An Improved Deep Learning Model for Electricity Price Forecasting

Accurate electricity price forecasting (EPF) is important for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Besides that, EPF becomes critically important for effective planning and efficient operation of a power system due to deregulation of electricity industry. However, accurate EPF is very challenging due to complex nonlinearity in the time series-based electricity prices. Hence, this work proposed two-fold contributions which are (1) effective time series preprocessing module to ensure feasible time-series data is fitted in the deep learning model, and (2) an improved long short-term memory (LSTM) model by incorporating linear scaled hyperbolic tangent (LiSHT) layer in the EPF. In this work, the time series pre-processing module adopted linear trend of the correlated features of electricity price series and the time series are tested by using Augmented Dickey Fuller (ADF) test method. In addition, the time series are transformed using boxcox transformation method in order to satisfy the stationarity property. Then, an improved LSTM prediction module is proposed to forecast electricity prices where LiSHT layer is adopted to optimize the parameters of the heterogeneous LSTM. This study is performed using the Australian electricity market price, load and renewable energy supply data. The experimental results obtained show that the proposed EPF framework performed better compared to previous techniques

Modelling Framework for Reducing Energy Loads to Achieve Net-Zero Energy Building in Semi-Arid Climate: A Case Study

Buildings consume a significant 40% of global energy, where, reducing the building energy consumption to a minimum, virtually zero, has become a thriving research area. Accordingly, this research aimed to determine and portray the huge potential of energy conservation in existing structures by making a retrofit at relatively low costs in finance strained economies. A walk-through of the survey of energy consuming appliances determined the energy consumption based on the power rating; the appliances were then virtually replaced and the reduced energy consumption was determined in terms of the cooling loads. Modelling these intervention using the hourly analysis program (HAP) showed significantly positive results. The pre- and post-retrofit model analysis of an institutional building in Pakistan exhibited significant potential for reducing the cooling load of 767 kW (218 TON) to 408 kW (116 TON) with an investment payback period of 2.5 years. The additional benefit is the reduced greenhouse gas (GHG) emissions which reduce the overall energy requirements. The study continues with the design of a solar energy source using the system advisor model (SAM) for the reduced energy demand of a retrofitted building. It is then concluded that using the available area, a solar energy source with a capital payback period of 5.7 years would bring an institutional building within its own energy footprint making it a net-zero building, since it will not be consuming energy from any other source outside of its own covered area. The study has the limitation to exposure and climate related conditions. In addition, the decline in heating and cooling loads represents model values which may vary when calculated after an actual retrofit for the same structure due to any site related issues

Detection, quantification and genotype distribution of HCV patients in Lahore, Pakistan by real-time PCR

Background: Hepatitis C virus (HCV) is considered as \u201cViral Time Bomb\u201d suggested by the World Health Organization and if it is not treated timely, it will lead towards cirrhosis and hepatocellular carcinoma (HCC). Objective: The purpose of the present research is to study possible risk factors, frequent genotypes of HCV and its association with different age groups. Methods: Suspected blood samples from HCV patients were collected from different hospitals of Lahore, Pakistan. Out of 1000 HCV suspected samples, 920 samples were found HCV positive detected by Anti-HCV ELISA, CobasR. kit. The quantification of HCV load was determined by HCV quantification kit and LINEAR ARRAY KIT (Roche) was used for genotype determination by Real-Time PCR (ABI). Statistical analysis was done by using Microsoft Excel. Results: Out of 920 subjects, 77 subjects (8.4%) were false positive and they were not detected by nested PCR. Three PCR positive samples were untypeable. Genotype 3 was predominant in Lahore which was 83.5%, whereas type 1 and 2 were 5.1% and 0.7% respectively. There were also mixed genotypes detected, 1 and 3 were 0.4%, 2 and 3 were 1.41% and 3 and 4 were 0.2% only. Male were more infected of HCV in the age <40 years and females >40years. Conclusion: The major risk factor for HCV transmission is by use of unsterilized razors/blades. It is necessary to spread awareness among the general population of Pakistan about HCV transmission risk factors. Regular physical examination at least once a year is recommended, so that early detection of HCV could be done

Optimal battery and fuel cell operation for energy management strategy in MG

Microgrid (MG) with optimal operating (OC) and capital cost (CC) is highly required. This study presents dual-mode energy management system (DM-EMS) operation with the objective of minimising the overall OC of the MG. DM-EMS determines the best mode to operate battery and fuel cell at a particular time and its duration to operate MG with minimum cost. The CC of an MG is mainly depending on battery size and battery life. In this study, novel battery sizing method by using the proposed life cycle cost (LCC) function is introduced. Considering the impact of battery size and life in the OC and CC of an MG, the proposed DM energy management strategy and novel battery sizing are concurrently optimised by using the proposed LCC function. The proposed strategy is validated using an MG system consisting of wind, battery storage and fuel cell. The results show that better optimal OC and CC with an accurate optimal battery capacity of an MG are achieved. In addition, analysis on the optimal battery size variations for different battery level [state of charges (SOCs)] is conducted. Changes in OCs and CC for the different range of initial battery SOC are also assessed. © The Institution of Engineering and Technology 2019

Optimal sizing and energy scheduling of isolated microgrid considering the battery lifetime degradation

The incessantly growing demand for electricity in today’s world claims an efficient and reliable system of energy supply. Distributed energy resources such as diesel generators, wind energy and solar energy can be combined within a microgrid to provide energy to the consumers in a sustainable manner. In order to ensure more reliable and economical energy supply, battery storage system is integrated within the microgrid. In this article, operating cost of isolated microgrid is reduced by economic scheduling considering the optimal size of the battery. However, deep discharge shortens the lifetime of battery operation. Therefore, the real time battery operation cost is modeled considering the depth of discharge at each time interval. Moreover, the proposed economic scheduling with battery sizing is optimized using firefly algorithm (FA). The efficacy of FA is compared with other metaheuristic techniques in terms of performance measurement indices, which are cost of electricity and loss of power supply probability. The results show that the proposed technique reduces the cost of microgrid and attain optimal size of the battery. © 2019 Sufyan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Operational Cost Minimization of Electrical Distribution Network during Switching for Sustainable Operation

Continuous increases in electrical energy demand and the deregulation of power systems have forced utility companies to provide high-quality and reliable services to maintain a sustainable operation and reduce electricity price. One way to continue providing the required services while simultaneously reducing operational costs is through minimizing power losses and voltage deviation in the distribution network. For this purpose, Network Reconfiguration (NR) is commonly adopted by employing the switching operation to enhance overall system performance. In the past, work proposed by researchers to attain switching sequence operation was based on hamming distance approach. This approach caused the search space to grow with the increase in total Hamming distance between the initial and the final configuration. Therefore, a method is proposed in this paper utilizing a Mixed Integer Second Order Cone Programming (MISOCP) to attain optimal NR to address this issue. The Hamming dataset approach is opted to reduce search space by considering only radial configuration solutions to achieve an optimal switching sequence. In addition, a detailed economic analysis has been performed to determine the saving after the implementation of the proposed switching sequence. The effectiveness of the proposed technique is validated through simulations on IEEE 33-bus distribution network and a practical 71-bus network in Malaysia. The result shows that the proposed method determined the optimal network configuration by minimizing the power losses for the 33 bus and 71-bus system by 34.14% and 25.5% from their initial configuration, respectively to maintain sustainable operation