Abstract

Presently, consequence of carbon emission from the unorganized industries has detrimental effect on the environment. In India silk industries are unorganized at the medium and small-scale level, where production of silk fiber through silk reeling process requires large amount of energy. Thermal energy demand for the silk reeling industries accounts for about 50% of the total fuel consumption where temperature range for the process are 25oC to 95oC. Electrical energy demand for different reeling units and equipment and for irrigation contributes most of the energy consumption from the remaining 50% fuel consumption. In the present study, a sustainable solution is provided for the cumulative electrical and thermal energy demand in silk reeling process. The methodology employs photovoltaic thermal with the conventional system to reduce the firewood consumption and to mitigate the carbon emission. In the study multi-domain energy conversion and performance of photovoltaic thermal is simulated using Simscape, Simscape electrical, Simscape fluid and Simulink package on MATLAB software. Total efficiency of photovoltaic thermal conversion system obtained from the simulation is 53.132% where electrical efficiency is 17.578% and thermal efficiency is 35.555%. For the production of 1kg reeled silk from cocoon, carbon emission is reduced from 76.11 kg/day for the conventional system to 29.372 kg/day and 28.203 kg/day for the open field photovoltaic thermal and roof-top photovoltaic thermal system respectively by considering the system life period of 10 years. Further, a relation between water and energy is shown in future work for more potent solution in terms of energy efficiency and carbon mitigation.

Abstract

The threats to the power systems being caused by extreme weather-related events are increasingly alarming. Safeguarding the Power System against all forms of instability requires adopting new and integrated approach. The integration of the distributed energy resources (DERs) into the Power grid especially at the distribution level has greatly changed the dynamics of the modern power system. Improving the resilience of the power grid by the application of DERs in black start application is explored in this study. The black start restoration is formulated as a dynamic optimisation problem and the restoration sequence capable of improving the grid resilience is developed. The implementation is carried out on the Nigerian 48-bus system and the simulated results demonstrate the DERs ability in contributing significantly to power system black start.

Abstract

Cycle humidification is an interesting and effective tool to increase the operational flexibility of micro Gas Turbines (mGTs). However, considering the future fuel diversification towards emission reduction and security of supply, it is essential to identify what the impact of the fuel is on the potential for cycle humidification. To determine this impact, in this paper, we present the results of a black box analysis towards this identification, using 2nd law analysis. Additionally, several advanced humidified cycles concepts have been simulated to assess their sensitivity towards fuel alteration. Black box results indicated that going towards hydrogen has no major impact on the cycle performance, while syngas clearly limits the potential: only injection up to 106 g/s is possible compared to the 123 g/s in the hydrogen case, leading to reduced electric efficiency of 38% compared to the 41% in the latter case. A similar observation was made when considering the specific humified cycles, where the preheated water injection option showed to be the best performing cycle. For all cases, however, using syngas led to a reduced cycle performance. Finally, none of the cycles could exploit the full black box potential and hence future work consists in the identification of the impact of fuel alteration on the more advanced humidified cycles, in an attempt to reach the full potential.

Abstract

Building energy consumption prediction is of great significance to realize intelligent decision-making of energy system and improve energy efficiency. A random forest (RF) prediction model optimized via the particle swarm optimization (PSO) algorithm is established to forecast the hourly electricity consumption of the building cluster consisting of interconnected multiple buildings. The accuracy, generalization and robustness are taken as evaluation indexes. In the case study, the building cluster located in Austin is adopted as an example to explore the predicted performance of the proposed PSO-RF model in different seasons. The results show that the hourly electricity consumption PSO-RF model of the building cluster can achieve highest accuracy, strongest generalization, and best robustness, compared with RF, decision tree (DT), XGBoost, and k-Nearest Neighbor (KNN) prediction models. Therefore, the proposed hybrid model can be used as a reliable tool for building cluster electricity consumption prediction and energy management.

Abstract

The application of rooftop solar photovoltaic (PV) systems is currently experiencing a significant increase as an alternative energy provider for different buildings in Malaysia. However, not many educational institutions other than university buildings install solar PV systems. The present work studies the potential of installing solar PV on the rooftop of the public skill training institutions with various roof tilted angles. Results show that even for small rooftop areas, the solar PV potential generation is significantly high and can accommodate the institute’s energy load demand.

Abstract

Electric power system is undergoing major changes, due to large-scale integration of renewable energy, complicated network structure and increased energy demand. Developing autonomous systems, intelligent and digital communication technologies are becoming more and more urgent to modernize, strengthen and stabilize the power grid. Among several innovative efforts, artificial intelligence (AI) is becoming more popular due to its human like thinking logic, advanced wisdom, reasoning, learning capability and knowledge representation. AI approaches have demonstrated precise, faster and scalable outcomes, thus can be ideal to handle complex non-linear power systems. The recent technological developments in AI and hybrid techniques, makes it possible for solving large-scale complex power systems problems like control, planning, scheduling, prediction etc. This article has briefly discussed seven major areas, where AI has been used easily to handle constraints such as power system stability assessments, power system forecasting studies, power quality problems, optimization of generation scheduling, 5G network data communications, SCADA intrusion detection and transformer fault identification.

Abstract

Energy costs constitute a significant expense for steel plants with electric arc furnaces (EAFs). To reduce energy costs, industrial processes actively participate in demand response by moving energy-consuming activities to low-price periods. On the other hand, conventional production scheduling mainly cares about the makespan via weighing the product demand and inventory. However, minimising energy costs and makespan are usually conflicting objectives. Therefore, we present a multi-objective mixed-integer linear programming (MOMILP) model for addressing the production scheduling problem of a steel plant with EAFs. The Normal Boundary Intersection (NBI) method is used to derive an evenly distributed Pareto frontier for better evaluating the trade-off between electricity costs reduction and the time saving of production completion. The effectiveness of the proposed model is demonstrated in the case study.

Abstract

Time Series (TS) analysis is a hot topic in Data Mining community. Currently, the detection of the operation state in energy system often relies only on human judgment or even on-site inspection. But TS analysis can help automatizing this task and has become attractive in the energy field. In this paper, we propose a method for detecting and recognizing system operating pattern based on change points and complex network features. We first explain how the change point detection method can be applied in different pipeline operation scenarios. The results obtained by this method can help TS to segment subsequences and extract shapelet. Then, the shapelet are transformed into the form of visibility graph. The structural features of such TS graphs corresponding to different operating patterns of the system are extracted. Finally, we validate the graph feature-based representation method on datasets from an oil pipeline system in China. We compare it with statistical features-based representation baseline method for classification tasks. The results show the interpretive and accuracy of our proposed method. The method could be a basis for intelligent detection and recognition in the field of energy systems.

Abstract

This paper presents an algorithm to compare traditional and innovative energy systems for maritime applications, adopting a multi-criteria method. The algorithm includes a large and updated database of market solutions. Two case studies are investigated: (i) a sailing yacht (ii) and a large-size cruise ship. For case (i), Fuel Cells represent a competitive solution, in particular considering navigation in emission control areas; the installation of electrical batteries is also evaluated. For case study (ii) Internal Combustion Engines are the best solution: the evaluation of alternative fuels (LNG, ammonia, methanol) is performed, also in dual-fuel configuration.

Abstract

In order to realize the renewable energy consumption and multi-energy coupling, it is necessary to study the optimal dispatching strategy of the integrated energy system. This paper aims to minimize the total operating cost of the integrated energy system, considers the randomness of distributed energy and the uncertainty of load, and adopts the method of model predictive control to conduct research. The main work is as follows: A typical cogeneration microgrid is given. The basic structure and energy flow relationship are established, and the mathematical model of each part of the integrated energy system is established. The optimal scheduling and control strategy of integrated energy system based on model predictive control is introduced, and the objective function and constraints of this paper are given. Finally, taking the winter integrated energy system of a district with distributed energy as an example, the effectiveness of the model predictive control method is verified by the comparison of different operation modes.