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The existing charging infrastructure needs expansion and upgrade with the growing fleet of electric vehicles (EV). The electric grids are largely affected by the uncontrolled charging cycles. To overcome this drawback, the hybrid charging stations are incorporated with battery storage and renewable energy sources. The power necessary from the grid can be buffered using a battery and renewable source attached to the charging station thereby avoiding the grid constraints and peaks. It has been a challenge to trace the origin of the battery’s energy till date. The battery energy storage and a simple photovoltaic system is incorporated in a hybrid EV charging station. Uncontrolled EV charging and its adverse effects can be overcome by this technology by accurately calculating the share of renewable energy derived from the battery. Multi-attribute utility theory is used for optimizing the EV charging level and scheduling the battery charging and discharging. Minimizing battery degradation and charging cost while maximizing the renewable energy from the battery and PV sources are the major criteria of optimization. Multicriteria optimization function is used along with the genetic algorithm optimization scheme to address the optimization issues. Optimal capacity of the battery and optimization strategy is affected by the preferences in decision making.

Journal of building performance simulation 14(1), 38-51 (2021). doi:10.1080/19401493.2020.1838611, Published by Taylor and Francis, London [u.a.]

As different batteries exhibit their own advantages and disadvantages, a single-type battery system for a mobile embedded system (such as smartphones and tablet PC) cannot overcome the inherent limitations of its target battery. For example, although the lithium cobalt oxide (LCO) battery is the most popular battery for mobile embedded systems due to its high energy density, mobile embedded systems equipped with LCO batteries suffer from rapid capacity degradation. In this letter, we target heterogeneous batteries in a mobile embedded system and propose a method for minimizing the capacity degradation of the batteries. To this end, we first analyze factors that affect capacity degradation, and choose dominant factors that are controllable in a mobile embedded system. We then build a battery degradation model considering the factors, and finally develop a battery scheduling algorithm that minimizes capacity degradation using the degradation model. Our evaluation with real experiments and simulations demonstrates the effectiveness of the proposed algorithm in minimizing capacity degradation.

In recent years, renewable energies and storage systems are bringing a significant contribution to power system. Due to the high penetration of non-predictable power sources, especially in the distribution networks, new challenges related to the integration and management of these new resources also combined with storage systems arose in power system research. Accordingly, the aim of this paper is to propose an innovative scheduling method for voltage regulation in low voltage networks. A simple type of contract for ancillary services that foresees the participation of active users endowed with a storage system coupled with a PV system is considered. The method allows to regulate the voltage in a distribution system by using a combined PV and storage system. The voltage regulation is achieved by setting the value of the inverter reactive power on the basis of sensitivity coefficients of the considered node, taking into account the capability curve constrains, as required by new grid codes. A possible tariff policy is also proposed for the ancillary service and for the operation of the realization of the scheduling algorithm. Simulation results demonstrate the effectiveness of the proposed method for the scheduling of the power system.

Recent development of renewable generation and increasing penetration of electric vehicles have led to large volumes of residential battery storage systems connected at distribution networks. In this paper, we propose a control algorithm for residential batteries that determines optimal day-ahead battery scheduling and operation with the aim to minimize household energy bills and in the context of dynamic Time of Use (ToU) electricity tariffs. The proposed formulation of the optimization problem takes into consideration the battery’s depreciation cost, which is determined by the accurate enumeration of battery cycles, including partial cycling i.e. battery cycles that do not start or end at 100% of State of Charge (SoC). A key advantage of the proposed formulation is that the problem can be solvable by use of linear programming. In addition, we study and compare the benefits of the optimisation-based algorithm with lifespan consideration to a simple heuristic-based battery control scheme and an optimisation-based algorithm without battery lifecycle consideration. Results show that battery lifespan consideration in the optimization algorithm does not necessarily yield to lower prosumer energy bills, when compared to other approaches, but it can lead to a lower depreciation cost of the battery.

The day-ahead PV power generation scenarios, which represent the possible output of PV, have a significant impact on the scheduling of the available flexibility in a smart building. In this paper, a scenario generation approach for the day-ahead production of a single PV system is presented. The proposed method is important in the context of single buildings where the self-consumption has to be optimized. LASSO, which is a data-driven method, is used in order to select relevant quantiles to capture CDF. Moreover, a statistical-based scenario generation is applied in order to compare the performance of the proposed method. The generated scenarios for day-ahead PV generation are used in a stochastic problem to minimize the expected operational cost of a building and manage the flexibility, which is battery in this case study. Finally, the proposed method has been applied to a real PV installation on the rooftop of EnergyVille-1, a research institute. The simulation results demonstrate that the proposed method is able to capture the dynamics of the system even with low number of scenarios, which leads to reduce the computational time of stochastic problem.

Coordinating the charging and discharging of a population of batteries with respect to load and generation forecasts decreases operating costs. However, in the case of asymmetric charging and discharging efficiencies the mathematical formulation of this problem is not straightforward. This paper compares the scheduling problem for a population of batteries in two formulations: mixed-integer (MINLP) and non-convex real-valued (NLP). In the case of the latter, the resulting separable NLP is solved using the Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN). In the case of the latter, the resulting MINLP is solved in hierarchical distributed fashion, we propose a mixed-integer extension of ALADIN. Real load and generation data is used to create an example of 54 batteries demonstrating improved performance for the MINLP formulation.

Optimal day-ahead scheduling of batteries relies on reliable day-ahead forecast of residential load profile, solar insolation, and time-of-use tariff. However, actual values may not be the same as the forecasted values due to various factors like weather, cloud cover, consumer behaviour, etc. This work aims at optimally scheduling the batteries installed in a residence to minimize the cost of power consumption from the grid and increasing the battery lifetime, while accounting for the variations in the forecasted load profile, solar insolation and time-of-use tariff. The proposed method uses short-term forecasts and employs a sliding window technique to generate optimal schedule on an hourly basis. The sliding window technique helps in adjusting for the variations in the forecasts used. Artificial bee colony (ABC) algorithm is used as the optimization technique during each window. Effect of allowable maximum depth-of-discharge, and sliding window size on the optimized cost are also studied. The results are compared with manual scheduling.

Different batteries have different desirable properties like energy density, peak power, recharge time, longevity, efficiency, etc. So, it is beneficial if we multiplex different types of batteries in a single device. In this paper, we look at ways of scheduling workloads over the multiplexed batteries to maximize the overall efficiency. We consider two ways to model the efficiency and give efficient solutions to the same.

The smart grid with its two-way communication and bi-directional power layers is a cornerstone in the combat against global warming. It allows for the large scale adoption of distributed (individually-owned) renewable energy resources such as solar photovoltaic systems. Their intermittency poses a threat to the stability of the grid which can be addressed by the introduction of energy storage systems. Determining the optimal capacity of a battery has been an active area of research in recent years. In this research an in-depth analysis of the relation between optimal capacity, and demand and generation patterns is performed for households taking part in a community-wide demand-side management scheme. The scheme is based on a non-cooperative dynamic game approach in which participants compete for the lowest electricity bill by scheduling their energy storage systems. The results are evaluated based on self-consumption, the peak-to-average ratio of the aggregated load, and potential cost reductions. Furthermore, the difference between individually-owned batteries to a centralised community energy storage system serving the whole community is investigated., Comment: 8 pages, 9 figures

This paper presents a reinforcement learning framework for solving battery scheduling problems in order to extend the lifetime of batteries used in electrical vehicles (EVs), cellular phones, and embedded systems. Battery pack lifetime has often been the limiting factor in many of today&rsquo, s smart systems, from mobile devices and wireless sensor networks to EVs. Smart charge-discharge scheduling of battery packs is essential to obtain super linear gain of overall system lifetime, due to the recovery effect and nonlinearity in the battery characteristics. Additionally, smart scheduling has also been shown to be beneficial for optimizing the system&rsquo, s thermal profile and minimizing chances of irreversible battery damage. The recent rapidly-growing community and development infrastructure have added deep reinforcement learning (DRL) to the available tools for designing battery management systems. Through leveraging the representation powers of deep neural networks and the flexibility and versatility of reinforcement learning, DRL offers a powerful solution to both roofline analysis and real-world deployment on complicated use cases. This work presents a DRL-based battery scheduling framework to solve battery scheduling problems, with high flexibility to fit various battery models and application scenarios. Through the discussion of this framework, comparisons have also been made between conventional heuristics-based methods and DRL. The experiments demonstrate that DRL-based scheduling framework achieves battery lifetime comparable to the best weighted-k round-robin (kRR) heuristic scheduling algorithm. In the meantime, the framework offers much greater flexibility in accommodating a wide range of battery models and use cases, including thermal control and imbalanced battery.

The use of mobile devices is limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries over the load to exploit recovery properties usually extends the system lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper we compare these scheduling schemes with the optimal scheduling scheme produced with a priced-timed automaton battery model (implemented and evaluated in Uppaal Cora). We see that in some cases the results of the simple scheduling schemes are close to optimal. However, the optimal schedules also clearly show that there is still room for improving the battery lifetimes.