Determining the Adjustment Baseline Parameters to Define an Accurate Customer Baseline Load

Demand response is an energy resource that has gained increasing importance in the context of competitive electricity markets and of smart grids. New business models and methods designed to integrate demand response in electricity markets and of smart grids have been published, reporting the need of additional work in this field. In order to adequately remunerate the participation of the consumers in demand response programs, improved consumers’ performance evaluation methods are needed. The methodology proposed in the present paper determines the characterization of the baseline approach that better fits the consumer historic consumption, in order to determine the expected consumption in absent of participation in a demand response event and then determine the actual consumption reduction. The defined baseline can then be used to better determine the remuneration of the consumer. The paper includes a case study with real data to illustrate the application of the proposed methodology.

Using Baseline Methods to Identify Non-technical Losses in the Context of Smart Grids

Demand response has gained increasing importance in the context of competitive electricity markets and smart grid environments. In addition to the importance that has been given to the development of business models for integrating demand response, several methods have been developed to evaluate the consumers’ performance after the participation in a demand response event. The present paper uses those performance evaluation methods, namely customer baseline load calculation methods, to determine the expected consumption in each period of the consumer historic data. In the cases in which there is a certain difference between the actual consumption and the estimated consumption, the consumer is identified as a potential cause of non-technical losses. A case study demonstrates the application of the proposed method to real consumption data.

Using baseline methods to identify non-technical losses in the context of smart grids

Demand response has gained increasing importance in the context of competitive electricity markets and smart grid environments. In addition to the importance that has been given to the development of business models for integrating demand response, several methods have been developed to evaluate the consumers' performance after the participation in a demand response event. The present paper uses those performance evaluation methods, namely customer baseline load calculation methods, to determine the expected consumption in each period of the consumer historic data. In the cases in which there is a certain difference between the actual consumption and the estimated consumption, the consumer is identified as a potential cause of non-technical losses. A case study demonstrates the application of the proposed method to real consumption data. © 2013 IEEE.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

The anatomical and biomechanical consequences of anterior vertebral stapling for the fusionless correction of scoliosis

Fusionless scoliosis surgery is an emerging treatment for idiopathic scoliosis as it offers theoretical advantages over current forms of treatment. Anterior vertebral stapling using a nitinol staple is one such treatment. Despite increasing interest in this technique, little is known about the effects on the spine following insertion, or the mechanism of action of the staple. The aims of this study were threefold; (1) to measure changes in the bending stiffness of a single motion segment following staple insertion, (2) to describe the forces that occur within the staple during spinal movement, and (3) to describe the anatomical changes that occur following staple insertion. Results suggest that staple insertion consistently decreased stiffness in all directions of motion. An explanation for the finding may be found in the outcomes of the strain gauge testing and micro-CT scan. The strain gauge testing showed that once inserted, the staple tips applied a baseline compressive force to the surrounding trabecular bone and vertebral end-plate. This finding would be consistent with the current belief that the clinical effect of the staples is via unilateral compression of the physis. Interestingly however, as each specimen progressed through the five cycles of each test, the baseline load on the staple tips gradually decreased, implying that the force at the staple tip-bone interface was decreasing. We believe that this was likely occurring as a result of structural damage to the trabecular bone and vertebral end-plate by the staple effectively causing ‘loosening’ of the staple. This hypothesis is further supported by the findings of the micro-CT scan. The pictures depict significant trabecular bone and physeal injury around the staple blades. These results suggest that the current hypothesis that stapling modulates growth through physeal compression may be incorrect, but rather the effect occurs through mechanical disruption of the vertebral growth plate.

Chronological categorization and decomposition of customer loads

The majority of distribution utilities do not have accurate information on the constituents of their loads. This information is very useful in managing and planning the network, adequately and economically. Customer loads are normally categorized in three main sectors: 1) residential; 2) industrial; and 3) commercial. In this paper, penalized least-squares regression and Euclidean distance methods are developed for this application to identify and quantify the makeup of a feeder load with unknown sectors/subsectors. This process is done on a monthly basis to account for seasonal and other load changes. The error between the actual and estimated load profiles are used as a benchmark of accuracy. This approach has shown to be accurate in identifying customer types in unknown load profiles, and is used in cross-validation of the results and initial assumptions.

Reduction in resting plasma granulysin as a marker of increased training load

Granulysin is a cytolytic granule protein released by natural killer cells and activated cytotoxic T lymphocytes. The influence of exercise training on circulating granulysin concentration is unknown, as is the relationship between granulysin concentration, natural killer cell number and natural killer cell cytotoxicity. We examined changes in plasma granulysin concentration, natural killer cell number and cytotoxicity following acute exercise and different training loads. Fifteen highly trained male cyclists completed a baseline 40-km cycle time trial (TT401) followed by five weeks of normal training and a repeat time trial (TT402). The cyclists then completed four days of high intensity training followed by another time trial (TT403) on day five. Following one final week of normal training cyclists completed another time trial (TT404). Fasting venous blood was collected before and after each time trial to determine granulysin concentration, natural killer cell number and natural killer cell cytotoxicity. Granulysin concentration increased significantly after each time trial (P<0.001). Pre-exercise granulysin concentration for TT403 was significantly lower than pre-exercise concentration for TT401 (-20.3 +/- 7.5%, P<0.026), TT402 (-16.7 +/- 4.3%, P<0.003) and 7T404 (-21 +/- 4.2%, P<0.001). Circulating natural killer cell numbers also increased significantly post-exercise for each time trial (P<0.001), however there was no significant difference across TT40 (P>0.05). Exercise did not significantly alter natural killer cell cytotoxicity on a per cell basis, and there were no significant differences between the four time trials. In conclusion, plasma granulysin concentration increases following moderate duration, strenuous exercise and is decreased in response to a short-term period of intensified training.

Demand response for residential appliances via Customer Reward Scheme

This paper proposes a reward based demand response algorithm for residential customers to shave network peaks. Customer survey information is used to calculate various criteria indices reflecting their priority and flexibility. Criteria indices and sensitivity based house ranking is used for appropriate load selection in the feeder for demand response. Customer Rewards (CR) are paid based on load shift and voltage improvement due to load adjustment. The proposed algorithm can be deployed in residential distribution networks using a two-level hierarchical control scheme. Realistic residential load model consisting of non-controllable and controllable appliances is considered in this study. The effectiveness of the proposed demand response scheme on the annual load growth of the feeder is also investigated. Simulation results show that reduced peak demand, improved network voltage performance, and customer satisfaction can be achieved.

Load profile based determination of distribution feeder configuration by dynamic programming

This paper presents a new method to determine feeder reconfiguration scheme considering variable load profile. The objective function consists of system losses, reliability costs and also switching costs. In order to achieve an optimal solution the proposed method compares these costs dynamically and determines when and how it is reasonable to have a switching operation. The proposed method divides a year into several equal time periods, then using particle swarm optimization (PSO), optimal candidate configurations for each period are obtained. System losses and customer interruption cost of each configuration during each period is also calculated. Then, considering switching cost from a configuration to another one, dynamic programming algorithm (DPA) is used to determine the annual reconfiguration scheme. Several test systems were used to validate the proposed method. The obtained results denote that to have an optimum solution it is necessary to compare operation costs dynamically.

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

Aspect-based opinion extraction from customer reviews

Text is the main method of communicating information in the digital age. Messages, blogs, news articles, reviews, and opinionated information abounds on the Internet. People commonly purchase products online and post their opinions about purchased items. This feedback is displayed publicly to assist others with their purchasing decisions, creating the need for a mechanism with which to extract and summarize useful information for enhancing the decision-making process. Our contribution is to improve the accuracy of extraction by combining different techniques from three major areas, named Data Mining, Natural Language Processing techniques and Ontologies. The proposed framework sequentially mines product’s aspects and users’ opinions, groups representative aspects by similarity, and generates an output summary. This paper focuses on the task of extracting product aspects and users’ opinions by extracting all possible aspects and opinions from reviews using natural language, ontology, and frequent “tag” sets. The proposed framework, when compared with an existing baseline model, yielded promising results.

Anti-cholinergic load, health care utilization, and survival in people with advanced cancer: a pilot study.

INTRODUCTION: Anti-cholinergic medications have been associated with increased risks of cognitive impairment, premature mortality and increased risk of hospitalisation. Anti-cholinergic load associated with medication increases as death approaches in those with advanced cancer, yet little is known about associated adverse outcomes in this setting. METHODS: A substudy of 112 participants in a randomised control trial who had cancer and an Australia modified Karnofsky Performance Scale (AKPS) score (AKPS) of 60 or above, explored survival and health service utilisation; with anti-cholinergic load calculated using the Clinician Rated Anti-cholinergic Scale (modified version) longitudinally to death. A standardised starting point for prospectively calculating survival was an AKPS of 60 or above. RESULTS: Baseline entry to the sub-study was a mean 62 +/- 81 days (median 37, range 1-588) days before death (survival), with mean of 4.8 (median 3, SD 4.18, range 1 - 24) study assessments in this time period. Participants spent 22% of time as an inpatient. There was no significant association between anti-cholinergic score and time spent as an inpatient (adjusted for survival time) (p = 0.94); or survival time. DISCUSSION: No association between anti-cholinergic load and survival or time spent as an inpatient was seen. Future studies need to include cognitively impaired populations where the risks of symptomatic deterioration may be more substantial.

Dietary glycemic index and glycemic load in relation to changes in body composition measures during adolescence:Northern Ireland Young Hearts Study

Background: Epidemiologic evidence on the influence of dietary glycemic index (GI) and glycemic load (GL) on the development of obesity is limited.

Objective: This prospective study examined the associations between dietary GI and GL and changes in body composition measures during adolescence.

Design: In a representative sample of Northern Irish adolescents aged 12 years at baseline and 15 years at follow-up (n=426), dietary intake was assessed by a diet history interview. Body composition measures included body mass index (BMI; kg m(-2)), BMI z-score, sum of four skinfold thicknesses, percentage body fat, fat mass index (FMI; kg m(-2)) and fat-free mass index (kg m(-2)).

Results: After adjustment for potential confounding factors, baseline GI was associated with increased change in FMI. Mean (95% confidence interval) values of changes in FMI according to tertiles of baseline GI were 0.41 (0.25, 0.57), 0.42 (0.26, 0.58) and 0.67 (0.51, 0.83) kg m(-2), respectively (P for trend=0.03). There was no significant association of baseline GI with changes in other body composition measures (P for trend0.054). Conversely, baseline GL showed no association with changes in any of the measures (P for trend0.41). Furthermore, changes in GI or GL were not associated with changes in any of the measures (P for trend0.16).

Conclusion: Dietary GI at age 12 years was independently associated with increased change in FMI between ages 12 and 15 years in a representative sample from Northern Ireland, whereas dietary GL showed no association with changes in any of the body composition measures examined.

Assessment of Domestic Load Suitable for Smart Consumer Load Participation

This study characterizes the domestic loads suitable to participate in the load participation scheme to make the power system more carbon and economically efficient by shifting the electricity demand profile towards periods when there is plentiful renewable in-feed.

A series of experiments have been performed on a common fridge-freezer, both completely empty and half full. The results presented are ambient temperature, temperature inside the fridge, temperature inside the drawer of the fridge, temperature inside the freezer, thermal time constants, power consumption and electric energy consumed.

The thermal time constants obtained clearly demonstrate the potential of such refrigeration load for Smart Customer Load Participation.

CO2 reduction and efficiency in road freight traffic: measures, baseline and potential

The objective of our research was to analyse the relevant logistic factors influencing energy efficiency in road freight transport, while quantifying the potential for CO2 reduction. We carried out a survey and linked fuel consumption to transport performance parameters in 50 German haulage companies during 2003. Efficiency ranges from 0.8 tkm to 26 tkm for 1 kg CO2 emissions. The results show a high potential for improvements, given a low level of efficiency in vehicle usage and load factor, scarce use of lightweight vehicle design, incorrectly selected vehicle class and a high proportion of empty runs. Efficiency measures are poorly applied.