Real-time scheduling for industrial load management

Vedova, Marco L. Della; Facchinetti, Tullio

doi:10.1109/energycon.2012.6348243

Cited by 14 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subramanian et al [13] modeled deferrable loads as tasks with attributes such as arrival time, departure time, and energy requirements, and proposed algorithms for allocating resources to these tasks based on their energy needs and/or deadlines. A similar approach was proposed in [14] for industrial loads. O'Brien and Rajagopal [15] used greedy algorithm to shift deferrable loads in such a way that the overall load profile matches a specified target profile.…”

Section: Demand Response For Industrial Plantsmentioning

confidence: 81%

See 1 more Smart Citation

Maintenance-centric energy management of industrial plants assisted by demand response

Mohagheghi

Raji

2014

2014 IEEE Industry Application Society Annual Meeting

View full text Add to dashboard Cite

Asset management is an integral part of every industrial plant. Here, the goal is to improve the efficiency of the system while reducing the cost of maintenance and risk of failure. Traditionally, asset management has been performed as time-based maintenance or corrective maintenance; however, in order to reduce the consequences of component failures on the operation of the overall system many plant managers have moved towards more advanced condition-based or reliabilitycentric maintenance techniques. Here, the asset manager would adopt a more proactive maintenance approach based on the condition of the component and its importance for the overall production plan. Although shutting down a process for maintenance purposes has long-term benefits, it may accrue costs associated with the lost revenue, inventory buildup or wasted labor. A solution is put forth in this paper that counteracts financial losses due to preventive maintenance with direct benefits gained from demand response (DR). Within this context, maintenance scheduling is coordinated with DR, which is used as a tool at the asset manger's disposal in order to offset these financial losses. A case study is presented for a sample industrial plant in order to further explain the design concepts.Index Terms-Asset management, demand response, energy management, industrial plant demand scheduling, maintenance scheduling, preventive maintenance.

show abstract

Section: Demand Response For Industrial Plantsmentioning

confidence: 81%

“…Optimization problem (13) has been solved subject to constraints (3)-(7), (10)- (12), (14), (15) in order to determine the workstation statuses that can be nominated for DR in response to I&C DR command from the utility. It has been assumed here that a DR command of 300kW for three hours has been sent to the plant.…”

Section: ) Iandc Demand Responsementioning

confidence: 99%

Maintenance-centric energy management of industrial plants assisted by demand response

Mohagheghi

Raji

2014

2014 IEEE Industry Application Society Annual Meeting

View full text Add to dashboard Cite

show abstract

“…In this area, Della Vedova and Facchinetti [17] developed a real-time scheduling approach to model and control the electrical availability. The aim was to reduce the presence of peaks of power consumption, which are negative for energy providers, for the grid and for the users, due to the fact that the presence of peaks reduces the grid efficiency (since the power grid must be designed for the peaks) and also because energy prices are based in peaks.…”

Section: State Of the Artmentioning

confidence: 99%

Improved time representation model for the simultaneous energy supply and demand management in microgrids

Silvente

Aguirre

Zamarripa

et al. 2015

Energy

View full text Add to dashboard Cite

This paper analyses the operational decision making procedures required to address the simultaneous management of energy supplies and requests in a microgrid scenario, in order to best accommodate arbitrary energy availability profiles resulting from an intensive use of renewable energy sources, and to extensively exploit the eventual flexibility of the energy requirements to be fulfilled. The optimization of the resulting short term scheduling problem in deterministic scenarios is addressed through a Mixed-Integer Linear Programming (MILP) mathematical model, which includes a new hybrid time formulation developed to take profit of the advantages of the procedures based on discrete time representations, while maintaining the ability to identify solutions requiring a continuous time representation, which might be qualitatively different to the ones constrained to consider a fixed time grid for decision-making. The performance of this new time representation has been studied, taking into account the granularity of the model and analyzing the associated trade-offs in front of other alternatives. The promising results obtained with this new formulation encourage further research regarding the development of decision-making tools for the enhanced operation of microgrids. Storage energy cost [m.u./kWh] Span of the time interval [h], Duration of consumption [h], Minimum power supply of source at interval [kW], Maximum power supply of source at interval [kW], Energy price to be sold to power grid at interval [m.u./kWh] , Minimum electricity storage of system at interval [kWh] , Maximum electricity storage of system at interval [kWh] , Initial storage level of system at interval [kWh] , , Period of time in which consumption is active at interval [h], Maximum initial time of consumption [h], Target initial time of consumption [h]  Binary variable indicating if consumption is active at interval t

show abstract

“…The energy demand side management of industrial process represents an emerging challenge. In this area, Della Vedova and Facchinetti (2012) developed a realtime scheduling approach to model and control the electrical availability. The aim was to reduce the presence of peaks of power consumption, which are negative for energy providers, for the grid and for the users, due to the fact that the presence of peaks reduces the grid efficiency and also because energy prices are based in peaks.…”

Section: Management Of Production and Demandmentioning

confidence: 99%

Improving the tactical and operational decision making procedures in chemical supply chains

Silvente Saiz

View full text Add to dashboard Cite

Nowadays, market trends have made companies modify the way of doing business. The current context of globalization, economic crisis, political conditions and competence among enterprises involves the continuous challenge for achieving their Key Performance Indicators. The successful achievement of these indicators depends on the Supply Chain (SC) efficiency. Thus, companies work towards the optimization of their overall SC in response to the competition from other companies as well as to take advantage of the flexibility in the restriction on world trade. This is done by the exploration of any resource flow (including raw materials and intermediates and final products, or energy), as well as any echelon of the Supply Chain network (such as suppliers, production plants, distribution centres and final markets). In this point, the Supply Chain Management addresses the process of planning, implementing and controlling the SC operations in an efficient way, throughout the management of material, information and financial flows across a network of entities within a SC. This includes the coordination and collaboration of processes and activities involved in this network. However, the complexity of considering the overall SC as well as the presence of uncertainty (demand, prices, process parameters) introduce more complexity in the coordination of all the activities or processes which take place through the SC. The aim of this thesis is to enhance the decision making process of industrial processes, by the development of new mathematical models to better coordinate all available information and to improve the synchronization production and demand, considering different time scales. Hence, this thesis presents a general overview of production process requirements within a SC and a review of the current state of the art, which has allowed to identify the open issues in the area in the context of Process Systems Engineering. Moreover, the first part of the thesis also presents an analysis of existing approaches, methods and tools used through this thesis. The second part of this work deals with the integrated management of production and demand constraints. This part first explores how the profitability of the SC can be improved by considering simultaneously production side and demand side management under deterministic conditions. Thus, discrete and hybrid time formulations have been presented to study the performance of the time representation. Furthermore, the discrete time formulation has been extended to deal with the external and internal uncertainty, through the implementation of a reactive approach. This part is also addresses the coordinated management of production and demand as well as the use of external and internal resources. Therefore, a new generalized mathematical formulation which integrates all resources involved in the production process within a supply chain is presented. The third part of this thesis is focused on the integrated SC optimization. Particularly, this part concerns the integration of hierarchical decision levels, by the exploitation of mathematical models that assess the consequences of considering simultaneously scheduling and planning decisions when designing a SC network. The Synthesis State Task Network concept is introduced to extend its typical representation of a process to incorporate information associated to the synthesis problem by the implementation of synthesis blocks. Finally, an integrated information management system based on an ontological framework is presented. The aim of this information platform is to coordinate all available information for decision making. This integrated platform will allow monitoring the real-time evolution of industrial processes within a supply chain. Moreover, this system may be used as an Operator Training System. Finally, the last part of this thesis provides the final conclusions and further work to be developed. A día de hoy, las tendencias del mercado han hecho que las empresas modifiquen su manera de hacer negocios. El contexto actual de globalización, crisis económica, condiciones políticas y la competencia entre las empresas implica un continuo desafío para conseguir los indicadores clave de rendimiento. Por lo tanto, las empresas trabajan para la optimización de su cadena de suministro (CS) en respuesta a la competencia de otras empresas, así como también para tomar ventaja de la flexibilidad en las restricciones del comercio mundial. Esto se hace mediante al análisis del flujo de materiales (incluyendo materias primas y productos intermedios y finales), así como cualquier escalón de la red de la CS (proveedores, plantas de producción, centros de distribución y mercados finales). Llegados a este punto, la gestión de la CS aborda el proceso de planificación, ejecución y control de las operaciones de la CS de forma eficiente, a través de la gestión de los materiales, la información y los flujos financieros a través de la red que conforma la CS. Esto incluye la coordinación de los diferentes procesos y actividades que participan en esta red. Sin embargo, la complejidad de considerar toda la CS en su conjunto, así como la presencia de incertidumbre (como por ejemplo, demanda, precios, parámetros de proceso) introducen una mayor complejidad en la coordinación de todas las actividades o procesos que tienen lugar en la CS. El objetivo de esta tesis es mejorar el proceso de toma de decisiones de los procesos industriales, por el desarrollo de nuevos modelos matemáticos que coordinen toda la información disponible y sincronizar la producción y la demanda, teniendo en cuenta diferentes escalas temporales. Por lo tanto, inicialmente esta tesis presenta una visión general de los procesos de producción, así como también una revisión del estado del arte, lo que ha permitido identificar las cuestiones abiertas en el área de la Ingeniería de Procesos de Sistemas. Además, en la primera parte de la tesis se presenta un análisis de los diferentes enfoques, metodologías y herramientas que se utilizan a lo largo de esta tesis. La segunda parte de este trabajo se ocupa de la gestión integrada de la producción y la demanda. Esta parte explora cómo la rentabilidad de la CS se puede mejorar teniendo en cuenta de manera simultánea le gestión de la producción y la demanda en condiciones determinísticas. Se ha desarrollado un modelo matemático que utiliza una formulación de tiempo discreto y una formulación híbrida, con el objetivo de evaluar el impacto de la representación temporal. Esta formulación se ha extendido para hacer frente a la incertidumbre tanto externa como interna, a través de la aplicación de un enfoque reactivo. Esta parte también trata la gestión coordinada de la producción y la demanda teniendo en cuenta la gestión de los recursos externos e internos. Para ello, se presenta una nueva formulación matemática generalizada que integra todos los recursos que intervienen en el proceso de producción. La tercera parte de esta tesis se centra en la optimización de la CS integrada. Particularmente, esta parte se refiere a la integración de diferentes niveles jerárquicos de decisión, mediante la explotación de modelos matemáticos que evalúan las consecuencias de considerar simultáneamente decisiones de diferentes niveles jerárquicos en una red de CS. La representación gráfica consistente en la red de síntesis y tareas-estados se presenta para incorporar la información asociada al problema de síntesis. Por último, se presenta un sistema de gestión integrado de información en un entorno ontológico, para coordinar toda la información disponible para la toma de decisiones. Esta plataforma permitirá seguir la evolución de procesos industriales, y también puede ser usado como un sistema de entrenamiento de operarios. Finalmente, la última parte de esta tesis ofrece las conclusiones finales y el trabajo futuro a desarrollar.

show abstract

Real-time scheduling for industrial load management

Cited by 14 publications

References 14 publications

Maintenance-centric energy management of industrial plants assisted by demand response

Maintenance-centric energy management of industrial plants assisted by demand response

Improved time representation model for the simultaneous energy supply and demand management in microgrids

Improving the tactical and operational decision making procedures in chemical supply chains

Contact Info

Product

Resources

About