Evaluation of the efficiency and resulting electrical and economic losses of photovoltaic home storage systems

The increase in electricity prices along with a decrease in the price of storage systems has led to a rapid expansion of the photovoltaic (PV) home storage system market, particularly in Germany. In order to be economically viable, PV home storage systems must fulfil certain performance criteria. The overall performance and achievable self-sufficiency ratio of a PV battery home storage system depends on (i) the efficiencies of the system components, (ii) the standby consumption, (iii) the reaction time of the home storage system as well as (iv) the intelligence of the overall system control software. So far, PV home storage system still show very big differences in their performance. However, poor system performance can result in the system being no longer economic viable. Up to now, there have been only a few studies that deal with the evaluation and systematic comparison of the performance of PV home storage systems. For this paper the performance of 12 commercially available PV-battery systems has been analysed with a focus on the overall system efficiency. The efficiency of the systems is mainly influenced by the battery efficiency, power conversion efficiency and standby consumption of the different system components. Therefore, a testing and evaluation method has been developed. In this work the method as well as the results of the systems are presented. A detailed study of the influence of the effects of the individual losses on both total energy and monetary losses was carried out. It is shown that power conversion has the greatest influence on energy and monetary losses. For the systems under evaluation the monetary losses per year due to battery efficiency losses range between 2 €/a and 40 €/a. Monetary losses due to conversion losses range between 33 €/a and 137 €/a and due to standby consumption between 1 €/a and 46 €/a. The individual losses can be summed up to give a total loss, which lies between 44 €/a and 174 €/a

Influence of Efficiency, Aging and Charging Strategy on the Economic Viability and Dimensioning of Photovoltaic Home Storage Systems

PV in combination with Li-ion storage systems can make a major contribution to the energy transition. However, large-scale application will only take place when the systems are economically viable. The profitability of such a system is not only influenced by the investment costs and economic framework conditions, but also by the technical parameters of the storage systems. The paper presents a methodology for the simulation and sizing of PV home storage systems that takes into account the efficiency of the storage systems (AC, DC standby consumption and peripheral consumption, battery efficiency and inverter efficiency), the aging of the components (cyclic and calendar battery aging and PV degradation), and the intelligence of the charging strategy. The developed methodology can be applied to all regions. In this paper, a sensitivity analysis of the influence of the mentioned technical parameters on the dimensioning and profitability of a PV home storage is performed. The calculation is done for Germany. Especially, battery aging, battery inverter efficiency and a charging strategy to avoid calendar aging have a decisive influence. While optimization of most other technical parameters only leads to a cost reduction of 1–3%, more efficient inverters can save up to 5%. Even higher cost reductions (more than 20%) can only be achieved using batteries that age less, especially batteries that are less sensitive to calendar aging. In individual cases, a small improvement in the efficiency of the storage system can also lead to higher costs. This is for example the case when smaller batteries are combined with a large PV system and the battery is used more due to the higher efficiency. This results in faster ageing and thus earlier replacement of the battery. In addition, the paper includes a detailed literature overview on PV home storage system sizing and simulation

Optimized Energy Management of a Solar and Wind Equipped Student Residence with an Innovative Hybrid Energy Storage System

Grid-connected Energy Storage Systems (ESS) are vital for transforming the current energy sector. Lithium-Ion Battery (LIB) technology is presently the most popular form of ESS, especially because of its fast response capability, efficiency, and reducing market prices, but is not always preferred for long-term storage, due to its relatively shorter lifetime. A Redox Flow Battery (RFB) on the other hand has a higher lifetime and better long-term storage capability, but has a higher upfront cost and reduced round trip efficiency. A Hybrid ESS (HESS) consisting of LIB and RFB offers the advantages of both technologies, thus making the ESS more economical and flexible to use while also improving the cycle lifetime of individual ESS. Such a grid-connected HESS is planned and installed for a student residence at Bruchsal having 126 apartments for 150 students and equipped with 220 kWp photovoltaics and 10.5 kWp wind-power. Real-time high-resolution data of the residence’s electrical load and energy generation are collected and used to optimally control the HESS. Additionally, the RFB is also used as heat storage, which supports partial heating requirements of the residence. In the present work, an Energy Management System (EMS) is deployed which not only controls this conglomerate but also optimizes its operations in real-time. The HESS is optimized two folds where it is operated with a fixed priority based strategy to improve the operational efficiency. Secondly using solar and load predictions, optimal charging schedules of the individual ESS are estimated. Based on the schedules the ESS are charged at its optimal charging points thus increasing charging efficiency and at the same time it avoids the ESS from staying at high SOC ranges for long time thus reducing ageing. Results based on real life operations based on the proposed methods are provided in this work

Comparison of Small EV Charging Station\u27s Load Forecasts and its Impact on the Operational Costs

For an energy management system (EMS) of a charging station (CS), information on future load is crucial. Existing models primarily focus on load forecasting for large charging stations. In this study, three different load forecasting models based on real data from a public CS with two charging points are developed. The models include two persistent models and one model that utilizes a machine learning algorithm. To assess the impact of forecasting accuracy on operational costs, a case study with dynamic electricity prices and a stationary battery storage is conducted. Using the load predictions, a mixed-integer linear programming problem is formulated to optimize the scheduling of the stationary battery charging