Wholesale energy market in a smart grid: Dynamic modeling and stability

Kiani, Arman; Annaswamy, Anuradha M.

doi:10.1109/cdc.2011.6161681

Cited by 34 publications

(16 citation statements)

References 12 publications

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“…The main objective of these DSM programs is to optimize and control the energy consumption automatically by considering scenarios with different energy suppliers and different energy users. A dual decomposition-based approach was successfully taken by some researchers [6,[23][24][25][26] for modeling these distributed optimization issues in an SG. Additionally, game-theoretic methods, including cooperative games, noncooperative games and evolutionary games, have been extensively employed as solution techniques for optimizing energy consumption by resolving the equilibriums of the games; because game-theoretic methods can capture the features of these interactions and study the possible outcomes and corresponding equilibriums [27].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Household Energy Management Based on a Double Cooperative Game Approach in the Smart Grid

et al. 2015

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Taking advantage of two-way communication infrastructure and bidirectional energy trading between utility companies and customers in the future smart grid (SG), autonomous energy management programs become crucial to the demand-side management (DSM). Most of the existing autonomous energy management schemes are for the scenario with a single utility company or the scenario with one-way energy trading. In this paper, an autonomous household energy management system with multiple utility companies and multiple residential customers is studied by considering the bidirectional energy trading. To minimize the overall costs of both the utility companies and the residential customers, the energy management system is formulated as a double cooperative game. That is, the interaction among the residential users is formulated as a cooperative game, where the players are the customers and the strategies are the daily schedules of their household appliances; and the interaction among the utility companies is also formulated as a cooperative game, where the players are the suppliers and the strategies are the proportions of the daily total energy they provide for the customers. Without loss of generality, the bidirectional energy trading in the double cooperative game is formulated by allowing plug-in electric vehicles (PEVs) to discharge and sell energy back. Two distributed algorithms will be provided to realize the global optimal performance in terms of minimizing the energy costs, which can be guaranteed at the Nash equilibriums of the formulated cooperative games. Finally, simulation results illustrated that the proposed double cooperative game can benefit both the utility companies and residential users significantly.

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Section: Introductionmentioning

confidence: 99%

Autonomous Household Energy Management Based on a Double Cooperative Game Approach in the Smart Grid

et al. 2015

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“…A wide range of possible approaches can be used to solve this problem including dynamic market mechanisms (e.g., [63], [78]). This in turn will directly enable a quantitative assessment of the distribution of DR devices, Buckets, Batteries and Bakeries and perhaps leads to an integration of a high percentage of RERs.…”

Section: Dr Program Behavior Referencesmentioning

confidence: 99%

Demand response in smart grids: Participants, challenges, and a taxonomy

Hansen

Knudsen

Annaswamy

2014

53rd IEEE Conference on Decision and Control

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In recent decades, moves toward higher integration of Renewable Energy Resources have called for fundamental changes in both the planning and operation of the overall power grid. One such change is the incorporation of Demand Response (DR), the process by which consumers can adjust their demand in a flexible manner. This paper presents a survey of various aspects of DR including the different types of participants, as well as the underlying challenges and the overall potential of DR when it comes to large-scale implementations. Benefits of DR as reported in the literature for performance metrics such as frequency control and price control, as well as methods for ensuring privacy are discussed. A quantitative taxonomy of DR recently proposed in the literature based on the inherent magnitude, run-time, and integral constraints is discussed and its integration with economic dispatch is explored.

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“…The overall network dynamics can be expressed by (2), (3), and (5) subjected to (7). The goal is to design the price signal λ(t) at the MO and the power p gn (t) distributed by the DNO such that the value of |e n (t)| can be as small as possible for n = 1, 2, ..., N , or equivalently, the stored energy s n (t) can achieve the desired energy levels n > 0 as close as possible.…”

Section: Dno and Momentioning

confidence: 99%

“…Ideally, the power users reduce/increase their power consumption at a high/low price [4]- [7] so that a well-designed pricing scheme can help balance the power supply and demand in the grid network. Various pricing schemes have been investigated, Copyright (c) 2015 IEEE.…”

Section: Introductionmentioning

confidence: 99%