The Sharing Economy for the Electricity Storage

Kalathil, Dileep; Wu, Chenye; Poolla, Kameshwar; Varaiya, Pravin

doi:10.1109/tsg.2017.2748519

Cited by 172 publications

(89 citation statements)

References 14 publications

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“…Cooperative game theoretic analysis of multiple demand response aggregators in a virtual power plant and their cost allocation has been tackled in [11]. Sharing of storage firms under a local spot market has been analyzed using noncooperative game theory [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Solar Energy Aggregation Under Various Billing Mechanisms

Chakraborty

Baeyens

Khargonekar

et al. 2019

IEEE Trans. Smart Grid

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Ongoing reductions in the cost of solar photovoltaic (PV) systems are driving their increased installations by residential households. Various incentive programs such as feed-in tariff, net metering, net purchase and sale that allow the prosumers to sell their generated electricity to the grid are also powering this trend. In this paper, we investigate sharing of PV systems among a community of households, who can also benefit further by pooling their production. Using cooperative game theory, we find conditions under which such sharing decreases their net total cost. We also develop allocation rules such that the joint net electricity consumption cost is allocated to the participants. These cost allocations are based on the cost causation principle. The allocations also satisfy the standalone cost principle and promote PV solar aggregation. We also perform a comparative analytical study on the benefit of sharing under the mechanisms favorable for sharing, namely net metering, and net purchase and sale. The results are illustrated in a case study using real consumption data from a residential community in Austin, Texas.

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Section: Introductionmentioning

confidence: 99%

Analysis of Solar Energy Aggregation Under Various Billing Mechanisms

Chakraborty

Baeyens

Khargonekar

et al. 2019

IEEE Trans. Smart Grid

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“…1) According to Proposition 1, p * i is the unique solution of (10) and satisfies ∑ i p * i = ∑ i D i . So p * i must be a feasible solution to the social optimal problem (12). Asp i is the optimal solution of (12), we always have…”

Section: Appendix D Proof Of Propositionmentioning

confidence: 99%

An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

Chen

Mei

Zhou

et al. 2020

IEEE Trans. Smart Grid

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This paper proposes a novel energy sharing mechanism for prosumers who can produce and consume. Different from most existing works, the role of individual prosumer as a seller or buyer in our model is endogenously determined. Several desirable properties of the proposed mechanism are proved based on a generalized game-theoretic model. We show that the Nash equilibrium exists and is the unique solution of an equivalent convex optimization problem. The sharing price at the Nash equilibrium equals to the average marginal disutility of all prosumers. We also prove that every prosumer has the incentive to participate in the sharing market, and prosumers' total cost decreases with increasing absolute value of price sensitivity. Furthermore, the Nash equilibrium approaches the social optimal as the number of prosumers grows, and competition can improve social welfare.

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“…The problem of scheduling multiple services is addressed in [6]- [9]. For previous work on sharing storage resources among multiple parties, see [10]- [13]. In [6], Mégel et al employ a model predictive control approach for co-optimizing simultaneous provision of local and system-wide services.…”

Section: A Literature Reviewmentioning

confidence: 99%

Sharing Energy Storage Between Transmission and Distribution

Elliott

Fernández-Blanco

Kozdras

et al. 2019

IEEE Trans. Power Syst.

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This paper addresses the problem of how best to coordinate, or "stack," energy storage services in systems that lack centralized markets. Specifically, its focus is on how to coordinate transmission-level congestion relief with local, distribution-level objectives. We describe and demonstrate a unified communication and optimization framework for performing this coordination. The congestion relief problem formulation employs a weighted 1-norm objective. This approach determines a set of corrective actions, i.e., energy storage injections and conventional generation adjustments, that minimize the required deviations from a planned schedule. To exercise this coordination framework, we present two case studies. The first is based on a 3-bus test system, and the second on a realistic representation of the Pacific Northwest region of the United States. The results indicate that the scheduling methodology provides congestion relief, cost savings, and improved renewable energy integration. The largescale case study informed the design of a live demonstration carried out in partnership with the University of Washington, Doosan GridTech, Snohomish County PUD, and the Bonneville Power Administration. The goal of the demonstration was to test the feasibility of the scheduling framework in a production environment with real-world energy storage assets. The demonstration results were consistent with computational simulations.Index Terms-Distribution system operator, energy storage system, mixed-integer linear programming, state of charge, transmission congestion, transmission system operator, unit commitment.

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The Sharing Economy for the Electricity Storage

Cited by 172 publications

References 14 publications

Analysis of Solar Energy Aggregation Under Various Billing Mechanisms

Analysis of Solar Energy Aggregation Under Various Billing Mechanisms

An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

Sharing Energy Storage Between Transmission and Distribution

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