Cost-optimal Control of Photovoltaic Systems with Battery Storage under Variable Electricity Tariffs

Kirchsteiger, Harald; Rechberger, Philipp J.; Steinmaurer, Gerald

doi:10.1007/s00502-016-0447-1

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The energy costs are then used as the quality measure that should be minimized and based on which the best solution candidates are selected for the next generation, where again some of them are selected for the children solution candidate creation [15]. The model used for this optimization (shown in Figure 1) was developed by Kirchsteiger et al [18] and was only slightly adapted for this work as described in [16].…”

Section: Methodsmentioning

confidence: 99%

Analysis of Battery Storage Usage of Heuristic Energy Flow Controllers

Kefer¹,

Kefer²,

Stöger³

et al. 2022

Atlantis Highlights in Engineering

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Due to the increasing usage of renewable energy sources like photovoltaic (PV) systems in the private sector and their fluctuations in production, energy management systems (EMS) are becoming more and more important. As such system use the produced energy as efficiently as possible, they also have an influence on a battery storage which is often installed together with the PV system. Nowadays batteries are already durable and cost efficient, but the possible negative effects an EMS might have on its usage and lifetime still need to be taken into account and be aware of. This is why this work analyses the battery usage of heuristic energy management controllers in detail and compares them to two existing energy management systems. It is shown that the heuristic energy management controllers are able to reduce the number of used battery cycles and therefore also the charged and discharged energy remarkable compared to the rulebased self-consumption optimization and the linear MPC, which also leads to a prolongation of the battery lifetime.

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Section: Methodsmentioning

confidence: 99%

Analysis of Battery Storage Usage of Heuristic Energy Flow Controllers

Kefer¹,

Kefer²,

Stöger³

et al. 2022

Atlantis Highlights in Engineering

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“…Different approaches are focused on the search of approximate solutions: one of these strategies is based on a probabilistic formulation [10], which is solved by an approach based on a model predictive control (MPC). In [11] proposes a dynamic programming (DP) approach and compares it with a linear optimization strategy and the related works of [12] and [13].…”

Section: State Of the Artmentioning

confidence: 99%

“…The required input data is the household load, PV production and electricity process. The dataset used was described in detail in [11]. It includes the electrical load from a typical single-family house sampled in 1-minute intervals, PV power measurements from a 5.8 kWp installation sampled at 5-minute intervals.…”

Section: Recorded Datamentioning

confidence: 99%

An Empirical Approach to Optimize Nonlinear Problems of Domestic Energy Management Systems

Carreras

Kirchsteiger

2023

Environmental and Climate Technologies

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Numerical optimization methods are used to reduce the operative costs and emissions of domestic houses comprising photovoltaic energy production and battery electrical storage combined with time-variant electricity prices. The modelling of the system comprises the different involved devices, energy flows and their constraints, and an objective function, which parametrizes the object of the optimization. The solution of the optimization problem defines the most adequate charging and discharging strategy of the battery into the future (prediction horizon). Power inverter efficiencies are usually modelled by assuming that they have constant values, and hence, that charging and discharging energy-flows lie on the most probably operating region of the inverter. A more realistic modelling of the power inverter efficiencies should consider a nonlinear parametrization of the efficiency curves. This consideration converts the optimization problem into a nonlinear one. It this paper, we modify a method to solve nonlinear optimization problems means iterations of linear optimization problems. The first iteration uses as seed values the solution of an optimization problem, which considers constant efficiencies of the battery inverter provided by the manufacturer of the battery. With the values of the solution of the optimization problem and with help of measured (dis)charging power curves and the optimized (dis)charging, new values of the efficiencies of the inverter of the battery will be determined, and the optimization problem will be with these values again computed. If a certain number of iterations is achieved or the values of the efficiencies converge, then the process stops.

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