Smart Grid Community Research Articles

Safe reinforcement learning for real-time automatic control in a smart energy-hub

Nowadays, multi-energy systems are receiving special attention from smart grid community owing to their high flexibility potentials integrating with multiple energy carriers. In this regard, energy hub is known as a flexible and efficient platform to supply energy demands with an acceptable range of affordability and reliability by relying on various energy production, storage and conversion facilities. Given the increasing penetration of renewable energy sources to promote a low-carbon energy transition, accurate economic and environmental assessment of energy hub, along with the real-time automatic energy management scheme has become a challenging task due to the high variability of renewable energy sources. Furthermore, the conventional model-based optimization approach requiring full knowledge of the employed mathematical operating models and accurate uncertainty distributions may become impractical for real-world applications. In this context, this paper proposes a model-free safe deep reinforcement learning method for the optimal control of a renewable-based energy hub operating in multiple energy carries while satisfying the physical constraints within the energy hub operation model. The main objective of this work is to minimize the system energy cost and carbon emission by considering various energy components. The proposed deep reinforcement learning method is trained and tested on a real-world dataset to validate its superior performance in reducing energy cost, carbon emission, and computational time with respect to the state-of-the-art deep reinforcement learning and optimized-based approaches. Moreover, the effectiveness of the proposed method in dealing with model operation constraints is evaluated on both training and test environments. Finally, the generalization performance for the learnt energy management scheme as well as the sensitivity analysis on storage flexibility and carbon price are also examined in the case studies.

Vulnerability and Impact Analysis of the IEC 61850 GOOSE Protocol in the Smart Grid.

IEC 61850 is one of the most prominent communication standards adopted by the smart grid community due to its high scalability, multi-vendor interoperability, and support for several input/output devices. Generic Object-Oriented Substation Events (GOOSE), which is a widely used communication protocol defined in IEC 61850, provides reliable and fast transmission of events for the electrical substation system. This paper investigates the security vulnerabilities of this protocol and analyzes the potential impact on the smart grid by rigorously analyzing the security of the GOOSE protocol using an automated process and identifying vulnerabilities in the context of smart grid communication. The vulnerabilities are tested using a real-time simulation and industry standard hardware-in-the-loop emulation. An in-depth experimental analysis is performed to demonstrate and verify the security weakness of the GOOSE publish-subscribe protocol towards the substation protection within the smart grid setup. It is observed that an adversary who might have familiarity with the substation network architecture can create falsified attack scenarios that can affect the physical operation of the power system. Extensive experiments using the real-time testbed validate the theoretical analysis, and the obtained experimental results prove that the GOOSE-based IEC 61850 compliant substation system is vulnerable to attacks from malicious intruders.

Development of Cluster-Based Energy Management Scheme for Residential Usages in the Smart Grid Community

Several efforts have been taken to promote clean energy towards a sustainable and green economy. Existing sources of electricity present some complications concerning consumers, utility owners, and the environment. Utility operators encourage household applicants to employ residential energy management (REM) systems. Renewable energy sources (RESs), energy storage systems (ESS), and optimal energy allocation strategies are used to resolve these difficulties. In this paper, the development of a cluster-based energy management scheme for residential consumers of a smart grid community is proposed to reduce energy use and monetary cost. Normally, residential consumers deal with household appliances with various operating time slots depending on consumer preferences. A simulator is designed and developed using C++ software to resolve the residential consumer’s REM problem. The benefits of the RESs, ESS, and optimal energy allocation techniques are analyzed by taking in account three different scenarios. Extensive case studies are carried out to validate the effectiveness of the proposed cluster-based energy management scheme. It is demonstrated that the proposed method can save energy and costs up to 45% and 56% compared to the existing methods.

Development of Home Energy Management Scheme for a Smart Grid Community

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller.

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.

Redox Behavior of a CuF2/Cu Electrode for an All-Solid-State Fluoride Shuttle Battery

Introduction The popularization of electrified vehicles connected to the smart grid community systems with stationary energy storage has attracted much attention as one of the solutions to global environmental problems. The critical technologies for such systems are rechargeable batteries with energy densities higher than that of conventional Li-ion batteries (LIBs). Among various candidates, all-solid-state fluoride-ion batteries (FIBs) operated by fluoride-ion transport [1] are promising owing to an ultra-high theoretical energy density over 5000 Wh/L [2].The FIBs currently we refer to as Fluoride Shuttle Batteries (FSBs) [3] were previously reported as a primary battery [4]. For the development of rechargeable FSBs, both electrolytes with high fluoride-ion conductivity and active materials that undergo reversible fluorination/de-fluorination reactions are crucial. Fluorite- and tysonite-type fluorides have been investigated as potential solid electrolytes since around the 1970s [ 5 , 6]. Recently, our group reported a series of PbxSn2-xF4 (x = 0.96~1.52) compounds [7] with a unique structure consisting of two alternating layers; a double Pb layer and a triple-layer, each flanked by a single Sn layer. The highest conductivity of 1.1 × 10-1 S/cm at 140 °C was achieved for fluorite-type Pb1.2Sn0.8F4 (x = 1.2). As for tysonite-type fluoride, Reddy et al. reported a possible rechargeable FIB using a La0.9Ba0.1F2.9 solid electrolyte [1] in combination with the various metal fluoride active materials such as CuF2, BiF3, and KBiF4. Among many metal fluorides, CuF2/Cu is a fascinating redox couple as the cathode active material owing to its large theoretical capacity (843 mAh/g) and high electro-motive-force (3.5 V vs. Li/Li+) [8]. However, full utilization of their theoretical capacity has not yet been demonstrated. In this study, we investigated the possible rate-determining steps in the CuF2/Cu cathode reaction using Pb1.2Sn0.8F4 (x = 1.2) with a high fluoride-ion conductivity. Experimental The Pb1.2Sn0.8F4 electrolyte was prepared by solid-state reaction using a mixture of PbF2 and SnF2 powders [7]. The fluoride-ion conductivity was measured by electrochemical impedance spectroscopy. The electrochemical cells were prepared by pressing into a 10 mm diameter pellet. Here, the cathode material was assembled using Cu, Pb1.2Sn0.8F4, and C in a 25:70:5 wt% ratio to facilitate both electronic and ionic conduction. Charge/discharge measurements were performed at 140 °C. Structural and electrochemical characterizations were conducted using TEM/SAED and operando XAFS. Results and Discussion The initial charge/discharge curves at 140 °C for the Cu cathode versus the PbF2 anode are shown in Fig. 1. The Cu cathode with Pb1.2Sn0.8F4 electrolyte exhibited a large charge (discharge) capacity of 522 (342) mAh/g. This capacity corresponds to 62 (41) % of the theoretical specific capacity even though the ionic conductivity of Pb1.2Sn0.8F4 was approximately 1000 times higher than that of La0.9Ba0.1F2.9 [1]. The Cu K-edge XAFS measurements during the charging process revealed the oxidation of Cu from Cu0 to Cu2+, and vice versa during the discharging process. The isosbestic points observed for all the spectra indicated that the conversion reaction involved the two phases, in agreement with the results of our DFT calculations [9]. In the TEM/SAED patterns of the cathode after charge, the distinct diffraction spots related to the CuF2 phase were observed on the surface of the metallic Cu particle. The fluoride-ion conductivity of CuF2 was 10-11 S/cm at 140 °C, which was very low compared to Pb1.2Sn0.8F4 and La0.9Ba0.1F2.9. These results suggest that the solid electrolyte may not be a dominant factor in the reaction on the cathode side, and alternatively smooth fluoride-ion diffusion inside the Cu active material plays a key role for redox behavior. As a next step, it may be effective to make the Cu active material a nano-composite with other fluorides, which is currently underway. Acknowledgments This work was supported by the New Energy and Industrial Technology Development Organization (NEDO) of Japan under the RISING and RISING2 projects.

Closed loop elastic demand control by dynamic energy pricing in smart grids

Broadcasting of dynamic energy price signals for consumer's demand response programs (agents) is an effective and feasible way for demand side load management in future smart grids. Particularly, under fluctuating generation conditions of distributed renewable sources, automated and online market management strategies based on dynamic pricing are necessary for persistently conservation of energy balance and reducing the risk of instant energy shortages in the smart grids. This study presents a control theoretic energy market management approach based on closed loop elastic demand control scheme by means of dynamic price signal broadcasting. A PI controller structure is used to regulate energy price signals for demand response agents of smart grid community. Thus, total energy demand can be governed to respond fluctuation of renewable energy generation. To illustrate an application, a renewable energy integrated microgrid management scenario was studied numerically. A first order dynamic system model with a piecewise linear price-demand response is used to represent overall demand elasticity of the microgrid and a renewable energy microgrid simulation scenario is developed in Matlab/Simulink simulation environment. Simulation of 90 MWh peak demand market demonstrate that the closed loop dynamic energy pricing can be useful to control the elastic demand for tracing fluctuating generation of renewable energy sources. It is concluded that dynamic energy pricing can be useful medium for energy demand control.

Multi-Period energy procurement policies for smart-grid communities with deferrable demand and supplementary uncertain power supplies

We analyze a multi-period energy procurement problem for an energy aggregator, which is responsible for the centralized control of energy procurement and consumption in a community with smart grid installed. The aggregator can delay time-adjustable demand in the community through the smart grid if necessary. Furthermore, the aggregator can supply renewable energy as a supplementary power source to traditional electricity markets with pre-announced day-ahead real-time prices. To determine the optimal procurement amount, the aggregator needs to make tradeoffs between two types of power supplies, namely, traditional energy with variable prices versus free renewable energy with uncertain supplies. We solve the aggregator’s problem using dynamic programming and show that the optimal procurement policy is to procure traditional energy only when the price is below a threshold, which depends on the statistics of the day-ahead real-time price, wind energy distribution and the time left until the end of horizon. Through numerical studies, we compare the optimal policy with two other commonly-used policies in the wind energy setting, procurement based on the estimated supplies of wind energy and procurement up to the arrived demand. The cost-savings of our optimal policy are remarkable if the day-ahead real-time price fluctuates considerably. We also examine the robustness of our optimal policy in the scenarios of using historical wind energy data.

Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities

In this paper, we propose two novel coalitional game theory-based optimization methods for minimizing the cost of electricity consumed by households from a smart community. Some households in the community may own renewable energy sources (RESs) conjoined with energy storage systems (ESSs). Some other residences own ESSs only, while the remaining households are simple energy consumers. We first propose a coalitional cost optimization method in which RESs and ESSs owners exchange energy and share their renewable energy and storage spaces. We show that by participating in the proposed game these households may considerably reduce their costs in comparison to performing individual cost optimization. We further propose another coalitional optimization model in which RESs and ESSs owning households not only share their resources, but also sell energy to simple energy consuming households. We show that through this energy trade the RESs and ESSs owners can further reduce their costs, while the simple energy consumers also gain cost savings. The cost savings obtained by the coalition are distributed among its members according to the Shapley value. Simulation examples show that the proposed coalitional optimization methods may reduce the electricity costs for the RESs and ESSs owning households by 18%, while the sole energy consumers may reduce their costs by 3%.

Thermally Resistant Film Formation on Carbonaceous Anode Materials Using Vinylene Carbonate Additive for Lithium-Ion Battery

Introduction Currently, the development of lithium ion batteries (LIBs) is toward large-scale ones because electric vehicles (EVs), hybrid electric vehicles (HEVs) and stationary energy storage systems for smart grid community are considered necessary from the viewpoint of clean energy and sustainability. However, the large-scale electronic applications are more likely used in high-temperature environment, thus increasing the risk of battery failure. To overcome this issue, much efforts are put on the development of electrolyte additives, among which vinylene carbonate (VC) is known as an effective one in enhancing the thermal stability of carbonaceous anode material. [1] Nevertheless, the film-forming mechanism induced by VC on the carbonaceous anode surface, particularly at elevated temperature, is not well understood. To clarify the contribution of VC to the formation of thermally resistant film, we use LC-MS together with direct analysis in real time mass spectrometry (DART-MS) to analyze the electrolyte and electrode surface. Experimental The test cell was CR2032 coin cell, in which LiFePO4 and carbonaceous material, namely graphite or hard carbon, are respectively used as cathode and anode materials. For the preparation of cathode, LiFePO4 powders were uniformly mixed with conductive agents and water-based acrylic binder. Similarly, the carbonaceous material was mixed with conductive agents and water-based acrylic binder to make the anode. In addition, the electrolyte was 1 M LiPF6 in a mixture of ethylene carbonate (EC) and diethyl carbonate (DEC) in a volume ration of 1:1, and VC was used as an additive. To evaluate the cell’s thermal stability, the charge/discharge cycling tests were performed at 60 °C. For the analysis, the cycled test cells were decomposed, followed by collecting the electrolyte and electrode under an environment with an ultra-low humidity. Next, the collected electrolyte and electrode were respectively moved to the LC-MS and DART-MS apparatuses to analyze the electrolyte and electrode surface. Results and Discussion The test cell with the VC additive shows better cycle performance than that without the additive. Since LiFePO4 is a thermally stable cathode material, the difference in cycle performance can thus be attributed to the interaction between the electrolyte and the carbonaceous anode material. According to the LC-MS analysis, VC is found to suppress the decomposition of lithium salt and solvent, leading to reduced organophosphates as well as carbonate oligomers. It is also found from the DART-MS analysis that VC can assist the formation of thermally resistant organophosphates on the anode surface, preventing further degradation of electrolyte at elevated temperature. This should be the reason for the improved cycle performance for the cell with the VC additive. Reference 1. M. Herstedt, H. Rensmo, H. Siegbahn, and K. Edströma, Electrochim, Acta, 49, 2351–2359 (2004).

Development of Binder Free Fiber-Type Li-Ni-Mn-O Cathode with High-Voltage Stability for Lithium-Ion Battery

Introduction With the advancement in electronic applications ranging from portable devices to large-scale ones, such as electric vehicles (EVs), hybrid electric vehicles (HEVs) and stationary energy storage systems for smart grid community, lithium ion batteries (LIBs) with high energy density and power density are seriously required. To meet the requirement, a battery design capable of affording more active material loading on an electrically conductive network without other additives is therefore preferred. Regarding the above issue, fiber-type electrodes, namely using carbon fibers as current collectors, have been developed to improve the battery performance. [1,2] Due to its binder-free electrode design, lithium ions can directly intercalate into and deintercalate from the host material based on an effective conductive connection, thus leading to improved electrochemical performances. To combine the advantages of carbon fiber and high-voltage active material, we develop a binder-free fiber-type cathode composed of nickel-manganese-based (LMO/LNMO) active material and evaluate its electrode performance. Experimental To prepare the fiber-type cathode, a sequential synthesis process, including electrodeposition, hydrothermal reaction and post-calcination treatment, was applied. In the first step, a carbon fiber tow aligned in the same orientation was partly immersed into a solution with nickel and manganese nitrate to carry out the electrodeposition for the formation of precursor. Secondly, the fiber precursor was rinsed, dried and then moved to an autoclave with a LiOH solution, where the hydrothermal treatment was performed under 100ºC for 20 h. After rinsing and drying, the fiber tow was calcined at 400ºC for 1 h and then a fiber-type LMO/LNMO cathode can be eventually obtained. To analyze the morphology and crystalline structure of the active material, SEM and XRD are respectively applied. For its electrochemical evaluation, a coin cell (CR2032) was used, in which the fiber-type cathode and lithium foil were used as working and counter/reference electrodes, respectively. In addition, the electrolyte was 1 M LiPF6 in a mixture of ethylene carbonate (EC) and diethyl carbonate (DEC) in a volume ration of 1:1. Results and Discussion Based on the SEM observation, the active materials, which are synthesized with different Mn/Ni ratio during the electrodeposition step, are found to differ in morphology. From the XRD analysis of the active materials, three crystalline phases of layered Li1.88[MnxNi(1-x)]1.12O3, spinel LiMn2O4 and LiNi0.5Mn1.5O4 crystalline phases can be identified. As for the electrochemical performance, the fiber-type LMO/LNMO cathode shows an initial discharge capacity of ca. 140 mAh/g with two plateaus at 4.7 and 4.0 V in its discharge profile. In addition, for the cycling test, the capacity retention at the 50th cycle is 98% and the coulomb efficiency for each cycle is above 90%, exhibiting a stable electrochemical behavior in the voltage range 2.5‒4.9 V. Therefore, it is suggested that the fiber-type cathode is beneficial for the reversible ion intercalation/deintercalation into and from the host structure of the active material LMO/LNMO, thus contributing to the improved electrode performance under high voltage operation.

Prediction of the Italian electricity price for smart grid applications

In this paper we address the problem of one day-ahead hourly electricity price forecast for smart grid applications. To this aim, we investigate the application of a number of predictive models for time-series, including methods based on empirical strategies frequently adopted in the smart grid community, Kalman Filters and Echo State Networks (ESNs). The considered methods have been suitably modified to address the electricity price forecast problem. Strategies based on daily re-adaptation of models’ parameters are taken into consideration as well. The predictive performance achieved by the considered models is assessed, and the methods are compared among each other on recent real data from the Italian electricity market. As a result of the comparison over three years data, ESN methods appear to provide the most accurate price predictions, which could imply significant economic savings in many smart grid activities, such as switching on power plants to support power generation from renewable sources, electric vehicle recharging or usage of household appliances.

High energy density rechargeable magnesium battery using earth-abundant and non-toxic elements.

Rechargeable magnesium batteries are poised to be viable candidates for large-scale energy storage devices in smart grid communities and electric vehicles. However, the energy density of previously proposed rechargeable magnesium batteries is low, limited mainly by the cathode materials. Here, we present new design approaches for the cathode in order to realize a high-energy-density rechargeable magnesium battery system. Ion-exchanged MgFeSiO4 demonstrates a high reversible capacity exceeding 300 mAh·g−1 at a voltage of approximately 2.4 V vs. Mg. Further, the electronic and crystal structure of ion-exchanged MgFeSiO4 changes during the charging and discharging processes, which demonstrates the (de)insertion of magnesium in the host structure. The combination of ion-exchanged MgFeSiO4 with a magnesium bis(trifluoromethylsulfonyl)imide–triglyme electrolyte system proposed in this work provides a low-cost and practical rechargeable magnesium battery with high energy density, free from corrosion and safety problems.

How internet concepts and technologies can help green and smarten the electrical grid

Several powerful forces are gathering to make fundamental and irrevocable changes to the century-old grid. The next-generation grid, often called the 'smart grid,' will feature distributed energy production, vastly more storage, tens of millions of stochastic renewable-energy sources, and the use of communication technologies both to allow precise matching of supply to demand and to incentivize appropriate consumer behaviour. These changes will have the effect of reducing energy waste and reducing the carbon footprint of the grid, making it 'smarter' and 'greener.' In this position paper, we discuss how the concepts and techniques pioneered by the Internet, the fruit of four decades of research in this area, are directly applicable to the design of a smart, green grid. This is because both the Internet and the electrical grid are designed to meet fundamental needs, for information and for energy, respectively, by connecting geographically dispersed suppliers with geographically dispersed consumers. Keeping this and other similarities (and fundamental differences, as well) in mind, we propose several specific areas where Internet concepts and technologies can contribute to the development of a smart, green grid. We also describe some areas where the Internet operations can be improved based on the experience gained in the electrical grid. We hope that our work will initiate a dialogue between the Internet and the smart grid communities.