Residential Energy Storage Management With Bidirectional Energy Control

Abstract

We consider the residential energy storage management system with integrated renewable generation and the availability of bidirectional energy flow from and to the grid through buying and selling. We propose a real-time bidirectional energy control algorithm, aiming to minimize the net system cost from energy buying and selling as well as battery deterioration and storage inefficiency within a given time period, subject to the battery operational constraints and energy buying and selling constraints. We formulate the problem as a stochastic control optimization problem. We then modify and transform this difficult problem into one that enables us to develop the realtime energy control algorithm through Lyapunov optimization. Our developed algorithm is applicable to arbitrary and unknown statistics of renewable generation, load, and electricity prices. It provides a simple closed-form control solution based on only current system states, and requires a minimum complexity for real-time implementation. Furthermore, the solution structure reveals how the battery energy level and energy prices affect the energy flow direction and storage decision. The proposed algorithm possesses a bounded performance guarantee to that of the optimal non-causal T -slot look-ahead control policy. Simulation shows the effectiveness of our proposed algorithm as compared with alternative real-time and non-causal algorithms, and demonstrates the effect of selling-to-buying price ratio and battery inefficiency on the storage behavior and system cost.

Optimal energy management of residential battery storage under uncertainty

“…In general, the electricity purchasing price ( λ p , t ) is designed to be no less than the selling price ( λ s , t ) 18,45 . In addition, , T > 0.…”

“…In general, the electricity purchasing price (λ p, t ) is designed to be no less than the selling price (λ s, t ). 18,45 In addition, fP net,t Á ð Þ≥0, T > 0. Thus, the Hessian matrix is positive semidefinite.…”

Real-Time Control of Battery Energy Storage Systems to Provide Ancillary Services Considering Voltage-Dependent Capability of DC-AC Converters

“…The formulated MINLP is solved with a two-stage approach in which the integer variables are solved in the first-stage and a nonlinear programming problem (NLP) is solved in the second-stage [16]. Reference [17] proposes an energy storage management based on a stochastic optimization problem to minimize the net system cost considering bidirectional energy flow with the grid. The stochastic optimization problem is reformulated and solved through the Lyapunov optimization approach which can deal with the uncertainties from renewable energy generation, power load and electricity price [17].…”

“…Reference [17] proposes an energy storage management based on a stochastic optimization problem to minimize the net system cost considering bidirectional energy flow with the grid. The stochastic optimization problem is reformulated and solved through the Lyapunov optimization approach which can deal with the uncertainties from renewable energy generation, power load and electricity price [17]. The authors of [17] prove the existence of the upper bound of the so called Lyapunov drift and use this upper bound to design the control algorithm to minimize a proposed drift-plus-cost metric.…”

Intelligent approach for residential load scheduling

“…Given the uncertainty associated with electricity prices, a probabilistic model was proposed to optimally manage electricity usage in a residential energy hub, containing energy converters, PHEV, heat storage and a PV cell [25]. A real‐time bidirectional energy‐control algorithm that manages the electricity flow to the grid and vice versa was provided in [26].…”