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.
more »
« less
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.
more »
« less
- Award ID(s):
- 2047306
- PAR ID:
- 10397906
- Date Published:
- Journal Name:
- 11TH BULK POWER SYSTEMS DYNAMICS AND CONTROL SYMPOSIUM
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Integration of distributed renewable energy sources (D- RES) has been introduced as a viable solution to offer cheap and clean energy to customers in decentralized power system. D- RES can offer local generation to flexible customers based on their servicing deadline and constraints, benefiting both D- RES owners and customers in terms of providing economic revenue and reducing the cost of supplied energy. In this context, this paper proposes a dynamic matching framework using model predictive control (MPC) to enable local energy sharing in power system operation. The proposed matching framework matches flexible customers with D- RES to maximize social welfare in the matching market, while meeting the customers' servicing constraints prior to their deadline. Simulations are conducted on a test power system using multiple matching algorithms across different load and generation scenarios and the results highlighted the efficiency of proposed framework in matching flexible customers with the appropriate supply sources to maximize social welfare in the matching market.more » « less
-
Integration of distributed renewable energy sources (D- RES) has been introduced as a viable solution to offer cheap and clean energy to customers in decentralized power system. D- RES can offer local generation to flexible customers based on their servicing deadline and constraints, benefiting both D- RES owners and customers in terms of providing economic revenue and reducing the cost of supplied energy. In this context, this paper proposes a dynamic matching framework using model predictive control (MPC) to enable local energy sharing in power system operation. The proposed matching framework matches flexible customers with D- RES to maximize social welfare in the matching market, while meeting the customers' servicing constraints prior to their deadline. Simulations are conducted on a test power system using multiple matching algorithms across different load and generation scenarios and the results highlighted the efficiency of proposed framework in matching flexible customers with the appropriate supply sources to maximize social welfare in the matching market.more » « less
-
Non-profit hunger relief organizations primarily depend on donors’ benevolence to help alleviate hunger in their communities. However, the quantity and frequency of donations they receive may vary over time, thus making fair distribution of donated supplies challenging. This paper presents a hierarchical forecasting methodology to determine the quantity of food donations received per month in a multi-warehouse food aid network. We further link the forecasts to an optimization model to identify the fair allocation of donations, considering the network distribution capacity in terms of supply chain coordination and flexibility. The results indicate which locations within the network are under-served and how donated supplies can be allocated to minimize the deviation between overserved and underserved counties.more » « less
-
more » « less
Increasing e-commerce activity, competition for shorter delivery times, and innovations in transportation technologies have pushed the industry toward instant delivery logistics. This paper studies a facility location and online demand allocation problem applicable to a logistics company expanding to offer instant delivery service using unmanned aerial vehicles or drones. The problem is decomposed into two stages. During the planning stage, the facilities are located, and product and battery capacity are allocated. During the operational stage, customers place orders dynamically and real-time demand allocation decisions are made. The paper explores a multi-armed bandit framework for maximizing the cumulative reward realized by the logistics company subject to various capacity constraints and compares it with other strategies. The multi-armed bandit framework provides about 7% more rewards than the second-best strategy when tested on standard test instances. A case study based in Portland Metro Area showed that multi-armed bandits can outperform the second-best strategy by more than 20%.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
