Ten quick tips for fuzzy logic modeling of biomedical systems

Fuzzy logic is useful tool to describe and represent biological or medical scenarios, where often states and outcomes are not only completely true or completely false, but rather partially true or partially false. Despite its usefulness and spread, fuzzy logic modeling might easily be done in the wrong way, especially by beginners and unexperienced researchers, who might overlook some important aspects or might make common mistakes. Malpractices and pitfalls, in turn, can lead to wrong or overoptimistic, inflated results, with negative consequences to the biomedical research community trying to comprehend a particular phenomenon, or even to patients suffering from the investigated disease. To avoid common mistakes, we present here a list of quick tips for fuzzy logic modeling any biomedical scenario: some guidelines which should be taken into account by any fuzzy logic practitioner, including experts. We believe our best practices can have a strong impact in the scientific community, allowing researchers who follow them to obtain better, more reliable results and outcomes in biomedical contexts.</p

Ten quick tips for fuzzy logic modeling of biomedical systems

Fuzzy logic is useful tool to describe and represent biological or medical scenarios, where often states and outcomes are not only completely true or completely false, but rather partially true or partially false. Despite its usefulness and spread, fuzzy logic modeling might easily be done in the wrong way, especially by beginners and unexperienced researchers, who might overlook some important aspects or might make common mistakes. Malpractices and pitfalls, in turn, can lead to wrong or overoptimistic, inflated results, with negative consequences to the biomedical research community trying to comprehend a particular phenomenon, or even to patients suffering from the investigated disease. To avoid common mistakes, we present here a list of quick tips for fuzzy logic modeling any biomedical scenario: some guidelines which should be taken into account by any fuzzy logic practitioner, including experts. We believe our best practices can have a strong impact in the scientific community, allowing researchers who follow them to obtain better, more reliable results and outcomes in biomedical contexts.</p

Flexibility From Distributed Multienergy Systems

Multienergy systems (MES), in which multiple energy vectors are integrated and optimally operated, are key assets in low-carbon energy systems. Multienergy interactions of distributed energy resources via different energy networks generate the so-called distributed MES (DMES). While it is now well recognized that DMES can provide power system flexibility by shifting across different energy vectors, it is essential to have a systematic discussion on the main features of such flexibility. This article presents a comprehensive overview of DMES modeling and characterization of flexibility applications. The concept of ``multienergy node'' is introduced to extend the power node model, used for electrical flexibility, in the multienergy case. A general definition of DMES flexibility is given, and a general mathematical and graphical modeling framework, based on multidimensional maps, is formulated to describe the operational characteristics of individual MES and aggregate DMES, including the role of multienergy networks in enabling or constraining flexibility. Several tutorial examples are finally presented with illustrative case studies on current and future DMES practical applications

Modeling Local Energy Market for Energy Management of Multi-Microgrids

The diffusion of distributed energy resources (DERs) has changed the supply-demand balance of power systems. One option to modernize the management of the electricity distribution is to operate the distribution system with interconnected micro-grids (MGs). However, the MG participation in wholesale energy and ancillary service markets creates several challenges in the interactions among the energy market managing entities. To solve these problems, local energy markets (LEMs) have been proposed, where the MGs can trade energy with each other under the management of the LEM manager (LEMM) to minimize their operation cost. In this paper, a local energy market is modeled for multi-MGs (MMGs) to minimize the operation cost of MGs individually and their social welfare in cooperation with each other. In such model, the optimal scheduling of the DERs in each MG is done through the market clearing process. To investigate the effectiveness of the proposed approach, the local energy market is applied to a distribution network with three MGs

Variability of behaviour in electricity load profile clustering: who does things at the same time each day?

UK electricity market changes provide opportunities to alter households' electricity usage patterns for the benet of the overall electricity network. Work on clustering similar households has concentrated on daily load proles and the variability in regular household behaviours has not been considered. Those households with most variability in reg- ular activities may be the most receptive to incentives to change timing. Whether using the variability of regular behaviour allows the creation of more consistent groupings of households is investigated and compared with daily load prole clustering. 204 UK households are analysed to nd repeating patterns (motifs). Variability in the time of the motif is used as the basis for clustering households. Dierent clustering algorithms are assessed by the consistency of the results. Findings show that variability of behaviour, using motifs, provides more consistent groupings of households across dierent clustering algorithms and allows for more ecient targeting of behaviour change interventions

Impact of Power-to-Gas on distribution systems with large renewable energy penetration

The exploitation of the Power-to-Gas (PtG) technology can properly support the distribution system operation in case of large penetration of Renewable Energy Sources (RES). This paper addresses the impact of the PtG operation on the electrical distribution systems. A novel model of the PtG plant has been created to be representative of the entire process chain, as well as to be compatible with network calculations. The structure of the model with the corresponding parameters has been defined and validated on the basis of measurements gathered on a real plant. The PtG impact on the distribution systems has then been simulated on two network models representing a rural and a semi-urban environment, respectively. The testing has been carried out by defining a set of cases that contain critical situations for the distribution network, caused by RES plant placement. The objectives of the introduction of PtG are the reduction of the reverse power flow, as well as the reduction of the overcurrent and overvoltage issues in the distribution system. The results obtained from annual simulations lead to considerable reduction (from 78 to 100%) of the reverse power flow with respect to the base case, and to alleviating (or even solving) the overcurrent and overvoltage problems of the networks. These results indicate PtG as a possible solution for guaranteeing a smooth transition towards decarbonized energy systems. The capacity factors of the PtG plants largely vary depending on the network topology, the RES penetration, the number of the PtG plants and their sizes. From the test cases, the performance in a rural network (where the minimum capacity factor is about 50%) resulted better than in a semi-urban network (where the capacity factor values range between 21% and 60%)

A Risk-Based Decision Framework for the Distribution Company in Mutual Interaction with the Wholesale Day-ahead Market and Microgrids

One of the emergent prospects for active distribution networks ( DN ) is to establish new roles to the distribution company ( DISCO ). The DISCO can act as an aggregator of the resources existing in the DN , also when parts of the network are structured and managed as microgrids ( MG s). The new roles of the DISCO may open the participation of the DISCO as a player trading energy in the wholesale markets, as well as in local energy markets. In this paper, the decision making aspects involving the DISCO are addressed by proposing a bilevel optimization approach in which the DISCO problem is modeled as the upper-level problem and the MG s problems and day-ahead wholesale market clearing process are modeled as the lower-level problems. To include the uncertainty of renewable energy sources, a risk-based two-stage stochastic problem is formulated, in which the DISCO 's risk aversion is modeled by using the conditional value at risk. The resulting nonlinear bilevel model is transformed into a linear single-level one by applying the Karush–Kuhn–Tucker conditions and the duality theory. The effectiveness of the model is shown in the application to the IEEE 33-bus DN connected to the IEEE RTS 24-bus power system

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

To reduce the risks of a new energy crisis and increase energy availability, the use of renewable energy sources (RES) is important and recommended. In Brazil, micro and small companies contribute about 25% of gross domestic product (GDP), and electric energy is employed intensively, so the importance of microgeneration is observable. This research aims to analyze the economic viability of the micro-generation wind energy project for micro and small businesses. Thus, three Brazilian states, Rio Grande do Norte, Rio Grande do Sul and Minas Gerais were considered, and different scenarios were proposed. A feasibility analysis is then performed, followed by a stochastic analysis using Monte Carlo simulation (MCS). Finally, models of artificial neural networks (ANN) are used to evaluate the relative importance (RI) of the variables. The results show that none of the states appears economically feasible under the conditions presented. In the stochastic analysis, the probability of viability is between 17% and 24% in all states, which shows the low probability of viability for microgeneration. Through ANN training, it was possible to calculate the RI, in which it is possible to identify the variables that have most impact on the net present value (NPV) in all states; it is considered the most important variable in the project's viability. In addition, the discussion explores the importance of public incentives for promoting investment in renewable energy, which can reduce investment costs and make it attractive to small and medium-sized businesses