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
An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview
اینترنت چارچوب انرژی با منابع انرژی توزیع شده ، پیشرانها و نیروگاه های مجازی در مقیاس کوچک: یک مرور کلی-2020
Current power networks and consumers are undergoing a fundamental shift in the way traditional energy systems were designed and managed. The bidirectional peer-to-peer (P–P) energy transactions pushed passive consumers to be prosumers. The future smart grid or the internet of energy (IoE) will facilitate the coordination of all types of prosumers to form virtual power plants (VPP). The paper aims to contribute to this growing area of research by accumulating and summarizing the significant ideas of the integration of distributed prosumers and small-scale VPP to the internet of energy (IoE). The study also reports the characteristics of IoE in comparison to the traditional grid and offers some valuable insights into the control, management and optimization strategies of prosumers, distributed energy resources (DERs) and VPP. As bidirectional P–P energy transaction by the prosumers is a crucial element of IoE, their management strategies including various demand-response approach at the customers’-levels are systematically summarized. The integration of DERs and prosumers to the VPP considering their functions, infrastructure, type, control objectives are also reviewed and summarized. Various optimization techniques and algorithm, and their objectives functions and the types of mathematical formulation that are used to manage the DERs and VPP are discussed and categorized systematically. Finally, the factors which affect the integration of DERs and prosumers to the VPP are identified.
Keywords: Bidirectional energy transactions | Distributed energy resources | Energy management | Internet of energy | Optimization techniques | Prosumers | Virtual power plant
Optimization strategies for Microgrid energy management systems by Genetic Algorithms
استراتژی های بهینه سازی برای سیستم های مدیریت انرژی میکرو گرید توسط الگوریتم های ژنتیک-2020
Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.
Keywords: Microgrids | Genetic algorithms | Fuzzy systems | Energy management systems
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
Flexibility management model of home appliances to support DSO requests in smart grids
مدل مدیریت انعطاف پذیری لوازم خانگی برای پشتیبانی از درخواست DSO در شبکه های هوشمند-2020
Several initiates have been taken promoting clean energy and the use of local flexibility towards a more sustainable and green economy. From a residential point of view, flexibility can be provided to operators using home-appliances with the ability to modify their consumption profiles. These actions are part of demand response programs and can be utilized to avoid problems, such as balancing/congestion, in distribution networks. In this paper, we propose a model for aggregators flexibility provision in distribution networks. The model takes advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision a request from a distribution system operator (DSO) or a balance responsible party (BRP). Due to the complex nature of the problem, evolutionary computation is evoked and different algorithms are implemented for solving the formulation efficiently. A case study considering 20 residential houses equipped each with seven types of home-appliances is used to test and compare the performance of evolutionary algorithms solving the proposed model. Results show that the aggregator can fulfill a flexibility request from the DSO/BRP by rescheduling the home-appliances loads for the next 24-h horizon while minimizing the costs associated with the remuneration given to end-users
Keywords: Demand response | Flexibility | Home appliances | Local energy management | Smart grids
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