Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv

Advanced Motor Control for Improving the Trajectory Tracking Accuracy of a Low-Cost Mobile Robot

This research was funded by the Grant PID2019-111278RB-C21 funded by MCIN/AEI/ 10.13039/501100011033 and “ERDF A way of making Europe”.Peer reviewedPublisher PD

Investigation of energy storage system and demand side response for distribution networks

PhD ThesisThe UK government has a target of achieving an 80% reduction in CO2 emissions with respect to the values from 1990 by 2050. Therefore, renewables based distributed generations (DGs) coupled with substantial electrification of the transport and heat sectors though low carbon technologies (LCTs), will be essential to achieve this target. The anticipated proliferation of these technologies will necessitate major opportunities and challenges to the operation and planning of future distribution networks. Smartgrid technologies and techniques, such as energy storage systems (ESSs), demand side response (DSR) and real time thermal ratings (RTTRs), provide flexible, economic and expandable solutions to these challenges without resorting to network reinforcement. This research investigates the use of ESS and DSR in future distribution networks to facilitate LCTs with a focus on the management and resolution of thermal constraints and steady state voltage limit violation problems. Firstly, two control schemes based on sensitivity factors and cost sensitivity factors are proposed. Next, the impacts of a range of sources of uncertainties, arising from existing and future elements of the electrical energy system, are studied. The impacts of electric vehicle charging are investigated with Monte Carlo simulation (MCS). Furthermore, to deal with uncertainties efficiently, a scheduling scheme based on robust optimization (RO) is developed. Two approaches have been introduced to estimate the trade-off between the cost and the probability of constraint violations. Finally, the performance of this scheme is evaluated. The results of this research show the importance of dealing with uncertainties appropriately. Simulation results demonstrate the capability and effectiveness of the proposed RO based scheduling scheme to facilitate DG and LCTs, in the presence of a range of source of uncertainties. The findings from this research provide valuable solution and guidance to facilitate DG and LCTs using ESS, DSR and RTTR in future distribution networks

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems

A simulation-based optimisation method to evaluate dynamic compensators for the improvement of LCC-HVDC performance in high source impedance power systems

Line commutated converter (LCC) high voltage direct current (HVDC) link dynamic performance is negatively affected by low alternating current (AC) system short circuit ratio (SCR) as viewed from the LCC-HVDC link converter stations. This is particularly evident at LCC-HVDC link converter stations operating as inverters subjected to large transient disturbances. Firstly, this thesis proposes a simulation-based optimisation method to evaluate black-box optimisation solver methods built with mature strategies against alternative solver methods using surrogate model strategies recently proposed in the research literature. The method uses the problem of tuning LCC-HVDC link controllers considering large transient behaviour modelled via electromagnetic transient (EMT) simulations as the underlying motivating problem on which the solver methods are tested. The results from the applied method confirm the suitability of applying the tested surrogate-based solver methods on LCC-HVDC link controller tuning problems. The surrogate-based solver methods’ performances are improved between 45.137% and 72.14% relative to the worst performing solver method using mature strategies. Secondly, this thesis proposes a method to quantitatively evaluate dynamic compensators’ ability to improve the dynamic performance of LCC-HVDC links inverting into low SCR AC systems. The method uses EMT simulations as part of a simulation-based optimisation using one of the aforementioned surrogate-based optimisation solver methods to make fair comparisons between different compensator types and compensator ratings. Multiple inverter system short circuit fault locations and inverter system equivalent source impedances are considered in the method. Compensators are evaluated by performance values calculated via performance functions applied to measured time domain variable results from the simulations. The method is able to successfully quantify and differentiate compensator type and rating superiorities when applied to a set of static VAr compensator (SVC), static synchronous compensator (STATCOM), and synchronous condenser study cases. In particular, the method results show that any type of compensator of any rating typically improves LCC-HVDC link dynamic performance compared to a compensator-less LCC-HVDC link. The best found improvement is 9.2035% relative to the Base study case for the integral square error (ISE) of direct current (DC)-side measured power of the LCC-HVDC link. The method results also show that synchronous condensers are the most effective compensator, with improvements between 7.5269% and 9.2035% relative to the compensator-less LCC-HVDC link when considering ISE of DC-side measured power. Similarly, SVCs provide improvements between 5.4759%, and 5.7968%, and STATCOMs provide improvements between -0.21144% and 6.9608%. Smaller-rated SVCs and STATCOMs provide better improvements compared with larger-rated SVCs and STATCOMs, using the compensator-less LCC-HVDC link as a baseline.Line commutated converter (LCC) high voltage direct current (HVDC) link dynamic performance is negatively affected by low alternating current (AC) system short circuit ratio (SCR) as viewed from the LCC-HVDC link converter stations. This is particularly evident at LCC-HVDC link converter stations operating as inverters subjected to large transient disturbances. Firstly, this thesis proposes a simulation-based optimisation method to evaluate black-box optimisation solver methods built with mature strategies against alternative solver methods using surrogate model strategies recently proposed in the research literature. The method uses the problem of tuning LCC-HVDC link controllers considering large transient behaviour modelled via electromagnetic transient (EMT) simulations as the underlying motivating problem on which the solver methods are tested. The results from the applied method confirm the suitability of applying the tested surrogate-based solver methods on LCC-HVDC link controller tuning problems. The surrogate-based solver methods’ performances are improved between 45.137% and 72.14% relative to the worst performing solver method using mature strategies. Secondly, this thesis proposes a method to quantitatively evaluate dynamic compensators’ ability to improve the dynamic performance of LCC-HVDC links inverting into low SCR AC systems. The method uses EMT simulations as part of a simulation-based optimisation using one of the aforementioned surrogate-based optimisation solver methods to make fair comparisons between different compensator types and compensator ratings. Multiple inverter system short circuit fault locations and inverter system equivalent source impedances are considered in the method. Compensators are evaluated by performance values calculated via performance functions applied to measured time domain variable results from the simulations. The method is able to successfully quantify and differentiate compensator type and rating superiorities when applied to a set of static VAr compensator (SVC), static synchronous compensator (STATCOM), and synchronous condenser study cases. In particular, the method results show that any type of compensator of any rating typically improves LCC-HVDC link dynamic performance compared to a compensator-less LCC-HVDC link. The best found improvement is 9.2035% relative to the Base study case for the integral square error (ISE) of direct current (DC)-side measured power of the LCC-HVDC link. The method results also show that synchronous condensers are the most effective compensator, with improvements between 7.5269% and 9.2035% relative to the compensator-less LCC-HVDC link when considering ISE of DC-side measured power. Similarly, SVCs provide improvements between 5.4759%, and 5.7968%, and STATCOMs provide improvements between -0.21144% and 6.9608%. Smaller-rated SVCs and STATCOMs provide better improvements compared with larger-rated SVCs and STATCOMs, using the compensator-less LCC-HVDC link as a baseline

Time decomposition of multi-period supply chain models

Many supply chain problems involve discrete decisions in a dynamic environment. The inventory routing problem is an example that combines the dynamic control of inventory at various facilities in a supply chain with the discrete routing decisions of a fleet of vehicles that moves product between the facilities. We study these problems modeled as mixed-integer programs and propose a time decomposition based on approximate inventory valuation. We generate the approximate value function with an algorithm that combines data fitting, discrete optimization and dynamic programming methodology. Our framework allows the user to specify a class of piecewise linear, concave functions from which the algorithm chooses the value function. The use of piecewise linear concave functions is motivated by intuition, theory and practice. Intuitively, concavity reflects the notion that inventory is marginally more valuable the closer one is to a stock-out. Theoretically, piecewise linear concave functions have certain structural properties that also hold for finite mixed-integer program value functions. (Whether the same properties hold in the infinite case is an open question, to our knowledge.) Practically, piecewise linear concave functions are easily embedded in the objective function of a maximization mixed-integer or linear program, with only a few additional auxiliary continuous variables. We evaluate the solutions generated by our value functions in a case study using maritime inventory routing instances inspired by the petrochemical industry. The thesis also includes two other contributions. First, we review various data fitting optimization models related to piecewise linear concave functions, and introduce new mixed-integer programming formulations for some cases. The formulations may be of independent interest, with applications in engineering, mixed-integer non-linear programming, and other areas. Second, we study a discounted, infinite-horizon version of the canonical single-item lot-sizing problem and characterize its value function, proving that it inherits all properties of interest from its finite counterpart. We then compare its optimal policies to our algorithm's solutions as a proof of concept.PhDCommittee Chair: George Nemhauser; Committee Member: Ahmet Keha; Committee Member: Martin Savelsbergh; Committee Member: Santanu Dey; Committee Member: Shabbir Ahme