Cash balance management: A comparison between genetic algorithms and particle swarm optimization

This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.

On the adaptation of broadcast transactions in token-passing fieldbus networks with heterogeneous transmission media

Broadcast networks that are characterised by having different physical layers (PhL) demand some kind of traffic adaptation between segments, in order to avoid traffic congestion in linking devices. In many LANs, this problem is solved by the actual linking devices, which use some kind of flow control mechanism that either tell transmitting stations to pause (the transmission) or just discard frames. In this paper, we address the case of token-passing fieldbus networks operating in a broadcast fashion and involving message transactions over heterogeneous (wired or wireless) physical layers. For the addressed case, real-time and reliability requirements demand a different solution to the traffic adaptation problem. Our approach relies on the insertion of an appropriate idle time before a station issuing a request frame. In this way, we guarantee that the linking devices’ queues do not increase in a way that the timeliness properties of the overall system turn out to be unsuitable for the targeted applications.

Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles

The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe r important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

Transmission network expansion planning considering uncertainty in demand

This paper presents two mathematical models and one methodology to solve a transmission network expansion planning problem considering uncertainty in demand. The first model analyzed the uncertainty in the system as a whole; then, this model considers the uncertainty in the total demand of the power system. The second one analyzed the uncertainty in each load bus individually. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The models presented are solved using a specialized genetic algorithm. The results obtained for several known systems from literature show that cheaper plans can be found satisfying the uncertainty in demand.

Explaining transactions in time banks in economic crisis

This article studies the services exchanged in a particular Spanish time bank. Using data from users and transactions, we analyse the users’ profile as well as the determinants of providing and receiving different services. Our results show that the representative user is a Spanish female, not married, middle aged, highly educated and unemployed. We also find differences in the personal characteristics driving the supply and demand of services.

Supply and Demand Side Limitations Affecting the Structure of Agriculture and the Rural Economy. Factor Markets Working Paper No. 21, February 2012

The paper criticises the neo-classical assumptions of perfect factor markets and of complete information, which constitute central elements in labour market theory. Based on literature review and on economic reports from transition economies, as well as developing countries and more advanced economies, this deliverable focuses on the structural impediments and imperfections which often characterise rural labour markets and which may prevent an efficient allocation of labour. According to empirical studies, transactions costs and rigidities hinder the well-functioning of labour markets and constrain labour adjustments. The paper attempts to classify the various limitations of rural labour markets from both supply and demand side, although the distinction is not always clear-cut as some problems occur on both sides. The identification of these issues is extremely important as it allows us to highlight the inefficiencies and the failures in labour markets and to understand their impact on labour allocation. In this context, market intervention is desirable and the paper provides particular support for rural development policies such as investments in human capital. Lastly, labour institutions can play a key role in promoting the well functioning of labour markets, thus it is fundamental that they are well in place.

An incentivized auction based group-selling approach for demand response management in V2G systems

Vehicle-to-Grid (V2G) system with efficient Demand Response Management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at EVs. An incentivilized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling based auction. In the proposed approach, a number of aggregators sit on the first level auction responsible to communicate with a group of EVs. EVs as bidders consider Quality of Energy (QoE) requirements and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.

Hierarchical Demand Response for Peak Minimization Using Dantzig-Wolfe Decomposition

Demand response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralized agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus, it is desirable to use a scalable decentralized algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for peak minimization based on Dantzig-Wolfe decomposition (DWD). In addition, a time weighted maximization option is included in the cost function, which improves the quality of service for devices seeking to receive their desired energy sooner rather than later. This paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

The rating of perceived exertion predicts intermittent vertical jump demand and performance

The aims of this study were (a) to assess the ability of the rating of perceived exertion (RPE) to predict performance (i.e. number of vertical jumps performed to a fixed jump height) of an intermittent vertical jump exercise, and (b) to determine the ability of RPE to describe the physiological demand of such exercise. Eight healthy men performed intermittent vertical jumps with rest periods of 4, 5, and 6s until fatigue. Heart rate and RPE were recorded every five jumps throughout the sessions. The number of vertical jumps performed was also recorded. Random coefficient growth curve analysis identified relationships between the number of vertical jumps and both RPE and heart rate for which there were similar slopes. In addition, there were no differences between individual slopes and the mean slope for either RPE or heart rate. Moreover, RPE and number of jumps were highly correlated throughout all sessions (r=0.97-0.99; P0.001), as were RPE and heart rate (r=0.93-0.97; P0.001). The findings suggest that RPE can both predict the performance of intermittent vertical jump exercise and describe the physiological demands of such exercise.

Defining Nutrient and Biochemical Oxygen Demand Baselines for Tropical Rivers and Streams in So Paulo State (Brazil): A Comparison Between Reference and Impacted Sites

Determining reference concentrations in rivers and streams is an important tool for environmental management. Reference conditions for eutrophication-related water variables are unavailable for Brazilian freshwaters. We aimed to establish reference baselines for So Paulo State tropical rivers and streams for total phosphorus (TP) and nitrogen (TN), nitrogen-ammonia (NH(4) (+)) and Biochemical Oxygen Demand (BOD) through the best professional judgment and the trisection methods. Data from 319 sites monitored by the So Paulo State Environmental Company (2005 to 2009) and from the 22 Water Resources Management Units in So Paulo State were assessed (N = 27,131). We verified that data from different management units dominated by similar land cover could be analyzed together (Analysis of Variance, P = 0.504). Cumulative frequency diagrams showed that industrialized management units were characterized by the worst water quality (e.g. average TP of 0.51 mg/L), followed by agricultural watersheds. TN and NH(4) (+) were associated with urban percentages and population density (Spearman Rank Correlation Test, P < 0.05). Best professional judgment and trisection (median of lower third of all sites) methods for determining reference concentrations showed agreement: 0.03 & 0.04 mg/L (TP), 0.31 & 0.34 mg/L (TN), 0.06 & 0.10 mg-N/L (NH(4) (+)) and 2 & 2 mg/L (BOD), respectively. Our reference concentrations were similar to TP and TN reference values proposed for temperate water bodies. These baselines can help with water management in So Paulo State, as well as providing some of the first such information for tropical ecosystems.

A New Approach for Video Adaptation through Overlay Services Networks

Video adaptation is an extensively explored content providing technique aimed at appropriately suiting several usage scenarios featured by different network requirements and constraints, user`s terminal and preferences. However, its usage in high-demand video distribution systems, such as CNDs, has been badly approached, ignoring several aspects of optimization of network use. To address such deficiencies, this paper presents an approach for implementing the adaptation service by exploring the concept of overlay services networks. As a result of demonstrate the benefits of this proposal, it is made a comparison of this proposed adaptation service with other strategies of video adaptation.

Postural activity of the diaphragm is reduced in humans when respiratory demand increases

1. Respiratory activity of the diaphragm and other respiratory muscles is normally co-ordinated with their other functions, such as for postural control of the trunk when the limbs move. The integration may occur by summation of two inputs at the respiratory motoneurons. The present study investigated whether postural activity of the diaphragm changed when respiratory drive increased with hypercapnoea. 2. Electromyographic (EMG) recordings of the diaphragm and other trunk muscles were made with intramuscular electrodes in 13 healthy volunteers. Under control conditions and while breathing through increased dead-space,subjects made rapid repetitive arm movements to disturb the stability of the spine for four periods each lasting 10 s, separated by 50 s. 3. End-tidal CO2, and ventilation increased for the first 60-120 s of the trial then reached a plateau. During rapid arm movement at the start of dead-space breathing, diaphragm EMG became tonic with superimposed modulation at the frequencies of respiration and arm movement. However, when the arm was moved after 60 s of hypercapnoea, the tonic diaphragm EMG during expiration and the phasic activity with arm movement were reduced or absent. Similar changes occurred for the expiratory muscle transversus abdominis, but not for the erector spinae. The mean amplitude of intra-abdominal pressure and the phasic changes with arm movement were reduced after 60 s of hypercapnoea. 4. The present data suggest that increased central respiratory drive may attenuate the postural commands reaching motoneurons. This attenuation can affect the key inspiratory and expiratory muscles and is likely to be co-ordinated at a pre-motoneuronal site.

Incorporating demand-side aspects into regional policy: variations in the importance of private investment decision factors across regions

Most regional programs focus on the supply side of regions, emphasizing the attraction conditions offered, such as infrastructure, labor skills, tax incentives, etc. This study analyzes one aspect of the demand side, that is, how investment decisions of private firms are made by asking the question: ""Do corporations decide the same way on investments in different parts of the territory?"" The paper analyzes the investments of 373 large Brazilian firms during 1996-2004. Based on the investment decisions of these firms, the role of sales, cash-flow, external financing, and working capital is investigated through regression analysis. The regional influence is captured by explanatory variables representing regional and firm characteristics, and by interaction dummies between the region and the main investment determinants. The results indicate significant differences across regions in the importance of investment determinants. This information is important for regional development policy, because different mechanisms should be used in different regions to foster private investments.