Optimal Electricity Pricing for Societal Infrastructure Systems

Alizadeh, Mahnoosh; Wai, Hoi-To; Goldsmith, Andrea; Scaglione, Anna

doi:10.24251/hicss.2017.366

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…Second, we plan to develop a stochastic (queueing-theoretical) model of P-AMoD, which explicitly captures the stochastic nature of demand for transportation and power, and enables the design of controllers that directly mitigate large-scale stochastic fluctuations. Third, we will extend our model to capture the scenario where multiple TSOs compete for customers while sharing the same transportation and power infrastructure, extending our previous results in [44]. Fourth, we will extend the P-AMoD model to capture other modes of provision of service, including heterogeneous fleets where vehicles may differ in size, seating capacity, and battery capacity, and ride-sharing mechanisms where multiple customers with similar origins and destinations can travel in the same vehicle.…”

Section: Discussionmentioning

confidence: 86%

On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms

Rossi

Iglesias

Alizadeh

et al. 2020

IEEE Trans. Control Netw. Syst.

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We study the interaction between a fleet of electric self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a joint model that captures the coupling between the two systems stemming from the vehicles' charging requirements, capturing time-varying customer demand, battery depreciation, and power transmission constraints. First, we show that the model is amenable to efficient optimization. Then, we prove that the socially optimal solution to the joint problem is a general equilibrium if locational marginal pricing is used for electricity. Finally, we show that the equilibrium can be computed by selfish transportation and generator operators (aided by a non-profit ISO) without sharing private information. We assess the performance of the approach and its robustness to stochastic fluctuations in demand through case studies and agent-based simulations. Collectively, these results provide a first-of-a-kind characterization of the interaction between AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.

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Section: Discussionmentioning

confidence: 86%

On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms

Rossi

Iglesias

Alizadeh

et al. 2020

IEEE Trans. Control Netw. Syst.

Self Cite

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Agent-Based Energy Consumption Scheduling for Smart Grids: An Auction-Theoretic Approach

et al. 2020

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The future smart grid would help to benefit both the users and the electricity providing companies from smart pricing techniques. In addition, smart pricing can be used to achieve social objectives and would in turn fluctuate wholesale market into demand side. Collecting abundant information regarding the users electricity consumption pattern is a challenging task for utility providing companies. That is, users may not be willing to expose their indigenous information without any incentive. In this paper an Optimal Energy Consumption Scheduling (OECS) mechanism is proposed to tackle this problem. An agent-based forecasting method is designed, which is capable of predicting energy consumption of each consumer with a lead-time of one hour. This forecasting is exploited to estimate the cost of buying required amount of energy from multiple suppliers. Consequently, based on the estimated required energy and cost, an auction mechanism is proposed to optimize the energy traded between consumers and multiple suppliers within a smart grid. The objectives include increased efficiency and cost reduction of electricity usage by the end users. The results and properties of the proposed OECS mechanism are studied, and it is shown that the auction technique is budget balanced for distribution of electrical energy among consumers from diverse renewable generation resources. Extensive numerical simulations are also conducted to show and prove the beneficial properties of OECS mechanism.

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