An Optimization Based Approach For Facility Energy Management With Uncertainties And Power Portfolio Optimization In Deregulated Electricity Markets With Risk Management PDF Download

Energy storage systems have become an essential technology to support the transition to low carbon energy by enabling valuable system flexibility. Coordinating the control of multiple building energy storage resources can provide energy flexibility to manage the power imbalance between energy supply and demand in the electric grid. This research focused on developing a new grid-interactive control framework for building portfolios by utilizing thermal energy storage assets. This work addressed the motivation of considering uncertainty in power procurement and operation, the value of the aggregator-based framework, the scalability of the computational framework, and the potential benefits to participants in the portfolio. To address these questions, this work presents an uncertainty- and risk-aware transactive control framework for an aggregator to coordinate the thermal energy storage (TES) assets of multiple buildings. A two-stage stochastic optimization framework was formulated for day-ahead energy procurement that considers uncertainties in building occupancy patterns, weather conditions, building demands, and real-time energy prices of the following day. A transactive market mechanism was introduced to determine the optimal TES operation by providing demand response incentives to customers and support grid reliability with the aggregated flexible building demand. In this dissertation, five research steps were completed to achieve the research objectives. The first study focused on the initial model development and validation. The capabilities of the model were assessed against different levels of power price realizations. According to the results, the proposed framework produced profits for the aggregator while providing the optimal TES operation to customers. It was also demonstrated that the value of stochastic solutions increased with the level of price volatility. This result implied that quantifying and understanding the nature of uncertainty is important in developing appropriate control strategies. After demonstrating the effectiveness of the model, the second study was to assess the customers' benefits when participating in the aggregator's portfolio compared to the individually controlled buildings. According to results, buildings within the portfolio could save up to 1%-3.7% of energy costs over individually optimized buildings. In addition to the energy costs, the customers could benefit in terms of TES utilization and risk management. The third study was to evaluate the performance of a computationally scalable framework to efficiently solve the large-scale stochastic problem. A smoothed variance-reduced accelerated gradient method was applied for a more complex building portfolio. The computational efficiency of the proposed approach was demonstrated in terms of lower peak memory usage over the existing algorithm. From the validation experiment with a large-scale building portfolio, it was demonstrated that the stochastic controller could achieve approximately 8.3% of energy costs with respect to the deterministic control framework. For the fourth step, the value of the stochastic controller was assessed. The performance of the supervisory controller was evaluated depending on the quality level of information. Unlike the deterministic approach using a single forecast, the stochastic framework could bring financial benefits in both day-ahead planning and real-time operation. However, both cases did not perform well when unexpected high demand was required due to the risk-neutral formulation. The fifth study developed a risk-averse control framework and evaluated the performance to improve upon the previous risk-neutral framework. Moreover, the relationship between risk-taking level and thermal energy storage sizing was discussed. The risk-averse control framework successfully avoided the financial loss from the unexpectedly high building demand and real-time power price spikes. In addition, the total energy cost savings by applying the risk-averse stochastic model were increased when more uncertainty and energy systems were included in the building. This dissertation demonstrated the significant impact of the developed framework in the management of uncertain and high-risk settings situations to support the coordination of flexible resources for grid reliability and sustainability. This research provides comprehensive information for optimal grid-interactive building design and controls integrating thermal energy storage. The results imply that there are advantages in implementation and scalability to the proposed framework keeping the majority of the intelligence at a high-level while requiring simple price-responsive controllers at the building level. This work also notes several future extensions and discusses the potential to adapt the framework for other planning and control problems in the building-to-grid domain.