A bi-objective optimization approach for selection of passive energy alternatives in retrofit projects under cost uncertainty
یک روش بهینه سازی دو هدفه برای انتخاب گزینه های انرژی منفعل در پروژه های مقاوم سازی تحت عدم اطمینان هزینه-2020
Improving energy performance of buildings is of particular importance in new construction and existing buildings. Building refurbishment is considered a practical pathway towards energy efficiency as the replacement of older buildings is at a slow pace. There are various ways of incorporating energy conservation measures in buildings through refurbishment projects. As such, we have to choose among various passive or active measures. In this study, we develop an integrated assessment model to direct energy management decisions in retrofit projects. Our focus will be on alternative passive measures that can be included in refurbishment projects to reduce overall energy consumption in buildings. We identify the relative priority of these alternatives with respect to their non- monetary (qualitative) benefits and issues using an analytic network process. Then, the above priorities will form a utility function that will be optimized along with the energy demand and retrofit costs using a multi-objective optimization model. We also explore various approaches to formulate the uncertainties that may arise in cost estimations and incorporate them into the optimization model. The applicability and authenticity of the proposed model is demonstrated through an illustrative case study application. The results reveal that the choice of the optimization approach for a retrofit project shall be done with respect to the extent of variations (uncertainties) in expected utilities (benefits) and costs for the alternative passive technologies.
Keywords: Construction technologies | assive energy measures | Building retrofit | Multi-Objective Optimization | Cost uncertainty | Fuzzy set theory
Enhancement the economical and environmental aspects of plus-zero energy buildings integrated with INVELOX turbines
تقویت جنبه های اقتصادی و زیست محیطی ساخت انرژی بالای صفر که با توربینهای بادی INVELOX یکپارچه شده اند-2020
A multi-objective energy management strategy for a plus-zero energy building during a year, incorporating renewable resources, air to water heat pump, micro-CHP, ventilation, energy storage systems and thermal-cooling-electrical loads have been proposed in this paper. In this strategy, a novel technology of wind turbine that has been known as INVELOX has been investigated and collaborated in ZEB planning to reach efficient plus-ZEB at lower cost and pollution. As well the building can sell and buy power to/from the upstream network. The total cost and pollution of the building have been considered as objective functions. Also, the effect of objective function priority on the planning of the building has considered. To make the results more realistic the wind speed and solar radiation of Kermanshah city in Iran have been used. The presented problem has modeled as a mixed-integer linear programming and the Epsilon constraint method and fuzzy satisfying approach have been used to solve and obtain the best solution. The final results show reducing the total cost and pollution by about 34.6% and 51.2% in cost priority, also 28.7% and 54.7% in pollution priority respectively, also increment in surplus power to sell to the grid and getting closer to reach plus-ZEB concept.
Keywords: Building energy management | Fuzzy satisfying | INVELOX wind turbine | Multi-objective optimization | Plus-zero energy building | Sustainable building
الگوریتم تکاملی چند هدفه مبتنی بر شبکه عصبی برای زمانبندی گردش کار پویا در محاسبات ابری
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 45
زمانبندی گردشکار یک موضوع پژوهشی است که به طور گسترده در محاسبات ابری مورد مطالعه قرار گرفته است و از منابع ابری برای کارهای گردش کار استفاده می¬شود و برای این منظور اهداف مشخص شده در QoS را لحاظ می¬کند. در این مقاله، مسئله زمانبندی گردش کار پویا را به عنوان یک مسئله بهینه سازی چند هدفه پویا (DMOP) مدل می¬کنیم که در آن منبع پویایی سازی بر اساس خرابی منابع و تعداد اهداف است که ممکن است با گذر زمان تغییر کنند. خطاهای نرم افزاری و یا نقص سخت افزاری ممکن است باعث ایجاد پویایی نوع اول شوند. از سوی دیگر مواجهه با سناریوهای زندگی واقعی در محاسبات ابری ممکن است تعداد اهداف را در طی اجرای گردش کار تغییر دهد. در این مطالعه یک الگوریتم تکاملی چند هدفه پویا مبتنی بر پیش بینی را به نام الگوریتم NN-DNSGA-II ارائه می¬دهیم و برای این منظور شبکه عصبی مصنوعی را با الگوریتم NGSA-II ترکیب می¬کنیم. علاوه بر این پنج الگوریتم پویای مبتنی بر غیرپیش بینی از ادبیات موضوعی برای مسئله زمانبندی گردش کار پویا ارائه می¬شوند. راه¬حل¬های زمانبندی با در نظر گرفتن شش هدف یافت می¬شوند: حداقل سازی هزینه ساخت، انرژی و درجه عدم تعادل و حداکثر سازی قابلیت اطمینان و کاربرد. مطالعات تجربی مبتنی بر کاربردهای دنیای واقعی از سیستم مدیریت گردش کار Pegasus نشان می¬دهد که الگوریتم NN-DNSGA-II ما به طور قابل توجهی از الگوریتم¬های جایگزین خود در بیشتر موارد بهتر کار می¬کند با توجه به معیارهایی که برای DMOP با مورد واقعی پارتو بهینه در نظر گرفته می¬شود از جمله تعداد راه¬حل¬های غیرغالب، فاصله¬گذاری Schott و شاخص Hypervolume.
|مقاله ترجمه شده|
Designing a short-term load forecasting model in the urban smart grid system
طراحی یک مدل پیش بینی بار کوتاه مدت در سیستم شبکه هوشمند شهری-2020
The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermittency of RE, it is challenging to design an accurate and reliable short-term load forecasting model. Recently, machine learning (ML) based forecasting models have been applied for short-term load forecasting whereas most of them ignore the importance of characteristics mining, parameters fine-tuning, and forecasting stability. To dissolve the above issues, a short-term load forecasting model is proposed that incorporates thorough data mining and multi-step rolling forecasting. To alleviate the chaos of short-term load, a de-noising method based on decomposition and reconstruction is used. Then, a phase space reconstruction (PSR) method is employed to dynamically determine the train-test ratios and neurons settings of the artificial neural network (ANN). Further, a multi-objective grasshopper optimization algorithm (MOGOA) is applied to optimize the parameters of ANNs. Case studies are conducted in the urban smart grid systems of Victoria and New South Wales in Australia. Simulation results show that the proposed model can forecast short-term load well with various measurement metrics. Multiple criterion and statistical evaluation also show the good performance of the proposed forecasting model in terms of accuracy and stability. To conclude, the proposed model achieves high accuracy and robustness, which will provide references to RE transitions and smart grid optimization, and offer guidance to sustainable city development.
Keywords: Smart grid | Short-term load forecasting | Neural networks | Multi-objective optimization algorithm | Urban sustainability
A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic modeling framework
یک رویکرد جدید برای برنامه ریزی بهینه چند منظوره از ناوگان های مقیاس بزرگ EV در یک شبکه توزیع هوشمند با توجه به چارچوب مدل سازی واقع گرایانه و تصادفی-2020
The ever-increasing number of grid-connected electric vehicles (EVs) has led to emerging new opportunities and threats in electrical distribution systems (DS). Developing a realistic model of EV interaction with the DS, as well as developing a strategy to optimally manage these interactions in line with distribution system operators (DSOs) intentions, are the most important prerequisites for gaining from this phenomenon especially in modern smart distribution systems (SDS). In this paper, a comprehensive model describing the electric vehicle integration to an SDS is presented by considering the real-world data from EV manufacturers and DSOs. Moreover, a novel energy management strategy (EMS) based on the multi-objective optimization problem (MOOP) is developed to fulfill the operational objectives of DSO and EV owner, including peak load shaving, loss minimization, and EV owner profit maximization. In this regard, an innovative dimension reduction approach is presented, to make it feasible to apply the heuristic optimization methods to a MOOP with a large number of decision variables. Thanks to this method, the improved electromagnetism like algorithm (IEMA) is employed to perform the multi-objective energy scheduling for a large-scale EV fleet. In addition, a novel method is devised for estimating the optimal hosting capacity of an SDS in adopting EVs without the need for sophisticated computations. The presented method is applied to the modified IEEE-33 bus test system. Obtained results reveal that employment of a realistic model concludes to more accurate results than a simplified model. In addition, the efficiency of the proposed EMS in satisfying EV owner and DSO objectives are approved by analyzing obtained computation results.
Keywords: Smart grid | Energy management | Electric vehicle | Vehicle to grid | Multi-objective optimization
Facilitating high levels of wind penetration in a smart grid through the optimal utilization of battery storage in microgrids: An analysis of the tradeoffs between economic performance and wind generation facilitation
تسهیل سطح بالای نفوذ باد در یک شبکه هوشمند از طریق استفاده بهینه از باتری در ریز شبکه ها : تجزیه و تحلیل مبادلات بین عملکرد اقتصادی و تسهیل تولید باد-2020
The aim of this paper was to investigate the trade-offs that can be achieved between optimizing the electricity costs of a building integrated microgrid, while simultaneously facilitating high levels of wind penetration in a smart grid. This study applied multi-objective optimization to obtain a daily charge and discharge schedule of a battery bank, which was used to both store electricity from the microgrid and smart grid and could also provide electricity to the building and the smart grid. Multi-objective optimization was employed due to the independent objectives of minimizing building operating cost and maximizing the facilitation of wind energy from the smart grid. The trade-offs between the two objectives were simulated, evaluated and analyzed. A priority weighting factor (α) was applied to each objective. The purpose of α was to vary the importance of each objective relative to the other in an inversely proportional manner. This enabled the algorithm to optimize the battery operating schedule for the economic performance of the microgrid, the facilitation of wind generation on the smart grid or for trade-offs in between. The results present a comprehensive evaluation of 96 scenarios with varying daily weather conditions, building electricity demand, electricity pricing, microgrid output and wind penetration from the smart grid. A multi-objective optimization approach was then applied for each of the 96 scenarios with 11 α values to determine optimal trade-offs in these scenarios. Generally for the 96 scenarios analyzed, when the α value was 20% or higher, the amount of extra wind generation facilitation obtained was negligible while microgrid operating costs continued to increase. The results showed that when changing from an α value of 0% to an α value of 20%, there was a large increase in wind generation facilitation compared to the corresponding increase in cost, with wind generation facilitation increasing from its minimum value to within 89% of its maximum value (10.7% to 14.3% of facilitated wind generation). The corresponding building cost increased from its minimum value to within 13% of its maximum value (€1.14/day to €1.37/day). This produced a cost of approximately €0.06 for every 1% increase in wind generation facilitation. In comparison to this, changing from an α value of 20% to an α value of 100% implied a cost of approximately €3.64 for every 1% increase in wind generation facilitation. These results indicated that smart grids with large percentages of wind penetration may be substantially aided by utilizing the storage capacity of building integrated microgrids for a relatively low monetary cost.
Keywords: Multi-objective optimization | Energy management | Wind penetration | Trade-off analysis | Battery | Microgrid
Behavior of crossover operators in NSGA-III for large-scale optimization problems
رفتار اپراتورهای متقاطع در NSGA-III برای مسائل بهینه سازی در مقیاس بزرگ-2020
Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usu- ally meet the requirements for online data processing because of their high compu- tational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algo- rithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable com- putational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simu- lated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the con- cept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.
Keywords: Electroencephalography | Large-scale optimization | Big data optimization | Evolutionary multi-objective optimization | NSGA-III | Crossover operator | Performance analysis
Multi-objective optimization model in a heterogeneous weighted network through key nodes identification in overlapping communities
مدل بهینه سازی چند هدفه در یک شبکه وزن دار ناهمگن از طریق شناسایی گره های کلیدی در جوامع همپوشانی -2020
Nowadays, it is possible to easily utilize positive and negative effects of neighbors on a social network to maximize diffusion of a novel product and profit of the seller. Hence, this paper aims to introduce a new mathematical model for a product pricing in non-competitive environment having multiple goals. The proposed model is designed while there are a monopole seller and several heterogeneous customers for a novel product. Considering various criteria, these customers are able to purchase the novel product including price, product quality, urgent need to have the product, and positive/negative externalities received from the neighbors. Moreover, they are able to comment in case of satisfaction or dissatisfaction with the product. However, the extent of influence depends on strength of the relations with neighbors that is considered in the proposed model with complete information and quantitative values. Proportionate to activating the neighbors, referral bonus is considered from the seller. To find influential nodes for the influence and exploit strategy implementation we propose a new overlapping community detection algorithm. In this algorithm, a new overlapping score based on non-member neighbor nodes connectivity is introduced to identify overlapping communities. Finally, we evaluate the efficiency of the proposed model, by implementing the proposed community detection algorithm in a real-world dataset. The results show that it is possible to obtain desired selling price in a fashion that maximum diffusion in the network happens and the seller achieves his desired profit under various management viewpoints.
Keywords: Diffusion | Weighted network | Non-competitive market | Monopoly pricing | Heterogeneous network | Overlapping community detection
Optimizing energy consumption and occupants comfort in open-plan offices using local control based on occupancy dynamic data
بهینه سازی مصرف انرژی و آسایش سرنشینان در دفاتر طرح باز با استفاده از کنترل محلی بر اساس اشغال داده های پویا -2020
Optimal management of buildings’ energy-consuming systems is of great importance for minimizing building energy consumption while satisfying the occupants. Since the operation of building systems are highly dependent on the presence of occupants, considering the dynamic occupancy information has become crucial to reflect the occupancy dynamism within offices and the random patterns in occupant behavior. Thus, occupancy-centered control strategies are required in order to enhance the energy management of buildings. On the other hand, the need for localized and customizable comfort controls is increasing in office buildings to improve the occupants’ satisfaction, and consequently their productivity. To this end, a framework aiming at developing optimal occupancy-centered local control strategies is proposed in this paper. A new simulation-based multi-objective optimization model of the energy consumption in offices is developed to exploit occupancy-related data and evaluate possible local control strategies to select the best ones. A set of real occupancy data collected over a period of one year is fed to the integrated simulation-based optimization model for investigating the energysaving potentials. Comparing the results shows that a considerable improvement in the indoor comfort condition can be achieved through the application of the proposed framework. We conclude that optimal control strategies not only provide demand-driven control solutions but also optimize building energy performance. The integrated model enables dynamic building energy management according to dynamic occupancy patterns. It avoids over-conditioning that is the result of the application of common practices, which control building systems based on the peak occupancy.
Keywords: Energy management system | Local control strategies | Dynamic occupancy profiles | Comfort | Multi-objective optimization | Simulation
A comprehensive review of energy management optimization strategies for fuel cell passenger vehicle
مروری کامل بر استراتژی های بهینه سازی مدیریت انرژی برای سلول سوختی وسیله نقلیه مسافر-2020
With the gradual maturity of fuel cell vehicle technology, it gives a better opportunity for the application of passenger vehicles. In this paper, the energy management optimization strategies of fuel cell passenger vehicle (FCPV) are summarized for the first time. Initially, in this review, the topological configurations of FCPV are classified systematically. The optimization objectives, energy consumption and fuel cell life, are proposed for FCPV. Then the energy management strategies (EMSs) are illustrated and analysed based on the optimization objectives above. In terms of the complex and changeable characteristics of FCPV driving conditions, the latest FCPV EMSs which depend on driving information prediction technologies are discussed and summarized. The purpose of this paper is providing references for the development of new generation FCPV energy management optimization strategies.
Keywords: FCPV | EMS | Multi-objective optimization | Fuel cell lifetime | Fuel consumption | Driving information prediction