Holistic approach for microgrid planning and operation for e-mobility infrastructure under consideration of multi-type uncertainties

Integrating renewable energys ources in sectors such as electricity, heat, and transportation must be structured in an economic, technological, and emission- efficient manner to address global environmental issues.Microgrids appear to be the solution for large-scale renewable energy integration in these sectors.The microgrid components must be optimally planned and operated to prevent high costs, technical issues, and emissions. Existing approaches for optimal microgrid planning and operation in the literature do not include a solution for e-mobility infrastructure. As a consequence, a compact e-mobility infrastructure metho- dology is provided.The development of e-mobility infrastructure has as sociated uncertainties (short and long-term). As a result, a new stochastic method re- ferred to as IGDM-DRO is proposed in this dissertation.The proposed method provides a risk-averse strategy for microgrid planning and operation by including long-term and short-term uncertainty related to e-mobility.The multi-cut ben- der decomposition is applied for IGDM-DRO to prevent the suggested method’s intractability.Finally, the deterministic and stochastic methodologies are com bined in an ovelholistic approach for microgrid design and operation in terms of cost and robustness.The proposed method ist ested on a new settlement area in Magdeburg, Germany, under three different EV development scenarios (nega- tive, trend, andpositive).The share for the number of electric vehicles reached 31 percent of conventional vehicles by the end of the planned horizon. As a result, the microgrid’s overall cost has been increased by 2.3 to 2.9 percent per electric vehicle.Three public electric vehicle charging stations will be required in the investigated settlement are a intrend 2031.The investigated settlement area will require a total cost of 127,029 € in the trend scenario.To achieve full robustness against long-term uncertainties,the cost of the microgrid needs to be increased by 80 percent

Stochastic Co-design of Storage and Control for Water Distribution Systems

Water distribution systems (WDSs) are typically designed with a conservative estimate of the ability of a control system to utilize the available infrastructure. The controller is subsequently designed and tuned based on the designed water distribution system. This sequential approach may lead to conservativeness in both design and control steps, impacting both operational efficiency and economic costs. In this work, we consider simultaneously designing infrastructure and developing a control strategy, the co-design problem, to improve the overall system efficiency. However, implementing a co-design problem for water distribution systems is a challenging task given the presence of stochastic variables (e.g. water demands and electricity prices). In this work, we propose a tractable stochastic co-design method to design the best tank size and optimal control parameters for WDS, where the expected operating costs are established based on Markov chain theory. We also give a theoretical result that investigates the average long-run co-design cost converging to the expected cost with probability 1. Furthermore, the method can also be applied to an existing WDS to improve operation of the system. We demonstrate the proposed co-design method on three examples and a real-world case study in South Australia

Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work

Heat pump aggregation, optimization and control

One tenth of anthropogenic greenhouse gas emissions are caused by heating and cooling buildings. Efficient electric heat pumps could significantly reduce these emissions, but face barriers to adoption related to costs, equipment selection and installation, and other factors. The goal of this thesis is to reduce emissions by lowering barriers to heat pump adoption. To this end, we investigate heat purchase agreements (HPAs), a new model of heat pump ownership, and develop supporting methods. In an HPA, users host heat pumps owned by an aggregator. The aggregator buys the heat pumps' electricity and sells their heat or cooling output to the users. We show that HPAs can lower barriers to adoption and benefit both the aggregator and the users. We also develop a method for fairly pricing heat and cooling. An HPA aggregator is responsible for selecting an appropriate heat pump for each user under uncertainty. We develop a data-driven selection method that provides probabilistic feasibility and optimality guarantees, and illustrate the method through simulations. An HPA aggregator operates a fleet of heat pumps. If the aggregator invests in sensing, communication and control capabilities, then they can provide services to the electricity grid by perturbing the heat pumps' power use. We develop methods for co-optimizing day-ahead capacity offers for the two highest-priced services, regulation and spinning reserve. In simulations, each heat pump offers 285--325 W of combined annual-average capacity and earns $25--75 of annual revenue. Providing these services could help grid operators integrate more renewable power, and thereby reduce emissions from electricity generation