Receding horizon operation control of a compressed natural gas station

In order to lower the cost of gas delivery to consumers who use compressed natural gas to propel their vehicles, operators of compressed natural gas stations in locations where electricity is sold under time-of-use tariff pricing can adopt station operation strategies that result in lower energy costs to secure their revenue. While finite horizon open loop control has shown the potential for significant savings on electricity costs while meeting the gas demand profile, receding horizon control can increase the robustness of the scheduling optimization by delivering a convergent solution for continuous operation of the plant. The present work shows the performance of the compressed natural gas station when the receding horizon control strategy is implemented achieving savings of up to 56.7% and a solution for continuous operation.10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China.http://www.elsevier.com/locate/procediaam2020Electrical, Electronic and Computer Engineerin

Decision problem structuring for selection of fixed firefighting systems

Active fire protection systems are an essential fire safety management tool, particularly in potentially high financial and risk consequence scenarios. In the UK and Europe over recent decades regulatory changes have been successful in creating an environment in which more innovation can take place. Increased numbers of fixed firefighting system types are now available to the user. However, not all systems offered are equal in terms of; suitability, cost, maturity of supporting knowledge, and overall performance or in-service reliability. Understanding of the systems performance and its limitations and how to match this to the assessed fire risk is incomplete among users. Experts are observing increasing numbers of what they consider to be poor fixed firefighting system choices leading to weaker fire safety designs, which is a cause of concern. Therefore the research aim is to verify that these concerns are founded and, that being the case, to develop a decision support system and related supporting resources to further this aspect of fire safety education and enable users to make better informed system selections. Thus, the focus of this research has been to develop a fixed firefighting system selection tool to complement existing legislation, which incorporates logic, rules and fire safety educational resources in a variety of formats to aid the fire safety design process. A variety of largely heuristic techniques have been used to aggregate data to form knowledge to underpin fixed firefighting system selection tool. In this form, the tool has been validated by experts as being a useful resource. The developed tool also provides ample opportunity for useful ongoing future development. The work recognises that cost and benefit are critical in the selection process. Supporting resources have been incorporated into the tool to assist users in evaluating the levels of reliability they might expect from a system in their circumstances. This tool has now been exposed to a wider audience of experts as part of an evaluation process. Findings include: that the tool is an innovative approach to promoting good fire safety designs, the tool efficiently provides useful fire safety education to users and the developed supporting resources which consider firefighting system reliability are helpful. This thesis and reference papers summarise the key stages of this research and tool development. The thesis concludes by outlining the progress achieved by this work and recommendations arising

A Heuristic Simulation and Optimization Algorithm for Large Scale Natural Gas Storage Valuation under Uncertainty

Natural gas storage valuation is an optimal scheduling of natural gas storage facilities. It is a complex predictive decision making research problem since it involves the financial decisions and the physical storage facility characteristics. The challenge arises from large scale stochastic input data sets and complex mathematical models. Research in the literature has been heavily focused on the financial facet of the valuation with little emphasis on the physical storage facility characteristics. The mathematical models and the solution approaches provided in the literature so far are also either overly simplified or are only relevant for very small scale problems. The contribution of this research is on the physical storage facility characteristics in combination with the financial aspect of the natural gas storage valuation. A large scale stochastic non-linear natural gas storage valuation problem that includes underground and aboveground storage facilities is formulated and solved efficiently. A new heuristic simulation and optimization natural gas storage valuation algorithm that handles a very complex and large size problems is proposed. The algorithm (i) decreases significantly the computation time from hundreds of days to fractions of a second, (ii) provides a reasonable solution quality, and (iii) incorporates all the possible underground and aboveground physical gas storage facility complexities. The research has both practical applications and mathematical significance. Practically, natural gas storage facility managers can use the models developed in this research as decision support tools to make a predictive storage decision under uncertainty within a reasonable time. Mathematically, a novel perspective to solving a non-linear natural gas storage facilities valuation problem is provided. Such approach can be used in a variety of applications; for instance, the algorithm can be applied to a high penetration of renewables to electric power grid and fluid flow network optimization among others

Real-Time Optimization of Interconnected Systems via Modifier Adaptation, with Application to Gas-Compressor Stations

The process industries are characterized by a large number of continuously operating plants, for which optimal operation is of economic and ecological importance. Many industrial systems can be regarded as an arrangement of several subsystems, where outputs of certain subsystems are inputs to others. This gives rise to the notion of interconnected systems. Plant optimality is difficult to achieve when the model used in optimization is inaccurate or in the presence of process disturbances. However, in the presence of plant-model mismatch, optimal operation can be enforced via specific real-time optimization methods. Specifically, this thesis considers so-called Modifier-Adaptation schemes which achieve plant optimality by direct incorporation of process measurements in the form of first-order corrections. As a first contribution, this thesis proposes a novel problem formulation for modifier adaptation. Specifically, it is focused on plants consisting of multiple interconnected subsystems that allows problem decomposition and application of distributed optimization strategies. The underlying key idea is the use of measurements and global plant gradients in place of an interconnection model. As a second contribution, this thesis investigates modifier adaptation for interconnected systems relying on local gradients by using an interconnection model. We show that the use of local information in terms of model, gradients and measurements is sufficient to optimize the steady-state performance of the plant. Finally, we propose a distributed modifier-adaptation algorithm that, besides the interconnection model and local gradients, employs a coordinator. For this scheme, we prove feasible-side convergence to the plant optimum, where a coordinator ensures that the local optimal inputs computed for each subsystem are consistent with the interconnection model. The experimental effort necessary to estimate the plant gradients increases with the number of plant inputs and may become intractable and sometimes not feasible or reliable for large-scale interconnected systems. The proposed approaches that use the interconnection model and local gradients overcome this problem. As an application case study of industrial relevance, this thesis investigates the problem of optimal load-sharing for serial and parallel gas compressors. The aim of load-sharing optimization is operating compressor units in an energy-efficient way, while at the same time satisfying varying load demands. We show how the structure of both the parallel and serial compressor configurations can be exploited in the design of tailored modifier adaptation algorithms based on efficient estimation of local gradients. Our findings show that the complexity of this estimation is independent of the number of compressors. In addition, we discuss gradient estimation for the case where the compressors are operating close to the surge conditions, which induces discontinuities in the problem