Maximizing the Social Welfare of Virtual Power Players Operation in Case of Excessive Wind Power

The integration of growing amounts of distributed generation in power systems, namely at distribution networks level, has been fostered by energy policies in several countries around the world, including in Europe. This intensive integration of distributed, non-dispatchable, and natural sources based generation (including wind power) has caused several changes in the operation and planning of power systems and of electricity markets. Sometimes the available non-dispatchable generation is higher than the demand. This generation must be used; otherwise it is wasted if not stored or used to supply additional demand. New policies and market rules, as well as new players, are needed in order to competitively integrate all the resources. The methodology proposed in this paper aims at the maximization of the social welfare in a distribution network operated by a virtual power player that aggregates and manages the available energy resources. When facing a situation of excessive non-dispatchable generation, including wind power, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. This method is especially useful when actual and day-ahead resources forecast differ significantly. The distribution network characteristics and concerns are addressed by including the network constraints in the optimization model. The proposed methodology has been implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20.310 consumers and 548 distributed generators, some of them non-dispatchable and with must take contracts. The implemented scenario corresponds to a real day in Portuguese power system.

Constrained Consumption Shifting Management in the Distributed Energy Resources Scheduling Considering Demand Response

Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.

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.

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

Optimization of Generation and Aggregated Consumption Shifting for Demand Response Programs Definition

The concept of demand response has drawing attention to the active participation in the economic operation of power systems, namely in the context of recent electricity markets and smart grid models and implementations. In these competitive contexts, aggregators are necessary in order to make possible the participation of small size consumers and generation units. The methodology proposed in the present paper aims to address the demand shifting between periods, considering multi-period demand response events. The focus is given to the impact in the subsequent periods. A Virtual Power Player operates the network, aggregating the available resources, and minimizing the operation costs. The illustrative case study included is based on a scenario of 218 consumers including generation sources.

Using Typical Consumption Profiles to Stablish the Consumption Level for Short Time Periods

The current practices in the consumption metering by electricity utilities is currently largely based on monthly consumption reading. The consumption metering device is always calculating the cumulative consumption. Then, it is possible to calculate the difference between the actual and the previous consumption evaluation in order to estimate the monthly consumption. The power systems planning needs in many aspects to handle consumption data obtained for shorter periods, namely in the Demand Response programs planning. The work presented in this paper is based on the application of typical consumption profiles that are previously defined for a certain power system area. Such profiles are then used in order to estimate the 15 minutes consumption for a certain consumer or consumer type.

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Consumption Management of Air Conditioning Devices for the Participation in Demand Response Programs

Demand Response has been taking over the years an extreme importance. There’s a lot of demand response programs, one of them proposed in this paper, using air conditioners that could increase the power quality and decrease the spent money in many ways like: infrastructures and customers energy bill reduction. This paper proposes a method and a study on how air conditioners could integrate demand response programs. The proposed method has been modelled as an energy resources management optimization problem. This paper presents two case studies, the first one with all costumers participating and second one with some of costumers. The results obtained for both case studies have been analyzed.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

Contextual and Environmental Awareness Laboratory for Energy Consumption Management

The recent changes on power systems paradigm requires the active participation of small and medium players in energy management. With an electricity price fluctuation these players must manage the consumption. Lowering costs and ensuring adequate user comfort levels. Demand response can improve the power system management and bring benefits for the small and medium players. The work presented in this paper, which is developed aiming the smart grid context, can also be used in the current power system paradigm. The proposed system is the combination of several fields of research, namely multi-agent systems and artificial neural networks. This system is physically implemented in our laboratories and it is used daily by researchers. The physical implementation gives the system an improvement in the proof of concept, distancing itself from the conventional systems. This paper presents a case study illustrating the simulation of real-time pricing in a laboratory.

Milk intake is inversely related to body mass index and body fat in girls

Dairy foods comprise a range of products with varying nutritional content. The intake of dairy products (DPs) has been shown to have beneficial effects on body weight and body fat. This study aimed to examine the independent association between DP intake, body mass index (BMI), and percentage body fat (%BF) in adolescents. A cross-sectional, school-based study was conducted with 1,001 adolescents (418 boys), ages 15–18 years, from the Azorean Archipelago, Portugal. Anthropometric measurements were recorded (weight and height), and %BF was assessed using bioelectric impedance analysis. Adolescent food intake was measured using a self-administered, semiquantitative food frequency questionnaire. Data were analyzed separately for girls and boys, and separate multiple linear regression analysis was used to estimate the association between total DP, milk, yogurt, and cheese intake, BMI, and %BF, adjusting for potential confounders. For boys and girls, respectively, total DP consumption was 2.6 ± 1.9 and 2.9 ± 2.5 servings/day (P = 0.004), while milk consumption was 1.7 ± 1.4 and 2.0 ± 1.7 servings/day (P = 0.001), yogurt consumption was 0.5 ± 0.6 and 0.4 ± 0.7 servings/day (P = 0.247), and cheese consumption was 0.4 ± 0.6 and 0.5 ± 0.8 servings/day (P = 0.081). After adjusting for age, birth weight, energy intake, protein, total fat, sugar, dietary fiber, total calcium intake, low-energy reporters, parental education, pubertal stage, and physical activity, only milk intake was negatively associated with BMI and %BF in girls (respectively, girls: β = −0.167, P = 0.013; boys: β = −0.019, P = 0.824 and girls: β = −0.143, P = 0.030; boys: β = −0.051, P = 0.548). Conclusion: We found an inverse association between milk intake and both BMI and %BF only in girls.

Association between dairy product intake and abdominal obesity in Azorean adolescents

BACKGROUND: Some studies have reported an inverse association between dairy product (DP) consumption and weight or fat mass loss. OBJECTIVES: The objective of our study was to assess the association between DP intake and abdominal obesity (AO) among Azorean adolescents. SUBJECTS/METHODS: This study was a cross-sectional analysis. A total of 903 adolescents (370 boys) aged 15--16 years was evaluated. Anthropometric measurements were collected (weight, height and waist circumference (WC)) and McCarthy’s cut-points were used to categorize WC. AO was defined when WC was X90th percentile. Adolescent food intake was assessed using a self-administered semiquantitative food frequency questionnaire and DP intake was categorized in o2 and X2 servings/day. Data were analyzed separately for girls and boys, and logistical regression was used to estimate the association between DPs and AO adjusting for potential confounders. RESULTS: The prevalence of AO was 54.9% (boys: 32.1% and girls: 70.7%, Po0.001). For boys and girls, DP consumption was 2.3±1.9 and 2.1±1.6 servings/day (P¼0.185), respectively. In both genders, the proportion of adolescents with WC o90th percentile was higher among individuals who reported a dairy intake of X2 servings/day compared with those with an intake o2 servings/day (boys: 71% vs 65% and girls: 36% vs 24%, Po0.05). After adjustments for confounders, two or more DP servings per day were a negative predictor of AO (odds ratio, 0.217; 95% confidence interval, 0.075 -- 0.633) only in boys. CONCLUSION: We found a protective association between DP intake and AO only in boys.

Novel LTCC-potentiometric microfluidic device for biparametric analysis of organic compounds carrying plastic antibodies as ionophores: Application to sulfamethoxazole and trimethoprim

Monitoring organic environmental contaminants is of crucial importance to ensure public health. This requires simple, portable and robust devices to carry out on-site analysis. For this purpose, a low-temperature co-fired ceramics (LTCC) microfluidic potentiometric device (LTCC/μPOT) was developed for the first time for an organic compound: sulfamethoxazole (SMX). Sensory materials relied on newly designed plastic antibodies. Sol–gel, self-assembling monolayer and molecular-imprinting techniques were merged for this purpose. Silica beads were amine-modified and linked to SMX via glutaraldehyde modification. Condensation polymerization was conducted around SMX to fill the vacant spaces. SMX was removed after, leaving behind imprinted sites of complementary shape. The obtained particles were used as ionophores in plasticized PVC membranes. The most suitable membrane composition was selected in steady-state assays. Its suitability to flow analysis was verified in flow-injection studies with regular tubular electrodes. The LTCC/μPOT device integrated a bidimensional mixer, an embedded reference electrode based on Ag/AgCl and an Ag-based contact screen-printed under a micromachined cavity of 600 μm depth. The sensing membranes were deposited over this contact and acted as indicating electrodes. Under optimum conditions, the SMX sensor displayed slopes of about −58.7 mV/decade in a range from 12.7 to 250 μg/mL, providing a detection limit of 3.85 μg/mL and a sampling throughput of 36 samples/h with a reagent consumption of 3.3 mL per sample. The system was adjusted later to multiple analyte detection by including a second potentiometric cell on the LTCC/μPOT device. No additional reference electrode was required. This concept was applied to Trimethoprim (TMP), always administered concomitantly with sulphonamide drugs, and tested in fish-farming waters. The biparametric microanalyzer displayed Nernstian behaviour, with average slopes −54.7 (SMX) and +57.8 (TMP) mV/decade. To demonstrate the microanalyzer capabilities for real applications, it was successfully applied to single and simultaneous determination of SMX and TMP in aquaculture waters.

Analysis of self-interference within DAG tasks

6th Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.

Double power laws, fractals and self-similarity

Power law (PL) distributions have been largely reported in the modeling of distinct real phenomena and have been associated with fractal structures and self-similar systems. In this paper, we analyze real data that follows a PL and a double PL behavior and verify the relation between the PL coefficient and the capacity dimension of known fractals. It is to be proved a method that translates PLs coefficients into capacity dimension of fractals of any real data.