Virtual power players demand response programs management based on locational marginal prices

Power systems have been suffering huge changes mainly due to the substantial increase of distributed generation and to the operation in competitive environments. Virtual power players can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. Resource management gains an increasing relevance in this competitive context, while demand side active role provides managers with increased demand elasticity. This makes demand response use more interesting and flexible, giving rise to a wide range of new opportunities.This paper proposes a methodology for managing demand response programs in the scope of virtual power players. The proposed method is based on the calculation of locational marginal prices (LMP). The evaluation of the impact of using demand response specific programs on the LMP value supports the manager decision concerning demand response use. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus network with intensive use of distributed generation.

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

Multilayer Architectures Based on a-SiC:H Material: Tunable Wavelength Filters in Optical Processing Devices

The characteristics of tunable wavelength filters based on a-SiC:H multilayered stacked pin cells are studied both theoretically and experimentally. The optical transducers were produced by PECVD and tested for a proper fine tuning of the cyan and yellow fluorescent proteins emission. The active device consists of a p-i'(a-SiC:H)-n/p-i(a-Si:H)-n heterostructures sandwiched between two transparent contacts. Experimental data on spectral response analysis, current-voltage characteristics and color and transmission rate discrimination are reported. Cyan and yellow fluorescent input channels were transmitted together, each one with a specific transmission rate and different intensities. The multiplexed optical signal was analyzed by reading out, under positive and negative applied voltages, the generated photocurrents. Results show that the optimized optical transducer has the capability of combining the transient fluorescent signals onto a single output signal without losing any specificity (color and intensity). It acts as a voltage controlled optical filter: when the applied voltages are chosen appropriately the transducer can select separately the cyan and yellow channel emissions (wavelength and frequency) and also to quantify their relative intensities. A theoretical analysis supported by a numerical simulation is presented.

Optical Transducers Based on Amorphous Si/SiC Photodiodes

Amorphous Si/SiC photodiodes working as photo-sensing or wavelength sensitive devices have been widely studied. In this paper single and stacked a-SiC:H p-i-n devices, in different geometries and configurations, are reviewed. Several readout techniques, depending on the desired applications (image sensor, color sensor, wavelength division multiplexer/demultiplexer device) are proposed. Physical models are presented and supported by electrical and numerical simulations of the output characteristics of the sensors.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Transmission costs allocation based on optimal re-dispatch

Congestion management of transmission power systems has achieve high relevance in competitive environments, which require an adequate approach both in technical and economic terms. This paper proposes a new methodology for congestion management and transmission tariff determination in deregulated electricity markets. The congestion management methodology is based on a reformulated optimal power flow, whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the transactions resulting from market operation. The proposed transmission tariffs consider the physical impact caused by each market agents in the transmission network. The final tariff considers existing system costs and also costs due to the initial congestion situation and losses. This paper includes a case study for the 118 bus IEEE test case.

Contexts-management strategy in considering the security in urban computing based on urban design

Urban Computing (UrC) provides users with the situation-proper information by considering context of users, devices, and social and physical environment in urban life. With social network services, UrC makes it possible for people with common interests to organize a virtual-society through exchange of context information among them. In these cases, people and personal devices are vulnerable to fake and misleading context information which is transferred from unauthorized and unauthenticated servers by attackers. So called smart devices which run automatically on some context events are more vulnerable if they are not prepared for attacks. In this paper, we illustrate some UrC service scenarios, and show important context information, possible threats, protection method, and secure context management for people.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Bid definition method for electricity markets based on an adaptive multiagent system

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.

Development of novel ionic liquids based on ampicillin

Novel ionic liquids containing ampicillin as an active pharmaceutical ingredient anion were prepared with good yields by using a new, efficient synthetic procedure based on the neutralization of a moderately basic ammonia solution of ampicillin with different organic cation hydroxides. The relevant physical and thermal properties of these novel ionic liquids based on ampicillin were also evaluated.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

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 training in control centres based on an ambient intelligence paradigm

This article describes a new approach in the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

Conflicts between employee preferences and ergonomic recommendations in shift scheduling: regulation based on consent is not sufficient

OBJECTIVE: Contribution to the discussion of the role of participation/consent of employees in working hours regulation. METHODS: Exploratory analysis of conflicts between preferences of employees and ergonomic recommendations in shift scheduling by analysing a large number of participative shift scheduling projects. RESULTS: The analysis showed that very often the pursuit of higher income played the major role in the decision making process of employees and employees preferred working hours in conflict with health and safety principles. CONCLUSIONS: First, the consent of employees or the works council alone does not ensure ergonomically sound schedules. Besides consent, risk assessment procedures seem to be a promising but difficult approach. Secondly, more research is necessary to check the applicability of recommendations under various settings, to support the risk assessment processes and to improve regulatory approaches to working hours.