Energy Management Policy Research Articles

A decision support system for sustainability prioritization of air pollution control technologies in energy and carbon management: Oil & gas industry of Iran

The environmental, economic, social, and technical challenges associated with implementing the air pollution control technologies (APCTs) in oil, gas, and petrochemical industries (OGPIs) necessitate a sustainability prioritization analysis. Environmental issues, economic parameters, social concerns, and technical performances all must assessed and weighted. Accomplishing such a crucial decision with rigorous potential environmental, economic, social, and technical effects needs a robust decision support system (DSS) for guiding the sustainability prioritization process of the technologies. This study purposes in developing a hybrid DSS by integrating the analytical network process (ANP), decision-making trial and evaluation laboratory (DEMATEL), and multi-objective optimization on the basis of ration analysis (MULTIMOORA) methods based on the fuzzy set theory for sustainability prioritization of implementing APCTs. Three cases of the petrochemical plant, gas refinery, and oil refinery in Iran are selected to assess and prioritize the most commonly used of APCTs including four alternatives of Electrostatic precipitators, Fabric filters, and Wet scrubbers, and Cyclones by the proposed DSS. The sustainability ranking of implementing the four technologies sequentially for three cases is obtained. The suggested DSS is feasible for group decision-making based on experts’ opinions and uncertain data. It can assist the stakeholders/decision-makers to attain the energy and carbon management (ECM) policies in three cases and apply to advance the oil & gas supply chains towards the targets of sustainability, green growth, and low-carbon economy. Finally, sensitivity analysis indicates the robustness of the proposed approach for the prioritization order.

The correlates of energy management practices and sales performance of small family food firms in Turkey

PurposeWhile pursuing energy management, firms simultaneously strive to boost sales as a path towards economic performance. Also, the literature suggests that family firms exhibit greater environmental commitment than their non-family counterparts. To examine these contentions, this review espouses contingency theory to interrogate the correlations of (1) energy consumption targets, (2) energy efficiency enhancing measures, (3) energy consumption monitoring and (4) the domestic sales performance of small family firms in Turkey's food sector.Design/methodology/approachData were sourced from the World Bank Enterprise Survey. A sample of 137 family firms in food production, processing and retail was analysed using non-linear structural equation modelling. Path coefficients were determined to estimate the extent to which energy management practices predict domestic sales.FindingsThe path analysis revealed that although energy consumption targets do not directly increase sales performance, they stimulate firms' energy efficiency enhancement measures and energy consumption monitoring to produce this effect by 21%.Research limitations/implicationsThe contingency lens espoused leaves room to capture further antecedents in small family food firms' technical, managerial, ownership, operational and architectural configuration that may also interact with or predict the propensity for energy management.Practical implicationsFor practitioners, the inherent findings demonstrate that there are firm-specific material benefits arising from adopting energy management practices. And, although small firms such as family food businesses may have low energy intensities, they can improve their sales performance by setting energy targets, installing energy efficiency enhancing measures and embarking on energy consumption monitoring.Social implicationsPublic stakeholders in Turkey such as the Ministry of Energy and Natural Resources, the General Directorate of Energy Affairs and affiliate institutions can reflect on these findings to develop a coherent national energy management policy for small firms. Such initiatives are especially relevant to Turkey and its ambitions to join the EU which requires member states to set up a national energy efficiency action plan.Originality/valueThis inquiry is one of the first to examine energy management in the food sector at the family firm level through the contingency lens. Theoretically, the results draw attention and shed new light on disparate energy management practices and their discrete yet substantial contribution to sales performance.

Long Short-Term Memory Network-Based Metaheuristic for Effective Electric Energy Consumption Prediction

The Electric Energy Consumption Prediction (EECP) is a complex and important process in an intelligent energy management system and its importance has been increasing rapidly due to technological developments and human population growth. A reliable and accurate model for EECP is considered a key factor for an appropriate energy management policy. In recent periods, many artificial intelligence-based models have been developed to perform different simulation functions, engineering techniques, and optimal energy forecasting in order to predict future energy demands on the basis of historical data. In this article, a new metaheuristic based on a Long Short-Term Memory (LSTM) network model is proposed for an effective EECP. After collecting data sequences from the Individual Household Electric Power Consumption (IHEPC) dataset and Appliances Load Prediction (AEP) dataset, data refinement is accomplished using min-max and standard transformation methods. Then, the LSTM network with Butterfly Optimization Algorithm (BOA) is developed for EECP. In this article, the BOA is used to select optimal hyperparametric values which precisely describe the EEC patterns and discover the time series dynamics in the energy domain. This extensive experiment conducted on the IHEPC and AEP datasets shows that the proposed model obtains a minimum error rate relative to the existing models.

Causal effects of population growth on energy utilization and environmental pollution: A system dynamics approach

Population growth will change the demand for food and energy resources and environmental pollution. Although early energy resources modeling has made vital efforts to model the energy system in the world, because of increasing complexity and integration of environmental, social, and economic functions, these models still need to be developed to show a system close to the real world to enhance sustainable management of natural resources. Hence, the main objective of this study is to design a system dynamics model for the food production system and energy demand in Iran in order to evaluate the effects of different population scenarios on key variables. In this regard, an integrated system dynamics simulation model was developed in Iran where managing energy resources is seriously challenging due to population growth and increasing food demand. The results of the behavioral test showed that the designed model can be used to investigate and simulate the effects of different population growth rate scenarios. Findings illustrated that by increasing population, if no further energy demand management policies were implemented, the total food demand and energy use increase by more than 1.35% and 3.31% respectively. Also, the annual air pollution change during 2014-2030 is expected to be around 4.41%. By changing the population growth rate in the form of population scenarios, the average annual energy demand in the first population scenario will be 20,277 barrels of crude oil and in the second population scenario will be 20049 barrels of crude oil. It seems that the change in the population growth rate will lead to an increase of 3.23% and 2.16% in average annual energy demand, respectively. The results showed that in the first population scenario, with a further increase in population variables, food demand and energy demand, the average change in pollution emission is 4.79%, which is at a higher level than the baseline conditions. In the second population scenario, changes in environmental pollution will be reduced to 4.31%. Therefore, given the effectiveness of population growth on the behavior of the energy system and pollution, the adoption of energy management policies should be considered by policy makers.

A Survey on Energy Harvesting Wireless Networks: Channel Capacity, Scheduling, and Transmission Power Optimization

This paper presents a survey on energy harvesting (EH) wireless communication networks focusing on channel capacity, transmission schemes, and power optimization. While many network researchers focus on energy management policies addressing the intermittency and randomness of the EH processes, but the channel capacity, and transmission power optimization have not been fully explored yet. In this paper, we provide a review and analysis of channel capacity, offline and online transmission schemes, and power optimization from an information theory perspective. By reviewing and analyzing wireless networking literature, we found that EH is a technologically feasible and economically viable paradigm for cost-effectiveness in the design and deployment of next-generation wireless networks. Finally, we identify open research problems and future research directions in the emerging field of EH wireless networks. We expect this study to stimulate more research endeavors to build energy-efficient scalable next-generation wireless network systems.

Design and implementation of methodology for the preparation of energy diagnoses

The current document describes the design and implementation of a methodology to elaborate electrical energy diagnostics. The sequence to perform a correct energy study is to initiate with a diagnostic type one and if necessary, continue with types two and/or three. What is not entirely clear, are the steps to follow in each of them. For the design, it has been made a literature analysis, energy management systems and policy. The methodology named DESMB consists of two phases, two flowcharts that explain the steps to follow and 14 formats that are evidence of energy diagnostic (ED). The implementation was made in a rate 2 commercial electric system (ES) rate 2. After the application of phase one, 10 problems were obtained, which led to the performance of phase two, where measurements were made for a 10 days period. The information obtained on this phase allowed to determine two opportunity fields: electric installation and illumination. The DESMB made it possible to systematize and standardize the activities to execute in the ES and they have been documented as evidence that facilitate the identification of potential saving areas of energy and cost.

Risk Constrained Energy Efficient Optimal Operation of a Converter Governed AC/DC Hybrid Distribution Network With Distributed Energy Resources and Volt-VAR Controlling Devices

Increasing penetration of direct current (dc) based distributed energy resources and dc loads in the conventional alternating current (ac) network necessitate the deployment of ac/dc hybrid distribution networks (HDNs). In view with the development of advanced energy management policy for ac/dc HDNs, unlike previous literatures, this article proposes a risk constrained energy efficient management algorithm by merging load shifting (LS) and conservation voltage reduction (CVR) techniques. The optimization framework aims to simultaneously minimize both true and conditional risk or conditional value at risk values of the expected energy cost under uncertain solar power generation, load demand, and upper grid energy price. In contrast with the available stochastic optimization process, in this article, two-point estimation strategy is employed in place of Monte Carlo simulation for scenario generation from the probability density functions of the uncertain parameters to reduce computational exertion. The proposed centralized optimization framework is initially developed as mixed integer nonconvex programming but to avoid computation complexity, the nonlinear components are replaced by their linear counterparts. Later, a new solution process named successive mixed integer linear programming (s-MILP) is proposed to obtain the optimal decisions for deployment of LS and CVR through smart inverters and volt-VAR controlling devices. Efficacy of the proposed technique is demonstrated on modified IEEE 33 bus ac/dc HDN and the most energy efficient operation is found by merging LS and CVR. Simulation outcomes prove fast and near optimal convergence of the s-MILP compared to conventional second-order conic programming relaxed mixed integer convex programming and piecewise linearization based MILP. Further, to assess the impact of network size on the solution time and optimality, the proposed advanced distribution network management systems strategy is employed on 132 bus ac/dc HDN.

Energy management for age of information control in solar-powered IoT end devices

In this paper, we propose several harvesting-aware energy management policies for solar-powered wireless IoT end devices that asynchronously send status updates for their surrounding environments to a network gateway device. For such devices, we aim at minimizing the average age of information (AoI) metric which has recently been investigated extensively for status update systems. The proposed energy management policies are obtained using discrete-time Markov chain-based modeling of the stochastic intra-day variations of the solar energy harvesting process in conjunction with the average reward Markov decision process formulation. With this approach, energy management policies are constructed by using the time of day and month of year information in addition to the instantaneous values of the age of information and the battery level. The effectiveness of the proposed energy management policies in terms of their capability to reduce the average AoI as well as improving upon the tail of the AoI distribution, is validated with empirical data for a wide range of system parameters.

Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification

Accurate building energy consumption prediction plays an important role in building energy management and energy policy. However, traditional prediction methods of building energy consumption fail to consider the running conditions of buildings in different periods, which results in the failure of best forecasting effect. This study presents a prediction strategy of building energy consumption based on ensemble learning and energy consumption patternclassification. Hourly meteorological data from a meteorological station and energy consumption data from an office building in New York City are used for this work. First, decision tree is employed to mining energy consumption patterns and classify energy consumption data into corresponding categories. Then, the ensemble learning method is employed to establish energy consumption prediction models for each pattern. Finally, the prediction accuracy of the proposed method is compared with other three methods, i.e., ensemble learning without energy consumption pattern classification, SVR and ANN. Also, the robustness of various methods is investigated by comparing their prediction performance under different training data amounts. Results show that there are four classified energy consumption patterns of the building and significant differences among them. The ensemble learning model with energy consumption pattern classification achieves the best prediction with 17.7%, 16.1%, 15.4%, 15.8%, 15.6% of CVRMSE under 20%, 40%, 60%, 80% and 100% data availability, respectively. It illustrates that the proposed strategy is reliable and effective. Additionally, this strategy can obtain acceptable performance with less training data, which is helpful to the application of energy consumption prediction.

A Review of Energy Consumption in Foundry Industry

Energy is a multiplying factor which allows man to translate various raw material and resources into useful items to meet the daily demands and necessities of human beings. Foundry technology involved casting from molten metal transferred into a mould and permitted to harden under varying conditions depending on the purpose. This paper reviews the foundry industry as an energy exhaustive industry, in the sense that it requires energy in various forms such as electrical, heat and mechanical to carry out various engineering processes to deliver the appropriate casting required. It also reviews the different methods are currently being employed to make the manufacturing process more efficient and each of them have led to an increase in energy demand. It is affirmed that the new methods involved in foundry and their energy management policy can led to more efficient energy consumption for casted product.

Energy management of hybrid electric vehicles with battery aging considerations: Wheel loader case study

This paper presents a multi-objective energy management strategy for hybrid electric vehicles. It aims at reducing fuel consumption and minimizing battery wear simultaneously while fulfilling system’s constraints. A control-oriented differential model is considered to account for battery aging effects, and an algorithm is developed to identify its parameters. The energy management is formulated as an optimal control problem and is solved by Pontryagin’s minimum principle. The controller is then implemented for a hybrid electric wheel loader to demonstrate its effectiveness. In short-term simulations for four drive cycles, behavior of the vehicle is compared to the case where the energy management policy does not encompass battery wear minimization. Long-term simulations suggest that there is a huge potential in extending battery life while the price to pay is a negligible increase in fuel consumption. It is observed that the proposed methodology works best for nonaggressive drive cycles.

Enhancing sustainability of the railway infrastructure: Trading energy saving and unavailability through efficient switch heating policies

Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.

A research on provide competitive advantage of energy use on the tourism sector

The tourism sector plays a locomotive role in the development of the country and in this regard, the energy factor plays a vital role in providing locally sourced conditions for competitive advantage. The purpose of this study is to reveal the relationship between energy production and the tourism sector within a competitive advantage. In this selected issue five countries (China, the USA, Germany, Russia, and Turkey) have been compared. The findings show that energy creates a competitive advantage in service infrastructure and transportation facilities, especially in tourism. Identifying the relationship between energy use and tourism will contribute to local governments to determine an appropriate strategy for energy management and sustainable tourism development policies, and reveal how energy will create a competitive advantage over the tourism sector.

Sustainability Analysis of Green Data Centers With CCHP and Waste Heat Reuse Systems

Data centers worldwide consume a large amount of energy and pose serious threats to the environment. Combined cooling, heating, and power (CCHP) and waste heat reuse (WHR) systems are two effective approaches to improve the data center energy efficiency. However, whether and to what extent the combination of them can bring further performance improvement have not been explored yet. Moreover, it is unclear how to manage the energy flow in such a hybrid system. In this article, we try to answer these questions by proposing a data center energy architecture that includes both CCHP and WHR technologies. We formulate the energy management problem as two optimization problems with different objectives. By analytically solving the optimization problems, two energy management strategies, namely, Following the electrical load with waste heat reuse (FEL-WHR) and operating cost-aware energy management with waste heat reuse (OCM-WHR), are developed. We evaluate the performance of proposed solutions by extensive simulations using a real-world workload trace from a production data center. Numerical results suggest that the CCHP and WHR technologies generally reduce the operating cost and are more environmental friendly. If the power generation efficiency of CCHP system is high, the impact of WHR will be more conspicuous (up to 8 percent improvement) and FEL-WHR is recommended because it achieves better sustainability at an operating cost comparable to OCM-WHR. Otherwise, WHR only improves the sustainability trivially, and the energy management policy should be carefully chosen according to the data center operator's objective.

Determination of the palm based biodiesel policy integration model as a renewable energy commodity

The increase in economic activity in the industrial sector and the rapid growth of the world population have stimulated an increase in energy demand. In 2004, Indonesia earned the status of a net importer of oil so that it becomes a challenge for the Indonesian government in developing the use of renewable energy to achieve ideal conditions for national energy security. Indonesia has the potential for large amounts of renewable energy sources, one of which is palm-based biodiesel. The mandatory biodiesel policy program was implemented in 2008 with a biodiesel content of 2.5% and gradually until 2019 with a biodiesel content of 30% (B30). The mandatory biodiesel policy is closely related to the achievement of the Sustainable Development Goals (SDGs), and the concept of maintaining the balance of Trilemma Energi. The current energy management and utilization policies in Indonesia continue to increase in line with modern life consumption patterns that require a more environmentally friendly energy variable for energy absorption in Indonesia, especially renewable energy. The purpose of this research is to determine the integration model of palm-based biodiesel policy as a renewable energy commodity to support energy security. This study uses several strategic frameworks by combining a quantitative approach through the perspective of the Balanced Scorecard (BSC) and measuring the technology coefficient using the Technology Contribution Coefficient (TCC), as well as a qualitative approach with the Business Model Canvas (BMC) and the design of the Omnibus Law. Data were collected through Focus Group Discussion (FGD) and Expert Opinion (EO) which were validated by Structural Equation Modeling-Partial Least Square (SEM-PLS) using a sample of 40 respondents from related agencies. The results showed that based on the SEM-PLS validation of 20 BSC perspective variables, two invalid variables were obtained, namely the variable efficiency port service cost and value-added creation which had a P value> 0.05. Meanwhile, Indonesia's TCC score is quite high, namely 0.787, which means that Indonesia is quite aggressive in developing biodiesel and its policies. Based on the results of the FGD expert, it was obtained that the BMC initiates the helicopters to view current biodiesel developments. And 10 regulations have been drafted into a proposed draft Omnibus Law through an action plan.

Reducing university energy use beyond energy retrofitting: The academic calendar impacts

With a strong commitment to sustainability, numerous universities set their carbon reduction targets, with the common measure being energy retrofitting of their campus buildings. Based on a complex understanding of the higher educational buildings’ energy consumption characteristics, this study addresses the role of the academic calendar that regulates the occupancy condition of campus buildings and consequently determines their energy consumption. Using an Australian university as a case study, the research collected energy data from its 122 campus buildings, analysed the data regarding the occupancy condition, and compared the data under two different academic calendars: semester and trimester. Among all campus building types, those used for teaching had the highest energy use, accounting for more than 50% of the whole campus energy consumption. Buildings that are used for research had the highest energy use intensity, at more than three times that of teaching. From a semester to a trimester academic calendar, the campus energy showed a reduction by 213,090 kWh per year (around 5% reduction). The energy consumption of teaching dedicated space decreased by 505,521 kWh per year (around 3% reduction), while that of research increased by 153,893 kWh per year (around 2% reduction). This indicated a decrease in teaching equivalent to an increase in research activities when transitioning to a more flexible enrolment calendar. The results suggest that the pattern of the occupancy condition regulated by the academic calendar should be adequately captured in the campus energy management and carbon reduction policy.

Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy

Smart grid technology based on renewable energy and energy storage systems are attracting considerable attention towards energy crises. Accurate and reliable model for electricity prediction is considered a key factor for a suitable energy management policy. Currently, electricity consumption is rapidly increasing due to the rise in human population and technology development. Therefore, in this study, we established a two-step methodology for residential building load prediction, which comprises two stages: in the first stage, the raw data of electricity consumption are refined for effective training; and the second step includes a hybrid model with the integration of convolutional neural network (CNN) and multilayer bidirectional gated recurrent unit (MB-GRU). The CNN layers are incorporated into the model as a feature extractor, while MB-GRU learns the sequences between electricity consumption data. The proposed model is evaluated using the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. Finally, our model is assessed over benchmark datasets that exhibited an extensive drop in the error rate in comparison to other techniques. The results indicated that the proposed model reduced errors over the individual household electricity consumption prediction (IHEPC) dataset (i.e., RMSE (5%), MSE (4%), and MAE (4%)), and for the appliances load prediction (AEP) dataset (i.e., RMSE (2%), and MAE (1%)).

Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach

Significant dependence on fossil fuels and freshwater shortage are common problems in remote and arid regions. In this context, the operation of a wind-solar-diesel-battery-reverse osmosis hybrid energy system has become a suitable option to solve this problem. However, owing to the uncertainties of renewable energy availability and load demand, it is a challenge for operators to develop an energy management scheme for such a system. This study aims to determine a real-time dynamic energy management strategy considering the uncertainties of the system. To this end, the energy management of a hybrid energy system is presented as an optimal control objective, and multi-targets are considered along with constraints. The information entropy theory is introduced to calculate the weight factor for the trade-off between different targets. Then, a deep reinforcement learning algorithm is adopted to solve this problem and obtain the optimal control policy. Finally, the proposed method is applied to a typical hybrid energy system, and numerous data are applied to train an agent to obtain the optimal energy management policy. Simulation results demonstrate that a well-trained agent can provide a better control policy and reduce costs by up to 14.17% in comparison with other methods.

Decision-making tools for sustainable planning and conceptual framework for the energy–water–food nexus

The impact assessment of energy strategies, more specifically those that promote an integrated approach on resource management in other sectors such as water and food, requires knowledge related to the evaluation of the quality and knowledge that may be estimated by quantitative means. The present paper makes inquiries into those knowledge requirements in addition to review the means used to obtain it—including the required entries and the results they provide. In response to the recognized problems in knowledge, this paper introduces a basic reference structure underlying a system to evaluate the way that a progressive development of inexhaustible energies in a particular geographical region can affect the demand of water and food. Then, the proposed conceptual framework constitutes a novel approach for energy policy makers which only consider partial impacts of the energy management. By considering the nexus of energy, water and food, energy management policies may be redefined and differences with current policies must be investigated.

Privacy‐cost trade‐offs in smart electricity metering systems

Trade-offs between privacy and cost are studied for a smart grid consumer, whose electricity consumption is monitored in almost real time by the utility provider (UP) through smart meter (SM) readings. It is assumed that an electrical battery is available to the consumer, which can be utilised both to achieve privacy and to reduce the energy cost by demand shaping. Privacy is measured via the mean squared distance between the SM readings and a target load profile, while time-of-use pricing is considered to compute the cost incurred. The consumer can also sell electricity back to the UP to further improve the privacy-cost trade-off. Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the target load profile is characterised. A more practical EMP, which optimises the energy management less frequently, is also considered. Numerical results are presented to compare the privacy-cost trade-off of these EMPs, considering various privacy indicators.