Stochastic Home Energy Management Systems with Varying Controllable Resources

Garifi, Kaitlyn; Baker, Kyri; Christensen, Dane; Touri, Behrouz

doi:10.1109/pesgm40551.2019.8973708

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…Since dEc ps,t À Á =dP b,t ≤ 0 (see (29)) and E(c b, t ) = 0, the minimum objective cost will be obtained when the battery power is at its maximum. Thus, P Ã bd,t = P Ã bc,t = 0.…”

Section: Appendix B: Exactness Of Relaxationmentioning

confidence: 99%

“…Since d Ec ps,t À Á =dP b,t ≤ 0 (see (29)), it is evident that the higher the battery power is, the lower the residents' expected electricity bills will be. In other words, given the value of ΔE Ã b,t , the minimum objective cost will be obtained when the battery power is at its maximum.…”

Section: Appendix B: Exactness Of Relaxationmentioning

confidence: 99%

“…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%

“…In Reference 28, the conditional value at risk is employed to treat with the battery overcharge and overdischarge risks. Reference 29 presents a chance‐constrained MPC model that ensures the resident's satisfaction using controllable household resources such as PV panels, electric vehicles, and batteries.…”

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%

See 4 more Smart Citations

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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“…Since dEc ps,t À Á =dP b,t ≤ 0 (see (29)) and E(c b, t ) = 0, the minimum objective cost will be obtained when the battery power is at its maximum. Thus, P Ã bd,t = P Ã bc,t = 0.…”

Section: Appendix B: Exactness Of Relaxationmentioning

confidence: 99%

Section: Appendix B: Exactness Of Relaxationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%