Energy management in Lucknow city

There are various approaches to energy and carbon management and the most appropriate approach is likely tochange over time. Selecting the appropriate approach is pivotal in determining how successful an organisation will be in achieving its energy and carbon management objectives. Over the last fifteen years, Gosford City Council has undergone numerous shifts in its approach to the management of energy and carbon across its operations. Council initially focused on reducing its carbon footprint, firstly by setting aspirational targets and followed by the setting of evidence based targets. In response to rising energy costs, Council shifted from a “carbon abatement” to an “energy management” focus in 2012. At this time, the sophistication of Council’s energy management program was vastly improved with the introduction of a corporate energy management information system and a revolving energy fund. In 2014, Council’s energy management program focused on “use less” and“pay less” levers. The first lever “use less” covered much the same ground as previous carbon abatement approaches, however, the second energy management lever, “pay less” unlocked significant additional value to Council. Pay less initiatives, such as energy procurement, load shifting, energy account management and bill validation resulted in energy cost savings of hundreds of thousands of dollars for Council. Council is now shifting from a tactical to a more strategic approach to energy management. An Energy Management Strategy is under development. The Energy Management Strategy will introduce an Energy Productivity Improvement Objective. This objective will focus on recognising the complete economic value of improved energy and carbon management. This should yield organisational productivity improvements and economic value in the local community. The strategy also introduces advanced energy metrics such as an energy cost index and asset class energy intensity metrics. The appropriate approach for Gosford City Council’s energy and carbon management has advanced in line with wider organisation objectives, values and maturity of its energy management systems.

Demand-side management (DSM) has a key role in the Smart Grid (SG) concept to control the demand-side consumption and reduce peak loads. The ability to shift peak loads and provide the energy efficiency through better demand-side management is currently one of the most promising approaches to solve problems related to peak demand. DSM has some different terms such as demand-side energy management (DSEM), load energy management (LEM), demand response (DR), and automated load management (ALM) and all terms refer to the balancing of energy generation and consumption. DSM includes all the process in demand energy systems such as utilities renovation operations, metering, energy pricing, monitoring, customer comfort, home energy management systems, and so on. In addition, DSM has superior advantage that it is less expensive to intelligently influence a load than to build a new power plant or install some electric storage device. All these DSM strategies are used for optimum and efficient energy consumption. In this chapter, the general perspective is given for DSM under SG concept and describes the DSM architecture and its benefits of the customer side and utility side. Also, it explains the DR techniques and classified DR programs and give some information about dynamic pricing and smart metering. Then the impact of DR programs is discussed in residential energy management perspectives. It also gives some details about home energy management (HEM) concept. Finally, it discusses about DSM standards. In conclusion, the existing DSM applications and what could be done in the future works are discussed.

The increasingly aggressive performance targets for new aircraft designs that drive fuel efficiency, emissions, noise, cost, and operability requirements necessitate a renewed focus on optimal onboard energy management for the aircraft systems and subsystems. Optimal energy management, which has traditionally been considered local to each aircraft subsystem, is now being expanded to include interactions among multiple subsystems to better optimize overall aircraft performance. The progressive trend toward the use of novel electric subsystems both opens up attractive opportunities for such a synergistic consideration of vehicle‐level energy optimality, and at the same time poses significant challenges of a technical and organizational nature. This chapter provides a broad overview of energy sources, types, and consumers for both conventional and novel subsystem architectures, identifying existing suboptimalities in energy utilization as well as opportunities and considerations for more effective total energy management. The technical and organizational challenges related to vehicle‐level treatment of energy optimality and management that modern aircraft designers face are presented and discussed. Several energy management strategies for thermal and electric systems are described and evaluated.

PurposeIran is currently among the countries with high energy consumption levels. Based on the statistics published on the country's hydrocarbon balance sheet, the industrial sector was the largest energy user of all the sectors, followed by the household and transportation sectors. Besides, production lines account for the highest percentage of the industrial sector energy consumption. Accordingly, this paper aims to investigate the effects of coordinated energy management and manufacturing strategies to increase energy management performance.Design/methodology/approachThis paper collected the required information on energy management and manufacturing from the experts of petrochemical companies; and oil and gas refineries and then examined their relationship. Moreover, the questionnaire tool was used to measure the independent variable.FindingsThe evaluations showed that organizations with coordinated and uncoordinated strategies do not exhibit equal energy management performance. Organizations with a coordinated combination of strategies have higher energy management performance than those with an uncoordinated combination of strategies. Combinations such as 11, 22, 33 and 44 are among the more coordinated combinations, which lead to higher performance.Originality/valueReviewing the studies in this regard revealed that limited and a handful of research papers were carried out on organizations' energy management strategies. None of the existing research has considered energy management strategies as a subsystem of an organization or specified its coordination with manufacturing strategies. However, this research has delved into this issue and our findings confirm certain assumptions of past studies and contribute to evaluating its effects on energy management performance.

This paper presents a decentralized demand response (DR) framework that, incorporating optimal multi-objective energy management strategies, addresses uncertainties in power networks. The power industry faces challenges in operational optimization due to uncertainties in generation and consumption while evaluating environmental impacts and long-term economic implications. This research introduces an innovative approach by combining advanced DR techniques with a robust energy management strategy designed for uncertain conditions, enhanced by sensitivity analysis to key system parameters. The article considers a network with distributed generating resources, including wind turbines, microturbines, photovoltaics, energy storage systems (ESS), and diesel generators, where generation is controlled hourly based on load fluctuations. Energy consumption optimization requires not only distributed energy generation but also DR to variations in demand, ensuring system reliability under diverse scenarios. Consumers play a crucial role in optimizing energy usage through incentive-based participation. To achieve the research goal of reducing generation and purchasing costs in power grids through optimal energy management and DR to fluctuations, a stochastic approach is employed to obtain the best outcomes. This paper proposes a novel method for optimizing energy consumption in power networks by integrating stochastic techniques to manage uncertainties and variable conditions. The findings show improved network efficiency and cost reduction, achieving a 15.62% decrease in voltage deviation, 37.08% reduction in load demand, 62.05% decrease in active losses, 81.25% reduction in reactive losses, and 33–45% reduction in Expected Energy Not Supplied (EENS).

Demand-side management, a new development in smart grid technology, has enabled communication between energy suppliers and consumers. Demand side energy management (DSM) reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules. Demand response (DR), distributed energy resources (DER), and energy efficiency (EE) are three categories of DSM activities that are growing in popularity as a result of technological advancements in smart grids. During the last century, the energy demand has grown significantly in tandem with the increase in the global population. This is related to the expansion of business, industry, agriculture, and the increasing use of electric vehicles. Because of the sharp increase in global energy consumption, it is currently extremely difficult to manage problems such as the characterization of home appliances, integration of intermittent renewable energy sources, load categorization, various constraints, dynamic pricing, and consumer categorization. To address these issues, it is critical to examine demand-side management (DSM), which has the potential to be a practical solution in all energy demand sectors, including residential, commercial, industrial, and agricultural. This paper has provided a detailed analysis of the different challenges associated with DSM, including technical, economic, and regulatory challenges, and has proposed a range of potential solutions to overcome these challenges. The PRISMA reviewing methodology is adopted based on relevant literature to focus on the issues identified as barriers to improving DSM functioning. The optimization techniques used in the literature to address the problem of energy management were discussed, and the hybrid techniques have shown a better performance due to their faster convergence speed. Gaps in future research and prospective paths have been briefly discussed to provide a comprehensive understanding of the current DSM implementation and the potential benefits it can offer for an energy management system. This comprehensive review of DSM will assist all researchers in this field in improving energy management strategies and reducing the effects of system uncertainties, variances, and restrictions.

Effective energy monitoring, reporting, and management strategies for wise energy usage is one of the objectives of Energy Management. Numerous researches have highlighted the extremely good profits of imposing business and industrial energy management measures. Notably, a number of those research display that extra financial savings may be found out in growing international locations. Unfortunately, industries in developing countries like Nigeria are lagging behind in the adoption of energy management measures and as such missing the benefits of implementation. This research study sets out to evaluate the energy consumption performance in manufacturing industry in order to showcase the gains of energy management in manufacturing industry. Data on weekly energy consumption (in MW) and weekly production of sugar (in Bags, 50kg/bag) were obtained from a sugar manufacturing company in southwestern Nigeria. Energy management data analysis and modeling was done using linear regression plot of energy consumption against production; energy intensity plot and cumulative sum of difference (CUSUM) plot respectively. The energy performance model was obtained from the linear regression plot and two parameters namely incremental energy consumed per bag (per kg) of sugar produced and “no-production” energy consumption are the performance measures. The model showed that the incremental energy consumed per bag (or per kg) is 0.00008 MW/Bag or 80W/Bag or 1600W/kg while the no-production energy consumption is 211.73 MW. Results also reveals that the no-production activities consumed energy more when compared with the actual energy used for production. CUSUM identified five periods when energy consumption gave higher and increased production thereby showing that CUSUM charts are more effective in detecting changes in energy consumption. The research study has shown how energy management data analysis can be helpful in taking decision that will enhance increased production and reduction of no-production energy consumption activities.
 Keywords: Energy management, CUSUM, Performance model, Energy, No-production energy consumption

Reducing fuel consumption has always been a major challenge to the automotive industry. Whereas first marketing aspects gave rise to innovative research, today the environmental regulations have become the main driving force behind new technologies. Historically, the research concentrated on improvements for the mechanical side of the vehicle. However, the introduction of Hybrid Electric Vehicles (HEV), where the propulsion power can also be delivered by an electric machine, definitely emphasizes the benefits of electro-mechanical solutions. With a secondary power source, the HEV can satisfy the vehicle power demand in various ways. An energy management (EM) strategy is needed to control this added freedom in a fuel-efficient way. At present, a broad range of EM strategies has been proposed in literature and several concepts have been implemented in series-production vehicles. Typically, the academic solutions focus on complex optimization techniques, arising from well defined mathematical problems. The engineering approach offers a sub-optimal strategy, based on heuristic rules. Nevertheless, both policies fail when the important vehicle characteristics for EM are not well understood. The main contribution of this thesis is to deduce a physical explanation of the EM problem for all HEV configurations, viz., the series-HEV, the parallel-HEV and the combined series/parallel-HEV. By having a good understanding of the vehicle properties of interest, it becomes possible to develop a model-based EM strategy that mimics the optimal solution, without the need for complex optimization routines, nor the necessity for having accurate predictions about the future driving cycle. The proposed causal strategy is directly suitable for on-line implementation in a vehicle. The primary goal of an EM strategy is to maximize the fuel efficiency of the vehicle. In practice, this requirement is often associated with operating the internal combustion engine (ICE) in its highest efficiency region. Nevertheless, this thesis reveals that this concept is only partially true. A better understanding of how to operate the ICE follows from two other properties: the slope of the fuel map and its fuel offset at idle speed. A formal optimization problem is formulated to prove that these properties also relate to a mathematical interpretation, and infer from the optimal solution. For all the HEV configurations mentioned above, a power-oriented vehicle model is derived. Next, a suitable EM strategy is proposed. This strategy originates from a non-causal global optimization, but through a physical understanding of the parameters of interest, it is translated into a causal on-line strategy. To cope with uncertainties in the future power demand, a feedback mechanism is added which automatically regulates the energy in the battery near a reference value. Contrary to standard control experience, this feedback control loop has a better performance if it incorporates a small bandwidth and a large tracking error. Simulation results for all HEVs demonstrate that the proposed EM strategy achieves a fuel economy which is almost equivalent to the optimal solution. Moreover, when the fuel costs for producing electric power are accurately known in advance, this strategy has the ability to further improve its performance. In practice, however, this requirement is inappropriate, since causality of the EM strategy is lost. An alternative methodology is presented to include road predictions into the causal EM strategy. By means of an electronic horizon, the prediction information is translated into a preferred reference trajectory for the energy stored in the battery. However, it will be demonstrated that the added value of having knowledge about the future driving cycle is limited, compared to the situation without prediction information. Finally, the EM concept can also be applied to the electric power net of vehicles with a traditional drive train, or micro HEVs. Here, the alternator takes the position of the electric machine. As a case-study, the EM strategy has been implemented in a Ford Mondeo vehicle. Vehicle experiments on a roller-dynamometer test-bench show that profits in fuel economy are achieved up to 2.6% for a typical driving cycle. Although the potential fuel benefits are limited for the vehicle under consideration, the return on investment is extremely high, since it requires primarily changes in the vehicle software.

The main objective of this paper is to review the integration of energy management for future smart energy systems. The authors hope to address the developing landscape of energy management in the context of new smart energy systems in this review. The paper conducts a thorough review of integrated energy management methodologies that maximize energy generation, consumption, and distribution within these systems. The study assesses the multifarious solutions that enable effective and sustainable energy consumption by considering many components such as renewable energy sources, storage technologies, demand-side management, and grid interactions. The authors present insights into the problems and opportunities inherent in realizing the potential of future smart energy systems through an in-depth assessment of recent research, case studies, and advances in energy management. The assessment focuses on the inherent problems and opportunities associated with pursuing integrated energy management in smart energy systems. The application of cutting-edge sensing, communication, and control technologies to electrical grids has been studied to increase resilience, efficiency, and dependability. Real-time monitoring, analysis, and optimization of energy flows are made possible by the integration of cutting-edge sensors, communication systems, and control algorithms into electrical grids. Variable renewable energy sources, such solar PV and wind power, may now be seamlessly integrated into the grid thanks to advancements in renewable energy integration technologies. Case studies have shown how smart grid technologies can optimize energy management and save system costs. Integrating various DERs into grid operations has been the main focus of advancements in energy management. The paper navigates through the intricate considerations that stakeholders must make to maintain the resilience and sustainability of future energy systems, from dealing with the intermittent nature of renewable sources to maximizing energy dispatch mechanisms. The study reveals the revolutionary potential of a holistic approach to energy management by studying the changing role of digital technologies, data analytics, and predictive algorithms. Finally, this review contributes to a better knowledge of integrated energy management techniques, opening the path for a more robust, responsive, and environmentally friendly energy landscape.

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

This paper investigates the application of utility theory in decision-making related to energy use behavior and management practice in the energy sector. By conducting a systematic literature review, this study aims to understand the theoretical and practical applications of utility theory in optimizing energy consumption and management strategies. The review targets a comprehensive collection of academic works that apply utility theory to various aspects of energy use behavior and management decisions, including efficiency initiatives, renewable energy adoption, and sustainable infrastructure development. A systematic literature review methodology was adopted, which encompassed a rigorous selection process to identify relevant studies, followed by a detailed analysis of how utility theory has been employed to influence energy-related decisions in residential, commercial, and industrial settings. The review findings were synthesized to outline the implications for both policy and practice, highlighting the role of utility theory in guiding more efficient and sustainable energy management practices. Through this exploration, the paper provides a discussion on bridging the gap between economic theoretical models and practical energy management applications. It also offers insights into how decision-making influenced by utility theory can lead to enhanced energy efficiency and sustainability. The findings offer valuable guidance for policymakers and energy managers in designing and implementing energy systems and policies that maximize utility while considering environmental and economic impacts. This paper serves to advance the theoretical framework of utility theory and its practical application in energy management, facilitating better-informed strategies that align with global sustainability goals.

The filter-based real-time energy management method has been proved practical and widely utilized in hybrid energy storage systems. However, the determination for the cutoff frequency of the energy-split filter is challenging. In this paper, an optimal filter-based energy management strategy is proposed for a battery/ultracapacitor electric vehicle to minimize the total energy consumption. A cost function of energy consumption for the cutoff frequency is established first. Considering the working condition of ultracapacitors, dynamic programming is adopted to obtain the optimal cutoff frequency series, i.e., the optimal energy distribution between batteries and ultracapacitors. Such an off-line optimization process is carried out under different driving cycles, e.g., urban and highway road conditions. Optimization results are used to determine the optimal cutoff frequency of a real-time filter-based energy management strategy. Simulation results indicate that the proposed strategy can minimize the total energy consumption of the hybrid energy storage system with ultracapacitors state of charge limitations being guaranteed. Compared with the existing real-time energy management strategies, the energy consumption is reduced 23.85% under aggressive acceleration conditions and 7.08% under urban conditions by the proposed strategy.

Recent advancement in demand side energy management system for optimal energy utilization

Hierarchical Operation of Electric Vehicle Charging Station in Smart Grid Integration Applications — An Overview

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS.