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
Intelligent energy management in off-grid smart buildings with energy interaction
مدیریت انرژی هوشمند در ساختمانهای هوشمند خارج از شبکه با تعامل انرژی-2020
The energy interaction between smart homes can be a solution for developing renewable energy systems in residential sections and optimal energy consumption in homes. The main objectives of such energy interactions are to increase consumer participation in energy management‘ boost economic efficiency‘ increase the user’s satisfaction by choosing between electricity sellers and buyers‘ and reduce the electricity purchased from the grid especially at peak hours. Thus, the innovations of this study includes defining an energy exchange method between smart buildings in an off-grid mode considering renewable energy systems, considering both thermal and electrical equilibrium and studying the lightning loads. it is assumed, here, that smart homes are off-grid‘ and the critical loads are supplied by the energy transfer between the homes using mixed integer linear programming. A compromise between the cost and time interval for using home appliances is considered to provide consumer’s comfort. An objective function is introduced considering programmable and non-programmable loads‘ thermal and electrical storages and lighting loads aiming to optimize the cost of energy between different smart buildings. Based on the method, which is tested in two different cases not only does the total cost of the smart buildings decrease but also the cost is reduced significantly when lightning loads are managed.
Keywords: Energy management | Smart homes | Smart microgrid | Energy storage system | Wind turbine
Projection of spatiotemporal variability of wave power in the Persian Gulf by the end of 21st century: GCM and CORDEX ensemble
پیش بینی تغییر پذیری مکانی و قدرت موج در خلیج فارس تا پایان قرن بیست و یکم: GCM و CORDEX-2020
This study investigates future variability of wave power in the Persian Gulf. The contribution of this paper is twofold: (a) to evaluate spatiotemporal resolutions, downscaling techniques and global circulation model (GCM) selection impacts running multi-climate models, and (b) to project wave energy resources and its variability by the end of 21st century using RCP4.5 and RCP8.5 as two different representative concentration pathways (RCPs). The SWAN (Simulating Waves Nearshore) model forcing with near surface wind components was employed for wave simulation. The numerical wave model was calibrated and validated using wave measurements by two buoys prior to wave energy computations. The results of wave models obtained from different climate models showed a wide range of variety for different climatic resources associated with GCM selection, temporal and spatial resolutions and downscaling approach. Outputs of the wave model forcing with 3 hourly wind data of CMCC-CM and CORDEX-MPI (Max Plank Institute) with daily temporal resolution were recognized as the models with the best performance. Using a weighted average of these two models, the wave characteristics were obtained and wave energy were computed for the historical and future periods. Temporal distribution of energy shows highly intra-annual and seasonal variability when the mean wave power for the strongest month exceeds 1000Watt per meter that is 10 times higher than the mean wave power in the weakest month. Similarly, a strong spatial variability in wave power distributionwas revealed where the middle part of the Gulf has found to have the highest energy and the eastern and northwestern regions have the lowest energy. The projections illustrated a decreasing trend on future wave energy up to 40% in the Iranian coastlines and lower rate of changes in the southern stripe of the study area.
Keywords: Renewable energy | Climate change | CORDEX | Representative concentration pathways | Energy management
ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse
مدل سازی ANN سیستم خنک کننده کولر CO2 COP در یک انبار هوشمند-2020
Industrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management system. The operating temperature and pressure measurements, as well as the operating frequency of compressors, are used in developing operational model of the cooling system, which outputs electrical consumption and refrigerant mass flow without the need for additional physical measurements. The presented model is superior to a generalized theoretical model, as it learns from data that includes individual compressor type characteristics. The results show that the presented approach is relatively precise with a Mean Average Percentage Error (MAPE) as low as 5%, using low resolution and asynchronous data from a case study system. The developed model is also tested in a laboratory setting, where MAPE is shown to be as low as 1.8%.
Keywords: Industrial cooling systems | Carbon dioxide refrigerant | Artificial neural networks | Coefficient of performance | Energy storage | Smart warehouse
Internet-of-things-based optimal smart city energy management considering shiftable loads and energy storage
مدیریت انرژی بهینه شهر هوشمند مبتنی بر اینترنت اشیا با توجه به بارهای قابل تغییر و ذخیره انرژی-2020
Formulating a novel mixed integer linear programing problem, this paper introduces an optimal Internet-of-Things-based Energy Management (EM) framework for general distribution networks in Smart Cities (SCs), in the presence of shiftable loads. The system’s decisions are optimally shared between its two main designed layers; a “core cloud” and the “edge clouds”. The EM of a Microgrid (MG), covered by an edge cloud, is directly done by its operator and the Distribution System Operator (DSO) is responsible for optimising the EM of the core cloud. Changing the load consumption pattern, based on market energy prices, for the edge clouds and their peak load hours, the framework results in decreasing the total operation cost of the edge clouds. Using the optimal trading power of the MGs aggregators as the input parameters of the core cloud optimisation problem, the DSO optimises the network’s total operation cost addressing the optimal scheduling of the energy storages. The energy storages are charged in low energy prices through the purchasing power from the market and discharged in high energy prices to meet the demand of the network and to satisfy the energy required by the edge clouds. As a result, the shiftable loads and the energy storages are used by the DSO and the MGs to meet the energy balance with the minimum cost.
Keywords: Energy management | Internet-of-Things | Microgrids | Optimal scheduling | Renewable energy sources
Optimal energy management of a residential-based hybrid renewable energy system using rule-based real-time control and 2D dynamic programming optimization method
مدیریت بهینه انرژی یک سیستم انرژی تجدیدپذیر ترکیبی مبتنی بر مسکونی با استفاده از کنترل زمان واقعی مبتنی بر قانون و روش بهینه سازی برنامه نویسی پویا 2D-2020
This paper presents a magnetically coupled hybrid renewable energy system (RES) for residential applications. The proposed system integrates the energies of a set of PV panels, a fuel cell stack, and a battery using a multi-winding magnetic link to supply a residential load. It can operate in multiple gridconnected and off-grid operation modes. An energy management unit including an off-line dynamic programming-based optimization stage and a real-time rule-based controller is designed to optimally control the power flow in the system according to the provided energy plan. The system is designed according to the required standards of the grid-connected residential RES. Different sections of the proposed system including steady-state operation, control techniques, energy management method and hardware design are studied in brief. A prototype of the proposed system is developed and experimentally tested for an energy management scenario considering both sunny and cloudy profiles of the PV generation. The energy distribution and cost analysis approved the benefits of the proposed system for residential consumers.
Keywords: Energy management | Real-time | Renewable energy system | Residential
Coordination of vehicle-to-home and renewable capacity resources for energy management in resilience and self-healing building
هماهنگی منابع ظرفیتی تجدید پذیر وسایل نقلیه به خانه برای مدیریت انرژی در ساختمان انعطاف پذیر و خود شفایی-2020
The home energy management is an efficient tool to manage energy in the buildings that organizes different technologies and mathematical techniques to minimize energy cost. Home energy management often utilizes renewable energy resources to supply load demand in the building. Current home energy management systems utilize one or several of the available hardware-software capacity resources to deal with energy consumption in the buildings. However, a comprehensive model including various hardware and software capacity resources may increase the flexibility of the model. In this regard, this paper studies an efficient paradigm for home energy management in the building connected to electric grid. The proposed model forms an energy hub including the hardware resources (i.e., vehicle-to-home, wind turbine, and diesel generator) and software tools (i.e., demand response program). All the capacity resources and grid power are optimally adjusted to minimize the daily operational cost of the building as well as improvement of resiliency and self-healing. Wind energy and load uncertainty are modeled through stochastic programming. The seasonal pattern is considered for loads, prices, and wind energy. Simulation results demonstrate that operating all capacity resources minimizes the daily operational cost. When the wind energy, demand response program, vehicle-to-home, and diesel generator are not utilized, the cost is increased by 900, 230, 84, and 322%, respectively. It is also confirmed that the building not only can operate when one of the components is not connected, but also it is able to supply the demand under off-grid operation.
Keywords: Demand response program | Home energy management | Resiliency | Stochastic mixed integer binary model | Vehicle to home | Wind turbine
Cost-aware renewable energy management: Centralized vs. distributed generation
مدیریت انرژی تجدید پذیر آگاه از هزینه: متمرکز در مقابل تولید توزیع شده-2020
We propose optimization strategies for cooperating households equipped with renewable energy assets and storage devices. We consider two system configurations: In the first configuration, households share access to an energy farm, where electricity is generated from renewable sources and stored in battery banks. In the second configuration, households are equipped with their own renewable energy sources and storage devices, and are allowed to share energy through the grid. The developed optimization model takes into account location and time-varying energy prices as well as energy transfer fees. To design our strategies, we first establish performance bounds, and compare the two configurations in terms of achievable savings and usability of renewable energy. Then, we devise real-time energy management algorithms by incorporating forecasting techniques in the proposed framework. Simulation results show that the proposed strategies outperform existing solutions by up to 10%. It is also shown that cooperative strategies outperform greedy approaches by up to 6.8%.
Keywords: Energy storage | Energy allocation | Cooperative strategies | Non-convex optimization
Utilizing renewable energy sources efficiently in hospitals using demand dispatch
استفاده از منابع انرژی تجدید پذیر با کارآیی در بیمارستان ها با استفاده از تقاضای ارسال -2020
Health centers and hospitals can be categorized as one of the major consumers of electrical energy in building sectors. Due to their competitive environment, they need to decrease their costs, including energy costs. On the other hand, environmental problems, lack of fossil fuels, and high energy consumption lead to using alternative energy generation methods like renewable energy sources (RESs). In this paper, we consider that the hospital can produce part of its energy from RESs for cost reduction and we implement demand dispatch energy program for using RESs efficiently. The challenge is that the main goal of hospital is providing health services not energy cost reduction. Therefore, we present a biobjective formulation for using RESs in hospitals in a way to minimize costs and dissatisfaction by scheduling the activities of the hospital by considering hospital’s specific constraints and limitations. With the help of the proposed model, hospitals will decrease energy costs while maintaining comfort of patients and surgeons at the same time. The model is solved using real data of a hospital in Iran, and sensitivity analysis on different parameters is done. The proposed model will cause reduction in energy cost of the hospital by implementing demand dispatch program for using RESs in the hospital.
Keywords: Renewable energy sources | Demand dispatch | Energy management | Health centers | Hospitals | Bi-objective
Renewable energy powered membrane technology: Energy buffering control system for improved resilience to periodic fluctuations of solar irradiance
فن آوری غشایی با انرژی قابل تجدید: سیستم کنترل بافر انرژی برای بهبود مقاومت در برابر نوسانات دوره ای تابش خورشیدی-2020
Energy management is required to enable autonomous photovoltaic-powered membrane (PV-membrane) desalination systems to make the optimal use of solar energy. In this paper, a novel charge controller based on pre-set voltage sensing thresholds was designed to optimise the energy from PV panels and supercapacitors (SCs). The control algorithms were established from the data derivations with high-temporal-resolution (1s) solar irradiance (SI) source, allowing for resilient system operation under variable conditions. The impacts of ramp rates, in both SI and PV output voltage (VPV) on the system, were systematically investigated. Under a worst-case scenario, with a rapid ramp rate of DVPV ¼ 2 V/s, the charge controller enabled the SCs to bridge the power gap to 6 min 20 s, permitting an additional 10 L of permeate water produced. The state-of-charge of the SCs varied from 11 to 86%, regardless of the magnitude of the ramp rate. The combination of the voltage thresholds (Vpump_on ¼ 160 V and Vpump_off ¼ 90 V) was determined to result in optimum system performance, realising a high permeate production at low specific energy consumption. It is concluded that the proposed charge controller is an effective method to enhance system resilience under worst-case solar conditions.
Keywords: Ramp rates | Charge controller | Supercapacitors | Photovoltaic | Reverse osmosis | Energy fluctuation