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 .

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.

Distribution network short term scheduling in Smart Grid context

The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and “green” operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a Mixed Integer Non-Linear Programming (MINLP) approach.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Distributed energy resources scheduling considering real-time resources forecast

Energy resource scheduling is becoming increasingly important, such as the use of more distributed generators and electric vehicles connected to the distribution network. This paper proposes a methodology to be used by Virtual Power Players (VPPs), regarding the energy resource scheduling in smart grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used.

Energy Resource Management under the Influence of the Weekend Transition Considering an Intensive use of Electric Vehicles

Energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and of massive electric vehicle is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with their owners. It takes into account these contracts, the users' requirements subjected to the VPP, and several discharge price steps. The full AC power flow calculation included in the model takes into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33-bus distribution network and V2G is used to illustrate the good performance of the proposed method.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Scalable and Efficient Configuration of TimeDivision Multiplexed Resources

Consumer-electronics systems are becoming increasingly complex as the number of integrated applications is growing. Some of these applications have real-time requirements, while other non-real-time applications only require good average performance. For cost-efficient design, contemporary platforms feature an increasing number of cores that share resources, such as memories and interconnects. However, resource sharing causes contention that must be resolved by a resource arbiter, such as Time-Division Multiplexing. A key challenge is to configure this arbiter to satisfy the bandwidth and latency requirements of the real-time applications, while maximizing the slack capacity to improve performance of their non-real-time counterparts. As this configuration problem is NP-hard, a sophisticated automated configuration method is required to avoid negatively impacting design time. The main contributions of this article are: 1) An optimal approach that takes an existing integer linear programming (ILP) model addressing the problem and wraps it in a branch-and-price framework to improve scalability. 2) A faster heuristic algorithm that typically provides near-optimal solutions. 3) An experimental evaluation that quantitatively compares the branch-and-price approach to the previously formulated ILP model and the proposed heuristic. 4) A case study of an HD video and graphics processing system that demonstrates the practical applicability of the approach.

Estudio de distribución y frecuencia de caries en preescolares de alto riesgo social

Objetivos: El presente trabajo establece en el escenario local, un patrón de distribución y severidad de caries en una población de riesgo social de niños preescolares, datos necesarios para la programación de recursos en programas de salud bucal Método: estudio descriptivo, correlacional y transversal sobre muestra intencionada de 150 niños preescolares de 4 y 5 años de edad, de ambos sexos, con actividad de caries, pertenecientes a escuelas públicas, del ámbito urbano-marginal-Distrito Pedro Molina-Mendoza. Rep. Argentina, cuyos padres hubieron firmado el consentimiento informado. Se registraron las lesiones de caries según diente y sitio, y la severidad según ICDAS II (Pitts, 2004). Se establecieron distribución de frecuencias para las variables diente, sitio y categoría de ICDAS II. Para establecer asociaciones entre variables se aplicó prueba de chi cuadrado, con un nivel de significación de 0.05. Resultados: 1º y 2º molares temporarios son los más afectados, principalmente 75 y 85. El valor 6 de ICDAS II es el más frecuente, seguido por valores 3 y 5. El 84 presenta mayor frecuencia de valores grado 6. La superficie oclusal es la más afectada (42.6%). Existe asociación entre diente y categoría de ICDAS II y entre diente y sitio de la lesión para 55, 52, 51, 61, 62, 63 y 64, 84 y 75. Conclusiones: la distribución y severidad de caries en denticióntemporaria de los niños estudiados evidencia una alta frecuencia de lesiones de caries en molares, y una necesidad de tratamiento complejo involucrando para su resolución niveles de atención sanitaria II y III que deberá encontrar como contraparte un sistema sanitario preparado para su resolución.

Extensiones a los métodos de planificación de sistemas de tiempo real críticos basados en prioridades

Los sistemas de tiempo real tienen un papel cada vez más importante en nuestra sociedad. Constituyen un componente fundamental de los sistemas de control, que a su vez forman parte de diversos sistemas de ingeniería básicos en actividades industriales, militares, de comunicaciones, espaciales y médicas. La planificación de recursos es un problema fundamental en la realización de sistemas de tiempo real. Su objetivo es asignar los recursos disponibles a las tareas de forma que éstas cumplan sus restricciones temporales. Durante bastante tiempo, el estado de la técnica en relación con los métodos de planificación ha sido rudimentario. En la actualidad, los métodos de planificación basados en prioridades han alcanzado un nivel de madurez suficiente para su aplicación en entornos industriales. Sin embargo, hay cuestiones abiertas que pueden dificultar su utilización. El objetivo principal de esta tesis es estudiar los métodos de planificación basados en prioridades, detectar las cuestiones abiertas y desarrollar protocolos, directrices y esquemas de realización práctica que faciliten su empleo en sistemas industriales. Una cuestión abierta es la carencia de esquemas de realización de algunos protocolos con núcleos normalizados. El resultado ha sido el desarrollo de esquemas de realización de tareas periódicas y esporádicas de tiempo real, con detección de fallos de temporización, comunicación entre tareas, cambio de modo de ejecución del sistema y tratamiento de fallos mediante grupos de recuperación. Los esquemas se han codificado en Ada 9X y se proporcionan directrices para analizar la planificabilidad de un sistema desarrollado con esta base. Un resultado adicional ha sido la identificación de la funcionalidad mínima necesaria para desarrollar sistemas de tiempo real con las características enumeradas. La capacidad de adaptación a los cambios del entorno es una característica deseable de los sistemas de tiempo real. Si estos cambios no estaban previstos en la fase de diseño o si hay módulos erróneos, es necesario modificar o incluir algunas tareas. La actualización del sistema se suele realizar estáticamente y su instalación se lleva a cabo después de parar su ejecución. Sin embargo, hay sistemas cuyo funcionamiento no se puede detener sin producir daños materiales o económicos. Una alternativa es diseñar el sistema como un conjunto de unidades que se pueden reemplazar, sin interferir con la ejecución de otras unidades. Para tal fin, se ha desarrollado un protocolo de reemplazamiento dinámico para sistemas de tiempo real crítico y se ha comprobado su compatibilidad con los métodos de planificación basados en prioridades. Finalmente se ha desarrollado un esquema de realización práctica del protocolo.---ABSTRACT---Real-time systems are very important now a days. They have become a relevant issue in the design of control systems, which are a basic component of several engineering systems in industrial, telecommunications, military, spatial and medical applications. Resource scheduling is a central issue in the development of real-time systems. Its purpose is to assign the available resources to the tasks, in such a way that their deadlines are met. Historically, hand-crafted techniques were used to develop real-time systems. Recently, the priority-based scheduling methods have reached a sufficient maturity level to be feasible its extensive use in industrial applications. However, there are some open questions that may decrease its potential usefulness. The main goal of this thesis is to study the priority-based scheduling methods, to identify the remaining open questions and to develop protocols, implementation templates and guidelines that will make more feasible its use in industrial applications. One open question is the lack of implementation schemes, based on commercial realtime kernels, of some of the protocols. POSIX and Ada 9X has served to identify the services usually available. A set of implementation templates for periodic and sporadic tasks have been developed with provisión for timing failure detection, intertask coraraunication, change of the execution mode and failure handling based on recovery groups. Those templates have been coded in Ada 9X. A set of guidelines for checking the schedulability of a system based on them are also provided. An additional result of this work is the identification of the minimal functionality required to develop real-time systems based on priority scheduling methods, with the above characteristics. A desirable feature of real-time systems is their capacity to adapt to changes in the environment, that cannot be entirely predicted during the design, or to misbehaving software modules. The traditional maintenance techniques are performed by stopping the whole system, installing the new application and finally resuming the system execution. However this approach cannot be applied to non-stop systems. An alternative is to design the system as a set of software units that can be dynamically replaced within its operative environment. With this goal in mind, a dynamic replacement protocol for hard real-time systems has been defined. Its compatibility with priority-based scheduling methods has been proved. Finally, a execution témplate of the protocol has been implemented.

The generalized multiprocessor periodic resource interface model for hierarchical multiprocessor scheduling

Composition is a practice of key importance in software engineering. When real-time applications are composed it is necessary that their timing properties (such as meeting the deadlines) are guaranteed. The composition is performed by establishing an interface between the application and the physical platform. Such an interface does typically contain information about the amount of computing capacity needed by the application. In multiprocessor platforms, the interface should also present information about the degree of parallelism. Recently there have been quite a few interface proposals. However, they are either too complex to be handled or too pessimistic.In this paper we propose the Generalized Multiprocessor Periodic Resource model (GMPR) that is strictly superior to the MPR model without requiring a too detailed description. We describe a method to generate the interface from the application specification. All these methods have been implemented in Matlab routines that are publicly available.