Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

Distribution network short term scheduling in Smart Grid context

The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and “green” operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a Mixed Integer Non-Linear Programming (MINLP) approach.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

Demand response in electrical energy supply: an optimal real time pricing approach

In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. This paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. The use of DemSi by a retailer in a situation of energy shortage, is presented. Load reduction is obtained using a consumer based price elasticity approach supported by real time pricing. Non-linear programming is used to maximize the retailer’s profit, determining the optimal solution for each envisaged load reduction. The solution determines the price variations considering two different approaches, price variations determined for each individual consumer or for each consumer type, allowing to prove that the approach used does not significantly influence the retailer’s profit. The paper presents a case study in a 33 bus distribution network with 5 distinct consumer types. The obtained results and conclusions show the adequacy of the used methodology and its importance for supporting retailers’ decision making.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Quality of life, life satisfaction and happiness in shift- and non-shiftworkers

OBJECTIVE: To investigate the quality of life, life satisfaction, happiness and demands of work in workers with different work schedules. METHODS: The survey was carried out on professional workers in social care. Some were shiftworkers whose schedule included night shifts (N=311), some were shiftworkers without night shifts (N=207) and some were non-shiftworkers (N=1,210). Surveys were mailed and the response rate was 86%. For the purpose of this study several variables were selected from the Survey: The Quality of Life Profile, which measures importance, satisfaction, control and opportunities in nine domains of life plus measures of happiness, life satisfaction and demands of work. RESULTS: While both groups of shiftworkers, compared to non-shiftworkers, reported needing more physical effort to complete their work, and reported 'being' more physically tired, no differences were found in reports of overall happiness, life satisfaction or total quality of life. However, night-shiftworkers reported greater percentage of time unhappy than the other two groups of workers. In analyses of the quality of life, night-shiftworkers were less satisfied with domains of spiritual 'being' and physical and community 'belonging' than day-shiftworkers and non-shiftworkers. They also reported having fewer opportunities to improve their physical 'being', leisure, and personal growth than the other two groups. CONCLUSIONS: Quality of life in specific domains in night-shiftworkers was rated worse than in other groups of workers. Domain-based quality of life assessment gives more information regarding the particular needs of workers than overall or global measures of well-being.

Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, balancing load among feeders and subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. The Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the Master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS. The effectiveness of the proposal is demonstrated through two examples extracted from the literature.

Intelligent SCADA for load control

A supervisory control and data acquisition (SCADA) system is an integrated platform that incorporates several components and it has been applied in the field of power systems and several engineering applications to monitor, operate and control a lot of processes. In the future electrical networks, SCADA systems are essential for an intelligent management of resources like distributed generation and demand response, implemented in the smart grid context. This paper presents a SCADA system for a typical residential house. The application is implemented on MOVICON™11 software. The main objective is to manage the residential consumption, reducing or curtailing loads to keep the power consumption in or below a specified setpoint, imposed by the costumer and the generation availability.

Symbolic dynamics and renormalization of non-autonomous k periodic dynamical systems

The purpose of this paper was to introduce the symbolic formalism based on kneading theory, which allows us to study the renormalization of non-autonomous periodic dynamical systems.

Family and non-family businesses on internal factors of entrepreneurship

Small firms are a major player in development. Thus, entrepreneurship is frequently attached to these rms and it must be present in daily management of factors such as planning and cooperation. We intend to analyze these factors, comparing familiar and non-familiar businesses. This study was conducted in a Portuguese region in the north of Portugal - Vale do Sousa . The results allow us to conclude that even with some managerial di erences it was not possible to identify distinct patterns between them. The main goal of this paper is to open research lines on important issues to distinguish familiar from non-familiar businesses.

Entrepreneurship Strategies in Family and Non-family Businesses

The goal of the present paper is to analyse the classic entrepreneurship strategies (Innovation, Risk and Proactivity) in small and medium-sized businesses. However as presented in the title, the study will go further by comparing the results of those strategies in familiar and nonfamiliar businesses. This study was carried on in construction and industry sectors, in the region of Vale do Sousa, in the north of Portugal. In order to classify businesses as familiar or non-familiar types two criterion were adopted: (1) Management Control, (2) Family Employability. On the opposite to some studies that present a larger percentage of familiar businesses in national and European entrepreneurial fabric, the criterion used leaded to a larger number of non-familiar businesses (53%). The results showed that in general SMEs in this region are not following entrepreneurship strategies. Analysing the entire sample without a separation of businesses by nature (familiar/non-familiar) only proactivity showed to be more present in the managerial decisions. There is a lack of innovation and risk culture. Comparing the groups only on proactivity tests was possible to verify some differences. It was concluded that non-familiar businesses are more proactive than familiar ones. Between those groups there are no statistical differences on the means of the variables innovation and risk. At the same time some tests were conducted to test the differences on the variable entrepreneurship. The results were similar to innovation and risk strategies: There are no significant differences on entrepreneurship between these groups of businesses.

Efeitos tridimensionais no escoamento em torno de cilindros confinados

Mestrado em Engenharia Química