This paper considers optimization problems of energy demand networks including aggregators and investigates strategic behavior of the aggregators. The participants of the network are a utility company, who plays a role of energy supply source, aggregators and a large number of consumers. We suppose that the network will be optimized by price response based or, in other words, market based optimization processes. We also suppose that the aggregator has a strategic parameter in its cost function and, by choosing the parameter strategically, the aggregator will try to pursue its own benefit. This general problem formulation will apply to a specific problem setting, where the aggregator possess battery storage with different specifications: The one is high-performance and expensive and the other is low-performance and cheap. The aggregator will choose total capacity of storage to be installed and a ratio of high-performance storage to low-performance storage as the strategic parameters and try to increase its own benefit. By using numerical examples, we show that the strategic decision making by the aggregator could provide useful insights in qualitative analysis of energy demand networks.
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Market mechanism to enable grid-aware dispatch of Aggregators in radial distribution networks
This paper presents a market-based optimization framework wherein Aggregators can compete for nodal capacity across a distribution feeder and guarantee that allocated flexible capacity cannot cause overloads or congestion. This mechanism, thus, allows Aggregators with allocated capacity to pursue a number of services at the whole-sale market level to maximize revenue of flexible resources. Based on Aggregator bids of capacity (MW) and network access price ($/MW), the distribution system operator (DSO) formulates an optimization problem that prioritizes capacity to the different Aggregators across the network while implicitly considering AC network constraints. This grid-aware allocation is obtained by incorporating a con- vex inner approximation into the optimization framework that prioritizes hosting capacity to different Aggregators. We adapt concepts from transmission-level capacity market clearing, utility demand charges, and Internet-like bandwidth allocation rules to distribution system operations by incorporating nodal voltage and transformer constraints into the optimization framework. Simulation based results on IEEE distribution networks showcase the effectiveness of the approach.
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- Award ID(s):
- 2047306
- NSF-PAR ID:
- 10397906
- Date Published:
- Journal Name:
- 11TH BULK POWER SYSTEMS DYNAMICS AND CONTROL SYMPOSIUM
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT The presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course.
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
