MEASUREMENT AND ENHANCEMENT OF THE RESILIENCE OF POWER SYSTEMS WITH A COMBINED DIESEL AND SOLAR POWER BACKUP

Power outages shut down facilities such as hospitals, shelters, and communication services. Each power system needs to be resilient to power outages. In a power system, resilience can be achieved by infrastructure hardening; smart meter (AMI), energy storage, micro grid, renewable energy and accessibility of critical components. Most critical systems, such as hospitals, have a backup power that is deiseal power generator. The resilience of such a power system refers to how a backup power can still supply the critical load or base load for such critical systems when facing to the prime power outage. This thesis studies how the resilience of such a power system can be quantitatively measured and whether a combined diesel and solar backup power can enhance the resilience of the entire power system with an affordable cost. Specifically, the hospitals in Saskatoon were taken as a study vehicle. A literature review was conducted first, which revealed that there was no satisfactory quantitative measurement available in literature for the resilience of power systems on the occasion of prime power outages. The overall objective of this thesis was thus to develop a quantitative measure for the resilience of power systems with a backup power when facing the prime power outage. The problem is in essence about the reliability of the backup power in the event of the prime grid power is disrupted. A general measure for the resilience of the backup power system (R for short), which can be multiple types of power generators, was developed, which was dimensionless (i.e., independent of the scale of the system). The measure was proved to be reasonable to the extreme cases (i.e., R=0, R=1). The use of the proposed measurement was illustrated for two situations of the backup power: (i) the backup power being a diesel power generator only, and (ii) the backup power being a combined diesel power generator and solar panel. The situation (i) corresponds to the current situation of the backup power in the hospitals in Saskatoon. The result shows that the resilience of the RUH (royal university hospital) is the highest one (R=70.5%) among the three hospitals in Saskatoon with the other two being SCH (Saskatoon City Hospital) and SPH (Saint Paul Hospital), and the resilience of SPH is the lowest one (R=54%). This result was in agreement with the experience of the managers of the hospitals. The economics of the combined backup power (diesel plus solar power generators) was studied with the help of a software system called SAM (system advisor model). Specifically, the power generated by and economic attributes of the solar panel of different sizes without battery storage were analyzed for the three hospitals, respectively. Note that the economic attributes are NPV (net present value) and payback time. The resilience of the combined backup power was calculated for different sizes of solar panels with the help of SAM and the proposed measure. The optimal design, namely the size of solar panel, was obtained in terms of the resilience and payback time; specifically, for the RUH, the size of solar panel is 700 KW (R of the solar panel alone is 35%; R of the combined backup power is 98%; the payback is 13.1 years, the capital cost is 1488490$), for the SPH, the size of solar panel is 500 KW (R of the solar panel alone is 25$), and for the SCH, the size of solar panel is 500 KW (R of the solar panel alone is 20%; R of the combined backup power is 94%; the payback is 10.4 years, the capital cost is $1060940). Besides, in the normal situation, the reduction of the grid power by solar power is about 7%. This research can thus conclude that the resilience of the backup power system of the hospitals in Saskatoon can be improved by adding solar panels with an acceptable cost payback time and at the same time the environmental sustainability, related to the fossil fuel power generation, can is also improved. The primary contribution of this thesis research is the provision of a quantitative measure for the resilience of a power system including a backup power, especially with respect to the recovery stage in the event of the prime power outage. The secondary contribution is the increase of the resilience of the power system of the hospitals in Saskatoon by 25% for SPH, 35% for RUH, and 20% for SCH and the reduction of the use of the grid power by 7% for the benefit to the environmental sustainability

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research

Computational intelligence techniques for energy storage management

Ph. D. ThesisThe proliferation of stochastic renewable energy sources (RES) such as photovoltaic and wind power in the power system has made the balancing of generation and demand challenging for the grid operators. This is further compounded with the liberalization of electricity market and the introduction of real-time electricity pricing (RTP) to reflect the dynamics in generation and demand. Energy storage sources (ESS) are widely seen as one of the keys enabling technology to mitigate this problem. Since ESS is a costly and energy-limited resource, it is economical to provide multiple services using a single ESS. This thesis aims to investigate the operation of a single ESS in a grid-connected microgrid with RES under RTP to provide multiple services. First, artificial neural network is proposed for day-ahead forecasting of the RES, demand and RTP. After the day-ahead forecast is obtained, the day-ahead schedule of energy storage is formulated into a mixed-integer linear programming and implemented in AMPL and solved using CPLEX. This method considers the impact of forecasting errors in the day-ahead scheduling. Empirical evidence shows that the proposed nearoptimal day-ahead scheduling of ESS can achieve a lower operating cost and peak demand. Second, a fuzzy logic-based energy management system (FEMS) for a grid-connected microgrid with RES and ESS is proposed. The objectives of the FEMS are energy arbitrage and peak shaving for the microgrid. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state-of-charge (SoC) of ESS, the power difference between RES and demand, and RTP. Instead of using a forecasting-based approach, the proposed FEMS is designed with the historical data of the microgrid. It determines the charge and discharge rate of the ESS in a rolling horizon. A comparison with other controllers with the same objectives shows that the proposed controller can operate at a lower cost and reduce the peak demand of the microgrid. Finally, the effectiveness of the FEMS greatly depends on the membership functions. The fuzzy membership functions of the FEMS are optimized offline using a Pareto based multi-objective evolutionary algorithm, nondominated sorting genetic algorithm- II (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy logic controller. A comparison was made against other control strategies with similar objectives are carried out at a simulation level. Empirical evidence shows that the proposed methodology can find more solutions on the Pareto front in a single run. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, UK. Furthermore, reserve service is added on top of energy arbitrage and peak shaving to the energy management system (EMS). As such multi-service of a single ESS which benefit the grid operator and consumer is achieved