Power consumption and generation in the electrical grid must be balanced at all times. This balance is maintained by the grid operator through the procurement of energy and regulation reserve services in the wholesale electricity market. Traditionally, these services could only be procured from generation resources. However, helped by the advances in the computational and communication infrastructure, the demand resources are increasingly being leveraged in this regard. In particular, the Heating, Ventilation and Air-Conditioning (HVAC) systems of buildings are gaining traction due to the consumption flexibility provided by their energy-shifting and fast-response capabilities.
The provision of energy and regulation reserve services in the wholesale market, from the perspective of a typical building’s HVAC system, can be construed in terms of two synergistic problems: an hourly deterministic optimization problem, referred to as Scheduling Problem, and a real-time (seconds timescale) stochastic control problem, referred to as Deployment Problem. So far, the existing literature has synthesized the solutions of these two problems in a simplistic, sequential/iterative manner without employing an integrated approach that captures explicitly their cost and constraint interactions. Moreover, the deployment problem has only been solved with classical controllers which are not optimal, whereas the non-convexities in the scheduling problem have been addressed with methods that are sensitive to initialization. The current approaches therefore do not fully optimize the decisions of the two problems either individually or collectively, and hence do not fully exploit the HVAC resource.
The goal of the proposed research is to have optimal decision-making across both the scheduling and deployment problems. Our approach proposes deriving an optimal control policy for the deployment problem, and expressing the corresponding expected sum of deployment costs over the hour (called ‘expected intra-hour costs’) as a closed-form analytic function of the scheduling decisions. The inclusion of these expected intra-hour costs into the scheduling problem allows the optimization of the hourly scheduling decisions, pursuant to the real-time use of the optimal deployment control policy. Thus, our approach captures the interaction of the two problems and optimizes decisions across timescales yielding a truly integrated solution. We investigate the estimation of the expected intra-hour costs (based on a myopic policy optimizing instantaneous tracking error and utility loss), and solve the integrated problem with tight relaxations, demonstrating the value and applicability of the approach. Further, we investigate the derivation of the optimal control policy for the deployment problem, formulating and solving it as discounted-cost infinite horizon Dynamic Program (DP) and Reinforcement Learning (RL) problems. We also characterize the optimal policy as a closed-form analytic mapping from state-space to action-space, and obtain the corresponding expected cost-to-go (a.k.a. expected intra-hour costs) as a closed-form analytic function of the scheduling decisions. We further illustrate that the optimal policy better captures the deployment costs compared to existing approaches.
As such, our work represents a structured, interpretable and automated way for the end-to-end consideration of energy and regulation reserve market participation, and can be extended to other demand side resources.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47937 |
| Date | 17 January 2024 |
| Creators | Aslam, Waleed |
| Contributors | Caramanis, Michael |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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