Developing a Framework for Determining Optimum Dispatch of Energy to a Building from Conventional and Renewable Sources

In an optimally designed grid-connected system with distributed energy resources where the grid plays the role of an energy buffer, it is interesting to analyze the economic feasibility to employ energy storage systems. A grid-connected system synchronizes with the power fluctuations, lowering the costs of energy compared to the cost of using conventional energy storage systems. An adaptive code is developed using computer programming that is used for lifetime simulation of the energy dispatch system with a specified time step and for optimization algorithm with comprehensive reliability/cost assessment. The results can be extended to a long period considering various economic factors. The programming code can be integrated with any system model, which can be flexibly implemented to any number of applications. In the present work, a strategic framework is developed for determining the optimal energy technology allocation to a typically selected commercial building located in the United States. The optimum design and management strategy of grid connected renewable generating systems composed of energy conversion units is considered. The provision of a hybrid system of energy storage is investigated. A genetic algorithm optimization-based approach is adopted for carrying out the optimization. The optimization of the set problem consisted of the minimization of the total lifecycle costs considered as the objective function, whereas the fulfillment of the users demand for energy was considered as the key constraint. The most suitable systems with an operation on hourly basis and the best strategy for the storage of energy were considered to generate the optimization results providing the optimal size and total cost of the system components. Furthermore, the possibility of using alternative energy dispatch systems was explored that might reduce the total lifetime costs below the cost of a benchmark case in which the entire demand for electricity is fulfilled from the grid. Four scenarios were analyzed to measure the impact of planning and operating the distributed energy resources: typical, off grid, on grid, feed-in-tariffs

INDIGO: An In Situ Distributed Gossip Framework for Sensor Networks

Abstract—With the onset of Cyber-Physical Systems (CPS), distributed algorithms on Wireless Sensor Networks(WSNs) have been receiving renewed attention. The distributed consensus problem is a well studied problem having a myriad of applications which can be accomplished using asynchronous distributed gossip algorithms on Wireless Sensor Networks(WSN). However, a practical realization of gossip algorithms for WSNs is found lacking in the current state of the art. In this paper, we propose the design, development and analysis of a novel in-situ distributed gossip framework called INDIGO. A key aspect of INDIGO is its ability to execute on a generic system platform as well as on a hardware oriented testbed platform in a seamless manner allowing easy portability of existing algorithms. We evaluate the performance of INDIGO with respect to the distributed consensus problem as well as the distributed optimization problem. We also present a data driven analysis of the effect, certain operating parameters like sleep time and wait time have on the performance of the framework and empirically attempt to determine a sweet spot. The results obtained from various experiments on INDIGO validate its efficacy, reliability and robustness and demonstrate its utility as a framework for the evaluation and implementation of asynchronous distributed algorithms

Rightsizing the Design of a Hybrid Microgrid

17 USC 105 interim-entered record; under review.The article of record as published may be found at http://dx.doi.org/10.3390/en14144273Selecting the sizes of distributed energy resources is a central planning element when designing a microgrid. Decision makers may consider several important factors, including, but not limited to, capacity, cost, reliability and sustainability. We introduce a method for rightsizing capacity that presents a range of potential microgrid design solutions, allowing decision makers to weigh their upsides and downsides based on a variety of measurable factors. We decouple component-specific modeling assumptions, energy management system logic and objective measurements from our simulation-based nested binary search method for rightsizing to meet power loads. In doing so, we develop a flexible, customizable and extensible approach to microgrid design planning. Aspects which have traditionally been incorporated directly in optimization-centric frameworks, such as resilience and reliability, can be treated as complementary analyses in our decoupled approach. This enables decision makers to gain exposure to a wide range of relevant information and actively participate in the microgrid design assessment process.Energy System Technology Evaluation ProgramOffice of Naval ResearchNaval Facilities Engineering Systems Command (NAVFAC)Naval Postgraduate Schoo

Design of Wireless Communication Networks for Cyber-Physical Systems with Application to Smart Grid

Cyber-Physical Systems (CPS) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. On one hand, CPS are generally large with components spatially distributed in physical world that has lots of dynamics; on the other hand, CPS are connected, and must be robust and responsive. Smart electric grid, smart transportation system are examples of emerging CPS that have significant and far-reaching impact on our daily life. In this dissertation, we design wireless communication system for CPS. To make CPS robust and responsive, it is critical to have a communication subsystem that is reliable, adaptive, and scalable. Our design uses a layered structure, which includes physical layer, multiple access layer, network layer, and application layer. Emphases are placed on multiple access and network layer. At multiple access layer, we have designed three approaches, namely compressed multiple access, sample-contention multiple access, and prioritized multiple access, for reliable and selective multiple access. At network layer, we focus on the problem of creating reliable route, with service interruption anticipated. We propose two methods: the first method is a centralized one that creates backup path around zones posing high interruption risk; the other method is a distributed one that utilizes Ant Colony Optimization (ACO) and positive feedback, and is able to update multipath dynamically. Applications are treated as subscribers to the data service provided by the communication system. Their data quality requirements and Quality of Service (QoS) feedback are incorporated into cross-layer optimization in our design. We have evaluated our design through both simulation and testbed. Our design demonstrates desired reliability, scalability and timeliness in data transmission. Performance gain is observed over conventional approaches as such random access

Feedback and time are essential for the optimal control of computing systems

The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems

Achieving High Reliability and Efficiency in Maintaining Large-Scale Storage Systems through Optimal Resource Provisioning and Data Placement

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues. First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience in maintaining large-scale storage systems. Second, in order to cost-effectively integrate solid-state drives (SSD) into large-scale storage systems, I design a holistic algorithm which can adaptively predict the popularity of data objects by leveraging temporal locality in their access pattern and adjust their placement among solid-state drives and regular hard disk drives so that the data access throughput as well as the storage space efficiency of the large-scale heterogeneous storage systems can be improved. Finally, I propose a new checkpoint placement optimization model which can maximize the computation efficiency of large-scale scientific applications while guarantee the endurance requirements of the SSD-based burst buffer in high performance hierarchical storage systems. All these models and algorithms are validated through extensive evaluation using data collected from deployed large-scale storage systems and the evaluation results demonstrate our models and algorithms can significantly improve the reliability and efficiency of large-scale distributed and parallel storage systems

A comprehensive model for the design of a microgrid under regulatory constraints using synthetical data generation and stochastic optimization

As renewable energy installation costs decrease and environmentally-friendly policies are progressively applied in many countries, distributed generation has emerged as the new archetype of energy generation and distribution. The design and economic feasibility of distributed generation systems is constrained by the operation of the microgrid, which has to consider the uncertainty of renewable energy sources, consumption habits and electricity market prices. In this paper, a mathematical model intended to optimize the design and economic feasibility of a microgrid is proposed. After a search in the state-of-the-art, weaknesses and strengths of existing models have been identified and taken into account for building the present model. The present model should be seen as a basis on which other models can be built upon, hence a complete definition of the different sub-models is stated: uncertainty modelling, optimization technique, physical constraints and regulatory framework. One of the main features presented is the generation of synthetic data in uncertainty modelling, employed to enhance the reliability of the model by taking into account a longer time horizon and a shorter time step. Results show significant details about energy management and prove the suitability of using a stochastic approach rather than deterministic or intuitive ones to perform the optimization.Peer ReviewedPostprint (published version