Coordination and Optimization of Power Distribution Systems with Stochastic Distributed Energy Resources using Artificial Intelligence

• Author / Creator

  Mohammed Al-Saffar

• High levels of penetration of distributed photovoltaic generators can cause serious overvoltage issues, especially during periods of high power generation and light loads. It is of vital importance to gain more understanding of the system and to prepare mitigation plans before the number of PV installations reaches a critical level. Therefore, properly assessing the PV hosting capacity is necessary. In this thesis, the hosting capacities of several real circuits in Alberta, Canada are evaluated using Monte Carlo simulation-based probabilistic power flow (MCS-based PPF) method. The examined circuits are located in the cities of Fort McMurray, Lloydminster, and Drumheller. These areas represent circuits of different
  sizes and complexities. The hosting capacities of the three regions were determined to be 10%, 60%, and 70%, respectively. Buses impacted by PV penetration were found in all three distribution networks. Factors influencing the PV hosting capacity are also identified and analyzed.

  There have been many solutions proposed to mitigate the voltage
  problems, some of them using battery energy storage systems (BESS) at the PV generation sites. In addition to their ability to absorb extra power during the light load periods, BESS can also supply additional power under high load conditions. However, their capacity may not be sufficient to allow charging every time when power absorption is desired. Therefore, typical PV/BESS may not fully prevent over-voltage problems in power distribution grids. This thesis develops a cooperative state of charge control scheme to alleviate the BESS capacity problem through Monte-Carlo Tree Search based reinforcement learning (MCTS-RL). The proposed intelligent method coordinates the distributed batteries from other regions to provide voltage regulation in a distribution network. Furthermore, the energy optimization process during the day hours and the simultaneous state of charge control are achieved using model predictive control (MPC). The proposed approach is demonstrated on two test cases, the IEEE 33 bus system and a practical medium size distribution system in Alberta Canada.

  Optimization technology is developing to the point of becoming a cost-effective enabler of increased utilization of power transfer assets. This research presents a smart decomposition technique for the traditional optimal power flow (OPF) algorithm to allow distributed optimal power flow (DOPF) calculations without relying on a centralized controller. Hence, it develops a feasible distributed architectures for the electric power industry. The proposed method is implemented using the same algorithm MCTS-RL. This reduces computational complexity and avoids difficulties associated with stochastic modeling often used to capture the random nature of distributed energy resources (DER) units and loads. The efficiency of the optimization process is improved when the DOPF reflects the fast response capability of the optimal solution. This contribution provides results for a real-time dispatchable resource and demonstrates the flexibility of RL to adapt to changes in system states, ultimately reducing the generation cost while maintaining the system security constraints.

  This thesis also develops a decomposition methodology for the traditional optimal power flow. It not only avoids the challenges associated with the stochastic nature of DERs and loads, but it also reduces the computational complexity of the conventional linear programming approach in the optimization problem. It does so using machine learning algorithms employed for two crucial tasks. First, MCTS-RL identifies clusters of network nodes to form a distributed architecture suitable for electric power transactions. Second, the network states updated by RL are used to execute conventional linear programming on a reduced set of lines identified during the previous step. The proposed approach is demonstrated through a real-time balancing electricity market constructed over the IEEE 69-bus system and enhanced using price signals based on distribution locational marginal prices. This application clearly shows the ability of the new technique to effectively coordinate multiple distribution system entities while maintaining system security constraints.

• Subjects / Keywords

  • Coordination
  • Optimization
  • Power Distribution System
  • Artificial Intelligence

• Graduation date

  Spring 2021

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-m0d1-8960

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.