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We focus on detecting the feeding behavior in predatory fish using implantable biologgers that record and analyze electrocardiogram (ECG) signals. We propose a novel processing pipeline for resource-constrained embedded systems that can infer higher-level information, such as heart-rate and feeding events, from the ECG signals in situ. Our main contributions are in proposing efficient event detection algorithms that can reliably detect fish feeding events from noisy heart-rate data based on the unique statistical properties of feeding-induced changes in the heart-rate. We evaluate our approaches using an in-house biologger that we surgically implant in twelve coral trout fish and use to collect data during an experiment for a period of ten weeks and show that our signal processing pipeline performs well with noisy ECG signals overall. Specifically, our heart-rate estimation algorithm achieves errors of less than one beat per minute even in scenarios where popular algorithms used by domain specialists perform poorly. Furthermore, our feeding detection algorithms offer improved accuracy compared with the state-of-the-art algorithms while requiring significantly reduced computational and energy resources. We implement the proposed heart-rate estimation and feeding detection algorithms on the biologger and evaluate the associated system overhead.

Due to high penetration of Distributed Renewable Energy Resources and their inflexible dispatch nature, modern and future electrical grids are facing technical challenges. New participants can offer the flexibility required by the grid through investments in energy storage and demand response capabilities. This article provides a comprehensive review on the concept of Energy Communities, their technical and economic motivations, available resources in the literature, trading schemes, price negotiation algorithms, and benefits for the grid. Furthermore, this review describes the policy framework in the European Union to make Energy Communities a reality. Moreover, a brief survey of related projects over the world is presented, along with a discussion around benefits, obstacles, and future research opportunities in this area.

Although blockchain offers a lot of benefits for the Internet-of-Things (IoT) network in terms of security, there are a number of barriers to its widespread adoption. Since many IoT networks consist of battery-powered devices with limited computing capabilities, one of those adoption barriers is the IoT end node itself. Several articles provided a general survey of the blockchain integration with IoT applications. However, a detailed analysis of this integration is missing, possible challenges are omitted, and no solutions that would enable the integration are presented. Furthermore, none of the surveys focused on reviewing public blockchains for IoT devices with low computational capabilities and limited energy resources. Therefore, the aim of this article is to provide the results of an in-depth study that covers challenges faced by existing public blockchains with IoT device integration. This article looks into resource-constrained IoT device classification and the wireless communication protocols used by them. It examines challenges that are required to be overcome for the successful integration of resource-constrained IoT end nodes with existing public blockchains. It then looks at the proposed solutions and their feasibility. A detailed review of possible security attacks on an IoT blockchain network is provided. As a result, a list of important characteristics that a public blockchain should possess in order to be integrated with a resource-constrained IoT network is proposed. Finally, the requirements for the IoT end nodes themselves are specified.

This article proposes a new framework for modeling plug-in electric vehicle (PEV) charging demand supported by solar photovoltaic (PV) energy resources in a distribution system. The proposed work focuses on modeling the stochastic nature of both PEV loads and PV generation while considering the effect of the temporal–spatial characteristics of the driver’s behavior, as well as solar irradiation and temperature. A trip chain, based on the Markov Chain Monte Carlo process, is developed to properly model PEV daily driving activities and the PV uncertainty. Charging facilities are assumed available at home, work, and fast-charging stations, having charging levels of 3.7, 6.6, and 50 kW, respectively. The proposed framework is examined, considering the National Household Travel Survey global data, as well as the city of Buffalo and New York state. The impact of varying the penetration levels of PEV and PV resources is also investigated. This work strengthens the proposed models in the literature by integrating the temporal–spatial characteristics of PEV charging demand into PV stochastic models. Resume —Cet article propose un nouveau cadre de modelisation de la demande de recharge de vehicules electriques rechargeables (PEV) soutenue par des ressources energetiques solaires photovoltaiques (PV) dans un systeme de distribution. Le travail propose se concentre sur la modelisation de la nature stochastique des charges PEV et de la production par PV tout en considerant l’effet des caracteristiques spatio-temporelles du comportement du conducteur, ainsi que l’irradiation solaire et la temperature. Une chaine de trajets, basee sur le processus de Monte-Carlo par Markov, est developpee pour modeliser correctement les activites quotidiennes concernant la conduite du PEV et l’incertitude des PV. Les installations de recharge sont supposees etre disponibles a la maison, au travail et aux bornes de recharge rapide, avec des niveaux de recharge de 3, 7; 6, 6 et 50 kW, respectivement. Le cadre propose est examine en tenant compte des donnees mondiales de l’enquete nationale sur les voyages des menages, ainsi que de la ville de Buffalo et de l’Etat de New York. L’impact de la variation des niveaux de penetration du PEV et des ressources PV est egalement etudie. Ce travail renforce les modeles proposes dans la litterature en integrant les caracteristiques temporelles-spatiales de la demande de charge PEV dans les modeles stochastiques PV.

Nuclear power has been declining in importance over the last quarter century, with its share of global electrical energy generation decreasing from 17.5 percent in 1996 to around 10 percent in 2019. Small modular and advanced nuclear reactors have been proposed as potential ways of dealing with the problems—specifically economic competitiveness, risk of accidents, link to proliferation and production of waste—confronting nuclear power technology. This perspective article examines whether these new designs can indeed solve these problems, with a particular focus on the economic challenges. It briefly discusses the technical challenges confronting advanced reactor designs and the many decades it might take for these to be commercialized, if ever. The article explains why the higher construction and operational costs per unit of electricity generation capacity will make electricity from small modular reactors more expensive than electricity from large nuclear power plants, which are themselves not competitive in today’s electricity markets. Next, it examines the potential savings from learning and modular construction, and explains why the historical record suggests that these savings will be inadequate to compensate for the economic challenges resulting from the lower generation capacity. It then critically examines arguments offered by advocates of these technologies about job creation and other potential uses of energy generated from these plants to justify subsidizing and constructing these kinds of nuclear plants. It concludes with an assessment of the markets for these technologies, suggesting that these are inadequate to justify constructing the necessary manufacturing facilities.

The optimal use of batteries in drones is a critical issue for achieving both reliable operation and maximum flight time. The key is to acquire accurate information about the state of charge (SoC) of the battery in runtime. Drones typically employ series-connected lithium-ion polymer (Li-Po) battery cells, whose SoC is affected by many environmental factors as well as flight patterns. In this article, we propose a scheme, called Optrone, which maximizes the flight time of a drone while safely using the battery. Understanding the implications of the factors affecting the SoC of the drone’s battery pack, we propose a three-level SoC, which is a metric for representing the SoC of a battery in runtime. We also provide various operating policies to users to improve the safety and efficiency of operating the drone. We implemented the prototype hardware and software for Optrone, and validated its operation in controlled and real environments. The experimental results in a controlled environment showed that the proposed three-level SoC poses less than 3% error and the operating policies achieved a flight time gain of 19.4%, while guaranteeing battery safety. We also observed a flight time gain of about 10% in real outdoor experiments, where the user rightly adheres to the advised Optrone policy.

Coordination of microgrids protections is an emerging research issue due to the increasing participation of distributed generation (DG) in power grids. This review paper aims to show the current weaknesses in the coordination of protections for microgrids, as well as the available solutions and future challenges. The review evidences that microgrid protection systems must face two important aspects. The first one is the dynamic behavior presented by the microgrids that take place due to the intermittency of energy resources. The second one is related to the operation modes of the microgrids, since they may operate either connected or disconnected from the distribution network. The literature review also allowed classifying the most used protection schemes for microgrids. A comprehensive analysis which performs a discusion of the most relevant future challenges regarding microgrid protection coordination is also presented.

This article develops a novel decentralized periodic energy trading framework for a pelagic islanded microgrid. Compared with situations of the microgrid connecting directly to the main grid and of the short-periodic energy trading system, in this article, we tackle the following challenges: the limitation of energy resources, the two types of end users (i.e., strategic users and normal users), the high uncertainty of renewable energy generations (REGs), and the nonconvexity of optimization problem. To overcome these four obstacles unavoidably existing in practice, we propose the models of user satisfaction with the priority power supply for strategic users. Robust optimization approach is adopted to address the uncertainty associated with the REGs. A nonconvex optimization problem derived from the game-theoretic approach and backward induction is solved via designing a decentralized bilevel iterative algorithm, then a Stackelberg equilibrium strategy is obtained, where the aggregator maximizes its revenue and each user minimizes its energy cost. Simulation results are provided to show the effectiveness and benefits of the proposed periodic energy trading framework for pelagic islanded microgrids.

To investigate the effect of the operating temperature on the performance of building integrated photovoltaic modules, two different crystalline silicon (c-Si) module types (glass/backsheet and glass/glass) with two different mounting configurations (ventilated and insulated) were installed in Canobbio, Switzerland and monitored over more than four years outdoors. The differences in the performance loss rates (PLRs) between module types and mounting configurations were determined by using the year-over-year methodology. The glass/backsheet module was markedly affected by the higher operating temperature: PLR changed from 0.01%/yr for ventilated to $-$ 0.42%/yr for insulated. However, the glass/glass module showed an opposite and unexpected trend: PLR changed from $-$ 0.10%/yr for ventilated to 0.26%/yr for insulated. Analysis of the IV curve parameters revealed that the decreased performance of the insulated glass/backsheet module was chiefly due to the loss in the fill factor with increased resistance; however, the gain in the short-circuit current ( I sc) was the main driver for the increased performance of the insulated glass/glass module. Real-world temperature coefficients were further examined and they were mostly found to be in line with the values measured indoors. Lower power–temperature and higher current–temperature dependence of the insulated glass/glass module, compared to the ventilated construction, supported the observed performance and current behavior of this module.

Consumers in the united kingdom typically spend, on average, p33 billion/year for electricity, of which a significant portion (21% in 2017) is paid to distribution companies for delivering electricity to customer premises. To protect consumers' interest, Ofgem (Office of Gas and Electricity Markets), the energy regulator in the United Kingdom, is actively reforming network charges so that they remain usable for the purpose of facilitating the industry?s transition to lowcarbon emissions.

The intermittency introduced by the increasing integration of distributed renewable energy sources is challenging the efficient operation of residential distribution systems. A promising solution to tackle this challenge is the implementation of residential demand response through responsive household appliances such as heat pumps, refrigeration devices, and energy storage units. In this article, a peer-to-peer energy trading platform among residential houses is proposed to coordinate demand response schemes and level off potential generation/consumption disturbances in the hour-ahead intraday context. First, the day-ahead and intraday energy management models for residential houses are established considering the characteristics of responsive household appliances and energy storages. The discomfort and possible economic losses for performing demand responses are quantified with respect to the risk preferences of residential customers. The peer-to-peer energy trading platform is developed and a double-auction mechanism employed to promote the collaborative demand response schemes in the face of disturbances. An optimal bidding strategy of residential houses is also proposed. The feasibility of the proposed models and bidding strategy are verified through case studies. It is also illustrated that the residential demand response schemes and intraday peer-to-peer energy trading are effective in managing the uncertainties of load demand and renewable generation.

© 2005-2012 IEEE. Powering small islands with reliable, affordable, and green electricity is a big challenge due to their dispersed geographical location with limited number of consumers and the heavy dependence on fossil fuels. This paper aims to address this challenge of reducing dependency on fossil fuel generators by providing an easy and feasible solution using available and accessible energy resources. The proposed method utilizes the bidirectional energy transfer mechanism available in electric boats (EBs) to support the consumers' power demand. It proposes a new real-time load-support (RTLS) system with a coordinated control using EBs, community generators, and battery energy storage systems. It analyzes the management of the intermittent source-dependent small-scale grid in real time, under various weather, load, and battery state-of-charge conditions. The RTLS system coordinates the customers' load demand with the available EBs, photovoltaics, and battery storage to provide efficient load support and to regulate the bus voltage and frequency. The efficacy of the proposed system is validated both computationally in a real network and in a laboratory setup. It is found that this novel system can substantially reduce the grid load demand and maintain the power quality under various load/source uncertainties and fault conditions. The system robustness is also evaluated considering undesirable conditions, such as severe three-phase faults and sudden EB disconnections. The performance of the proposed method is compared with that of the day-ahead load management approach to validate its effectiveness under various scenarios.

Cascaded utilization of natural gas, electric power, and heat could leverage synergetic effects among these energy resources, precipitating the advent of integrated energy systems. In such infrastructures, energy hub is an interface among different energy systems, playing the role of energy production, conversion and storage. The capacity of energy hub largely determines how tightly these energy systems are coupled and how flexibly the whole system would behave. This paper proposes a data-driven two-stage robust stochastic programming model for energy hub capacity planning with distributional robustness guarantee. Renewable generation and load uncertainties are modelled by a family of ambiguous probability distributions near an empirical distribution in the sense of Kullback-Leibler (KL) divergence measure. The objective is to minimize the sum of the construction cost and the expected life-cycle operating cost under the worst-case distribution restricted in the ambiguity set. Network energy flow in normal operating conditions is considered; demand supply reliability in extreme conditions is taken into account via robust chance constraints. Through duality theory and sampling average approximation, the proposed model is transformed into an equivalent convex program with a nonlinear objective and linear constraints, and is solved by an outer-approximation algorithm which entails solving only linear program. Case studies demonstrate the effectiveness of the proposed model and method.

In Pakistan, the performance of conventional electrical grids is inefficient, resulting in severe energy crises. To overcome the alarming challenges persisting in the energy grids, Pakistan must focus on system protection, grid reliability, distribution and transmission, and power quality. The inefficiencies in grid protection and management signify an overall problematic energy scenario. The solutions to these problems include the improvement of domestic, commercial, and industrial demand-side management and the reduction in distribution network losses. A smart grid (SG) is a critical requirement as it can overcome the shortcomings of the existing grid owing to its promising features, enhanced consumer empowerment, utmost security; efficient and optimized energy flow; and demand-supply management. Thus, the SG is essential to overcome the energy crisis in Pakistan and achieve the standards of other developed nations in the energy sector. This study aims to highlight the significant prospects of SGs within Pakistan with the key objectives of its availability requirements. We compare the energy scenario in Pakistan with that of other countries and recommend various aspects that require improvement through SG implementation. Additionally, we discuss the incorporation of renewable energy resources and present a market analysis regarding SGs to illustrate the SG scenario and its implementation in Pakistan. Moreover, we analyze and evaluate detailed taxonomies of energy generation, energy projects, renewable energy assessment, power market trends in Pakistan, and the basic requirements of SGs. Furthermore, a critical analysis of the energy sector in Pakistan is elaborated, which describes the possibilities, requirements, and strengths pertaining to the transformation of the modern electric grid with respect to the China-Pakistan Economic Corridor. Thus, we believe that our work is more versatile in improving the energy system of Pakistan for the implementation of the SG.

Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWO-based approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.

Demand response (DR) is an important factor contributing to achieve a balance between energy production and demand in Smart Grids. DR plays a key role in the use of residential energy allowing to improve the load management, electrical grid reliability, to reduce energy demand during peak hours and to minimize the use of energy in face of increasing energy prices. This article proposes a Model Predictive Control (MPC) strategy to manage the energy resources of a residential microgrid combined with DR techniques, such as load curtailment, that promotes short term reduction of electricity demand in pre-defined hours. In particular, the presented approach encompasses degradation issues of the Energy Storage System (ESS), the cost of the electricity, renewable energy generation, and other operational constraints. The developed control strategy is able to maximize microgrid economical benefit, while minimizing the degradation of the ESS, reducing electricity consumption during the day, and fulfilling the different operational constraints. The proposed strategy is validated in an experimental renewable-energy based microgrid platform for different climatic conditions. The obtained results demonstrate and verify the effectiveness of the proposed control and management strategy.

Transmission–distribution coordinated energy management (TDCEM) is recognized as a promising solution to the challenge of high distributed energy resource (DER) penetration, but there is a lack of a distributed computation method that universally and effectively works for the TDCEM. To bridge this gap, this paper presents a generalized master–slave-splitting (G-MSS) method. This method is based on a general-purpose transmission–distribution coordination model called G-TDCM, which enables the G-MSS to be applicable to most of the central functions of the TDCEM. In this G-MSS method, a basic heterogeneous decomposition (HGD) algorithm is first derived from the heterogeneous decomposition of the coupling constraints in the optimality conditions of the G-TDCM. Its optimality and convergence properties are proved. Then, inspired by the sufficient conditions for convergence, a modified HGD algorithm that utilizes the subsystem's response function is developed and demonstrated to converge faster. The distributed G-MSS method is then demonstrated to successfully solve a series of central functions of the TDCEM, e.g., power flow, contingency analysis, voltage stability assessment, economic dispatch, and optimal power flow. The severe issues of over-voltage and erroneous assessment of the system security that are caused by DERs are thus resolved by the G-MSS method with modest computation cost.

The increasing penetration of variable energy resources in modern electric power systems requires additional flexibility in reserve provision to maintain reliable and efficient grid operations. However, full recognition and appropriate compensation of this flexibility is difficult to ensure within current power market designs due to rigidity in reserve definitions and requirements. This paper proposes a new mixed-integer linear programming formulation for the optimal clearing of a day-ahead market based on swing contracts with dynamically updated regulation reserve zones. Five-bus and 30-bus test cases are used to illustrate the effectiveness of the proposed new market design.

Autonomous electric vehicles (AEVs) are gaining ground around the world. They are equipped with intelligent decision-making capabilities that allow them to optimize on their battery usage and engage in energy trading for profit maximization. This paper envisions a market model, where energy aggregators considers buying and selling electricity from AEVs. In this system, the aggregators reside on cloudlets at the edge of a cloud computing system for prompt communication and negotiation with AEVs. Whether they are parked or en-route, AEVs can be crowdsourced to provide energy to consumers at needed times. This study proposes double auction models to incentivize AEV agents to participate in the market based on their time preferences and energy pricing. The proposed system implements dynamic pricing structures, which are applicable to various scenarios of energy trading. To evaluate the proposed system, this paper provides extensive theoretical analysis and simulation experiments to demonstrate that the proposed auctioning models are computationally efficient, truthful, individually rational, and budget balanced.

In this paper, we present an event-driven automatic demand response (EADR) framework for online operation of residential microgrids (RMGs). Our framework involves comprehensive analysis of the schedulable ability (SA) for residential energy resources (RERs), and the uncertainties of both sides are also considered. Specifically, we first construct an EADR architecture to minimize the total operation cost and maintain supply–demand balance, and the event analyses are provided. Then, we define the concept of SA for RERs and also establish an SA evaluation system taking the real-time and historical information of the RERs into account. On these bases, SA-based interactions between RERs and EADR system and state machine are introduced to trigger the execution of the online EADR in a close-loop way. Furthermore, to implement this framework in practice, we functionally decompose the overall EADR architecture into event manager, EADR server, and a group of local controllers, and also design distributed online algorithms for each of them. The key idea of the designed algorithms is to operate the RMG simulating its real-life dynamics. Numerical simulations on a practical RMG verify the effectiveness of the proposed approach.

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost.

Distributed energy resources (DERs), including distributed renewable generation, have changed the operational, commercial, and regulatory dynamics of power systems. The growth of DERs has challenged the classic organization of power systems, which was generally based on centralized supply from larger plants, and there has been a broad, worldwide discussion among institutions and agents of the electricity industry about the character of future energy systems in the face of DERs. A fundamental question arises: will the future energy supply be dominated by large-scale power plants, developed by traditional agents and acting in the wholesale market, or by DERs? Some see a DER-dominant future in which millions of proactive energy consumers choose to install DERs behind their electricity meters, revolutionizing the traditional model. Others believe that the future power system will simply be an integrated mix of centralized energy resources and DERs, with most of the traditional model remaining intact.

The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish-Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city.

Due to uncertainty and dynamic characteristics from intermittent energy and load demand response (DR), the optimal operation of the hybrid energy system is a great challenge. This article proposes an event-triggered multiagent coordinated optimization strategy with two-layered architecture. First, the price-bidding-based DR model is proposed with different stakeholders, and it also deduces the optimal bidding price with the Nash equilibrium theory. Then, four agents are designed to control different kinds of energy resources: agent 1 mainly analyzes the uncertainty or randomness caused by intermittent power, agent 2 takes charge of the dynamic economic dispatch (DED) within thermal units, agent 3 manages the optimal scheduling of energy storage, and agent 4 mainly undertakes the load-shifting strategy from consumers. In the upper-layer level, all agents coordinate together to ensure the stability of the hybrid energy system with an event-triggered mechanism, and the intelligent control approach mainly depends on switching ON/OFF power generators or curtailing system load, and the consensus algorithm is utilized to optimize the subsystem problem in the lower-layer level. Furthermore, the simulation results can further verify the efficiency of the proposed method, and it also reveals that the event-triggered multiagent optimization strategy can be a promising way to solve the hybrid energy system problem.

The main focus of the work presented in this paper is on the outage management of interconnected microgrids during islanded operation after being disconnected from the utility main supply. The proposed two-stage load restoration technique is formulated as a Mixed Integer Linear Programming (MILP) optimization problem with the sole objective of optimally restoring the maximum number of disconnected loads. The proposed technique is applied to a distribution system comprising of several microgrids, i.e., Multi-Microgrid (MMG) distribution system. In this proposed technique, the power transactions between individual microgrids are managed through (1) determining the schedule of local energy resources, and (2) by obtaining the control signals pertaining to local flexible loads. Flexible load control signals are prioritized through two incorporated Demand Response (DR) programs, namely emergency load shedding and preemptive load shifting. To quantify the restoration technique performance, a new index-denoted as restoration technique success index (SI)-is proposed. The effectiveness of the proposed restoration technique is verified through different test case scenarios and the obtained results are discussed.

Nowadays, the world is facing the dual crisis of the energy and environment, and renewable energy, such as wave energy, can contribute to the improvement of the energy structure of the world, enhance energy supply and improve the environment in the framework of sustainable development. Long-term prediction of the significant wave height (SWH) is indispensable in SWH-related engineering studies and is exceedingly important in the assessment of wave energy in the future. In this paper, the spatial and temporal characteristics of wave energy in the South China Sea (SCS), and adjacent waters are analyzed. The results show that there are abundant wave energy resources in the waters around the Taiwan Strait, the Luzon Strait, and the north part of the SCS with annual average SWH (SWH) of over 1.4 m and obvious increasing trend. Then, the SARIMA approach considers the relationship between the current time and the values, residuals at some previous time and the periodicity of the SWH series are proposed to forecast the SWH in the SCS and adjacent waters. The results obtained are promising, showing good performance of the prediction of monthly average SWH in the SCS and adjacent waters.

Today’s energy resources are closer to consumers due to sustainable energy and advanced technology. To that end, ensuring a precise prediction of energy consumption at the buildings’ level is vital and significant to manage the consumed energy efficiently using a robust predictive model. Growing concern about reducing the energy consumption of buildings makes it necessary to predict the future energy consumption precisely using an optimizable predictive model. Most of the previously proposed methods for energy consumption prediction are conventional prediction methods that are normally designed based on the developer’s knowledge about the hyper-parameters. However, the time lag inputs and the network’s hyper-parameters of learning methods need to be adjusted to have a more accurate prediction. This paper proposes a novel hybrid prediction approach based on the evolutionary deep learning (DL) method that is combining genetic algorithm with long short-term memory and optimizing its objective function with time window lags and the network’s hidden neurons. The performance of the presented optimization predictive model is investigated using public building datasets of residential and commercial buildings for very short-term prediction, and the results indicate that the evolutionary DL models have better performance than conventional and regular prediction models.

One of the most promising use cases of 5G/IMT2020 is the unmanned aerial vehicle (UAV). Due to their small size, the UAVs are resource constraint devices. To this end, this paper proposes an offloading algorithm for UAVs to assist in the execution of computationally intensive tasks. The proposed algorithm provides two UAV offloading methods. The first offloading method is the air-offloading, where a UAV can offload its computing tasks to nearby UAVs that have available computing and energy resources. The second offloading method is the ground-offloading, which enables the offloading of tasks to an edge cloud server from the multi-level edge cloud units connected to ground stations. The proposed algorithm is energy- and latency-aware, i.e., it selects the execution device and the offloading method based on the latency and energy constraints. The intensive algorithm simulation over reliable conditions for various scenarios with different cases for each scenario is conducted and results are presented.

Advances in bilateral communication technology foster the improvement and development of home energy management system (HEMS). This paper proposes a new HEMS to optimally schedule home energy resources (HERs) in a high rooftop photovoltaic penetrated environment. The proposed HEMS includes three stages: forecasting , day-ahead scheduling , and actual operation . In the forecasting stage, short-term forecasting is performed to generate day-ahead forecasted photovoltaic solar power and home load profiles; in the day-ahead scheduling stage, a peak-to-average ratio constrained coordinated HER scheduling model is proposed to minimize the one-day home operation cost; in the actual operation stage, a model predictive control based operational strategy is proposed to correct HER operations with the update of real-time information, so as to minimize the deviation of actual and day-ahead scheduled net-power consumption of the house. An adaptive thermal comfort model is applied in the proposed HEMS to provide decision support on the scheduling of the heating, ventilating, and air conditioning system of the house. The proposed approach is then validated based on Australian real datasets.

Wireless energy harvesting is promising to be a new opportunity to prolong the lifetime of rechargeable sensors in the Internet of Things. However, how to recharge the sensors and manage charging energy is still a problem. Most existing methods are based on robots or vehicles carrying battery packs to charge sensors. They are vulnerable to the sensors distributed in complex terrain and have high hardware maintaining cost. To address this issue, we present crowd-charging (CC), a novel crowdsourcing-based wireless energy charging model for rechargeable sensors. It leverages smart devices carried by users as mobile crowd energy resources (chargers) to provide wireless energy to sensors. The challenge is how to incentivize and allocate mobile users to optimize the total charging quality of all the sensors. Besides monetary energy revenues, we design a bonus game to entertain participated users. We also propose three user allocation algorithms, CC algorithm (CCA), and two improved versions of CCA, i.e., CC and CC . The improved algorithms give great improvement for our model and they have different advantages suitable for different situations. We conduct extensive simulations and demonstrate the effectiveness of our algorithms with numerical results.

This paper presents a risk-based stochastic model to sizing a photovoltaic (PV) /diesel/storage hybrid power system of a merchant marine vessel. Integrating renewable, in particular PV, energy resources offer significant advantages to bulk transportation by reducing the greenhouse gas emissions, improving the energy efficiency, and increasing the energy security. To this end, the sizing problem of PV/diesel/storage merchant marine vessel hybrid power system would optimally determine appropriate configuration of the PV system and energy storage system to design hybrid power system of a merchant marine vessel in an optimal and reliable manner. The uncertainty related to the hourly global solar radiation and its effect on the output power of the PV system is taken into account and modeled using proper scenario generation methods. Additionally, the scenario reduction technique is applied for the domination of dimensionality. Furthermore, an appropriate risk measurement, the conditional value-at-risk methodology, is incorporated with the proposed stochastic model to quantify the potential risk of sizing the problem. Finally, the proposed model is applied to a comprehensive test case to illustrate the efficiency and the applicability of the proposed approach.

Following FERC Order 755 in October 2011, wholesale markets in the US adopted a two-part compensation structure in their regulation markets, paying for regulation capacity made available as well as the quantity of regulation actually provided. In this letter, we first present a mathematical model for Pennsylvania–Jersey–Maryland market clearing process for their energy and regulation markets. We then compute the optimal offer of a single energy resource offering its services into such an energy and regulation market. Finally, we empirically illustrate our offer strategy through case studies.

In this paper, a robust energy management system is proposed for islanded microgrids, which at the same time considers static modeling of system frequency. The aim of this paper is to manage frequency excursions produced from load and renewable generation fluctuations. In microgrids, the use of inertialess and small-scale energy resources risks the frequency stability. In order to overcome this problem, first, the frequency-dependent behavior of the distributed energy resources is formulated precisely within the centralized hierarchical energy and reserve management context of a microgrid. Then, in order to handle microgrid uncertainties in a robust way, information gap decision theory (IGDT) technique is proposed. Furthermore, to address a robust hierarchical energy and frequency reserve management architecture, the problem is transmitted into a single-level mixed integer linear programming model and solved appropriately over a 24-h scheduling time horizon. Numerical simulation results obtained on a typical islanded smart microgrid are presented, including the demand response mechanism. The IGDT can help microgrid central controller (MGCC) to make operational decisions in front of major uncertainties. The obtained results verify that through the proposed IGDT-based energy management system, the MGCC can effectively stabilize the microgrid frequency along with its economic targets while considering severe uncertainties.

Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.

There is a growing concern over addressing the adverse effects of variations in the output power of distributed generators such as photovoltaic generation (PVG) systems that continue to be widely introduced into power networks. Nowadays, most network operators are requiring these intermittent energy resources to seek compliance with new regulations pertaining to the restriction of their export power fluctuations. This paper aims to investigate the smoothing of the export power fluctuations primarily attributed to clouds passing over the PVG plant, which are traditionally compensated by integrating a battery storage (BS) system. The idea of incorporating short-term solar prediction information into the conventional smoothing approach is examined to indicate how it affects the engagement of BS in the smoothing process. Afterward, an enhanced solar forecasting scheme based on whole-sky imaging is proposed and its performance is demonstrated through several real-time experiments complemented with simulation studies. The results reveal that the proposed PVG smoothing strategy is capable of successfully filtering rapid export power fluctuations to an acceptable extent and the conventional generation reserves will experience a negligible amount of remaining undesired power variation. This clearly bears out the hypothesis of battery-less PVG regulation.

The increasing penetration of variable energy resources has led to more uncertainties in power systems. Flexible Ramping Products (FRP) have been adopted by several electricity markets to manage the uncertainties. We reveal that neglected line congestion for FRP may not only cause infeasibility, but also result in a failure of cost recovery. To address the deliverability issues on FRP, this paper proposes a new concept, Deliverable Robust Ramping Products (DRRP), in real-time markets. The DRRP includes generation ramping reserve and generation capacity reserve. The DRRP is deliverable and immunized against any predefined uncertainty. It also fully addresses the bid cost recovery issue caused by the line congestion in existing FRPs. The prices of DRRP are derived within the Affine Adjustable Robust Optimization (AARO) framework. These prices can be used to identify valuable reserves among available reserves and quantify the values of flexible resources that provide reserves. This paper also proposes a general approach to obtaining the time-decoupled prices for DRRP and generation, which can be used for the market settlement of the first interval only in real-time markets. Simulations on a 3-bus system and the IEEE 118-bus system are performed to illustrate the concept of DRRP and the advantages of DRRP compared to existing FRP.

The construction and operation of the ultrahigh voltage power grid has solved the problem of optimal allocation of energy resources and played a key role in promoting social development. However, concerns about its safety have kept growing in recent years. In order to analyze the reliability of ultrahigh voltage AC/DC system combined with the quasi-Monte Carlo method and PSD-BPA simulation, the reliability model of various components and system is established, and the simulation speed is improved with accuracy ensured. In order to ensure the compatibility and efficiency of the software, we design the BPA-Matpower data interface, and the data translation from BPA to Matpower is realized by fourth conversion. Then, we propose an index system for reliability evaluation, concise and easy to compute, to reflect the reliability of the UHV grids and the characteristics of AC and DC systems. Based on actual regional powergrid, we compare the model in this paper with the traditional sequential Monte Carlo method, and carry out the analysis of the component sensitivity to study the multi-fault characteristics and the weakness of the system, and verify the scientific and effective result of this paper.

A virtual power plant (VPP) is formulated and developed as a service-centric aggregator that enables the market integration of distributed energy resources and simultaneously supports cooperation with the distribution system operator (DSO) in addressing the issue of network usage. A suitable schedule of interactions and communications between aggregators, market operators, system operators, generators, and consumers, regarding electricity market participation and network operation is proposed and presented in a sequence diagram. The cooperation on congestion management in the distribution network is highlighted as solution to relieve network constraints via the optimal adjustment of active and reactive power of VPP resources while maximizing renewable energy integration across the pool under management. The VPP reduces uncertainty affiliated with input data by employing the latest forecasts through a rolling horizon approach in the planning stage. Thanks to the flexibility of the VPP to perform rescheduling in accordance with agreements it negotiated with its resources, it becomes possible to refrain from undesirable curtailments. Both the market-integrative and the service-centric roles of the VPP are verified through modeling and simulation with a benchmark European distribution network. The results confirm the added value of the proposed VPP in enhancing the integration of wind and solar power.

This work describes a technique to define a single parametric equation that estimates remote conditions within a distribution network, in an online setting, without dedicated telemetry. In this novel approach, a departure from conventional state estimation is explored to facilitate a fully decentralized operation. Derived based on fundamental treatment of ac power flow, the electrical behavior of a section of network is defined to a tractable constraint space using regression analysis. In this non-iterative technique, a measurement set consisting of the local voltage magnitude, active and reactive power injections at a single node are the input to pre-computed polynomials. Remote current flow, active power and reactive power flow as well as remote and local voltages and sensitivities are estimated in a direct calculation from period to period. Accurate estimations are also found in the presence of transducer errors. To assess the applicability of this technique at differing voltage levels a range of reactance to resistance ratios are considered. Science Foundation Ireland

Distributed energy resources (ERs), featured with small-scale power generation technologies and renewable energy sources, are considered as necessary supplements for smart grid. To ensure that merged resources contribute effectively to the grid, data generated by consumer side should be shared among the ERs. However, it also introduces challenges of the protection of consumer privacy. To address these difficulties, we propose a new framework to share data in smart grid by leveraging new advances in homomorphic encryption and proxy re-encryption. Our proposed framework allows ERs to analyze consumer data while ensuring consumer privacy. An additional benefit of our proposed framework is that consumer data is transmitted over the smart grid only once. Furthermore, we present a concrete scheme falling into the proposed framework. Extensive analysis shows that the concrete scheme is secure and efficient.

The rise of distributed energy resources (DERs) can enhance the efficiency of system operations by providing flexibility services to the different agents involved, but they also pose a major resource allocation problem. This study considers three different agents procuring DER services: distribution system operators (DSOs) for local congestion management, transmission system operators (TSOs) for system-wide reserve deployment, and retailers for hedging against network usage tariffs based upon peak-load pricing. A variety of market mechanisms are identified to co-ordinate these needs, and three schemes are developed in detail. These are separate markets for each agent, co-ordinated Shapley value allocations for TSO and DSO, and a co-ordinated mechanism including retailers. These designs are evaluated on a realistic distribution network in Britain for two operational days. The results show a more efficient dispatch from the TSO–DSO co-ordinated procurement over independent sequential procurements. However, the inclusion of retailers in the joint dispatch is surprisingly less attractive due to the lack of improvement in social welfare and the undesirable impacts on the DSO.

The integration of microgrids that depend on the renewable distributed energy resources with the current power systems is a critical issue in the smart grid. In this paper, we propose a non-cooperative game-theoretic framework to study the strategic behavior of distributed microgrids that generate renewable energies and characterize the power generation solutions by using the Nash equilibrium concept. Our framework not only incorporates economic factors but also takes into account the stability and efficiency of the microgrids, including the power flow constraints and voltage angle regulations. We develop two decentralized update schemes for microgrids and show their convergence to a unique Nash equilibrium. Also, we propose a novel fully distributed PMU-enabled algorithm which only needs the information of voltage angle at the bus. To show the resiliency of the distributed algorithm, we introduce two failure models of the smart grid. Case studies based on the IEEE 14-bus system are used to corroborate the effectiveness and resiliency of the proposed algorithms., 11 pages; This paper has been accepted to publish in IEEE Transactions on Smart Grid. This is the final version

Plug-in electric vehicles (PEVs) present a promising solution to mitigate greenhouse gas emissions but on the other hand, their increased penetration can impact power system operation, particularly so in an isolated microgrid. Similarly, demand response (DR) has the potential to provide significant flexibility in the operation of an isolated microgrid with limited generation capacity, by altering the demand and introducing an elasticity effect. This paper proposes a new mathematical model for optimal scheduling of energy resources and smart management of loads which includes smart charging of PEVs, DR, and operation of battery energy storage systems (BESSs), for isolated microgrids. Different case studies are developed to examine isolated microgrid operations when the demand increases, and how the energy management model copes with such increase. The proposed model develops energy management strategies considering the network constraints and different objective functions from the perspective of the microgrid operator as well as from the owners of PEVs and BESS.

Energy Internet is a vision of future power systems, which will achieve highly efficient interconnection among various types of energy resources, storage, and loads, and enable P2P energy delivery on a large scale. To enhance the flexibility and efficiency of Energy Internet, this article employs the methodology of SDN in Energy Internet and proposes an SDEI architecture. In the SDEI, the control, data, and energy planes are separated. The control plane dynamically reconfigures the data and energy planes and achieves flexible cooperation between them. We then focus on applying an SDN approach to energy router networking, and build a hierarchical energy control architecture that can implement the programmability of energy flow and allow P2P energy delivery from a high-level view. Additionally, we present a case study of future EVs, for which two applications within the SDEI framework are proposed to improve energy efficiency and green energy utilization.

Electric power systems are facing major challenges because of the increase in penetration of energy resources (ERs). This paper focuses on composite system reliability based planning for ERs, and presents novel clustering techniques based approaches to determine the optimal location, size and year of installation of ERs in the system. The K-means clustering and Fuzzy C-means clustering techniques are applied to the set of reliability indices, Load Not Served per Interruption (LNSI), which are determined using nodal minimal cut sets. The nodal minimal cut sets are obtained using an optimal power flow (OPF) based approach in this paper. Once the optimal sizes and locations of ERs are obtained, the earliest year of penetration is determined using an adequacy check algorithm. Detailed studies presented considering the IEEE RTS demonstrate the applicability of the proposed technique.

Since a microgrid consists of various distributed energy resources and local loads, integration of a distributed grid and multiple microgrids leads to an integrated hybrid energy system. This paper focuses on improving profit, economy, security, and dynamic performance of the integrated hybrid energy system by means of developing different areas of management and control strategies upon four-level hierarchical multiagent system. The level 1 agent implements optimal price bidding strategies for high profit of the overall system. The level 2 agent optimizes the energy management strategies for economic operation of each microgrid. The level 3 agent executes coordinated switching control for maintaining security of each microgrid. The level 4 agent facilitates local hybrid control of each distributed energy resource for guaranteeing dynamic performance. Finally, validity of the proposed scheme is tested by means of simulation study.

As research and development of renewable and clean energy based systems is advancing rapidly, the nonlinear source emulator (NSE) is becoming very essential for testing of maximum power point trackers or downstream converters. Renewable and clean energy sources play important roles in both terrestrial and nonterrestrial applications. However, most existing NSEs have only been concerned with simulating energy sources in terrestrial applications, which may not be fast enough for testing of nonterrestrial applications. In this paper, a high-bandwidth NSE is developed that is able to simulate the behaviors of a typical nonlinear source under different critical conditions that can happen during their operations. The proposed 200-W NSE, which consists of a fourth-order output filter buck converter and a novel nonlinear small-signal reference generator, can quickly react not only to an instantaneous change in the input source but also to a load step between nominal and open circuit. Moreover, all of these operation modes have a very fast settling time of only 10 $\mu$ s, which is hundreds of times faster than that of existing works. This attribute allows for higher speed and a more efficient maximum power point tracking algorithm. The proposed NSE, therefore, offers a superior dynamic performance among devices of the same kind.