Sizing of electric thermal storage under real time pricing

Daryanian, B.; Bohn, Roger E.

doi:10.1109/59.221246

Cited by 23 publications

(18 citation statements)

References 7 publications

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“…We summarize the convergence property of the iterates specified above, whose proof is relegated to Appendix C. 4 See Appendix C for more details.…”

Section: Edk Ementioning

confidence: 99%

“…Here P2 is an auxilliary problem we construct to bridge the infinite horizon storage control problem P1 to online optimization problems (4). It has the following form…”

Section: P1: Minimize Limmentioning

confidence: 99%

“…Here constraints (10b) and (10c) ensures that at the solution, these local auxiliary variables must be equal to the corresponding true variables. The (scaled) dual variables 4 corresponding to constraints (10b) and (10c) are denoted by η i and ξ e , respectively. We proceed to state the task-based distributed ADMM.…”

Section: A Node-edge Reformulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed Online Modified Greedy Algorithm for Networked Storage Operation Under Uncertainty

Qin

Chow

Yang

et al. 2015

IEEE Trans. Smart Grid

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Abstract-The integration of intermittent and stochastic renewable energy resources requires increased flexibility in the operation of the electric grid. Storage, broadly speaking, provides the flexibility of shifting energy over time; network, on the other hand, provides the flexibility of shifting energy over geographical locations. The optimal control of storage networks in stochastic environments is an important open problem. The key challenge is that, even in small networks, the corresponding constrained stochastic control problems on continuous spaces suffer from curses of dimensionality, and are intractable in general settings. For large networks, no efficient algorithm is known to give optimal or provably near-optimal performance for this problem. This paper provides an efficient algorithm to solve this problem with performance guarantees. We study the operation of storage networks, i.e., a storage system interconnected via a power network. An online algorithm, termed Online Modified Greedy algorithm, is developed for the corresponding constrained stochastic control problem. A sub-optimality bound for the algorithm is derived, and a semidefinite program is constructed to minimize the bound. In many cases, the bound approaches zero so that the algorithm is near-optimal. A task-based distributed implementation of the online algorithm relying only on local information and neighbor communication is then developed based on the alternating direction method of multipliers. Numerical examples verify the established theoretical performance bounds, and demonstrate the scalability of the algorithm.

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“…We summarize the convergence property of the iterates specified above, whose proof is relegated to Appendix C. 4 See Appendix C for more details.…”

Section: Edk Ementioning

confidence: 99%

“…Here P2 is an auxilliary problem we construct to bridge the infinite horizon storage control problem P1 to online optimization problems (4). It has the following form…”

Section: P1: Minimize Limmentioning

confidence: 99%

Section: A Node-edge Reformulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Online Modified Greedy Algorithm for Networked Storage Operation Under Uncertainty

Qin

Chow

Yang

et al. 2015

IEEE Trans. Smart Grid

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“…Furthermore, flexible or controllable demand provides another ubiquitous source of storage. Deferrable loads -including many thermal loads, loads of internet data-centers and loads corresponding to charging electric vehicles (EVs) over certain time intervals [2], [3] -can be interpreted as storage of demand [4]. Other controllable loads which can possibly be shifted to an earlier or later time, such as thermostatically controlled loads (TCLs), may be modeled and controlled as a storage with negative lower bound and positive upper bound on the storage level [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Online Modified Greedy algorithm for storage control under uncertainty

Qin

Chow

Yang

et al. 2016

2016 IEEE Power and Energy Society General Meeting (PESGM)

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Abstract-This paper studies the general problem of operating energy storage under uncertainty. Two fundamental sources of uncertainty are considered, namely the uncertainty in the unexpected fluctuation of the net demand process and the uncertainty in the locational marginal prices. We propose a very simple algorithm termed Online Modified Greedy (OMG) algorithm for this problem. A stylized analysis for the algorithm is performed, which shows that comparing to the optimal cost of the corresponding stochastic control problem, the sub-optimality of OMG is controlled by an easily computable bound. This suggests that, albeit simple, OMG is guaranteed to have good performance in cases when the bound is small. Meanwhile, OMG together with the sub-optimality bound can be used to provide a lower bound for the optimal cost. Such a lower bound can be valuable in evaluating other heuristic algorithms. For the latter cases, a semidefinite program is derived to minimize the suboptimality bound of OMG. Numerical experiments are conducted to verify our theoretical analysis and to demonstrate the use of the algorithm.

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Thermal Energy Storage

Flake

1999

Wiley Encyclopedia of Electrical and Electronics Engineering

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Sizing of electric thermal storage under real time pricing

Cited by 23 publications

References 7 publications

Distributed Online Modified Greedy Algorithm for Networked Storage Operation Under Uncertainty

Distributed Online Modified Greedy Algorithm for Networked Storage Operation Under Uncertainty

Online Modified Greedy algorithm for storage control under uncertainty

Thermal Energy Storage

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