Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) 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. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

Emergency energy resource management in SmartGrids

Energy resources management can play a very relevant role in future power systems in a SmartGrid context, with intensive penetration of distributed generation and storage systems. This paper deals with the importance of resource management in incident situations. The paper presents DemSi, an energy resources management simulator that has been developed by the authors to simulate electrical distribution networks with high distributed generation penetration, storage in network points and customers with demand response contracts. DemSi is used to undertake simulations for an incident scenario, evidencing the advantages of adequately using flexible contracts, storage, and reserve in order to limit incident consequences.

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

Support System for Rational Use of Electric Energy

This paper presents the system developed to promote the rational use of electric energy among consumers and, thus, increase the energy efficiency. The goal is to provide energy consumers with an application that displays the energy consumption/production profiles, sets up consuming ceilings, defines automatic alerts and alarms, compares anonymously consumers with identical energy usage profiles by region and predicts, in the case of non-residential installations, the expected consumption/production values. The resulting distributed system is organized in two main blocks: front-end and back-end. The front-end includes user interface applications for Android mobile devices and Web browsers. The back-end provides data storage and processing functionalities and is installed in a cloud computing platform - the Google App Engine - which provides a standard Web service interface. This option ensures interoperability, scalability and robustness to the system.

A Multiple Criteria Utility-based Approa h for the Unit Commitment with Wind Power and Pumped Storage Hydro

The integration of wind power in eletricity generation brings new challenges to unit commitment due to the random nature of wind speed. For this particular optimisation problem, wind uncertainty has been handled in practice by means of conservative stochastic scenario-based optimisation models, or through additional operating reserve settings. However, generation companies may have different attitudes towards operating costs, load curtailment, or waste of wind energy, when considering the risk caused by wind power variability. Therefore, alternative and possibly more adequate approaches should be explored. This work is divided in two main parts. Firstly we survey the main formulations presented in the literature for the integration of wind power in the unit commitment problem (UCP) and present an alternative model for the wind-thermal unit commitment. We make use of the utility theory concepts to develop a multi-criteria stochastic model. The objectives considered are the minimisation of costs, load curtailment and waste of wind energy. Those are represented by individual utility functions and aggregated in a single additive utility function. This last function is adequately linearised leading to a mixed-integer linear program (MILP) model that can be tackled by general-purpose solvers in order to find the most preferred solution. In the second part we discuss the integration of pumped-storage hydro (PSH) units in the UCP with large wind penetration. Those units can provide extra flexibility by using wind energy to pump and store water in the form of potential energy that can be generated after during peak load periods. PSH units are added to the first model, yielding a MILP model with wind-hydro-thermal coordination. Results showed that the proposed methodology is able to reflect the risk profiles of decision makers for both models. By including PSH units, the results are significantly improved.

Macronutrients and energy balance in obesity.

Energy balance is the difference between metabolizable energy intake and total energy expenditure. Energy intake is difficult to measure accurately; changes in body weight, for example, are not a good measure of the adequacy of energy intake, because fluctuations in body weight are common even if the overall trend is toward weight loss. It is now customary to assess energy requirements indirectly from total energy expenditure. Total energy expenditure consists of basal metabolism, postprandial thermogenesis, and physical activity. Energy expenditure is related to both body weight and body composition. A reduction in total energy expenditure accompanies weight loss, because basal metabolic rate decreases with the loss of lean tissue mass. Similarly, with weight gain, there is an increase in basal metabolic rate, because lean tissue mass grows to support the increase in fat tissue mass. Excess energy intake over energy expenditure causes weight gain and an accompanying increase in total energy expenditure. Following a period of adaptation, total energy expenditure will match energy intake and body weight will stabilize at a higher level. This same relationship holds for weight loss. Respiratory quotient (measured in steady state) is an indication of the proportion of energy expenditure derived from fat and carbohydrate oxidation. Over long periods of time, fat balance is equivalent to energy balance, as an excess of fat intake over fat oxidation causes fat storage.

Electrochemical studies on Metal phthalocyanines

The primary aim of these investigations was to probe the elecnuchemical and material science aspects of some selected metal phthalocyanines(MPcs).Metal phthalocyanines are characterised by a unique planar molecular structure. As a single class of compounds they have been the subject of ever increasing number of physicochemical and technological investigations. During the last two decades the literature on these compounds was flooded by an outpour of original publications and patents. Almost every branch of materials science has benefited by their application-swface coating, printing, electrophotography, photoelectrochemistry, electronics and medicine to name a few.The present study was confined to the electrical and electrochemical properties of cobalt, nickel, zinc. iron and copper phthalocyanines. The use of soluble Pes as corrosion inhibitor for aluminium was also investigated.In the introductory section of the thesis, the work done so far on MPcs is reviewed. In this review emphasis is given to their general methods of synthesis and the physicochemical properties.In phthalocyanine chemistry one of the formidable tasks is the isolation of singular species. In the second chapter the methods of synthesis and purification are presented with necessary experimental details.The studies on plasma modified films of CoPe, FePc, ZnPc. NiPc and CuPc are also presented.Modification of electron transfer process by such films for reversible redox systems is taken as the criterion to establish enhanced electrocatalytic activity.Metal phthalocyanines are p- type semiconductors and the conductivity is enhanced by doping with iodine. The effect of doping on the activation energy of the conduction process is evaluated by measuring the temperature dependent variation of conductivity. Effect of thennal treatment on iodine doped CoPc is investigated by DSC,magnetic susceptibility, IR, ESR and electronic spectra. The elecnucatalytic activity of such doped materials was probed by cyclic voltammetry.The electron transfer mediation characteristics of MPc films depend on the film thickness. The influence of reducing the effective thickness of the MPc film by dispersing it into a conductive polymeric matrix was investigated. Tetrasulphonated cobalt phthalocyanine (CoTSP) was electrostatically immobilised into polyaniline and poly(o-toluidine) under varied conditions.The studies on corrosion inhibition of aluminium by CoTSP and CuTSP and By virtue of their anionic character they are soluble in water and are strongly adsorbed on aluminium. Hence they can act as corrosion inhibitors. CoTSP is also known to catalyze the reduction of dioxygen.This reaction can accelerate the anodic dissolution of metal as a complementary reaction. The influence of these conflicting properties of CoTSP on the corrosion of aluminium was studied and compared with those of CuTSP.In the course of these investigations a number of gadgets like cell for measuring the electrical conductivity of solids under non-isothermal conditions, low power rf oscillator and a rotating disc electrode were fabricated.

Studies on Energy Exchange and Upper Ocean Thermat Structure in Arabian Sea and Heat Transport in Northern Indian Ocean

The thesis focused Studies on Energy Exchange and Upper Ocean Thermal Structure in Arabian Sea and Heat Transport in Northern Indian Ocean. The present thesis is an attempt to understand the upper ocean thermal characteristics at selected areas in the western and eastern Arabian Sea in relation to surface energy exchange and dynamics, on a climatological basis. It is also aimed to examine, the relative importance of different processes in the evolution of SST at the western and eastern Arabian Sea. Short-term variations of energy exchange and upper ocean thermal structure are also investigated. Climatological studies of upper ocean thermal structure and surface energy exchange in the western and eastern parts of Arabian Sea bring out the similarities/differences and the causative factors for the observed features. Annual variation of zonally averaged heat advection in north Indian Ocean shows that maximum export of about 100 W/m2 occurs around 15ON during southwest monsoon season. This is due to large negative heat storage caused by intense upwelling in several parts of northern Indian Ocean. By and large, northern Indian Ocean is an area of heat export