Robust Optimization for SCED in AC-HVDC Power Systems

Wind power is a clean, renewable and low-carbon resource for power generation that has received increasing attention in power systems over the last few decades. There are two main challenges associated with the large-scale integration of wind power plants in the power system: i) the intermittent nature of wind power results in prediction errors that can greatly impact the system's operational security and reliability requirements, and ii) large-scale offshore wind farms are typically located far from onshore loads and require new developments in the transmission system of power grids, e.g., realization of mixed alternating current-high voltage direct current (AC-HVDC) power systems, which will introduce new reliability requirements to the system operator. The security-constrained economic dispatch (SCED) problem deals with determining a power dispatch schedule, for all generating units, that minimizes the total operational cost, while taking into account system reliability requirements. Robust optimization (RO) has recently been used to tackle wind power uncertainty in the SCED problem. In the literature of RO, the budget of uncertainty was proposed to adjust the solution conservatism (robustness) such that higher budgets of uncertainty correspond to more conservative solutions. This thesis shows that the budget of uncertainty approach may not be meaningful for problems with RHS uncertainty since increasing the budget of uncertainty by more than a certain threshold may not always impact the level of conservatism. This thesis proposes a new tractable two-stage robust optimization model that effectively incorporates the budget of uncertainty in problems with RHS uncertainty, controls the level of conservatism, and provides meaningful insights on the trade-off between robustness and cost. Furthermore, this thesis examines the applicability of the proposed robust approach for the SCED problem in mixed AC-HVDC power systems with large integration of wind power. The proposed robust SCED model considers the impact of wind power curtailment on the operational cost and reliability requirements of the system. Extensive numerical studies are provided to demonstrate the economic and operational advantages of the proposed robust SCED model in mixed AC-HVDC systems from five aspects: the effectiveness of the budget of uncertainty, robustness against uncertainty, contribution to real-time reliability, cost efficiency, and power transfer controllability

Risk-based Stochastic Continuous-time Scheduling of Flexibility Reserve for Energy Storage Systems

This paper develops a novel risk-based stochastic continuous-time model for optimizing the role of energy storage (ES) systems in managing the financial risk imposed to power system operation by large-scale integration of uncertain renewable energy sources (RES). The proposed model is formulated as a two-stage continuous-time stochastic optimization problem, where the generation of generating units, charging and discharging power of ES, as well as flexibility reserve capacity from both resources are scheduled in the first stage, while the flexibility reserve is deployed in the second stage to offset the uncertainty of RES generation in each scenario. The Conditional Value at Risk (CVaR) is integrated as the risk metric measuring the average of the higher tail of the system operation costs. The proposed model is implemented on the IEEE Reliability Test System using load and solar power data of CAISO. Numerical results demonstrate that the proposed model enables the system operators to effectively utilize the flexibility of ES and generating units to minimize the system operation cost and renewable energy curtailment at a given risk tolerance level

Risk mitigation of poor power quality issues of standalone wind turbines:An efficacy study of synchronous reference frame (SRF) control

This paper validates and presents the efficiency and performance of Synchronous Reference Frame (SRF) control as a mitigating control in managing risks of high volatility of electric current flows from the wind turbine generator to the distributed load. High volatility/fluctuations of electricity (high current, voltage disturbance) and frequency are hazards that can trip off or, in extreme cases, burn down a whole wind turbine generator. An advanced control scheme is used to control a Voltage Source Converter (VSC)-based three-phase induction generator with a Battery Energy Storage System (BESS). For the purpose of risk mitigation of harmonics, this scheme converts three-phase input quantity to two-phase Direct Current (DC) quantity (dq) so that the reactive power compensation decreases the harmonics level. Thus, no other analog filters are required to produce the reconstructed signal of fundamental frequency. In this paper, the values of Proportional Integral (PI) regulators are calculated through the “MONTE CARLO” optimization tool. Furthermore, risk analysis is carried out using bowtie, risk matrix and ALARP (as low as reasonably practicable) methods, which is the novelty based on the parametric study of this research work. The results reveal that by inducting proposed SRF control into the Wind Energy Conversion System (WECS), the risks of high fluctuations and disturbances in signals are reduced to an acceptable level as per the standards of IEEE 519-2014 and EN 50160. The proposed work is validated through running simulations in MATLAB/Simulink with and without controls

Flexibility Characterization of Sustainable Power Systems in Demand Space: A Data-Driven Inverse Optimization Approach

The deepening of the penetration of renewable energy is challenging how power system operators cope with their associated variability and uncertainty. The inherent flexibility of dispathchable assets present in power systems, which is often ill-characterized, is essential in addressing this challenge. Several proposals for explicit flexibility characterization focus on defining a feasible region that secures operations either in generation or uncertainty spaces. The main drawback of these approaches is the difficulty in visualizing this feasibility region when there are multiple uncertain parameters. Moreover, these approaches focus on system operational constraints and often neglect the impact of inherent couplings (e.g., spatial correlation) of renewable generation and demand variability. To address these challenges, we propose a novel data-driven inverse optimization framework for flexibility characterization of power systems in the demand space along with its geometric intuition. The approach captures the spatial correlation of multi-site renewable generation and load using polyhedral uncertainty sets. Moreover, the framework projects the uncertainty on the feasibility region of power systems in the demand space, which are also called loadability sets. The proposed inverse optimization scheme, recast as a linear optimization problem, is used to infer system flexibility adequacy from loadability sets