table 2: U={2007, 2008, 2009}, ...
Embodiment 2
[0226] Assuming that scientific research results are evaluated, the evaluation index set U={u 1 ,u 2 ,u 3}={academic level, social benefit, economic benefit}, the respective weights are obtained according to the previous steps w={0.3, 0.3, 0.4}
[0227] Determine the comment set as V={V 1 ,V 2 ,V 3 ,V 4}={very good, good, average, bad}
[0228] A single factor evaluation is carried out for each evaluation index of this achievement, such as academic level, 50% of the experts think it is "very good", 30% of the experts think it is "good", and 20% of the experts think it is "average". The single factor evaluation result of the level is R 1 =(0.5, 0.3, 0.2, 0)
[0229] The evaluation results of all factors are
[0230] R = R 1 R 2 R 3 ...
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