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The significant market growth of stationary electrical energy storage systems both for private and commercial applications has raised the question of battery lifetime under practical operation conditions. Here, we present a study of two 8 kWh lithium-ion battery (LIB) systems, each equipped with 14 lithium iron phosphate/graphite (LFP) single cells in different cell configurations. One system was based on a standard configuration with cells connected in series, including a cell-balancing system and a 48 V inverter. The other system featured a novel configuration of two stacks with a parallel connection of seven cells each, no cell-balancing system, and a 4 V inverter. The two systems were operated as part of a microgrid both in continuous cycling mode between 30% and 100% state of charge, and in solar-storage mode with day–night cycling. The aging characteristics in terms of capacity loss and internal resistance change in the cells were determined by disassembling the systems for regular checkups and characterizing the individual cells under well-defined laboratory conditions. As a main result, the two systems showed cell-averaged capacity losses of 18.6% and 21.4% for the serial and parallel configurations, respectively, after 2.5 years of operation with 810 (serial operation) and 881 (parallel operation) cumulated equivalent full cycles. This is significantly higher than the aging of a reference single cell cycled under laboratory conditions at 20 °C, which showed a capacity loss of only 10% after 1000 continuous full cycles.
This paper presents the use of model predictive control (MPC) based approach for peak shaving application of a battery in a Photovoltaic (PV) battery system connected to a rural low voltage gird. The goals of the MPC are to shave the peaks in the PV feed-in and the grid power consumption and at the same time maximize the use of the battery. The benefit to the prosumer is from the maximum use of the self-produced electricity. The benefit to the grid is from the reduced peaks in the PV feed-in and the grid power consumption. This would allow an increase in the PV hosting and the load hosting capacity of the grid.
The paper presents the mathematical formulation of the optimal control problem
along with the cost benefit analysis. The MPC implementation scheme in the
laboratory and experiment results have also been presented. The results show
that the MPC is able to track the deviation in the weather forecast and operate
the battery by solving the optimal control problem to handle this deviation.
Cell lifetime diagnostics and system be-havior of stationary LFP/graphite lithium-ion batteries
(2018)
The increase in households with grid connected Photovoltaic (PV) battery system poses challenge for the grid due to high PV feed-in as a result of mismatch in energy production and load demand. The purpose of this paper is to show how a Model Predictive Control (MPC) strategy could be applied to an existing grid connected household with PV battery system such that the use of battery is maximized and at the same time peaks in PV energy and load demand are reduced. The benefits of this strategy are to allow increase in PV hosting capacity and load hosting capacity of the grid without the need for external signals from the grid operator. The paper includes the optimal control problem formulation to achieve the peak shaving goals along with the experiment set up and preliminary experiment results. The goals of the experiment were to verify the hardware and software interface to implement the MPC and as well to verify the ability of the MPC to deal with the weather forecast deviation. A prediction correction has also been introduced for a short time horizon of one hour within this MPC strategy to estimate the PV output power behavior.
In rural low voltage grid networks, the use of battery in the households with a grid connected Photovoltaic (PV) system is a popular solution to shave the peak PV feed-in to the grid. For a single electricity price scenario, the existing forecast based control approaches together with a decision based control layer uses weather and load forecast data for the on–off schedule of the battery operation. These approaches do bring cost benefit from the battery usage. In this paper, the focus is to develop a Model Predictive Control (MPC) to maximize the use of the battery and shave the peaks in the PV feed-in and the load demand. The solution of the MPC allows to keep the PV feed-in and the grid consumption profile as low and as smooth as possible. The paper presents the mathematical formulation of the optimal control problem along with the cost benefit analysis . The MPC implementation scheme in the laboratory and experiment results have also been presented. The results show that the MPC is able to track the deviation in the weather forecast and operate the battery by solving the optimal control problem to handle this deviation.
In dieser Arbeit werden die außentemperaturgeführte Vorlauftemperaturregelung (Standard-TABS-Strategie), ein Verfahren das auf einer multiplen linearen Regression basiert (AMLR-Strategie) und ein Verfahren, das unter dem Obergriff der modellprädiktiven Regelung (MPC-Strategie) zusammengefasst werden kann, untersucht. Anhand der Simulationsergebnisse und des Integrationsaufwandes in die Gebäudeautomation des Seminargebäudes wurde eine Fokussierung auf die AMLR-Strategie vorgenommen.
The building sector is one of the main consumers of energy. Therefore, heating and cooling concepts for renewable energy sources become increasingly important. For this purpose, low-temperature systems such as thermo-active building systems (TABS) are particularly suitable. This paper presents results of the use of a novel adaptive and predictive computation method, based on multiple linear regression (AMLR) for the control of TABS in a passive seminar building. Detailed comparisons are shown between the standard TABS and AMLR strategies over a period of nine months each. In addition to the reduction of thermal energy use by approx. 26% and a significant reduction of the TABS pump operation time, this paper focuses on investment savings in a passive seminar building through the use of the AMLR strategy. This includes the reduction of peak power of the chilled beams (auxiliary system) as well as a simplification of the TABS hydronic circuit and the saving of an external temperature sensor. The AMLR proves its practicality by learning from the historical building operation, by dealing with forecasting errors and it is easy to integrate into a building automation system.
Three real-lab trigeneration microgrids are investigated in non-residential environments (educational, office/administrational, companies/production) with a special focus on domain-specific load characteristics. For accurate load forecasting on such a local level, à priori information on scheduled events have been combined with statistical insight from historical load data (capturing information on not explicitly-known consumer behavior). The load forecasts are then used as data input for (predictive) energy management systems that are implemented in the trigeneration microgrids. In real-world applications, these energy management systems must especially be able to carry out a number of safety and maintenance operations on components such as the battery (e.g. gassing) or CHP unit (e.g. regular test runs). Therefore, energy management systems should combine heuristics with advanced predictive optimization methods. Reducing the effort in IT infrastructure the main and safety relevant management process steps are done on site using a Smart & Local Energy Controller (SLEC) assisted by locally measured signals or operator given information as default and external inputs for any advanced optimization. Heuristic aspects for local fine adjustment of energy flows are presented.
In this study, a high-performance controller is proposed for single-phase grid-tied energy storage systems (ESSs). To control power factor and current harmonics and manage time-shifting of energy, the ESS is required to have low steady-state error and fast transient response. It is well known that fast controllers often lack the required steady-state accuracy and trade-off is inevitable. A hybrid control system is therefore presented that combines a simple yet fast proportional derivative controller with a repetitive controller which is a type of learning controller with small steady-state error, suitable for applications with periodic grid current harmonic waveforms. This results in an improved system with distortion-free, high power factor grid current. The proposed controller model is developed and design parameters are presented. The stability analysis for the proposed system is provided and the theoretical analysis is verified through stability, transient and steady-state simulations.