• Energy Research

Abstract Traditional electricity grid offers demand side management (DSM) programs for industrial plants and commercial buildings; there is no such program for residential consumers because of the lack of effective automation tools and efficient information and communication technologies (ICTs). Smart Grid is, by definition, equipped with modern automation tools such as home energy management system (HEMS), and ICTs. HEMS is an intelligent system that performs planning, monitoring and control functions of the energy utilization within premises. It is intended to offer desirable demand response according to system conditions and price value signaled by the utility. HEMS enables smart appliances to counter demand response programs according to the comfort level and priority set by the consumer. Demand response can play a key role to ensure sustainable and reliable electricity supply by reducing future generation cost, electricity prices, CO2 emission and electricity consumption at peak times. This paper focuses on the review of HEMSs and enabled demand response (DR) programs in various scenarios as well as incorporates various DR architectures and models employed in the smart grid. A comprehensive case study along with simulations and numerical analysis has also been presented.

Appliances scheduling and direct load control are major tools of Demand Side Management (DSM); however, in many scenarios, these tools come to threshold for effective use due to certain limitations. Prosumers are those energy consumers who also produce energy and can share it with grid or other users. Demand-supply gap can be smartly minimized by energy sharing and management with integration of storage system at end users in smart grid environment. In this paper, prosumer based energy sharing and management model with integration of storage system is presented. MATLAB simulations have been performed for validation of the proposed model. Results show that if users are equipped with storage system then demand-supply gap can be reduced remarkably.

Abstract In this era, energy is essential to life and its continuous and ubiquitous supply brings much needed economic growth and prosperity to a region. Energy Management (EnM) contributes to the controlling and monitoring of energy and has recently become the subject of great significance. EnM involves analysis, conservation and imperative actions that leads to the minimization of the system losses for efficient use of available conventional and renewable energy resources of the region. This study evaluates the energy management in South Asia (SA) region, as it houses one-fourth of the world's total population with India, Pakistan and Bangladesh being the most populous countries. The study accesses the regional energy mix, distributed generation strategy plans, and present the trends in EnM over the past few decades. South Asian Countries (SACs), especially Pakistan, India and Bangladesh have a huge potential of coal i.e. 17,550, 90,085 and 884 Million Tonnes, respectively. India has rightly explored nearly 70% of its coal potential, whereas Pakistan and Bangladesh despite having the largest potential of the coal have focused on oil (35.4%) and natural gas (91.5%) exploration respectively. It is important to note that India experiences the highest technical losses of 23.5%, followed by 17% percent and 14.11% losses by Pakistan and Bangladesh respectively. Energy Intensity (EI) values for Afghanistan, Bhutan and Sri Lanka have listed these countries as low EI terrain region. Pakistan, India and Bangladesh have EI values ranging between 10,000 and 20,000 btu per $2000 constant. SACs countries have monopolistic electricity markets, however steps are taken to partially implement competition and monopoly regulation by privatization and ensuring equal conditions for all vendors. Analysis show that the SACs are presently far behind to meet their energy demands. The paper also recommends introduction and implementation of effective EnM policies along with large scale utilization of non-conventional energy resources, which will bring necessary changes and will help SACs to overcome economic challenges. The study also concludes that the inclusion of renewable energy resources and development of competitive electricity markets would overall improve the EnM in SA.

Power generation and supply management is becoming an increasingly difficult task with the increase in population. One of the solutions is intelligent control of power consumption in the households where most of power consumption is due to high load appliances like Electric Water Heater (EWH) in winter and fridge in summer. This paper presents an over view of the EWH use for Demand Side Management (DSM). To reduce the consumption of EWH, the US Electrical Power Research Institute (EPRI) model has been focused in context of DSM in smart grid. Moreover, use of Binary Particle Swarm Optimization (BPSO) has been elaborated to implement a direct load control strategy for EWH management.

The robust and accurate load forecasting is necessary to ensure effective power market operations and optimize load dispatch strategies. Deep learning models have recently gained popularity because of their strong ability to learn data patterns. However, conventional deep-learning models still encounter difficulties in precisely predicting complex load patterns. This paper addresses the difficulties of forecasting intricate load patterns, where conventional deep learning models often fail. Therefore, a novel quantile long short-term memory network with dual attention is proposed for hour-ahead short-term load forecasting. By combining dual attention processes with quantile regression-based long short-term memory networks, the proposed framework effectively captures the temporal dependencies of the complex load pattern. The gates recurrent unit and hybridized methodologies of recurrent neural networks are among the baseline techniques against which the proposed method is thoroughly tested using datasets from Panama City and the Islamabad Electric Supply Company. The proposed quantile long short-term memory network with dual attention mechanism has demonstrated notable performance gains with 2.35% and 5.36% reduction in mean absolute percentage error in comparison to the best-performing models, from the set of baseline models, for the Panama and IESCO datasets, respectively. These results demonstrate the proposed method's effectiveness in providing more improved and accurate forecasts for enhanced grid stability and economic dispatch efficiency.

Summary Load management (LM) is supposed to have a vital role in future energy management systems. This article presents an overview and comparison of LM techniques along with related technologies and implementation challenges in smart grid. The article also covers consumer and utility concerns in context of LM to enhance readers' intuition about the topic. Two major categories of LM techniques, incentive-based and dynamic pricing-based schemes, have been discussed and compared. Most commonly used incentive-based direct load control is elaborated in detail. Dynamic pricing-based energy consumption scheduling schemes, featuring peak load reduction and consumers' energy cost minimization at residential level, are also emphasized. Furthermore, the article includes a description of dynamic pricing-based home energy management and associated optimization techniques as well as comparison of the latest schemes. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Pakistan is facing severe energy crisis in spite of the fact that nature has blessed her with huge energy potential. Short fall of electricity supply in the country is increasing and has been recorded up to 4522 MW in 2010. This deficit reached to 7000 MW in May, 2011. A comprehensive review of Pakistan's energy sector is presented in this paper. Energy potential, major issues of energy sector and energy import options are discussed. Issues like poor management, combined cycle capacity, low hydro power share, circular debt and energy security have been covered. Energy potential assessment includes hydro solar, wind, coal, nuclear, hydrogen cells, geo-thermal, ocean resources and bio mass. Future prediction calculations are based upon country's current and world's average per capita energy consumption. Current oil and gas reserves of the country contribute to only 5 percent and 48.8 percent of the energy mix and at the current rate will be exhausted by 13 and 16 years respectively. The overwhelming dependence of the energy sector on imported fossil fuels may create a situation of energy security threat. However dependence upon the energy import options cannot be avoided in order to lessen the severity of energy crisis in near future. Some of the energy import options are: Turkmanistan, Afghanistan, Pakistan and India (TAPI); Iran, Pakistan and India (IPI) gas pipelines; Liquefied Natural Gas (LNG) from Qatar etc. On the other hand exploitation of vast renewable potential like hydro, solar and wind requires serious attention. Exploitation of indigenous coal resources would also be a key for solving energy crisis in the long run. In summary, this paper presents energy potential assessment in context of major issues, future predictions and impact of energy import options. This in turn provides a big, clear and brighter picture of the country's energy sector.

AbstractIn this paper, a home energy management (HEM) scheme based on appliances coordination has been proposed for future smart grids. This scheme is based on communication among home appliances, a central energy management unit (EMU), smart meter and the storage unit inside home. A wireless sensor home area network (WSHAN) using ZigBee protocol is employed for relaying messages among different entities involved in our proposed HEM scheme. The performance of WSHAN is analyzed with respect to different networking properties. HEM implementation will lead to socially and economically beneficial environment by addressing the consumers’ and utilities concerns. Increased savings, better peak load management and reduction in peak to average ratio are some of the benefits achieved by proposed scheme. Appropriate use of HEMs in a system integrated with distributed resources along with appliances co-ordination and dynamic pricing scheme provides the optimized solutions for energy management issues in smart grids as confirmed by simulation results.