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Renewable energy generation will play an important role in solving the climate change problem. With renewable electricity generation increasing, there will be some significant changes in electric power systems, notably through smaller generators embedded in the distribution network. Historically insignificant volumes of Embedded Generation (EG) mean that traditionally it has been treated by the transmission system operator as negative load, with its impact on the dynamic behaviour of power systems neglected. However, with the penetration level increasing, EG would start to influence the dynamics and stability of the transmission network. Hence the dynamic behaviour of distribution network cannot be neglected any more. In most cases, a detailed distribution network model is not always available or necessary for the study of transmission network dynamics and stability. Thus a dynamic equivalent model of the distribution network that keeps its essential dynamic behavior, is required. Most existing dynamic equivalencing methods are based on the assumption that the detailed information of the complete power system is known. Dynamic equivalencing methods based on coherency of the machines have been applied to transmission networks but cannot be applied to distribution networks due to their radial structure. Hence an alternative methodology has been developed in this project to derive the dynamic equivalent model of the distribution network using system identification, without the detailed information of the distribution network necessarily known. Case studies have been accomplished in PSS/E on a model of the Scottish transmission network with the distribution network in Dumfries and Galloway. Embedded generation with a certain penetration level in either conventional generation or DFIG wind generation has been added to the model of the distribution network. The dynamic equivalent models of the distribution network are compared with the original distribution network model using a series of indicators. A constant power model has also been involved in the comparison to illustrate the advantage of using the dynamic equivalent to represent the distribution network. The results suggest that a proper dynamic equivalent model derived using this methodology may have better agreement to the original power system dynamic response than constant power equivalent. A discussion on factors that influence the performance of the dynamic equivalent model, is given to indicate the proper way to use this methodology. The major advantage of the dynamic equivalencing methodology developed in this project is that it can potentially use the time series obtained from measurements to derive the dynamic equivalent models without knowing detailed information on the distribution network. The derived dynamic equivalent, in a simple spate-space form, can be implemented in commercial simulation tools, such as PSS/E.

Clock mesh is widely used in microprocessor designs for achieving low clock skew and high process variation tolerance. Clock mesh optimization is a very diffcult problem to solve because it has a highly connected structure and requires accurate delay models which are computationally expensive. Existing methods on clock network optimization are either restricted to clock trees, which are easy to be separated into smaller problems, or naive heuristics based on crude delay models. A clock mesh sizing algorithm, which is aimed to minimize total mesh wire area with consideration of clock skew constraints, has been proposed in this research work. This algorithm is a systematic solution search through rigorous Sequential Quadratic Programming (SQP). The SQP is guided by an efficient adjoint sensitivity analysis which has near-SPICE(Simulation Program for Integrated Circuits Emphasis)-level accuracy and faster-than-SPICE speed. Experimental results on various benchmark circuits indicate that this algorithm leads to substantial wire area reduction while maintaining low clock skew in the clock mesh. The reduction in mesh area achieved is about 33%.

Master of Agribusiness / Department of Agricultural Economics / Keith Harris / For an Evergreen Seed Agent, making the financial decision to invest in bulk infrastructure is a large capital investment and requires a great deal of considerations. This thesis determines the soybean sales volume required for an investment in bulk soybean infrastructure to become financially feasible. A total of 1,456 soybean sales data points were used to run a regression model. Based on the results of the regression model, a correlation was determined between those agents that have bulk soybeans and soybean sales growth in comparison to those agents that did not have bulk soybeans. A “Bulk Soybean Decision Tool” was constructed and demonstrates costs and earnings of an Evergreen Seed Agent over a five-year period. The financial feasibility analysis concluded that the soybean volume required to consider investing in bulk soybeans is 8,488 units. In addition to a break-even analysis, three other base year volume scenarios were demonstrated. At the 3,233 unit base soybean sales volume, it is not financially feasible to invest in bulk. At the 10,265 unit base soybean sales volume, it is finically feasible to invest in bulk soybeans. Lastly, at the 18,912 unit base soybean sales volume, it is finically feasible to invest in bulk soybean infrastructure. The capabilities of the “Bulk Soybean Decision Tool” are significant for any Evergreen Seed Agent considering investing in bulk soybean infrastructure. Ensuring that an Evergreen Seed Agent is making a sound financial investment in bulk soybean infrastructure will allow for an increased adoption in infrastructure, resulting in increased soybean sales volume across the distribution network.

Yes / This paper proposes a probabilistic multi-objective optimization method for the operation of three-phase distribution networks incorporating active network management (ANM) schemes including coordinated voltage control and adaptive power factor control. The proposed probabilistic method incorporates detailed modelling of three-phase distribution network components and considers different operational objectives. The method simultaneously minimizes the total energy losses of the lines from the point of view of distribution network operators (DNOs) and maximizes the energy generated by photovoltaic (PV) cells considering ANM schemes and network constraints. Uncertainties related to intermittent generation of PVs and load demands are modelled by probability density functions (PDFs). Monte Carlo simulation method is employed to use the generated PDFs. The problem is solved using ɛ-constraint approach and fuzzy satisfying method is used to select the best solution from the Pareto optimal set. The effectiveness of the proposed probabilistic method is demonstrated with IEEE 13- and 34- bus test feeders.

Lured by the promise of bigger sales, companies are increasingly looking to raise the volume of international trade. Consequently, the amount of bulk products carried in containers and transported overseas exploded because of the flexibility and reliability of this type of transportation. However, minimizing the logistics costs arising from the container flow management across different terminals has emerged asa major problem that companies and affiliated third-party logistics firms face routinely. The empty tankcontainer allocation problem occurs in the context of intermodal distribution systems management and transportation operations carried out by logistic service provider companies. This paper considers the time-evolving supply chain system of an international logistic service provider company that transports bulk products loaded in tank containers via road, rail and sea. In such system, unbalanced movements of loaded tank containers forces the company to reposition empty tank containers. The purpose of this paper is to develop a mathematical model that supports tactical decisions for flow management of empty tank containers. The problem involves dispatching empty tank containers of various types to the meet on-time delivery requirements and repositioning the other tank containers to storage facilities, depots and cleaning stations. To this aim, a mixed-integer linear programming (MILP) multiperiod optimization model is developed. The model is analyzed and developed step by step, and its functionality is demonstrated by conducting experiments on the network from our case study problem, within the boarders of Europe. The case study constitutes three different scenarios of empty tank container allocation. The computational experiments show that the model finds good quality solutions, and demonstrate that cost and modality improvements can be achieved in the network The sensitivity analysis employs a set of data from our case study and randomly selected data to highlight certain features of the model and provide some insights regarding the model’s behavior.

Municipal water distribution systems are operated and maintained by utilities whose first priority is the safe and reliable provision of drinking water to consumers. The cost to move and treat water through distribution networks is significant and can account for up to 80% of a utility’s energy costs. As these networks age, operating and maintenance costs continue to increase due to higher incidences of leaks and breaks and increased pipe friction leading to higher energy use. Many utilities are considering water conservation as a strategy to reducing their energy consumption by reducing the amount of water being pumped and treated in their jurisdictions. This work studies the pumping energy response of a distribution system when water conservation strategies are implemented in small geographic areas in the network. A water conservation plan is tailored to each defined area by specifying which conservation measures are feasible to implement, desired by the customer, and are attractive to the utility based on a potential return on investment in the form of reduced electricity bills to pump and treat water. Energy intensity and energy elasticity indicators are developed to assess the mechanical energy used in a network to distribute water to end-users. A case study for the City of Kingston water distribution system is presented. The distribution system studied indicated that when water conservation strategies produced marginal water savings, the energy response was inelastic to changes in water demand. The amount of energy required to move one cubic metre of water through the network increased with higher water savings because the percent savings of water was higher than the percent savings of pumping energy. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-05-31 14:20:18.363

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The amount of distributed generation (DG) is increasing worldwide, and it is located in distribution networks close to consumers or even in the consumers¡¦ side of the meter. Therefore, the net demand to be supplied through transmission and distribution networks may decrease, allowing to postpone reinforcement of existing networks. This thesis presents a methodology for assessing the potential benefits of using non--constructional reinforcement strategies to relieve distribution network congestion and increase the utilization of the network assets. Due to the randomness of involved variables (load demand patterns, DG hourly production, DG availability, etc.), a simulation approach is used to model the uncertainties. The benefits of DG, energy storage (ES), and demand response (DR) on congestion relief and investment deferment are evaluated. The analyzed items include: the distribution network investment avoided cost, levelized annual cost, hourly overload probability, and hourly overload risk. Simulation results indicate the potential benefits of non--traditional strategies in increasing the distribution network utilization and relieving network congestion.

In the next decade, electric vehicles (EV) will be heading to the road in a fast speed. Utility company would have no control over the future EV charging points or stations, and no direct control over periods and frequency of EV charging that could cause great effects to the existing distribution network operations if not well planned. Distribution system operation and expansion planning would become more complicated due to the high degree of uncertainty of the EV charging demand. Markov model is used in this study to calculate the probabilities and locations of EV charging. To mitigate the loading and voltage quality problem, feeder reconfiguration is proposed. The problem is formulated as an stochastic programming program with an objective function of minimizing total switching and system loss costs, and subject to radial structure of the distribution network and security constraints. The problem is solved by a binary particle swarm optimization technique. Test results indicate that feeder reconfiguration can be exercised to match loading patterns of different types of feeders (residential, commercial and industrial) with various stochastic charging scenarios, and consequently, reduce the impacts of EV charging and optimize the use of the existing network.