Energy Storage in Madeira, Portugal: Co-optimizing for Arbitrage, Self-Sufficiency, Peak Shaving and Energy Backup

International audienceEnergy storage applications are explored from a prosumer (consumers with generation) perspective for the island of Madeira in Portugal. These applications could also be relevant to other power networks. We formulate a convex co-optimization problem for performing arbitrage under zero feed-in tariff, increasing self-sufficiency by increasing self-consumption of locally generated renewable energy, provide peak shaving and act as a backup power source during anticipated and scheduled power outages. Using real data from Madeira we perform short and long timescale simulations in order to select end-user contract which maximizes their gains considering storage degradation based on operational cycles. We observe energy storage ramping capability decides peak shaving potential, fast ramping batteries can significantly reduce peak demand charge. The numerical experiment indicates that storage providing backup does not significantly reduce gains performing arbitrage and peak demand shaving. Furthermore, we also use AutoRegressive Moving Average (ARMA) forecasting along with Model Predic-tive Control (MPC) for real-time implementation of the proposed optimization problem in the presence of uncertainty

Model Predictive Control Design for Unlocking the Energy Flexibility of Heat Pump and Thermal Energy Storage Systems

Heat pump and thermal energy storage (HPTES) systems, which are widely utilized in modern buildings for providing domestic hot water, contribute to a large share of household electricity consumption. With the increasing integration of renewable energy sources (RES) into modern power grids, demand-side management (DSM) becomes crucial for balancing power generation and consumption by adjusting end users' power consumption. This paper explores an energy flexible Model Predictive Control (MPC) design for a class of HPTES systems to facilitate demand-side management. The proposed DSM strategy comprises two key components: i) flexibility assessment, and ii) flexibility exploitation. Firstly, for flexibility assessment, a tailored MPC formulation, supplemented by a set of auxiliary linear constraints, is developed to quantitatively assess the flexibility potential inherent in HPTES systems. Subsequently, in flexibility exploitation, the energy flexibility is effectively harnessed in response to feasible demand response (DR) requests, which can be formulated as a standard mixed-integer MPC problem. Numerical experiments, based on a real-world HPTES installation, are conducted to demonstrate the efficacy of the proposed design.Comment: submitted to The 8th IEEE Conference on Control Technology and Applications (CCTA) 2024, 7 page

BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification & removal of storage & computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security & QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order & generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS & security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments

Energy Storage in Madeira, Portugal: Co-optimizing for Arbitrage, Self-Sufficiency, Peak Shaving and Energy Backup

International audienceEnergy storage applications are explored from a prosumer (consumers with generation) perspective for the island of Madeira in Portugal. These applications could also be relevant to other power networks. We formulate a convex co-optimization problem for performing arbitrage under zero feed-in tariff, increasing self-sufficiency by increasing self-consumption of locally generated renewable energy, provide peak shaving and act as a backup power source during anticipated and scheduled power outages. Using real data from Madeira we perform short and long timescale simulations in order to select end-user contract which maximizes their gains considering storage degradation based on operational cycles. We observe energy storage ramping capability decides peak shaving potential, fast ramping batteries can significantly reduce peak demand charge. The numerical experiment indicates that storage providing backup does not significantly reduce gains performing arbitrage and peak demand shaving. Furthermore, we also use AutoRegressive Moving Average (ARMA) forecasting along with Model Predic-tive Control (MPC) for real-time implementation of the proposed optimization problem in the presence of uncertainty

Impact of household heterogeneity on community energy storage in the UK

The increasing penetration of Decentralised Energy Resources (DERs) into the residential sector along with a reduction in their subsidy in many countries requires innovative approaches to ensure economic viability. Whilst applications of Household Energy Storage (HES) have been widely investigated and deployed, in recent years communities have been identified as a key scale for energy systems, particularly for energy storage. Community Energy Storage (CES) is therefore a promising alternative deployment model to assist the roll-out of DERs. The power and energy demand may vary significantly with the demographic composition of community; therefore, it is important to evaluate the operation of HES and CES for different communities and hence to assign suitable energy storage options to corresponding objectives. In this work, an Agent Based Model (ABM) is developed that includes household demand heterogeneities, as well as HES and CES, and photovoltaic (PV) systems. The single household models can be aggregated to a community, and hence it is able to simulate the interaction between households in a local, grid connected, energy system. A battery degradation model is also included in order to reproduce the capacity fade of a Li-ion battery over time. The impact on battery performance of the heterogeneous demand within communities is explored using typical performance indicators, such as Self-Consumption Rate (SCR), Self-Sufficiency Rate (SSR) and battery cycle counts

OS-Based Sensor Node Platform and Energy Estimation Model for Health-Care Wireless Sensor Networks

Accurate power and performance figures are critical to assess the effective design of possible sensor node architectures in Body Area Networks (BANs) since they operate on limited energy storage. Therefore, accurate power models and simulation tools that can model real-life working conditions need to be developed and validated with real platforms. In this paper we propose a sensor node platform designed for health-care applications and a validated simulation model based on event-driven operating system simula- tion that can be used to accurately analyze performance and power consumption in BANs composed of multiple nodes. Thus, this model can be employed to tune the node architecture and communication layer for different working conditions, applications and topologies of BANs. In this paper we validate the proposed simulation model on different real life applications and working conditions. Our results show variations of less than 4% between the presented simulation framework and measurements in the final platforms

High Performance GNRFET Devices for High-Speed Low-Power Analog and Digital Applications

Indiana University-Purdue University Indianapolis (IUPUI)Recent ULSI (ultra large scale integration) technology emphasizes small size devices, featuring low power and high switching speed. Moore's law has been followed successfully in scaling down the silicon device in order to enhance the level of integration with high performances until conventional devices failed to cop up with further scaling due to limitations with ballistic effects, and challenges with accommodating dopant fluctuation, mobility degradation, among other device parameters. Recently, Graphene based devices o ered alternative approach, featuring small size and high performances. This includes high carrier mobility, high carrier density, high robustness, and high thermal conductivity. These unique characteristics made the Graphene devices attractive for high speed electronic architectures. In this research, Graphene devices were integrated into applications with analog, digital, and mixed signals based systems. Graphene devices were briefly explored in electronics applications since its first model developed by the University of Illinois, Champaign in 2013. This study emphasizes the validation of the model in various applications with analog, digital, and mixed signals. At the analog level, the model was used for voltage and power amplifiers; classes A, B, and AB. At the digital level, the device model was validated within the universal gates, adders, multipliers, subtractors, multiplexers, demultiplexers, encoders, and comparators. The study was also extended to include Graphene devices for serializers, the digital systems incorporated into the data structure storage. At the mixed signal level, the device model was validated for the DACs/ADCs. In all components, the features of the new devices were emphasized as compared with the existing silicon technology. The system functionality and dynamic performances were also elaborated. The study also covered the linearity characteristics of the devices within full input range operation. GNRFETs with a minimum channel length of 10nm and an input voltage 0.7V were considered in the study. An electronic design platform ADS (Advanced Design Systems) was used in the simulations. The power amplifiers showed noise figure as low as 0.064dbs for class A, and 0.32 dbs for class B, and 0.69 dbs for class AB power amplifiers. The design was stable and as high as 5.12 for class A, 1.02 for class B, and 1.014 for class AB. The stability factor was estimated at 2GHz operation. The harmonics were as low as -100 dbs for class A, -60 dbs for class B, and -50dbs for class AB, all simulated at 1GHz. The device was incorporated into ADC system, and as low as 24.5 micro Watt power consumption and 40 nsec rise time were observed. Likewise, the DAC showed low power consumption as of 4.51 micro Watt. The serializer showed as minimum power consumption of the order of 0.4mW. These results showed that these nanoscale devices have potential future for high-speed communication systems, medical devices, computer architecture and dynamic Nano electromechanical (NEMS) which provides ultra-level of integration, incorporating embedded and IoT devices supporting this technology. Results of analog and digital components showed superiority over other silicon transistor technologies in their ultra-low power consumption and high switching speed

Real-time state of charge estimation in thermal storage vessels applied to a smart polygeneration grid

In thermal grids and district heating, thermal storage devices play an important role to manage energy demand. Additionally, in smart polygeneration grids, thermal energy storage devices are essential to achieve high flexibility in energy demand management at relatively low cost. In this scenario, accurate evaluation of state of charge of storage vessels based on available measurements is critical. The aim of this paper is to develop and compare three different models for state of charge estimation in stratified water tanks (discrete temperature measurements) and the related application in an experimental polygeneration grid with a real-time management tool. The first model is based on the empirical calculation of the state of charge considering the thermal power difference between generation and consumption, and afterwards correction based on measured temperatures. The second model is a mathematical approach considering a pre-defined temperature shape fitted with experimental data. The latter model is based on a 1-D physical approach using a multi-nodal method forced on the basis of the measured temperatures. The models were compared considering an experimental test performed in the polygeneration laboratory by the Thermochemical Power Group (TPG). As a result of the comparative analysis, the first model was selected for applications in complex polygeneration grids, due to its good compromise between accuracy and computational effort. Several tests were carried out to demonstrate the performance of the empirical approach selected for the thermal storage model and the economic benefit related to the utilization of this vessel. The experimental plant, constituted by two different prime movers (a 100 kW microturbine and a 20 kW internal combustion engine) and a thermal storage tank, was able to demonstrate the performance of a real-time management tool. For this reason, special attention was devoted to the variable cost comparisons. The novelty of this work lies in the development of the real-time management tool coupled with a thermal storage model by considering the simplified modelling approach. This is an essential requisite for complex polygeneration grids including hundreds or thousands of prime movers and thermal storage devices. Additionally, it is important to state that in such cases the required real-time performance could be difficult to obtain. The results, produced with the innovative and flexible experimental rig, demonstrate the positive impact of thermal storage as well as the effective management performance of this quite simple dispatching approach. Another important novel aspect regards this experimental assessment considering both specific 3-h tests andextended conditions typical of a possible real application

Green Approach for Joint Management of Geo-Distributed Data Centers and Interconnection Networks

Every time an Internet user downloads a video, shares a picture, or sends an email, his/her device addresses a data center and often several of them. These complex systems feed the web and all Internet applications with their computing power and information storage, but they are very energy hungry. The energy consumed by Information and Communication Technology (ICT) infrastructures is currently more than 4\% of the worldwide consumption and it is expected to double in the next few years. Data centers and communication networks are responsible for a large portion of the ICT energy consumption and this has stimulated in the last years a research effort to reduce or mitigate their environmental impact. Most of the approaches proposed tackle the problem by separately optimizing the power consumption of the servers in data centers and of the network. However, the Cloud computing infrastructure of most providers, which includes traditional telcos that are extending their offer, is rapidly evolving toward geographically distributed data centers strongly integrated with the network interconnecting them. Distributed data centers do not only bring services closer to users with better quality, but also provide opportunities to improve energy efficiency exploiting the variation of prices in different time zones, the locally generated green energy, and the storage systems that are becoming popular in energy networks. In this paper, we propose an energy aware joint management framework for geo-distributed data centers and their interconnection network. The model is based on virtual machine migration and formulated using mixed integer linear programming. It can be solved using state-of-the art solvers such as CPLEX in reasonable time. The proposed approach covers various aspects of Cloud computing systems. Alongside, it jointly manages the use of green and brown energies using energy storage technologies. The obtained results show that significant energy cost savings can be achieved compared to a baseline strategy, in which data centers do not collaborate to reduce energy and do not use the power coming from renewable resources