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The increasing integration of distributed household photovoltaics (PVs) and electric vehicles (EVs) may further aggravate voltage violations and unbalance of low-voltage distribution networks (LVDNs). DC distribution networks can increase the accommodation of PVs and EVs and mitigate mutilple power quality problems by the flexible power regulation capability of voltage source converters. This paper proposes schemes to establish hybrid AC/DC LVDNs considering the conversion of the existing three-phase four-wire low-voltage AC systems to DC operation. The characteristics and DC conversion constraints of typical LVDNs are analyzed. In addition, converter configurations for typical LVDNs are proposed based on the three-phase four-wire characteristics and quantitative analysis of various DC configurations. Moreover, an optimal planning method of hybrid AC/DC LVDNs is proposed, which is modeled as a bi-level programming model considering the annual investments and three-phase unbalance. Simulations are conducted to verify the effectiveness of the proposed optimal planning method. Simulation results show that the proposed optimal planning method can increase the integration of PVs while simultaneously reducing issues related to voltage violation and unbalance.

The object of this study is underground gas storage facilities (UGSF) as part of the gas transmission system (GTS), in the process of joint operation of which a significant synergistic effect is achieved. The problem under consideration is to ensure the joint effective operation of the integrated UGSF and GTS complex as a single thermal-hydraulic one. A method of daily calculation of the maximum productivity of underground gas storage facilities has been devised. The optimization potential of UGSF operating modes has been studied. It is shown to range within 11‒20 %. The problems of planning the operation of UGSF have been stated and solved both under gas pumping modes and gas withdrawal modes. An algorithm for planning gas withdrawal modes at intervals of peak-free UGSF operation been developed. The achieved computational complexity of problem-solving algorithms is in the range of 2‒10 seconds. The problem of combining simultaneous operation of UGSF under an optimal mode for fuel gas and ensuring the necessary peak operation of UGSF at projected time intervals has also been considered. The joint UGSF performance was calculated at the projected time intervals according to the established criteria. At the same time, thermal-hydraulic coordination of UGSF operating modes with the operation of the GTS main gas pipeline system with which they are integrated was ensured. The problem was solved as a result of the implementation of a universal approach to the construction of functional models of complex systems – a single information support, the representation of the structure of the system in terms of graphs, the statement of proper mathematical problems, the development of methods for guaranteed convergence of systems with different mathematical representations of equations, the development of computational algorithms for combinatorial optimization of minimum complexity processes with discrete and irregular influences on their behavior

The work is devoted to the study of the technology of urban wastewater treatment, specially designed to remove the biogenic element phosphorus. An analytical review of the achievements and publications on the study of the process of wastewater dephosphotation by the reagent method is given. Based on the analysis carried out, the results of our own experimental studies are proposed and substantiated. The dependence of the effect of dephosphotation of wastewater on the dose of the reagent when using mineral coagulants, taking into account changes in environmental conditions, has been established. To optimize the cleaning process, mathematical modeling methods based on influencing and determining factors were used. Graphs and regression equation obtained, which determine the dependence of the residual concentration of wastewater phosphates on the dosing conditions of the aluminum polyoxychloride reagent.

The parametric uncertainties inherent in the models of renewable and sustainable energy systems (RSESs) make the associated decision-making processes of integrated resource operation, planning, and designing profoundly complex. Accordingly, intelligent energy management strategies are recognised as an effective intervention to efficiently accommodate the variability inherent in various input data and integrate distributed demand-side flexibility resources. To identify the key methodological and content gaps in the area of stochastic dispatch and planning optimisation of RSESs in the presence of responsive loads, this paper systematically reviews and synthetically analyses 252 relevant peer-reviewed academic articles. The review reveals that academic studies have utilised a wide variety of methods for the joint quantification of uncertainties and procurement of demand response services, while optimally designing and scheduling RSESs. However, to minimise simulation-to-reality gaps, research is needed into more integrated energy optimisation models that simultaneously characterise a broader spectrum of problem-inherent uncertainties and make behaviourally-founded use of flexible demand-side resources. More specifically, the review finds that while the research in this area is rich in thematic scope, it is commonly associated with strong simplifying assumptions that disconnect the corresponding approaches from reality, and thereby obscure the real challenges of transferring simulations into the real world. Accordingly, based on the descriptive analyses conducted and knowledge gaps identified, the paper provides useful insights into myriad possibilities for new research to more effectively utilise the potential of responsive loads, whilst simultaneously characterising the most salient problem-inherent parametric sources of uncertainty, during the investment planning and operational phases of RSESs.

Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.

Existing research on on-grid microgrid planning is carried out with a free trading assumption and without considering the limitation of annual electricity exchange. Therefore, the existing planning and sizing scheme may be not viable for the application of partially self-sufficient microgrid (PSSMG) with a limited amount of electricity exchange. To address this issue, a new planning method for PSSMG is proposed in this paper considering the limited annual electricity exchanging amount (AEEA). The sizing model and energy management are linearized and simultaneously integrated into one model, which could be solved in polynomial time. In order to effectively reduce the number of variables of a full year horizon and to cope with the uncertainty both of DGs and loads, a data-driven method based on K-means algorithm is utilized to choose a set of typical days that are representative of historical data of one full year. Finally, the validity and effectiveness of the proposed model are verified by comparative numerical simulations, and the sensitivity of limited AEEA to the planning scheme is analyzed.

As more and more renewable energy sources like wind and solar power are added to the electricgrid, reliable sources of power like hydropower become more important. Hydropower isabundant in Scandinavia, and helps to maintain a stable and reliable grid with added irregularitiesfrom wind and solar power, as well as more fluctuations in demand. Aside from the reliabilityaspect of hydropower, power producers want to maximize their profit from sold electricity. InSweden, power is bid to the spot market at Nord Pool every day, and a final spot price is decidedwithin the electricity market. There is a different electricity price each hour of the day, so it ismore profitable to generate power during some hours than others.There are many other factors that can change when it is most profitable for a hydropower plant tooperate, like how much local inflow of water there is. Hydropower production is an ideal case forusing optimisation models, and they are widely used throughout industry already. Though theoptimisation calculations are done by a computer, there is a lot of manual work from the spottraders that goes into specifying the inputs to the model, such as local inflow, price forecasts, andperhaps most importantly, market strategy. Due to the large amount of work that needs to be donefor each hydropower plant, many of the smaller power plants are not optimised at all, but are leftto run on an automatic control that typically tries to maintain a constant water level. In Fortum,this is called, VNR, or vattennivåreglering (water level regulation).The purpose of this thesis is to develop an optimisation algorithm for a small hydropower plant,using Fortum owned and operated Båthusströmmen as a test case. An optimisation model is builtin Fortum’s current modelling system and is tested for 2016. In addition, a mathematical model isalso built and tested using GAMS. It is found that by optimising the plant instead of running it onVNR, an increase of about 15-16% in profit could be, Tillförlitliga energikällor som vattenkraft blir allt viktigare vart eftersom elkraftsystemet utökasmed fler förnybara energikällor som vindkraft och solenergi. I Norden finns det rikligt medvattenkraft, vilket bidrar till att upprätthålla ett stabilt och pålitligt elnät även med ökadeoregelbundenheter från vindkraft och solkraft samt större variationer i efterfrågan. Bortsett frånvattenkraftens tillförlitlighetsaspekter vill kraftproducenter maximera sin vinst från såld el. ISverige läggs dagligen bud på effektvolym till spotmarknaden Nord Pool och ett slutgiltigtmarknadspris bestäms därefter av elmarknaden. Varje timme under dygnet motsvarar ett enskiltelpris, därmed är det mer lönsamt att generera effekt under de timmar där priset är som högst.Det finns många andra faktorer som påverkar när det är mest lönsamt för ett vattenkraftverk attproducera el, exempelvis hur stort det lokala inflödet av vatten är. Vattenkraftproduktion är idealtför tillämpning av optimeringsmodeller, vilka är vanligt förekommande inom verksamhetsområdet.Även om optimeringsberäkningarna utförs av en dator innebär optimeringen mycket manuelltarbete för Fortums elhandlare som specificerar indata till modellen. Exempel på indata är lokaltinflöde, prisprognoser och kanske viktigast av allt marknadsstrategi. På grund av den storamängden arbete som fordras för varje vattenkraftverk, optimeras inte produktionen för många avde småskaliga kraftverken utan de regleras automatiskt med mål att upprätthålla en konstantvattennivå. Denna typ av reglering kallas vattennivåreglering, VNR.Syftet med examensarbetet var att utveckla en optimeringsalgoritm för ett småskaligtvattenkraftverk, där Fortumägda vattenkraftverket Båthusströmmen används som testobjekt. Enoptimeringsmodell utvecklades i Fortums befintliga system och testades för 2016. Dessutom haren matematisk modell utvecklats och testades med GAMS. Det konstaterades att genom attoptimera produktionen från vattenkraftverket istället för att reglera den

In order to achieve the optimal efficiency in the operation of a power system, it is required to optimally schedule the power generating units, which is typically known as economic dispatch problem, a highly complex non-linear constrained optimization problem. There have been a lot of research efforts made to date in tackling this prevailing issue. Various numerical optimization techniques have been previously employed to solve this problem algorithmically reported in the literature, but the main problem from which most traditional algorithms suffer to provide good quality solutions is due to their stagnancy problem, i.e. trapping into the local optimum, while searching for optimal solutions. Considering the shortcomings of heuristic algorithms, in this thesis, an improved bees algorithm (IBA) is being proposed for solving dynamic economic dispatch (DED) problem of generating units considering prohibited operating zones for small- and large-scale power system, which can be seen as the first main practical contribution of this thesis. Additional techniques, such as constraint handling technique, adaptive patch size and limited search space, are also integrated into the traditional bees algorithm, thereby resulting in significant improvement in the overall performance of the proposed IBA technique. Here, DED problem is performed over the whole dispatch period (24 Hour) while satisfying the operational constraints such as prohibited operating zones, ramp rate limits, spinning reserve, system load demand and transmission losses. To validate the effectiveness and robustness of the proposed method in comparison with other traditional swarm-based optimization algorithms, simulations were performed to test the proposed method for DED problem considering six-unit, fifteen-unit, and forty-unit system, respectively. Numerical results obtained from the proposed algorithm are compared with the bees algorithm (BA), genetic algorithm (GA) and particle swarm optimization (PSO) affi

The paper presents a model for optimizes the resources used in the overhead distribution network planning, which allows the deployment of transformers considering coverage and capacity of them. It shows a routing model of the media voltage network based of minimum Steiner tree to find the best route in a geo-referenced area. The planning has been made over a geo-referenced scenery with data from OpenStreetMap platform, with the purpose of locations the transformers and the topology of the net area real. Results imply a starting point for electricity distribution companies to establish work plans for de expansion and planning of the electricity distribution network considering the variability at the demand present., Este trabajo presenta un modelo para optimizar los recursos utilizados en la planeación de una red de distribución aérea, la cual permite el despliegue de los transformadores considerando su cobertura y su capacidad. Muestra un modelo de enrutamiento de la red de media tensión basado en minimun Steiner tree para encontrar la mejor ruta en una zona georreferenciada. La planeación se realiza sobre un escenario georreferenciado con datos de la plataforma OpenStreetMap, con el fin de que la localización de los transformadores y la topología de la red sean reales. Los valores obtenidos muestran un punto de partida para que las empresas de distribución eléctrica establezcan planes de trabajo para la expansión y planificación de la red eléctrica de distribución, considerando la variabilidad a la que se presenta la demanda.

In order to achieve the optimal efficiency in the operation of a power system, it is required to optimally schedule the power generating units, which is typically known as economic dispatch problem, a highly complex non-linear constrained optimization problem. There have been a lot of research efforts made to date in tackling this prevailing issue. Various numerical optimization techniques have been previously employed to solve this problem algorithmically reported in the literature, but the main problem from which most traditional algorithms suffer to provide good quality solutions is due to their stagnancy problem, i.e. trapping into the local optimum, while searching for optimal solutions. Considering the shortcomings of heuristic algorithms, in this thesis, an improved bees algorithm (IBA) is being proposed for solving dynamic economic dispatch (DED) problem of generating units considering prohibited operating zones for small- and large-scale power system, which can be seen as the first main practical contribution of this thesis. Additional techniques, such as constraint handling technique, adaptive patch size and limited search space, are also integrated into the traditional bees algorithm, thereby resulting in significant improvement in the overall performance of the proposed IBA technique. Here, DED problem is performed over the whole dispatch period (24 Hour) while satisfying the operational constraints such as prohibited operating zones, ramp rate limits, spinning reserve, system load demand and transmission losses. To validate the effectiveness and robustness of the proposed method in comparison with other traditional swarm-based optimization algorithms, simulations were performed to test the proposed method for DED problem considering six-unit, fifteen-unit, and forty-unit system, respectively. Numerical results obtained from the proposed algorithm are compared with the bees algorithm (BA), genetic algorithm (GA) and particle swarm optimization (PSO) affi

In order to achieve the optimal efficiency in the operation of a power system, it is required to optimally schedule the power generating units, which is typically known as economic dispatch problem, a highly complex non-linear constrained optimization problem. There have been a lot of research efforts made to date in tackling this prevailing issue. Various numerical optimization techniques have been previously employed to solve this problem algorithmically reported in the literature, but the main problem from which most traditional algorithms suffer to provide good quality solutions is due to their stagnancy problem, i.e. trapping into the local optimum, while searching for optimal solutions. Considering the shortcomings of heuristic algorithms, in this thesis, an improved bees algorithm (IBA) is being proposed for solving dynamic economic dispatch (DED) problem of generating units considering prohibited operating zones for small- and large-scale power system, which can be seen as the first main practical contribution of this thesis. Additional techniques, such as constraint handling technique, adaptive patch size and limited search space, are also integrated into the traditional bees algorithm, thereby resulting in significant improvement in the overall performance of the proposed IBA technique. Here, DED problem is performed over the whole dispatch period (24 Hour) while satisfying the operational constraints such as prohibited operating zones, ramp rate limits, spinning reserve, system load demand and transmission losses. To validate the effectiveness and robustness of the proposed method in comparison with other traditional swarm-based optimization algorithms, simulations were performed to test the proposed method for DED problem considering six-unit, fifteen-unit, and forty-unit system, respectively. Numerical results obtained from the proposed algorithm are compared with the bees algorithm (BA), genetic algorithm (GA) and particle swarm optimization (PSO) affi

As more and more renewable energy sources like wind and solar power are added to the electricgrid, reliable sources of power like hydropower become more important. Hydropower isabundant in Scandinavia, and helps to maintain a stable and reliable grid with added irregularitiesfrom wind and solar power, as well as more fluctuations in demand. Aside from the reliabilityaspect of hydropower, power producers want to maximize their profit from sold electricity. InSweden, power is bid to the spot market at Nord Pool every day, and a final spot price is decidedwithin the electricity market. There is a different electricity price each hour of the day, so it ismore profitable to generate power during some hours than others.There are many other factors that can change when it is most profitable for a hydropower plant tooperate, like how much local inflow of water there is. Hydropower production is an ideal case forusing optimisation models, and they are widely used throughout industry already. Though theoptimisation calculations are done by a computer, there is a lot of manual work from the spottraders that goes into specifying the inputs to the model, such as local inflow, price forecasts, andperhaps most importantly, market strategy. Due to the large amount of work that needs to be donefor each hydropower plant, many of the smaller power plants are not optimised at all, but are leftto run on an automatic control that typically tries to maintain a constant water level. In Fortum,this is called, VNR, or vattennivåreglering (water level regulation).The purpose of this thesis is to develop an optimisation algorithm for a small hydropower plant,using Fortum owned and operated Båthusströmmen as a test case. An optimisation model is builtin Fortum’s current modelling system and is tested for 2016. In addition, a mathematical model isalso built and tested using GAMS. It is found that by optimising the plant instead of running it onVNR, an increase of about 15-16% in profit could be, Tillförlitliga energikällor som vattenkraft blir allt viktigare vart eftersom elkraftsystemet utökasmed fler förnybara energikällor som vindkraft och solenergi. I Norden finns det rikligt medvattenkraft, vilket bidrar till att upprätthålla ett stabilt och pålitligt elnät även med ökadeoregelbundenheter från vindkraft och solkraft samt större variationer i efterfrågan. Bortsett frånvattenkraftens tillförlitlighetsaspekter vill kraftproducenter maximera sin vinst från såld el. ISverige läggs dagligen bud på effektvolym till spotmarknaden Nord Pool och ett slutgiltigtmarknadspris bestäms därefter av elmarknaden. Varje timme under dygnet motsvarar ett enskiltelpris, därmed är det mer lönsamt att generera effekt under de timmar där priset är som högst.Det finns många andra faktorer som påverkar när det är mest lönsamt för ett vattenkraftverk attproducera el, exempelvis hur stort det lokala inflödet av vatten är. Vattenkraftproduktion är idealtför tillämpning av optimeringsmodeller, vilka är vanligt förekommande inom verksamhetsområdet.Även om optimeringsberäkningarna utförs av en dator innebär optimeringen mycket manuelltarbete för Fortums elhandlare som specificerar indata till modellen. Exempel på indata är lokaltinflöde, prisprognoser och kanske viktigast av allt marknadsstrategi. På grund av den storamängden arbete som fordras för varje vattenkraftverk, optimeras inte produktionen för många avde småskaliga kraftverken utan de regleras automatiskt med mål att upprätthålla en konstantvattennivå. Denna typ av reglering kallas vattennivåreglering, VNR.Syftet med examensarbetet var att utveckla en optimeringsalgoritm för ett småskaligtvattenkraftverk, där Fortumägda vattenkraftverket Båthusströmmen används som testobjekt. Enoptimeringsmodell utvecklades i Fortums befintliga system och testades för 2016. Dessutom haren matematisk modell utvecklats och testades med GAMS. Det konstaterades att genom attoptimera produktionen från vattenkraftverket istället för att reglera den

In order to achieve the optimal efficiency in the operation of a power system, it is required to optimally schedule the power generating units, which is typically known as economic dispatch problem, a highly complex non-linear constrained optimization problem. There have been a lot of research efforts made to date in tackling this prevailing issue. Various numerical optimization techniques have been previously employed to solve this problem algorithmically reported in the literature, but the main problem from which most traditional algorithms suffer to provide good quality solutions is due to their stagnancy problem, i.e. trapping into the local optimum, while searching for optimal solutions. Considering the shortcomings of heuristic algorithms, in this thesis, an improved bees algorithm (IBA) is being proposed for solving dynamic economic dispatch (DED) problem of generating units considering prohibited operating zones for small- and large-scale power system, which can be seen as the first main practical contribution of this thesis. Additional techniques, such as constraint handling technique, adaptive patch size and limited search space, are also integrated into the traditional bees algorithm, thereby resulting in significant improvement in the overall performance of the proposed IBA technique. Here, DED problem is performed over the whole dispatch period (24 Hour) while satisfying the operational constraints such as prohibited operating zones, ramp rate limits, spinning reserve, system load demand and transmission losses. To validate the effectiveness and robustness of the proposed method in comparison with other traditional swarm-based optimization algorithms, simulations were performed to test the proposed method for DED problem considering six-unit, fifteen-unit, and forty-unit system, respectively. Numerical results obtained from the proposed algorithm are compared with the bees algorithm (BA), genetic algorithm (GA) and particle swarm optimization (PSO) affi

The paper presents a model for optimizes the resources used in the overhead distribution network planning, which allows the deployment of transformers considering coverage and capacity of them. It shows a routing model of the media voltage network based of minimum Steiner tree to find the best route in a geo-referenced area. The planning has been made over a geo-referenced scenery with data from OpenStreetMap platform, with the purpose of locations the transformers and the topology of the net area real. Results imply a starting point for electricity distribution companies to establish work plans for de expansion and planning of the electricity distribution network considering the variability at the demand present., Este trabajo presenta un modelo para optimizar los recursos utilizados en la planeación de una red de distribución aérea, la cual permite el despliegue de los transformadores considerando su cobertura y su capacidad. Muestra un modelo de enrutamiento de la red de media tensión basado en minimun Steiner tree para encontrar la mejor ruta en una zona georreferenciada. La planeación se realiza sobre un escenario georreferenciado con datos de la plataforma OpenStreetMap, con el fin de que la localización de los transformadores y la topología de la red sean reales. Los valores obtenidos muestran un punto de partida para que las empresas de distribución eléctrica establezcan planes de trabajo para la expansión y planificación de la red eléctrica de distribución, considerando la variabilidad a la que se presenta la demanda.

In order to achieve the optimal efficiency in the operation of a power system, it is required to optimally schedule the power generating units, which is typically known as economic dispatch problem, a highly complex non-linear constrained optimization problem. There have been a lot of research efforts made to date in tackling this prevailing issue. Various numerical optimization techniques have been previously employed to solve this problem algorithmically reported in the literature, but the main problem from which most traditional algorithms suffer to provide good quality solutions is due to their stagnancy problem, i.e. trapping into the local optimum, while searching for optimal solutions. Considering the shortcomings of heuristic algorithms, in this thesis, an improved bees algorithm (IBA) is being proposed for solving dynamic economic dispatch (DED) problem of generating units considering prohibited operating zones for small- and large-scale power system, which can be seen as the first main practical contribution of this thesis. Additional techniques, such as constraint handling technique, adaptive patch size and limited search space, are also integrated into the traditional bees algorithm, thereby resulting in significant improvement in the overall performance of the proposed IBA technique. Here, DED problem is performed over the whole dispatch period (24 Hour) while satisfying the operational constraints such as prohibited operating zones, ramp rate limits, spinning reserve, system load demand and transmission losses. To validate the effectiveness and robustness of the proposed method in comparison with other traditional swarm-based optimization algorithms, simulations were performed to test the proposed method for DED problem considering six-unit, fifteen-unit, and forty-unit system, respectively. Numerical results obtained from the proposed algorithm are compared with the bees algorithm (BA), genetic algorithm (GA) and particle swarm optimization (PSO) affi

In this paper we propose a framework for task allocation in human-robot collaborative assembly planning. Our framework distinguishes between two main layers of abstraction and allocation. In the higher layer we use an abstract world model, incorporating a multi-agent human-robot team approach in order to describe the collaborative assembly planning problem. From this, nominal coordinated skill sequences for every agent are produced. In order to be able to treat humans and robots as agents of the same form, we move relevant differences/peculiarities into distinct cost functions. The layer beneath handles the concrete skill execution. On atomic level, skills are composed of complex hierarchical and concurrent hybrid state machines, which in turn coordinate the real-time behavior of the robot. Their careful design allows to cope with unpredictable events also on decisional level without having to explicitly plan for them, instead one may rely also on manually designed skills. Such events are likely to happen in dynamic and potentially partially known environments, which is especially true in case of human presence. © 2017 IEEE

Smart metering is used to control, monitor and know the system status in real time; to this effect, the incorporation of smart grids primarily benefits the electrical system; similarly, the reuse of infrastructure and cellular spectrum help mitigate the time and cost of its implementation. In order to reduce traffic and saturation of cellular networks, this paper aims at determining the optimal route for information transmission analyzing the optimal routing through distances and optimal routing through traffic flow. This analysis helps determine what the optimal route is, when there is no traffic on the wireless network, or when there is prolonged traffic, and what the traffic tendencies are, that may occur by excessive information transmission of smart meters to electric distribution companies, La medición inteligente se emplea para controlar, monitorear y conocer el estado del sistema en tiempo real; por ese motivo la incorporación de redes inteligentes beneficia primordialmente al sistema eléctrico; con la reutilización de la infraestructura y del espectro celular ayuda a mitigar el tiempo y el costo de su implementación. Con la finalidad de reducir el tráfico y la saturación de las redes celulares, se propone determinar las rutas factibles, un ruteo óptimo para el envío de la información, por medio de un análisis de enlaces óptimo por medio de distancia y también por flujo de tráfico. Gracias a este análisis se determina cual es la ruta óptima cuando no existe tráfico en la red celular, cuando existe un tráfico prolongado, y cuáles son las tendencias de tráfico que se pueden producir por el envío excesivo de la información de los medidores inteligentes hacia las empresas eléctricas de distribución.

Smart metering is used to control, monitor and know the system status in real time; to this effect, the incorporation of smart grids primarily benefits the electrical system; similarly, the reuse of infrastructure and cellular spectrum help mitigate the time and cost of its implementation. In order to reduce traffic and saturation of cellular networks, this paper aims at determining the optimal route for information transmission analyzing the optimal routing through distances and optimal routing through traffic flow. This analysis helps determine what the optimal route is, when there is no traffic on the wireless network, or when there is prolonged traffic, and what the traffic tendencies are, that may occur by excessive information transmission of smart meters to electric distribution companies, La medición inteligente se emplea para controlar, monitorear y conocer el estado del sistema en tiempo real; por ese motivo la incorporación de redes inteligentes beneficia primordialmente al sistema eléctrico; con la reutilización de la infraestructura y del espectro celular ayuda a mitigar el tiempo y el costo de su implementación. Con la finalidad de reducir el tráfico y la saturación de las redes celulares, se propone determinar las rutas factibles, un ruteo óptimo para el envío de la información, por medio de un análisis de enlaces óptimo por medio de distancia y también por flujo de tráfico. Gracias a este análisis se determina cual es la ruta óptima cuando no existe tráfico en la red celular, cuando existe un tráfico prolongado, y cuáles son las tendencias de tráfico que se pueden producir por el envío excesivo de la información de los medidores inteligentes hacia las empresas eléctricas de distribución.

Many real world domains present a non-deterministic behaviour, mostly due to unpredictable environmental conditions. In this context, strong planning, i.e., finding a plan which is guaranteed to achieve the goal regardless of non-determinism, is a significant research challenge for both the planning and the control communities. In particular, the problem of cost-optimal strong planning has not been addressed so far. In this paper we provide a formal description of the cost-optimal strong planning problem in non-deterministic finite state systems, present an algorithm to solve it with good complexity bounds and formally prove the correctness and completeness of the proposed algorithm. Furthermore, we present experimental results showing the effectiveness of the proposed approach on a meaningful case study.

Many real world domains present a non-deterministic behaviour, mostly due to unpredictable environmental conditions. In this context, strong planning, i.e., finding a plan which is guaranteed to achieve the goal regardless of non-determinism, is a significant research challenge for both the planning and the control communities. In particular, the problem of cost-optimal strong planning has not been addressed so far. In this paper we provide a formal description of the cost-optimal strong planning problem in non-deterministic finite state systems, present an algorithm to solve it with good complexity bounds and formally prove the correctness and completeness of the proposed algorithm. Furthermore, we present experimental results showing the effectiveness of the proposed approach on a meaningful case study.

This article introduces the concept of gap-singularities to underline particular phenomena related to the choice of the outputs in some non-linear systems. It is shown that, when bounds on the variables are considered, this property induces a topological isolation of the gap-singular trajectories in the outputs domain. In the context of differentially flat systems, a procedure to detect gap-singularities is provided because it is related to the existence of a singularity of flatness. The examples that are presented in this article show that taking gap-singularities into account can improve some trajectory planning algorithms based on an optimal control approach like in the Nonlinear Trajectory Generation software.

The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities of problem specific knowledge, the most widely adopted approach to automated planning is search. The topic of this thesis is the development of methods for achieving effective search control for domain-independent optimal planning through the construction of admissible heuristics. The particular planning problem considered is the so called “classical” AI planning problem, which makes several restricting assumptions. Optimality with respect to two measures of plan cost are considered: in planning with additive cost, the cost of a plan is the sum of the costs of the actions that make up the plan, which are assumed independent, while in planning with time, the cost of a plan is the total execution time – makespan – of the plan. The makespan optimization objective can not, in general, be formulated as a sum of independent action costs and therefore necessitates a problem model slightly different from the classical one. A further small extension to the classical model is made with the introduction of two forms of capacitated resources. Heuristics are developed mainly for regression planning, but based on principles general enough that heuristics for other planning search spaces can be derived on the same basis. The thesis describes a collection of methods, including the hm, additive hm and improved pattern database heuristics, and the relaxed search and boosting techniques for improving heuristics through limited search, and presents two extended experimental analyses of the developed methods, one comparing heuristics for planning with additive cost and the other concerning the relaxed search technique in the context of planning with time, aimed at discovering the characteristics of problem domains that determine the relative effectiveness of the compared methods. Results indi

The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities of problem specific knowledge, the most widely adopted approach to automated planning is search. The topic of this thesis is the development of methods for achieving effective search control for domain-independent optimal planning through the construction of admissible heuristics. The particular planning problem considered is the so called “classical” AI planning problem, which makes several restricting assumptions. Optimality with respect to two measures of plan cost are considered: in planning with additive cost, the cost of a plan is the sum of the costs of the actions that make up the plan, which are assumed independent, while in planning with time, the cost of a plan is the total execution time – makespan – of the plan. The makespan optimization objective can not, in general, be formulated as a sum of independent action costs and therefore necessitates a problem model slightly different from the classical one. A further small extension to the classical model is made with the introduction of two forms of capacitated resources. Heuristics are developed mainly for regression planning, but based on principles general enough that heuristics for other planning search spaces can be derived on the same basis. The thesis describes a collection of methods, including the hm, additive hm and improved pattern database heuristics, and the relaxed search and boosting techniques for improving heuristics through limited search, and presents two extended experimental analyses of the developed methods, one comparing heuristics for planning with additive cost and the other concerning the relaxed search technique in the context of planning with time, aimed at discovering the characteristics of problem domains that determine the relative effectiveness of the compared methods. Results indi

The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities of problem specific knowledge, the most widely adopted approach to automated planning is search. The topic of this thesis is the development of methods for achieving effective search control for domain-independent optimal planning through the construction of admissible heuristics. The particular planning problem considered is the so called “classical” AI planning problem, which makes several restricting assumptions. Optimality with respect to two measures of plan cost are considered: in planning with additive cost, the cost of a plan is the sum of the costs of the actions that make up the plan, which are assumed independent, while in planning with time, the cost of a plan is the total execution time – makespan – of the plan. The makespan optimization objective can not, in general, be formulated as a sum of independent action costs and therefore necessitates a problem model slightly different from the classical one. A further small extension to the classical model is made with the introduction of two forms of capacitated resources. Heuristics are developed mainly for regression planning, but based on principles general enough that heuristics for other planning search spaces can be derived on the same basis. The thesis describes a collection of methods, including the hm, additive hm and improved pattern database heuristics, and the relaxed search and boosting techniques for improving heuristics through limited search, and presents two extended experimental analyses of the developed methods, one comparing heuristics for planning with additive cost and the other concerning the relaxed search technique in the context of planning with time, aimed at discovering the characteristics of problem domains that determine the relative effectiveness of the compared methods. Results indi

The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities of problem specific knowledge, the most widely adopted approach to automated planning is search. The topic of this thesis is the development of methods for achieving effective search control for domain-independent optimal planning through the construction of admissible heuristics. The particular planning problem considered is the so called “classical” AI planning problem, which makes several restricting assumptions. Optimality with respect to two measures of plan cost are considered: in planning with additive cost, the cost of a plan is the sum of the costs of the actions that make up the plan, which are assumed independent, while in planning with time, the cost of a plan is the total execution time – makespan – of the plan. The makespan optimization objective can not, in general, be formulated as a sum of independent action costs and therefore necessitates a problem model slightly different from the classical one. A further small extension to the classical model is made with the introduction of two forms of capacitated resources. Heuristics are developed mainly for regression planning, but based on principles general enough that heuristics for other planning search spaces can be derived on the same basis. The thesis describes a collection of methods, including the hm, additive hm and improved pattern database heuristics, and the relaxed search and boosting techniques for improving heuristics through limited search, and presents two extended experimental analyses of the developed methods, one comparing heuristics for planning with additive cost and the other concerning the relaxed search technique in the context of planning with time, aimed at discovering the characteristics of problem domains that determine the relative effectiveness of the compared methods. Results indi