Optimal Residential Battery Storage Operations Using Robust Data-Driven Dynamic Programming

Zhang, Nan; Leibowicz, Benjamin D.; Hanasusanto, Grani A.

doi:10.1109/tsg.2019.2942932

Cited by 28 publications

(16 citation statements)

References 20 publications

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“…In our setting, we only use the information from user mains, which is already available to DSOs. In [10], a methodology for optimal residential battery operation in a single house is proposed, to minimize electricity costs. Finally, the approaches in [11], [12] focus on the scheduling of Electric Vehicles (EVs) only.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries

Melatti

Mari

Mancini

et al. 2022

IEEE Trans. Ind. Electron.

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Within electrical distribution networks, substation constraints management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation constraints management can be measured as the reduction of constraints violations w.r.t. unmanaged demand. Home batteries hold the promise of enabling efficient and user-oblivious substation constraints management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. We present a novel two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields nearoptimal substation constraints management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraints management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy. NOMENCLATUREs EDN substation T s set of time slots for substation s U set of houses (users) t time slot (element of T ) u house (element of U ) T u set of time slots for house u; note that, for all notation depending on an house index u, if the house is understood, u is not shown T u,P set of time slots in T u in which the EV is plugged-in on house u τ l duration (in minutes

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Section: Introductionmentioning

confidence: 99%

“…Finally, the approaches in [11], [12] focus on the scheduling of Electric Vehicles (EVs) only. However, in our setting, we are interested in SCM, which is not addressed in [9], [8], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries

Melatti

Mari

Mancini

et al. 2022

IEEE Trans. Ind. Electron.

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“…The day‐ahead scheduling, that is, the second category of solution, needs only one‐shot optimization from the global perspective to obtain the all‐day operation plan, which is beneficial for the residents to set battery schedules and the utilities to preunderstand the user's power consumption. To include the next day's uncertain conditions, stochastic optimization based on probability distributions and robust optimization based on certain variable bounds have been introduced to optimal battery energy management 21‐34 . This work considers the uncertainty in the residential microgrid's daily operation and studies the day‐ahead and intraday optimal scheduling of household batteries.…”

Section: Introductionmentioning

confidence: 99%

“…Reference 32 proposes an operation method for the multienergy system integrating the energy storage, which can fully utilize the flexibility of the multienergy system and energy storage to accommodate the uncertainties. Reference 33 considers the worst‐case expected electricity cost in residential battery storage operations and proposes data‐driven DP algorithms that raise the break‐even battery cost. For building the ambiguity set in the robust chance‐constrained problem, distributionally robust optimization is applied in the recent Reference 34.…”

Section: Introductionmentioning

confidence: 99%

“…To include the next day's uncertain conditions, stochastic optimization based on probability distributions and robust optimization based on certain variable bounds have been introduced to optimal battery energy management. [21][22][23][24][25][26][27][28][29][30][31][32][33][34] This work considers the uncertainty in the residential microgrid's daily operation and studies the day-ahead and intraday optimal scheduling of household batteries.…”

Section: Introductionmentioning

confidence: 99%

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Optimal energy management of residential battery storage under uncertainty

Zhou

Feng

et al. 2021

Int Trans Electr Energ Syst

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Summary Backgroud The uncertainty associated with renewable generation and loads imposes various challenges for the economic operation of microgrids. On the residential side, energy storage systems are implemented with high expectations to enhance photovoltaic power consumption and electricity price arbitrage. Aims This paper proposes an expectation‐oriented stochastic model for optimal energy management of household batteries to cope with the uncertain electricity generation and consumption. Materials & Methods The exact cost expectation of daily operation is formulated as an alternative to the conventional scenario‐based average approximation. A predictive control‐based battery scheduling mechanism is developed further to apply the proposed model to intraday energy storage management. Results Simulation results validate the convexity of the proposed model. Discussion Although nonlinear in nature, the resultant optimization problem is proven to be convex, which ensures computable global optimal solutions for the proposed model. Conclusion Simulation results demonstrate the effectiveness of the proposed scheduling strategy compared with existing methods.

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Effective Analysis of Industry-Relevant Cyber-Physical Systems via Statistical Model Checking

Pappagallo

2022

AIxIA 2021 – Advances in Artificial Intelligence

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Optimal Residential Battery Storage Operations Using Robust Data-Driven Dynamic Programming

Cited by 28 publications

References 20 publications

A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries

A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries

Optimal energy management of residential battery storage under uncertainty

Effective Analysis of Industry-Relevant Cyber-Physical Systems via Statistical Model Checking

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