Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

Energy resource scheduling considering reactive power management

The reactive power management is an important task in future power systems. The control of reactive power allows the increase of distributed energy resources penetration as well as the optimal operation of distribution networks. Currently, the control of reactive power is only controlled in large power units and in high and very high voltage substations. In this paper a reactive power control in smart grids paradigm is proposed, considering the management of distributed energy resources and of the distribution network by an aggregator namely Virtual Power Player (VPP).

An efficient method for optimal location and sizing of fixed and switched shunt capacitors in large distribution systems

This paper proposes a computationally efficient methodology for the optimal location and sizing of static and switched shunt capacitors in large distribution systems. The problem is formulated as the maximization of the savings produced by the reduction in energy losses and the avoided costs due to investment deferral in the expansion of the network. The proposed method selects the nodes to be compensated, as well as the optimal capacitor ratings and their operational characteristics, i.e. fixed or switched. After an appropriate linearization, the optimization problem was formulated as a large-scale mixed-integer linear problem, suitable for being solved by means of a widespread commercial package. Results of the proposed optimizing method are compared with another recent methodology reported in the literature using two test cases: a 15-bus and a 33-bus distribution network. For the both cases tested, the proposed methodology delivers better solutions indicated by higher loss savings, which are achieved with lower amounts of capacitive compensation. The proposed method has also been applied for compensating to an actual large distribution network served by AES-Venezuela in the metropolitan area of Caracas. A convergence time of about 4 seconds after 22298 iterations demonstrates the ability of the proposed methodology for efficiently handling large-scale compensation problems.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Mitochondria are the main source and one of the targets of Pb (lead)-induced oxidative stress in the yeast Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae is a useful model organism for studying lead (Pb) toxicity. Yeast cells of a laboratory S. cerevisiae strain (WT strain) were incubated with Pb concentrations up to 1,000 μmol/l for 3 h. Cells exposed to Pb lost proliferation capacity without damage to the cell membrane, and they accumulated intracellular superoxide anion (O2 .−) and hydrogen peroxide (H2O2). The involvement of the mitochondrial electron transport chain (ETC) in the generation of reactive oxygen species (ROS) induced by Pb was evaluated. For this purpose, an isogenic derivative ρ0 strain, lacking mitochondrial DNA, was used. The ρ0 strain, without respiratory competence, displayed a lower intracellular ROS accumulation and a higher resistance to Pb compared to the WT strain. The kinetic study of ROS generation in yeast cells exposed to Pb showed that the production of O2 .− precedes the accumulation of H2O2, which is compatible with the leakage of electrons from the mitochondrial ETC. Yeast cells exposed to Pb displayed mutations at the mitochondrial DNA level. This is most likely a consequence of oxidative stress. In conclusion, mitochondria are an important source of Pb-induced ROS and, simultaneously, one of the targets of its toxicity.

Nanoparticles based Permeable Reactive Barriers as an Eco-efficient Technology for nitrate remediation in soil and groundwater

The need to increase agricultural yield led, among others, to an increase in the consumption of nitrogen based fertilizers. As a consequence, there are excessive concentrations of nitrates, the most abundant of the reactive nitrogen (Nr) species, in several areas of the world. The demographic changes and projected population growth for the next decades, and the economic shifts which are already shaping the near future are powerful drivers for a further intensification in the use of fertilizers, with a predicted increase of the nitrogen loads in soils. Nitrate easily diffuses in the subsurface environments, portraying high mobility in soils. Moreover, the presence of high nitrate loads in water has the potential to cause an array of health dysfunctions, such as methemoglobinemia and several cancers. Permeable Reactive Barriers (PRB) placed strategically relatively to the nitrate source constitute an effective technology to tackle nitrate pollution. Ergo, PRB avoid various adverse impacts resulting from the displacement of reactive nitrogen downstream along water bodies. A four stages literature review was carried out in 34 databases. Initially, a set of pertinent key words were identified to perform the initial databases searches. Then, the synonyms of those initial key words were used to carry out a second set of databases searches. The third stage comprised the identification of other additional relevant terms from the research papers identified in the previous two stages. Again, databases searches were performed with this third set of key words. The final step consisted of the identification of relevant papers from the bibliography of the relevant papers identified in the previous three stages of the literature review process. The set of papers identified as relevant for in-depth analysis were assessed considering a set of relevant characterization variables.

A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context

In the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present the best compromise solution. The proposed methodology has been tested in the 33-bus distribution network. The case study shows the results of three different scenarios for the economic, technical, and multi-objective perspectives, and the results demonstrated the importance of incorporating the reactive scheduling in the distribution network using the multi-objective perspective to obtain the best compromise solution for the economic and technical perspectives.

Monetary and fiscal policies interactions in a monetary union with country-size asymmetry

Within a country-size asymmetric monetary union, idiosyncratic shocks and national fiscal stabilization policies cause asymmetric cross-border effects. These effects are a source of strategic interactions between noncoordinated fiscal and monetary policies: on the one hand, due to larger externalities imposed on the union, large countries face less incentives to develop free-riding fiscal policies; on the other hand, a larger strategic position vis-à-vis the central bank incentives the use of fiscal policy to, deliberately, influence monetary policy. Additionally, the existence of non-distortionary government financing may also shape policy interactions. As a result, optimal policy regimes may diverge not only across the union members, but also between the latter and the monetary union. In a two-country micro-founded New-Keynesian model for a monetary union, we consider two fiscal policy scenarios: (i) lump-sum taxes are raised to fully finance the government budget and (ii) lump-sum taxes do not ensure balanced budgets in each period; therefore, fiscal and monetary policies are expected to impinge on debt sustainability. For several degrees of country-size asymmetry, we compute optimal discretionary and dynamic non-cooperative policy games and compare their stabilization performance using a union-wide welfare measure. We also assess whether these outcomes could be improved, for the monetary union, through institutional policy arrangements. We find that, in the presence of government indebtedness, monetary policy optimally deviates from macroeconomic to debt stabilization. We also find that policy cooperation is always welfare increasing for the monetary union as a whole; however, indebted large countries may strongly oppose to this arrangement in favour of fiscal leadership. In this case, delegation of monetary policy to a conservative central bank proves to be fruitful to improve the union’s welfare.

A disfunção redox espinhal na dor neuropática

As espécies reativas de oxigénio (ROS) estão envolvidas no desenvolvimento de dor neuropática. No entanto, a aplicação clínica de moléculas antioxidantes no tratamento desta patologia tem demonstrado pouca eficácia. A inibição da NADPH oxidase (NOX), uma das principais fontes de ROS, poderá ser uma boa estratégia terapêutica. O nosso grupo verificou que a apocinina (inibidor da NOX) melhora parcialmente os sintomas de dor neuropática e a disfunção redox espinhal no modelo SNI (spared nerve injury). De forma a melhorar este efeito terapêutico, o presente estudo insere-se num projeto maior, que visa identificar as isoformas da NOX envolvidas na fisiopatologia da doença e avaliar o efeito da administração de inibidores específicos para essas isoformas. Assim, propusemo-nos a avaliar a disfunção redox espinhal em fases precoces dador neuropática periférica induzida pelo modelo SNI no Rato, relacionando-a com os comportamentos de dor demonstrados pelos animais. Foram constituídos três grupos experimentais: SNI, sham e naïve, com subgrupos testados e sacrificados aos dias 1, 3, 7 e 14 após a cirurgia. Avaliou-se a sensibilidade mecânica (vonFrey e pinprick) e ao frio (acetona) dos animais, sacrificaram-se e recolheram-se as medulas espinhais para análise imunohistoquímica, com marcadores de dano oxidativo no DNA e de dano nitrosativo. Ao contrário dos animais sham, que demonstraram um comportamento muito próximo dos naïve, os animais SNI desenvolveram alodínia mecânica e ao frio e hiperalgesia mecânica na pata ipsilateral. No entanto, o dano oxidativo no corno dorsal ipsilateral da medula espinhal apresentou-se idêntico nos grupos SNI e sham ao longo dos 14 dias de estudo, não havendo também diferenças entre os cornos ipsi e contralateral à lesão nervosa. É possível que o desenvolvimento de dor neuropática nos animais SNI não se faça acompanhar de disfunção redox espinhal, pelo menos até aos 14 dias pós indução. O facto de a lesão nervosa no modelo SNI se localizar numa porção distal do ciático, ao contrário de outros modelos em que o stresse oxidativo espinhal foi já descrito, poderia explicar essas diferenças. Em todo o caso, considerando que os resultados comportamentais obtidos indicam que as cirurgias SNI e sham causam diferentes níveis de sensibilização nos animais, parece-nos fulcral prolongar os tempos de neuropatia, e executar uma avaliação do estado redox com outros marcadores, de forma a elucidar se, de facto, existem ROS envolvidas nesta sensibilização e, em caso positivo, poder identificar essas espécies, bem como as suas fontes.

An heuristic approach for optimal location of gas supply units in transportation system

The best places to locate the Gas Supply Units (GSUs) on a natural gas systems and their optimal allocation to loads are the key factors to organize an efficient upstream gas infrastructure. The number of GSUs and their optimal location in a gas network is a decision problem that can be formulated as a linear programming problem. Our emphasis is on the formulation and use of a suitable location model, reflecting real-world operations and constraints of a natural gas system. This paper presents a heuristic model, based on lagrangean approach, developed for finding the optimal GSUs location on a natural gas network, minimizing expenses and maximizing throughput and security of supply.The location model is applied to the Iberian high pressure natural gas network, a system modelised with 65 demand nodes. These nodes are linked by physical and virtual pipelines – road trucks with gas in liquefied form. The location model result shows the best places to locate, with the optimal demand allocation and the most economical gas transport mode: by pipeline or by road truck.

Optimal location of gas supply units in natural gas system network

Natural gas industry has been confronted with big challenges: great growth in demand, investments on new GSUs – gas supply units, and efficient technical system management. The right number of GSUs, their best location on networks and the optimal allocation to loads is a decision problem that can be formulated as a combinatorial programming problem, with the objective of minimizing system expenses. Our emphasis is on the formulation, interpretation and development of a solution algorithm that will analyze the trade-off between infrastructure investment expenditure and operating system costs. The location model was applied to a 12 node natural gas network, and its effectiveness was tested in five different operating scenarios.

Natural gas system operation: lagrangean optimization techniques

To comply with natural gas demand growth patterns and Europe´s import dependency, the gas industry needs to organize an efficient upstream infrastructure. The best location of Gas Supply Units – GSUs and the alternative transportation mode – by phisical or virtual pipelines, are the key of a successful industry. In this work we study the optimal location of GSUs, as well as determining the most efficient allocation from gas loads to sources, selecting the best transportation mode, observing specific technical restrictions and minimizing system total costs. For the location of GSUs on system we use the P-median problem, for assigning gas demands nodes to source facilities we use the classical transportation problem. The developed model is an optimisation-based approach, based on a Lagrangean heuristic, using Lagrangean relaxation for P-median problems – Simple Lagrangean Heuristic. The solution of this heuristic can be improved by adding a local search procedure - the Lagrangean Reallocation Heuristic. These two heuristics, Simple Lagrangean and Lagrangean Reallocation, were tested on a realistic network - the primary Iberian natural gas network, organized with 65 nodes, connected by physical and virtual pipelines. Computational results are presented for both approaches, showing the location gas sources and allocation loads arrangement, system total costs and gas transportation mode.

Mergers and acquisitions as strategies of business expansion in the pharmaceutical industry

Introduction: The present paper deals with the issue of the increasing usage of corporation mergers and acquisitions strategies within pharmaceutical industry environment. The aim is to identify the triggers of such business phenomenon and the immediate impact on the financial outcome of two powerful biopharmaceutical corporations: Pfizer and GlaxoSmithKline, which have been sampled due to their successful approach of the tactics in question. Materials and Methods: In order to create an overview of the development steps through mergers and acquisitions, the historical data of the two corporations has been consulted, from their official websites. The most relevant events were then associated with adequate information from the financial reports and statements of the two corporations indulged by web-based financial data providers. Results and Discussions: In the past few decades Pfizer and GlaxoSmithKline have purchased or merged with various companies in order to monopolize new markets, diversify products and services portfolios, survive and surpass competitors. The consequences proved to be positive although this approach implies certain capital availability. Conclusions: Results reveal the fact that, as far as the two sampled companies are concerned, acquisitions and mergers are reactions at the pressure of the highly competitive environment. Moreover, the continuous diversification of the market’s needs is also a consistent motive. However, the prevalence and the eminence of mergers and acquisition strategies are conditioned by the tender offer, the announcer’s caliber, research and development status and further other factors determined by the internal and external actors of the market.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

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.