On-site Battery Research Articles

Hierarchical online energy management for residential microgrids with Hybrid hydrogen–electricity Storage System

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.

A smart platform (BEVPro) for modeling, evaluating, and optimizing community microgrid integrated with buildings, distributed renewable energy, electricity storage, and electric vehicles

To achieve net-zero emissions, smart microgrid technologies like building-electric-vehicle (building-EV) energy networks with distributed renewable energy (RE) and energy storage are receiving growing attention. Nonetheless, there are few easy-to-use simulation platforms for conveniently and fast modeling and optimizing building-EV energy networks since it involves many research fields. In this study, a novel open-sourced simulating platform, BEVPro, was proposed to easily and accurately model and evaluate building-EV networks integrating buildings, REs, onsite electric batteries, and EVs. Firstly, the development of BEVPro was introduced. Then, a series of simulations on a community microgrid with 30 buildings, solar panels, onsite batteries, and 30 EVs, was conducted via BEVPro to optimize the investigated network. To be specific, in the comparison of annual energy amounts and grid powers for validating the accuracy of BEVPro with TRNSYS, the differences are smaller than 1.1% and 6.4%, respectively. Then the influences of different quantities of energy components (like the EV) were explored, and the four rule-based strategies were compared via BEVPro, of which the optimal strategy with the largest economic benefits (reducing 61.5% energy cost for residents) was selected for the parametric optimization of the energy components and control strategy, to further improve the energy flexibility of the building-EV network.

Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

Integrated Energy Flexibility Management at Wastewater Treatment Facilities.

On-site batteries, low-pressure biogas storage, and wastewater storage could position wastewater resource recovery facilities as a widespread source of industrial energy demand flexibility. This work introduces a digital twin method that simulates the coordinated operation of current and future energy flexibility resources. We combine process models and statistical learning on 15 min resolution sensor data to construct a facility's energy and water flows. We then value energy flexibility interventions and use an iterative search algorithm to optimize energy flexibility upgrades. Results from a California facility with anaerobic sludge digestion and biogas cogeneration predict a 17% reduction in electricity bills and an annualized 3% return on investment. A national analysis suggests substantial benefit from using existing flexibility resources, such as wet-weather storage, to reduce electricity bills but finds that new energy flexibility investments are much less profitable in electricity markets without time-of-use incentives and plants without existing cogeneration facilities. Profitability of a range of energy flexibility interventions may increase as a larger number of utilities place a premium on energy flexibility, and cogeneration is more widely adopted. Our findings suggest that policies are needed to incentivize the sector's energy flexibility and provide subsidized lending to finance it.

Transactive Operational Framework for Internet Data Centers in Geo-Distributed Local Energy Markets

Internet data centers (IDCs), which can regulate the spatial and temporal load distribution, and manage on-site energy resources, are promising candidates to enhance the connected distribution network's operation. The recent emergence of local energy trading provides a new opportunity for IDCs to trade energy with end energy customers in low-voltage distribution networks and enhance their operational energy efficiency, and this has not been well investigated in the literature. Motivated by this, this paper proposes a two-stage transactive operation framework for a group of geo-distributed IDCs to engage in local energy markets. In the first stage, an ex-ante bidding model is proposed, which optimally schedules the IDCs' cyber-energy resources (computing requests and on-site battery energy storage system) and determines energy trading prices in the local energy markets. The bidding model aims to minimize the cloud service provider (CSP)'s total cost. In the second stage, a real-time energy balancing model is proposed to adjust the IDCs' energy volumes and faciliate them to offer energy balancing services to the power distribution networks in terms of alleviating energy supply-demand imbalances. The coupling relationship among the IDCs' operation strategies, energy trading prices, and energy balancing signals are modeled in the framework. Extensive numerical case studies are implemented to demonstrate the effectiveness of the proposed framework. The simulation results show that the framework can reduce a considerable proportion of total operation cost for networked IDCs and can facilitate the CSP to effectively assist power distribution networks in balancing the energy supply and demand in real-time operation.

Augmented Random Search with Artificial Neural Networks for energy cost optimization with battery control

Intermittent renewable energy production and dynamic load must be balanced through appropriate control of integrated energy storage to account for the temporal discrepancy among power supply and demand. Intelligent control systems are required to anticipate and optimize the charging and discharging of energy storage. In recent years, reinforcement learning based techniques have been applied to a multitude of problems, including building integrated energy storage solutions. In this work, the focus is on the application of reinforcement learning based techniques to the specific energy optimization problem of controlling a battery energy storage system in a smart warehouse. This paper adopts data from a real operational battery energy storage system installed in a smart warehouse, integrated with photovoltaic, for food distribution on the west coast of Norway. In the smart warehouse, an intelligent energy management system controls the on-site battery energy storage using machine learning predictions of load and photovoltaic production, and an optimization algorithm is presented to generate a schedule for effective utilization of battery energy storage in coordination with a thermal storage system. This paper presents the combination of the augmented random search reinforcement algorithm with artificial neural networks as a basis to design an intelligent energy management system for controlling energy flows of battery energy storage systems to minimize the energy cost. The developed algorithm finds very promising solutions in the considered case-study of a smart house for energy cost minimization through a battery energy storage system, achieving an average of 99.2% accuracy across 10 seeded trials.

Energy management of Internet data centers in multiple local energy markets

Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs’ cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method.

Power Loss Minimization of Off-Grid Solar DC Nano-Grids—Part I: Centralized Control Algorithm

Peer-to-peer interconnection of households having on-site batteries, multi-port converters and solar panels to form a multi-port converter-enabled solar DC nano-grid is an emerging approach for providing affordable energy access in rural areas. Battery charge and discharge losses, distribution losses and converter losses are significant problem when operating such nano-grids. This paper presents a centralized control algorithm that can help address the power loss problem. The proposed algorithm uses a new problem formulation where the power loss problem is formulated as a two-stage convex optimization problem. The first stage of the optimization problem is an optimal battery dispatch problem for determining optimal battery charge and discharge currents. The second stage is an optimal current flow problem for determining optimal distribution voltages which corresponds to the optimal battery currents. Simulation results of the nano-grid show that the proposed algorithm can minimize the nano-grid power losses while facilitating the power exchange between the households. The proposed algorithm is suitable for small nano-grids where privacy of households is not a concern. In Part II of this paper we propose a distributed control algorithm that preserves the privacy of the households especially where the size of the nano-grid is large.

Performance Analysis of VRLA Battery for DC Load at Telecommunication Base Station

The high level of power outage in Sukabumi-Cianjur area has influenced the operations of telecommunication industry in the vicinity. This has shortened the battery life at the Base Station (BTS). This study aims to analyze the performance of a (new) VRLA battery against a DC load (BTS) to support the continuity of BTS operation in case of a power outage. The research method used is a (new) battery charge-discharge procedure. Parameters are analyzed by determining the on-site battery discharge duration, the pressure at the battery terminals between cells during backup, and the capacity of the rectifier module to support fast charging. To support fast charging, the rectifier with the formula N+1 and C-rate is 10% and C15 is 15% of the battery capacity. The internal impedance value is 3.4 mΩ and the battery terminal pressure (torque) is 9-11 N/m. The battery performance analysis of the four BTS shows that two of them managed to do a backup, while the other two did not provide good performance.

Techno-Economic Assessment of Battery Electric Trains and Recharging Infrastructure Alternatives Integrating Adjacent Renewable Energy Sources

Battery electric multiple units (BEMU) are an effective path towards a decarbonized regional rail transport on partly electrified rail lines. As a means of sector coupling, the BEMU recharging energy demand provided through overhead line islands can be covered from decentralized renewable energy sources (RES). Thus, fully carbon-free electricity for rail transport purposes can be obtained. In this study, we analyze cost reduction potentials of efficient recharging infrastructure positioning and the feasibility of covering BEMU energy demand by direct-use of locally produced renewable electricity. Therefore, we set up a model-based approach which assesses relevant lifecycle costs (LCC) of different trackside electrification alternatives comparing energy supply from local RES and grid consumption. The model-based approach is applied to the example of a German regional rail line. In the case of an overhead line island, the direct-use of electricity from adjacent wind power plants with on-site battery storage results in relevant LCC of EUR 173.4 M/30a, while grid consumption results in EUR 176.2 M/30a whereas full electrification results in EUR 224.5 M/30a. Depending on site-specific factors such as existing electrification and line lengths, BEMU operation and partial overhead line extension can lead to significant cost reductions of recharging infrastructure as compared to full electrification.

Coordinated Planning of Extreme Fast Charging Stations and Power Distribution Networks Considering On-Site Storage

The extreme fast charging (XFC) technology helps to reduce refueling time, alleviate mile anxiety, extend driving range and finally promote the popularity of electric vehicles (EVs). However, it would also pose great challenges on the power grid infrastructure especially distribution networks, due to the large-scale and intermittent power demand. This paper proposes a coordinated planning method for power distribution networks and XFC EV charging stations, with the on-site batteries considered. Firstly, considering the traffic flow pattern, the operation of XFC stations is analyzed on both energy and power demand. Secondly, the coordinated planning model is developed to satisfy the time-varying XFC load, with both transportation and electricity constraints considered. In addition, the on-site batteries are introduced to flatten the XFC energy used and supplement its power supply. The case studies have verified the effectiveness of the proposed method. The influence of XFC on the distribution networks and the effects of the on-site storage are also studied.

Enhanced electricity generation in rice paddy-field microbial fuel cells supplemented with iron powders

Microbial fuel cells installed in rice paddy fields (RP-MFCs) are able to serve as on-site batteries for operating low-power environmental sensors. In order to increase the utility and reliability of RP-MFCs, however, further research is necessary for boosting the power output. Here we examined several powdered iron species, including zero valent iron (ZVI), goethite, and magnetite, for their application to increasing power outputs from RP-MFCs. Soil around anodes was supplemented with either of these iron species, and RP-MFCs were operated for several months during the transplanting and harvesting. It was found that power outputs from RP-MFCs supplemented with ZVI were more than double the outputs from control (not supplemented with iron species) and other RP-MFCs, even after iron corrosion was ceased, and the maximum power density reached 130 mW/m2 (per projected area of the anode). Metabarcoding of 16S rRNA gene amplicons suggested that several taxa represented by fermentative and exoelectrogenic bacteria were substantially increased in MFCs supplemented with ZVI. Results suggest that ZVI lowers oxidation/reduction potential around anodes, activates anaerobic microbes involved in the conversion of organic matter into electricity and increases power output from RP-MFCs.

Data analysis of a monitored building using machine learning and optimization of integrated photovoltaic panel, battery and electric vehicles in a Central European climatic condition

Climate change is the biggest challenge of the present era. Buildings being one of the largest consumers of energy can help address this issue in various ways. The objective of the present study is to simulate and optimize a photovoltaic-based energy system integrated with onsite battery and electric vehicles for a demo site in Belgium in the Central European climatic conditions. The energy system of the demo site consists of photovoltaic-thermal panels, providing electricity and heat to the building, a battery as well as several long- and short-term thermal storages to reduce electricity curtailment and import. In the present study, the focus is on the optimization of the electric vehicle and battery charging and the import from the electrical network. Monitored data from the demo site is processed using a machine learning method to identify overproduction curtailment and electric vehicle charging events and to generate profiles of the photovoltaic (PV) energy production, feasible time windows and energy requirements for electric vehicle charging and building’s energy demand. These profiles and data are then used in an optimization method using linear and mixed integer programming to minimize the annual cost of the purchased electricity. Different case studies in terms of PV capacity, battery capacity and electric vehicle charging scenarios are investigated to compare against the performance of the real system. Various criteria, such as the curtailment of the PV production, imported energy cost, onsite energy fraction and matching indicators and lastly the net-zero energy balance of the building are considered to compare the results of the case studies. The study shows that machine learning can be used for the investigation of the monitored data, which can be later used for simulating and optimizing the case studies. The results shows that PV panels with 20 kWp (PV generation to demand ratio of 97%) and battery capacity of 16.1 kWh can result in annual energy cost saving of up to 807€ (31% reduction) with PV curtailment ratio of 34%, compared with the installed real demo building that has 10 kWp PV panels (PV generation to demand ratio of 49%) and battery capacity of 32.2 kWh. This would lead to an increase in the onsite-energy fraction indicator from 45% to 68%. Moreover, this combination can bring the building closer to the net-zero energy balance if the surplus electricity is exported rather than being curtailed. The optimization of the energy system in terms of equipment sizing and operation control is needed to increase the techno-economic performance of the whole system.

Economic performance assessment of building integrated photovoltaic system with battery energy storage under grid constraints

In this work, a typical South Norwegian house with BIPV system is considered for potential application of on-site battery energy storage. Economic and technical operation performance analysis has revealed that such a system can perform techno-economically much better compared to its counterpart. Techno-economic performance of BIPV system with appropriate energy storage has been evaluated for grid power supply limits with appropriate energy tariff for minimizing the annualized energy cost. It has been assumed that the capital costs associated with PV has been included in the building mortgage and expected that in the near future, the grid power supply limits would be introduced and therefore the BIPV house owner may need to install the battery with additional investments and it may affect the techno-economic performance of the BIPV energy system. It has been observed that during month-wise appropriate grid power supply limits, battery with BIPV system can perform much better through energy throughput compare to other reported scenarios. The correlation analysis has been presented for techno-economic performance indicators and observed that there is a strong correlation among PV energy production and grid energy supply. Correlation analysis among the technical and economic performance indicators may be useful for techno-economic of BIPV system.

Distributed Consensus for Output Power Regulation of DFIGs With On-Site Energy Storage

This paper proposes a novel distributed control architecture for output power regulation of doubly fed induction generator (DFIG) based wind turbines (WTs) with on-site battery energy storage systems (BESSs). The proposed distributed control architecture receives information from adjacent WTs+BESSs to control the DFIG's grid side converter (GSC) and the storage unit for output active/reactive power regulation and energy management purposes. The proposed method guarantees the equal active power sharing and energy management of WTs+BESSs in various wind speed conditions. A distributed controller is also designed to respond to the reactive power demand and share the reactive load demand between the GSC and BESS controllers based on their capacities. Furthermore, a controller is designed to account for the communication delays caused by the communication systems based on the IEEE standards. The proposed method is tested on the modified IEEE 14-bus power system with 10 WTs+BESSs.

Aging-aware predictive control of PV-battery assets in buildings

This paper presents an aging-aware model predictive control (MPC) approach for operation of sustainable buildings with on-site photovoltaic (PV) and battery systems. On-site batteries can be used to collect excess PV power during low demand hours and to discharge when PV generation is not enough to meet loads. To further increase the value of the PV-battery assets, batteries can be proactively charged/discharged to shed peak demands for utility cost reduction. The battery life span is highly dependent on the operation strategy and aggressive charge/discharge actions lead to faster battery capacity degradation and higher overall operation cost due to more frequent replacement. The control strategy developed in this study incorporates a convex battery capacity loss model, derived from a physically-based degradation model, to capture the battery aging cost in the control optimization. The degradation model used in the control optimization consists of a set of convex and piece-wise linear sub-models to approximate the battery degradation effect at different time instances. A convex MPC problem is solved in the proposed control strategy to seek the optimal balance between the building utility cost and the battery life-cycle cost. Whole month simulation tests were carried out for a typical office building with on-site PV and battery assets. To quantify the benefits of the proposed control approach, three baseline strategies were also simulated that represent the best current practices. Test results showed that conventional battery control strategies seeking minimum utility cost could double the battery degradation rate and lead to a significant increase in the battery life-cycle cost. The proposed strategy could foresee the tradeoff between the utility cost and battery aging cost and make decisions accordingly. It was demonstrated that the proposed control solution could reduce the utility cost by 9% with moderate battery degradation increase, leading to overall building operation cost reduction up to 4%.

A Chance-Constraints-Based Control Strategy for Microgrids With Energy Storage and Integrated Electric Vehicles

An online optimal control strategy for power flow management in microgrids with on-site battery, renewable energy sources, and integrated electric vehicles (EVs) is presented in this paper. An optimization problem in the form of a mixed integer linear program is formulated. It is executed over a rolling time horizon using predicted values of the microgrid electricity demand, renewable energy generation, EV connection and disconnection times, and the EV state of charge at time of connection. The solution to this optimization problem provides the on-site storage and EV charge/discharge powers. Both bidirectional and unidirectional charging scenarios are considered for EVs. The proposed optimal controller maximizes economic benefits and ensures user-specified charge levels are reached at the time of EV disconnection from the microgrid. By formulating the problem as a stochastic chance constraints optimization, significant improvement is shown in the system robustness over conventional rolling horizon controller, while dealing with uncertainties in the predictions of demand/generation, and EV state of charge and connection/disconnection times. Results of Monte Carlo simulations show that the proposed chance constraints-based controller is highly effective in reducing cost and meeting the user desired EV charge level at time of disconnection from the microgrid, even in the presence of uncertainty.

Comparative Analysis of Load-Shaping-Based Privacy Preservation Strategies in a Smart Grid

A key enabler for the smart grid is the fine-grained monitoring of power utilization. Although such a mechanism is helpful in the optimization of the whole electricity generation, distribution, and consumption cycle, it also creates opportunities for the potential adversaries in deducing the activities and habits of the subscribers. In fact, by utilizing the standard and readily available tools of nonintrusive load monitoring (NILM) techniques on the metered electricity data, many details of customers’ personal lives can be easily discovered. Therefore, prevention of such adversarial exploitations is of utmost importance for privacy protection. One strong privacy preservation approach is the modification of the metered data through the use of on-site storage units in conjunction with renewable energy resources. In this study, we introduce a novel mathematical programming framework to model eight privacy-enhanced power-scheduling strategies inspired and elicited from the literature. We employ all the relevant techniques for the modification of the actual electricity utilization (i.e., on-site battery, renewable energy resources, and appliance load moderation). Our evaluation framework is the first in the literature, to the best of our knowledge, for a comprehensive and fair comparison of the load-shaping techniques for privacy preservation. In addition to the privacy concerns, we consider monetary cost and disutility of the users in our objective functions. Evaluation results show that privacy preservation strategies in the literature differ significantly in terms of privacy, cost, and disutility metrics.

PV based EV charging at universities using supplied historical PV output ramp

Abstract Optimal PV based EV charging is performed either by predicting or by measuring PV output. However, due to the uncertainties associated with the variables involved, both the charging methods are not only disposed to significant voltage limit violations but also may cause PV energy harvest reduction. Hence, a methodology is proposed in this paper, which utilizes the measured PV output of a given sample and the supplied historical ramp to predict the PV output of the immediate next sample using a non-iterative method. The charging rates of the EV population are, subsequently, adjusted in the interval between the successive samples based on the predicted PV output with the help of a proposed SOC based charging strategy. The proposed methodology has been tested at the University of Queensland (UQ)’s solar and parking facilities coupled with its electric grid. The results show that aside from reducing the probability of voltage limit violations (PVV), the proposed methodology can increase the PV energy harvest. Moreover, it is cost-effective as compared to the conventional method such as onsite battery energy storage deployment.

A PERSPECTIVE IMAGING STUDY OF AGEING (PISA): GENETIC RISK PREDICTION FOR STUDYING AN AT-RISK POPULATION FOR ALZHEIMER’S DISEASE

While the burden of dementia occurs late in life, neuropathology accumulates for decades prior to onset of dementia. It is imperative to develop methods to identify those at the very early stage of dementia. This is the aim of the Prospective Imaging Study of Aging: Genes, Brain and Behaviour (PISA) study, which seeks to (1) Identify healthy middle-aged adults at high risk of dementia; (2) Discover biological and imaging markers of early neuropathology; (3) Identify modifiable risk factors; (4) Establishing a cohort of preclinical patients for future clinical trial. The main study is a longitudinal cohort study on at risk younger adults (40–70 yrs) with no clinical symptom. This is achieved by levering our extensive in-house cohorts, comprising ∼16,000 individuals in the target age range with available GWAS data. A genetically enriched cohort was selected to study the preclinical Alzheimer's disease (AD), based on APOE genotype and polygenic risk scores (PRS). The main study began early 2016. We have verified the predictin accuracy of PRS score on data from ADNI, AddNeuroMed, Sydney MAS, OATS (presented in another abstract at AAIC). We developed an in-house online questionnaire to collect longitudinal data on cognition and lifestyle from the total cohort, and an onsite, multidisciplinary test battery to collect high-quality data from a subset of the cohort. As part of this onsite battery, we have developed and tested a comprehensive suite of imaging sequences on MRI and PET, including high resolution T1, T2, QSM, fMRI, ASL, and amyloid-PET (Florbetaben). A novel naturalistic fMRI paradigm is designed to study perception, attention and episodic memory. Furthermore, smart sensing devices are given to participants to collect continuous data on lifestyle factors, such as physical activities and sleep.