A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Demand response management in power systems using a particle swarm optimization approach

Competitive electricity markets have arisen as a result of power-sector restructuration and power-system deregulation. The players participating in competitive electricity markets must define strategies and make decisions using all the available information and business opportunities.

Environmental data management in the Sado Estuary

Estuaries are perhaps the most threatened environments in the coastal fringe; the coincidence of high natural value and attractiveness for human use has led to conflicts between conservation and development. These conflicts occur in the Sado Estuary since its location is near the industrialised zone of Peninsula of Setúbal and at the same time, a great part of the Estuary is classified as a Natural Reserve due to its high biodiversity. These facts led us to the need of implementing a model of environmental management and quality assessment, based on methodologies that enable the assessment of the Sado Estuary quality and evaluation of the human pressures in the estuary. These methodologies are based on indicators that can better depict the state of the environment and not necessarily all that could be measured or analysed. Sediments have always been considered as an important temporary source of some compounds or a sink for other type of materials or an interface where a great diversity of biogeochemical transformations occur. For all this they are of great importance in the formulation of coastal management system. Many authors have been using sediments to monitor aquatic contamination, showing great advantages when compared to the sampling of the traditional water column. The main objective of this thesis was to develop an estuary environmental management framework applied to Sado Estuary using the DPSIR Model (EMMSado), including data collection, data processing and data analysis. The support infrastructure of EMMSado were a set of spatially contiguous and homogeneous regions of sediment structure (management units). The environmental quality of the estuary was assessed through the sediment quality assessment and integrated in a preliminary stage with the human pressure for development. Besides the earlier explained advantages, studying the quality of the estuary mainly based on the indicators and indexes of the sediment compartment also turns this methodology easier, faster and human and financial resource saving. These are essential factors to an efficient environmental management of coastal areas. Data management, visualization, processing and analysis was obtained through the combined use of indicators and indices, sampling optimization techniques, Geographical Information Systems, remote sensing, statistics for spatial data, Global Positioning Systems and best expert judgments. As a global conclusion, from the nineteen management units delineated and analyzed three showed no ecological risk (18.5 % of the study area). The areas of more concern (5.6 % of the study area) are located in the North Channel and are under strong human pressure mainly due to industrial activities. These areas have also low hydrodynamics and are, thus associated with high levels of deposition. In particular the areas near Lisnave and Eurominas industries can also accumulate the contamination coming from Águas de Moura Channel, since particles coming from that channel can settle down in that area due to residual flow. In these areas the contaminants of concern, from those analyzed, are the heavy metals and metalloids (Cd, Cu, Zn and As exceeded the PEL guidelines) and the pesticides BHC isomers, heptachlor, isodrin, DDT and metabolits, endosulfan and endrin. In the remain management units (76 % of the study area) there is a moderate impact potential of occurrence of adverse ecological effects and in some of these areas no stress agents could be identified. This emphasizes the need for further research, since unmeasured chemicals may be causing or contributing to these adverse effects. Special attention must be taken to the units with moderate impact potential of occurrence of adverse ecological effects, located inside the natural reserve. Non-point source pollution coming from agriculture and aquaculture activities also seem to contribute with important pollution load into the estuary entering from Águas de Moura Channel. This pressure is expressed in a moderate impact potential for ecological risk existent in the areas near the entrance of this Channel. Pressures may also came from Alcácer Channel although they were not quantified in this study. The management framework presented here, including all the methodological tools may be applied and tested in other estuarine ecosystems, which will also allow a comparison between estuarine ecosystems in other parts of the globe.

Distributed MPC for Thermal Comfort and Load Allocation with Energy Auction

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited shared energy resource using an integrated demand-side management approach that involves a power price auction and an appliance loads allocation scheme. The control objective for each subsystem (house or building) aims to minimize the energy cost while maintaining the indoor temperature inside comfort limits. In a distributed coordinated multi-agent ecosystem, each house or building control agent achieves its objectives while sharing, among them, the available energy through the introduction of particular coupling constraints in their underlying optimization problem. Coordination is maintained by a daily green energy auction bring in a demand-side management approach. Also the implemented distributed MPC algorithm is described and validated with simulation studies.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

GFRP waste reinforced polymer mortars: a new waste management solution for GFRP scrap material

To date, glass fibre reinforced polymer (GFRP) waste recycling is very limited and restricted by thermoset nature of binder matrix and lack of economically viable enduse applications for the recyclates. In this study, efforts were made in order to recycle grinded GFRP waste proceeding from pultrusion production scrap, into new and sustainable composite materials. For this purpose, GFRP waste recyclates, a mix of powdered and fibrous materials, were incorporated into polyester based mortars as fine aggregate and filler replacements, at different load contents (between 4% up to 12% of total mass) and particle size distributions. Potential recycling solution was assessed by mechanical behaviour of resultant GFRP waste modified polymer mortars. Test results revealed that GFRP waste filled polymer mortars present improved flexural and compressive behaviour over unmodified polyester based mortars, thus indicating the feasibility of GFRP waste reuse in concrete-polymer composites.

Consistency and Context Management in a Multi Agent Belief Revision Testbed

Multi-agent architectures are well suited for complex inherently distributed problem solving domains. From the many challenging aspects that arise within this framework, a crucial one emerges: how to incorporate dynamic and conflicting agent beliefs? While the belief revision activity in a single agent scenario is concentrated on incorporating new information while preserving consistency, in a multi-agent system it also has to deal with possible conflicts between the agents perspectives. To provide an adequate framework, each agent, built as a combination of an assumption based belief revision system and a cooperation layer, was enriched with additional features: a distributed search control mechanism allowing dynamic context management, and a set of different distributed consistency methodologies. As a result, a Distributed Belief Revision Testbed (DiBeRT) was developed. This paper is a preliminary report presenting some of DiBeRT contributions: a concise representation of external beliefs; a simple and innovative methodology to achieve distributed context management; and a reduced inter-agent data exchange format.

Intelligent Distributed Environmental Decision Support System

This article discusses the development of an Intelligent Distributed Environmental Decision Support System, built upon the association of a Multi-agent Belief Revision System with a Geographical Information System (GIS). The inherent multidisciplinary features of the involved expertises in the field of environmental management, the need to define clear policies that allow the synthesis of divergent perspectives, its systematic application, and the reduction of the costs and time that result from this integration, are the main reasons that motivate the proposal of this project. This paper is organised in two parts: in the first part we present and discuss the developed Distributed Belief Revision Test-bed — DiBeRT; in the second part we analyse its application to the environmental decision support domain, with special emphasis on the interface with a GIS.

Knowledge management systems and intellectual capital measurement in portuguese organizations: a case study

This paper presents the results of an exploratory study on knowledge management in Portuguese organizations. The study was based on a survey sent to one hundred of the main Portuguese organizations, in order to know their current practices relating knowledge management systems (KMS) usage and intellectual capital (IC) measurement. With this study, we attempted to understand what are the main tools used to support KM processes and activities in the organizations, and what metrics are pointed by organizations to measure their knowledge assets.

Virtual Power Players Internal Negotiation and Management in MASCEM

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in very helpful sophisticated tools. This paper presents a new methodology for the management of coalitions in electricity markets. This approach is tested using the multi-agent market simulator MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), taking advantage of its ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market and internally, with their members in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. A case study using real data from the Iberian Electricity Market is performed to validate and illustrate the proposed approach.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

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.

Real-Time Simulation of energy Management in a Domestic Consumer

Recent and future changes in power systems, mainly in the smart grid operation context, are related to a high complexity of power networks operation. This leads to more complex communications and to higher network elements monitoring and control levels, both from network’s and consumers’ standpoint. The present work focuses on a real scenario of the LASIE laboratory, located at the Polytechnic of Porto. Laboratory systems are managed by the SCADA House Intelligent Management (SHIM), already developed by the authors based on a SCADA system. The SHIM capacities have been recently improved by including real-time simulation from Opal RT. This makes possible the integration of Matlab®/Simulink® real-time simulation models. The main goal of the present paper is to compare the advantages of the resulting improved system, while managing the energy consumption of a domestic consumer.

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.

A Communication and Resources Management Scheme to Support the Smart Grid Integration of Multiplayers Access to Resources Information

The increasing and intensive integration of distributed energy resources into distribution systems requires adequate methodologies to ensure a secure operation according to the smart grid paradigm. In this context, SCADA (Supervisory Control and Data Acquisition) systems are an essential infrastructure. This paper presents a conceptual design of a communication and resources management scheme based on an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). The methodology is used to support the energy resource management considering all the involved costs, power flows, and electricity prices leading to the network reconfiguration. The methodology also addresses the definition of the information access permissions of each player to each resource. The paper includes a 33-bus network used in a case study that considers an intensive use of distributed energy resources in five distinct implemented operation contexts.