با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Industrial smart and micro grid systems e A systematic mapping study
سیستم های هوشمند و ریز شبکه صنعتی و یک مطالعه نقشه برداری منظم-2020
Energy efficiency and management is a fundamental aspect of industrial performance. Current research presents smart and micro grid systems as a next step for industrial facilities to operate and control their energy use. To gain a better understanding of these systems, a systematic mapping study was conducted to assess research trends, knowledge gaps and provide a comprehensive evaluation of the topic. Using carefully formulated research questions the primary advantages and barriers to implementation of these systems, where the majority of research is being conducted with analysis as to why and the relative maturity of this topic are all thoroughly evaluated and discussed. The literature shows that this topic is at an early stage but already the benefits are outweighing the barriers. Further incorporation of renewables and storage, securing a reliable energy supply and financial gains are presented as some of the major factors driving the implementation and success of this topic.
Keywords: Industrial smart grid | Industrial micro grid | Systematic mapping study | Strategic energy management | Industrial facility optimization | Renewable energy resources
A preference-based demand response mechanism for energy management in a microgrid
مکانیسم پاسخ تقاضا مبتنی بر اولویت برای مدیریت انرژی در یک ریز شبکه -2020
In this work, a preference-based, demand response (DR) multi-objective optimization model based on real-time electricity price is presented to solve the problem of optimal residential load management. The purpose of such a model is threefold: 1) to minimize the costs associated with consumption; 2) to minimize the inconvenience caused to consumers; and 3) to minimize environmental pollution. Potential solutions to the underlying multi-objective optimization problem are subject to a set of electrical and operational constraints related to home appliances categories and the utilization of distributed energy resources (DER) and energy storage systems (ESS). The use of the proposed model is illustrated in a realistic microgrid scenario where suitable solutions were found by the Non-Dominated Sorting Genetic Algorithm III (NSGA-III). These solutions determine new operational periods for home appliances as well as the utilization of DER and ESS for the planning horizon while considering consumer preferences. Besides helping consumers to take advantage of the benefits offered by DR, the experimental results show that the multi-objective DR model together with the NSGA-III algorithm can effectively minimize energy-consumption costs as well as reduce inconvenience costs and environmental pollution.
Keywords: Demand Response | Microgrid | Optimization | NSGA-III | Smart grid
Techno-economic evaluation of PV based institutional smart microgrid under energy pricing dynamics
ارزیابی فنی و اقتصادی میکروگرید هوشمند نهادی مبتنی بر PV تحت پویایی قیمت گذاری انرژی-2020
The solar photovoltaic (PV) system with battery energy storage have a lot of potential to provide reliable and cost-effective electricity and to contribute in micro-grid operation. However, the operational performance of such type of micro-grid system depends on many factors (e.g. techno-economic sizing, energy management among the sources, market energy prices dynamics, energy dispatch strategies, etc.). In this paper, a typical Indian institutional energy system has considered for techno-economic performance evaluation for operating as a smart micro-grid under market energy pricing dynamics. The institutional energy system has integrated PV, battery storage and DG for operating as a smart microgrid. An operational energy dispatch strategy for micro-grid has proposed and evaluated for maximizing the local energy resources utilization with contemplation of peak demand and grid outage conditions under market energy pricing dynamics. With techno-economic sizing of PV, battery and DG of considered system; the peak demand has reduced by 10%, DG contribution by 92% and annual energy savings by 45% compare to operation of base system. With proposed energy management strategy, the annual battery energy throughput has increased from 0.4% to 10%, and the DG’s contribution has decreased from 7% to 5% with 10% reduction in levelized cost of energy (CoE) compare to case with techno-economic sizing of PV, battery and DG for considered system. With inclusion of electrical energy pricing dynamics scenario, it has observed that the CoE has increased by 89% with change in time-of-use (ToU) tariff from 100% to 200% and considering energy-selling price to the grid at 100%. However, 8% reduction in the CoE has observed, when the energy-selling price to grid has increased from 100% to 200% at ToU of 100%. The results from this work are going to be useful for developing electrical tariff policies for promoting the PV based institutional micro-grid system under market energy pricing dynamics.
Keywords: Smart micro-grid | Market energy pricing dynamics | Techno-economics | Solar photovoltaic | Energy management strategy
Ensuring the reduction in peak load demands based on load shifting DSM strategy for smart grid applications
تضمین کاهش تقاضای بار اوج بر اساس تغییر استراتژی DSM برای کاربردهای شبکه هوشمند-2020
In recent year the concept of prosumers (energy producer as well as the consumer) is becoming more popular, and on the other side, demand response has gained attention especially in the smart and micro grid systems. In such power networks, the behaviors of prosumers are highly variable due to many factors and few are price depended, incentive depended etc., Due to the highly dynamic nature of the prosumers, the peak load management is becoming crucial in the power system operation and control especially in smart grids, in which demand side management (DSM) application thought to be useful. This paper presents a simulation study on the role of DSM in the peak load management for a residential community. Here, load shitting-based DSM technique is used. This study considered the daily consumption patterns (hour wise) of electricity using the four types of electric loads. Results showed a considerable decrease in the peak load when compared to the load patterns before applying DSM. The average demand per day in summer is 4.521 kW, and in winter is 3.871 kW. The summary of peak load demands per day in kW without DSM are 14.712 kW, and 18.18 kW for summer and winter respectively. With the load shifting based DSM technique, the peak load per day are reduced to 9.6 kW in the summer season and 9.672 kW in the winter season.
Keywords: Smart grid | demand side management | load shifting | peak loads | load management | energy management
Efficient Load Control Based Demand Side Management Schemes Towards a Smart Energy Grid System
برنامه های مدیریت کنترل تقاضا مبتنی بر کنترل بار کارآمد به سمت یک سیستم شبکه انرژی هوشمند-2020
In this paper, we propose ecient load scheduling based demand side management schemes for the objective of peak load reduction. We propose two heuristic algorithms, named G-MinPeak and LevelMatch, which are based on the generalized two-dimensional strip packing problem, where each of the appliances has their specic timing requirements to be fullled. Furthermore, we have proposed some improvement schemes that try to modify the resulted schedule from the proposed heuristic algorithms to reduce the peak. All the proposed algorithms and improvement schemes are experimented using benchmark data sets for performance evaluation. Extensive simulation studies have been conducted using practical data to evaluate the performance of the algorithms in real life. The results obtained show that all the proposed methodologies are thoroughly eective in reducing peak load, resulting in smoother load proles. Specically, for the benchmark datasets, the deviation from the optimal values has been about 6% and 7% for Level- Match and G-MinPeak algorithms respectively and by using the improvement schemes the deviations are further reduced up to 3% in many cases. For the practical datasets, the proposed improvement schemes reduce the peak by 5:21 ???? 7:35% on top of the peaks obtained by the two proposed heuristic algorithms without much computation overhead.
Keywords: Demand side management | direct load control | heuristic algorithm | scheduling | energy management | smart grid
Agent negotiation in an IoT-Fog based power distribution system for demand reduction
عامل مذاکره در سیستم توزیع برق مبتنی بر IoT-Fog برای کاهش تقاضا-2020
Growing energy demand is calling for an effective energy management. In smart homes all devices are connected to Internet by means of Internet of Things. There is a possible means of studying the consumer usage pattern and accordingly forecast their energy demand. Multi Agents has been used in computer science for a long time and applied for lot of applications for replicating the job of human. So towards monitoring and controlling the cyber physical systems, these multi agent system has been applied in smart transportation, smart cities, Smart Grid and so. This paper proposes a Multi-agent System (MAS) for smart energy management in an IoT based system. Inspired by the competition in human societies for accepting best proposals: this work proposes an Agent Negotiation system for demand reduction. The Agents in IoT system negotiate with the meter agent for accepting a proposal which will reduce the peak hour usage. The negotiation agent also negotiates with the meter agent for using energy when the availability of renewables are surplus. This negotiation is done with hundreds and thousands of homes thus helping Utilities to meet the supply-demand effectively. Consumers get the best pricing based on the accepted policies.
Keywords: Internet of Things | Multi-agent system | Negotiation | Distribution automation | Smart grid
Hierarchical system model for the energy management in the smart grid: A game theoretic approach
مدل سیستم سلسله مراتبی برای مدیریت انرژی در شبکه هوشمند: یک رویکرد تئوری بازی-2020
Nowadays, Demand Side Management (DSM) in the Smart Grids (SG) opens new perspectives on the management of the prosumers’ demands. The use of Information and Communication Technologies (ICT) empowers SG with the capability of supporting two-way energy and information flows, facilitating the integration of renewable energy into the grid and empowering the consumer with tools for optimizing energy consumption and costs. Many researches have highlighted on the active role of consumers in the DSM. However, they limited their role to either negotiating with the providers about the unit energy price, or trading their surplus of energy, if any, with other prosumers or providers. In this paper, we introduce a hierarchical system model where multiple providers and prosumers interact to define the best price and demands. We highlight the capacity of a prosumer to produce energy and minimize the dependency on the providers in the overall proposed energy management. The latter will be able to maximize his satisfaction by trading energy, first, with other prosumers with a suitable price, and by interacting with the providers, if any remaining energy needed, in a distributed way. Hence, we optimize the price and demands while considering multiple constraints and different providers and prosumers by establishing a Stackelberg game to model two types of interactions: (1) prosumer–prosumer and (2) provider–consumer. We prove that a unique equilibrium solution exists and simulation results show that our proposed approach optimizes energy consumption and price.
Keywords:Demand side management | Energy trading | Prosumers | Providers | Game theory
Smart users for smart technologies: Investigating the intention to adopt smart energy consumption behaviors
کاربران هوشمند برای فناوری های هوشمند: بررسی قصد اتخاذ رفتارهای مصرف انرژی هوشمند-2020
Smart grid technologies have the potential to overcome several environmental, social, and technical problems associated with the current electricity production and consumption modes. However, the adoption of smart grids requires a change in users’ behavior. This study investigates the individual-level motivational factors that affect the intention to adopt “smart consumption and production behaviors”. Understanding these factors is important for implementing effective behavioral change initiatives that would facilitate the diffusion of smart grids. Ajzens theory of planned behavior is applied to explain the formation of such an adoption intention. With a questionnaire survey distributed to a random sample of consumers, an elicitation study is conducted to obtain salient modal beliefs. Data collected from the main survey are analyzed using structural equation modeling. Consistent with the theory, the results of the structural equation analysis reveals that attitude, subjective norm, and perceived behavioral control positively affect the adoption intention. Specifically, the study finds that the variable with the highest estimated loading factor perceived behavioral control, and the most important belief linked with attitude is energy savings. Further investigation indicates that the added exogenous variable, resistance to change, has a negative influence on intention. Results confirm the explanatory power of the theoretical model and provide valuable knowledge for individuals and institutions interested in facilitating the diffusion of smart grids via the implementation of behavioral change initiatives.
Keywords: Adoption of innovation | Consumers’ behavior | Energy management | Smart grid | Theory of planned behavior
Blockchain for Internet of Energy management: Review, solutions, and challenges
بلاکچین برای مدیریت انرژی اینترنت: بررسی ، راه حل ها و چالش ها-2020
After smart grid, Internet of Energy (IoE) has emerged as a popular technology in the energy sector by integrating different forms of energy. IoE uses Internet to collect, organize, optimize and manage the networks energy information from different edge devices in order to develop a distributed smart energy infrastructure. Sensors and communication technologies are used to collect data and to predict demand and supply by consumers and suppliers respectively. However, with the development of renewable energy resources, Electric Vehicles (EVs), smart grid and Vehicle-to-grid (V2G) technology, the existing energy sector started shifting towards distributed and decentralized solutions. Moreover, the security and privacy issues because of centralization is another major concern for IoE technology. In this context, Blockchain technology with the features of automation, immutability, public ledger facility, irreversibility, decentralization, consensus and security has been adopted in the literature for solving the prevailing problems of centralized IoE architecture. By leveraging smart contracts, blockchain technology enables automated data exchange, complex energy transactions, demand response management and Peer-to-Peer (P2P) energy trading etc. Blockchain will play vital role in the evolution of the IoE market as distributed renewable resources and smart grid network are being deployed and used. We discuss the potential and applications of blockchain in the IoE field. This article is build on the literature research and it provides insight to the end-user regarding the future IoE scenario in the context of blockchain technology. Lastly this article discusses the different consensus algorithm for IoE technology.
Keywords: Consensus algorithm | Blockchain | Internet of Energy | Smart grid | Vehicle-to-grid
Home energy management system based on task classification and the resident’s requirements
سیستم مدیریت انرژی خانگی بر اساس طبقه بندی وظیفه و الزامات ساکنان-2020
In this paper, an approach for home energy management system is introduced that is based on task classification. The problem is to find the best task activation plan regarding the resident’s requirements and appliances constraints, considering time of use pricing. We show that to have a best task activation plan it is necessary to specify four specific values for each task. In this regard, a quadratic utility function is derived based on the law of diminishing marginal utility in microeconomic. With the parameters such as sensitivity index and monetary equivalent value, each task’s utility function can be configured in accordance to the resident’s personal preferences. In this model, the concept of tasks interaction are also taken into account, for the first time. The problem is transformed to a mixed integer nonlinear programming problem, so that the available commercial solvers can successfully solve it in an acceptable solving time. The outputs of numerical examples show that they are reasonable and can be considered as optimal or near optimal results. The results indicate that the less sensitive the resident is to changing his/her desire task parameters, the more profit he/she will get. It is also shown that by increasing the monetary equivalent value, the task activation is shifted toward the times that are more preferable or has more favorite environment conditions. These results as well as the acceptable solving times, show that the proposed approach can be a promising model for home energy management systems in the future smart homes.
Keywords: Home Energy Management System (HEMS) | Load scheduling | Smart grid | Task classification | Utility function | Time preference effect