Internet of Things (IoT) and mobile edge computing (MEC) architectures are common in real-time application scenarios for improving the reliability of service responses. Energy conservation (EC) and energy harvesting (EH) are significant concerns in such architectures due to the self-sustainable devices and resource-constraint edge nodes. The density of the users and service requirements are further reasons for energy conservation and the need for energy harvesting in these scenarios. This article proposes decisive task scheduling for energy conservation (DTS-EC). The proposed energy conservation method relies on conditional decision-making through classification disseminations and energy slots for data handling. By classifying the energy requirements and the states of the mobile edge nodes, the allocation and queuing of data are determined, preventing overloaded nodes and dissemination. This process is recurrent for varying time slots, edge nodes, and tasks. The proposed method is foun... [more]

Energy management for multi-home installation of solar PhotoVoltaics (solar PVs) combined with Electric Vehicles’ (EVs) charging scheduling has a rich complexity due to the uncertainties of solar PV generation and EV usage. Changing clients from multi-consumers to multi-prosumers with real-time energy trading supervised by the aggregator is an efficient way to solve undesired demand problems due to disorderly EV scheduling. Therefore, this paper proposes real-time multi-home energy management with EV charging scheduling using multi-agent deep reinforcement learning optimization. The aggregator and prosumers are developed as smart agents to interact with each other to find the best decision. This paper aims to reduce the electricity expense of prosumers through EV battery scheduling. The aggregator calculates the revenue from energy trading with multi-prosumers by using a real-time pricing concept which can facilitate the proper behavior of prosumers. Simulation results show that the pr... [more]

Short-term hydrothermal scheduling problem plays an important role in maintaining a high degree of economy and reliability in power system operational planning. Since electric power generation from fossil fired plants forms a major part of hydrothermal generation mix, therefore their emission contributions cannot be neglected. Hence, multi-objective short term hydrothermal scheduling is formulated as a bi-objective optimization problem by considering (a) minimizing economical power generation cost, (b) minimizing environmental emission pollution, and (c) simultaneously minimizing both the conflicting objective functions. This paper presents a non-dominated sorting disruption-based oppositional gravitational search algorithm (NSDOGSA) to solve multi-objective short-term hydrothermal scheduling (MSHTS) problems and reveals that (i) the short-term hydrothermal scheduling problem is extended to a multi-objective short-term hydrothermal scheduling problem by considering economical productio... [more]

Future European strategies to reduce dependence on foreign markets for energy supply and energy production will rely on the further exploitation of the primary sector. Lignocellulosic feedstock for bioenergy production is a valuable candidate, and dedicated crops such as giant reed (Arundo donax L.), miscanthus (Miscanthus × giganteus), reed canary grass (Phalaris arundinacea L.), and switchgrass (Panicum virgatum L.) have been proven to be suitable for extensive cultivation on marginal lands. The present review aimed at providing a comprehensive picture of the mechanical strategies available for harvesting giant reed, miscanthus, reed canary grass, and switchgrass that are suitable for the possible upscaling of their supply chain. Since harvesting is the most impactful phase of a lignocellulosic supply chain in dedicated crops, the associated performance and costs were taken into account in order to provide concrete observations and suggestions for future implementation. The findings... [more]

The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energy consumption controls and planning. Non-intrusive load monitoring (NILM) is the analytical method used to monitor the energy and disaggregate total electrical usage into appliance-related signals as an alternative to installing multiple electricity meters in the building. However, despite considerable progress, there are a limited number of tools dedicated to the problem of reliable and complete energy disaggregation. This paper presents an experiment consisting in designing an electrical system with electrical energy receivers, and then starting NILM disaggregation using machine learning algorithms (MLA). The quality of this disaggregation was assessed using dedicated indicators. Subsequently, the quality of these MLA was also verified us... [more]

Combined heat and power (CHP) is crucial for promoting thorough energy conservation and advanced energy use, aimed toward greenhouse gas reduction. Solid oxide fuel cell (SOFC)-CHP is expected to be introduced as a measure against global warming and has been the focus of attention, and this study examined the effects of its introduction. This study introduces a linear programming evaluation model that can simulate optimized facility configuration and operation, based on the power supply and demand. The novelty of the proposed model is the consideration of geographic characteristics, which influences parameters dependent on gas transportation infrastructure and electricity. A sensitivity analysis was conducted considering the number of units and location of SOFC-CHP introductions in the National Capital Region of Japan. As a result, it was predicted that SOFC-CHP would likely begin to be introduced in areas where there is a large shadow price difference between electricity and gas at ea... [more]

The decarbonisation of the building sector is crucial for Portugal’s goal of achieving economy-wide carbon neutrality by 2050. To mobilize communities towards energy efficiency measures, it is important to understand the primary drivers and barriers that must be overcome through policymaking. This paper aims to review existing Energy Policies and Actions (EPA) in Portugal and assess their effectiveness in improving Energy Efficiency (EE) and reducing CO2 emissions in the building sector. The Local Energy Planning Assistant (LEPA) tool was used to model, test, validate and compare the implementation of current and alternative EPAs in the North of Portugal, including the national EE plan. The results indicate that electrification of heating and cooling, EE measures, and the proliferation of Renewable Energy Sources (RES) are crucial for achieving climate neutrality. The study found that the modelling of alternative EPAs can be improved to reduce investment costs and increase Greenhouse G... [more]

The goal of this paper is to use the variable returns to scale (VRS)-slacks-based measure (SBM)-data envelopment analysis (DEA) method to compare the energy resilience of different economies and areas. This study looks at the energy resilience scores of 26 economies from Europe, the Americas, and the Asia-Pacific area. It does this by looking at twelve sub-indicators in three dimensions: society, the economy, and the environment. According to the computational results, seventeen of these economies’ total energy resilience achieved top-tier performance. South Korea, ranked 18th, is only second to these seventeen economies and is followed by, among others, Turkey, Luxembourg, Poland, Italy, Belgium, the Slovak Republic, the Czech Republic, and Hungary. Twelve of the twenty European economies, all three American economies, and two Asia-Pacific economies are relatively energy-resilient. There are sixteen economies in society dimensions, seventeen economies in economy dimensions, and sevent... [more]

With the increasing integration of HVDC tie-lines, the regional power systems in both the energy-exporting area and the energy-importing area have been gradually evolving into “strong DC, weak AC” systems. In this paper, a multi-period transmission expansion planning optimization model is proposed for an energy-exporting power grid with hybrid AC/DC interface. While the existing literature has not considered the dynamic security problem in TEP, this paper adopts the conventional total transfer capacity (TTC) index to evaluate the security limit of hybrid AC/DC interface under different transmission expansion schemes. Multiple objectives are considered to reduce the investment cost while promoting the consumption of renewables by enhancing the total transfer capacity of hybrid AC/DC interface. The non-dominated sorting genetic algorithm-II (NSGA-II) is used to compute the optimal solution for the proposed multi-period multi-objective transmission expansion planning problem. A case study... [more]

With the gradual depletion of fossil energy sources and the improvement in environmental protection attention, efficient use of energy and reduction in carbon emissions have become urgent issues. The integrated electricity and heating energy system (IEHS) is a significant solution to reduce the proportion of fossil fuel and carbon emissions. In this paper, a stochastic optimization model of the IEHS considering the uncertainty of wind power (WP) output and carbon capture power plants (CCPs) is proposed. The WP output in the IEHS is represented by stochastic scenarios, and the scenarios are reduced by fast scenario reduction to obtain typical scenarios. Then, the conventional thermal power plants are modified with CCPs, and the CCPs are equipped with flue gas bypass systems and solution storage to form the integrated and flexible operation mode of CCPs. Furthermore, based on the different load demand responses (DRs) in the IEHS, the optimization model of the IEHS with a CCP is construct... [more]

In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cle... [more]

Several technical, computational, and economic barriers have caused curtailing a share of renewable-based power generation, especially in systems with higher penetration levels. The Mobile Battery Energy Storage (MBES) can cope with this problem considering the spatial and temporal distribution of the curtailed energy. Accordingly, a new operation model is proposed for optimal scheduling of the MBES in a distribution network with wind and photovoltaic (PV) resources. The network experiences curtailment situations because of bus overvoltage, feeder overload, and power over-generation. The MBES is a truck-mounted battery system compacted in a container. The proposed model seeks to determine the optimal spatio-temporal and power−energy status of the MBES to achieve a minimum curtailment ratio. The model considers transportation time and cost of the MBES efficiently while both active and reactive power exchanges are modeled. The model is linear, without convergence and optimality problems,... [more]

Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power consumption, respectively and discusses the influence of weather parameters and schedule parameters on the prediction accuracy. The results demonstrate that using the LSTM algorithm to accurately predict the electricity consumption of air conditioners is more challenging than predicting lighting electricity consumption. To improve the prediction accuracy of air conditioning power consumption, two... [more]

This article deals with the new challenges facing modernising railways in Poland. We look at the problem of the efficiency of the power supply system (3 kV DC) used in the context of the increasing use of electric vehicles, which have a higher demand for electricity than the old type. We present and characterise the power supply system in use, pointing out its weaknesses. We consider a case study. The load of the power supply network generated by the rolling stock used in Poland was examined using a microsimulation. A real train timetable was taken into account on a fragment of one of the most important railway line sections in one of the urban agglomerations. Then the results were compared with the results of a microsimulation in which old units were replaced by new trains. These tests were carried out in several variants. We found critical points in the scheduling of railway system use. Our results indicate that it is becoming increasingly necessary to take into account the permissib... [more]

Integrated energy systems have become an important research topic in the pursuit of sustainable energy development. This paper examines regional integrated energy systems, presents the typical architecture of regional integrated energy systems, and builds an integrated energy system model. Two evaluation indexes are proposed: the integrated energy self-sufficiency rate and the expected energy deficiency index. Based on these evaluation indexes and taking into account the uncertainty of wind power generation, a bi-level optimization model based on meta-heuristic algorithms and multi-objective programming is established to solve the problem of regional integrated energy system planning under different load structures and for multi-period and multi-scenario operation modes. A quantum evolutionary algorithm is combined with genetic algorithms to solve the problem.

Climate change and expected weather patterns in the long-term threaten the livelihood inside oases settlements in arid lands, particularly under the recurring heat waves during the harsh months. This paper investigates the impact of climate change on the outdoor thermal comfort within a multifamily housing neighborhood that is considered the most common residential archetype in Algerian Sahara, under extreme weather conditions in the summer season, in the long-term. It focuses on assessing the outdoor thermal comfort in the long-term, based on the Perceived Temperature index (PT), using simulation software ENVI-met and calculation model RayMan. Three different stations in situ were conducted and combined with TMY weather datasets for 2020 and the IPCC future projections: A1B, A2, B1 for 2050, and 2080. The results are performed from two different perspectives: to investigate how heat stress evolution undergoes climate change from 2020 till 2080; and for the development of a mathematica... [more]

This study aims to plan a cost-minimizing charging schedule for electric buses with fast charging stations. The paper conceptualizes the problem as a three-stage procedure, which is oriented around the participation of an electric bus aggregator in a day-ahead energy auction. First, the aggregation stage determines the bid parameters of buses. With bid parameters, aggregated cost-minimizing charging plans are obtained in the second stage conceived as the hourly day-ahead auction. The disaggregation of hourly plans into feasible minutely charging schedules is the third stage. The main contribution is the formulation of mixed-integer linear programming aggregation models to determine charging availability expressed as minimum and maximum hourly energy requirements taking into account detailed, minutely characteristics and constraints of the charging equipment and the buses. No price forecasts are required, and the plans adjust to the wholesale prices of energy. Defining only a few aggreg... [more]

The implementation of monitoring tools and energy management systems (EnMSs) supports companies in their long-term energy efficiency strategies, and they are essential to analyse the effectiveness of energy performance improvement actions (EPIAs). The first fundamental step towards increasing energy efficiency is the development of energy audits (EAs). EAs provide comprehensive information about the energy usage in a specific facility, identifying and quantifying cost-effective EPIAs. The crucial role of these tools in clean energy transition is remarked by the European Energy Efficiency Directive (EED), which promotes the implementation of EAs and EnMS programmes. The purpose of this work is to better understand the link between EnMSs (specifically ISO 50001) and EAs in the EED Article 8 implementation in two industrial and two tertiary sectors in Italy. Moreover, the impact of company size, energy monitoring systems, and EnMSs on planned and/or implemented EPIAs is analysed. Our find... [more]

The power system is changing towards a decarbonized one. The Kyoto protocol and the Paris climate agreement have prompted many nations to approve energy policies based on volatile renewable energy sources (RESs). However, the integration into the grid of the power generated by RESs as well as the electrification of the heating, gas and transportation sectors is becoming a huge challenge. Planning industrial and tertiary sites as net-zero energy systems (NZESs) might contribute to advance the solutions of fully integrating volatile RESs into the power system. This study aims to point out the importance of planning large energy consumer sites such as NZESs, and to depict a holistic modeling approach for this. The methodology is based on a multi-layer approach, which focuses on on-site power generation by RESs, on the improvement of energy efficiency, and on the increase of system flexibility. A qualitative case study has been conducted. It considers the planning of a Net-Zero Energy Data... [more]

Construction time and time overruns for infrastructure projects have been frequently studied; however, the construction time of power plants has not been studied. This lack of study is problematic, as more renewable energy power plants, such as wind and solar, are planned for many jurisdictions. Accurately estimating the construction time of a power plant will assist construction planning, budget estimates, and policy development encouraging the use of more renewable sources. The construction times of utility scale power plants in Canada were studied using publicly available data. Multiple linear regression analysis techniques were applied to the data to generate construction time estimation functions for all power plants together, and for individual technologies. The analyses reveal that construction time is sensitive to jurisdiction and the decade of construction, indicating that decisions made by individual Canadian provincial governments at different times had statistically signifi... [more]

Optimal scheduling strategy of integrated energy systems (IES) with combined cooling, heating and power (CCHP) has become increasingly important. In order to make the scheduling strategy fit to the practical implementation, this paper proposes a variable performance parameters temperature−flowrate scheduling model for IES with CCHP. The novel scheduling model is established by taking flowrate and temperature as decision variables directly. In addition, performance parameters are treated as variables rather than constants in the proposed model. Specifically, the efficiencies of the gas turbine and the waste heating boiler are estimated with the partial load factor, and the coefficient of performance (COP) of the electrical chillers and heat pumps are estimated with the partial load factor and outlet water temperature. Then, to deal with the model nonlinearities caused by considering the variability of COPs, the COP-expansion method is developed by adopting a specific representation of t... [more]

Energy firms are the beneficiaries and initiators of innovation, and energy investments are a crucial area of business activity that is specially protected in any country. This is no wonder, as energy security is the basis for the functioning of states and economies. The Internet of Things and Big Data create both new challenges and new threats. This study aimed to identify the potential threats and determine preventive measures, as well as to establish the agile principles related to energy firms’ logistics. The method of the narrative summary in combination with the literature searching method was used. Two conclusions emerged: first, research serves to develop the discipline of management science; second, the identification of risks associated with innovation serves practitioners. In addition, the study defined further research directions.

Power system operators are in search of proven solutions to improve the penetration levels of distributed generators (DGs) in the grid while minimizing cost. This transition is driven, among others, by global climate concerns, the growing power demand, the need for greater flexibility, the ageing grid infrastructure and the need to diversify sources of energy production. Distributed renewables would not easily substitute the conventional electric grid system, perhaps because the latter is a well-established technology and it would not be prudent to abandon it, while the new distributed renewable energy technologies are generally not adequately developed to support the total load. Thus, it is becoming increasingly necessary to consider sustainable options such as integrating renewable energy sources into the existing power grid. This study is a review that is mainly hinged on distributed generation (DG) classification, the challenges of DG to grid integration, practical options used in... [more]

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of E... [more]

The maximization of output from variable renewable energy (VRE) sources considering system operational constraints (SOCs) is a traditional method for maximizing VRE generators’ profits. However, in wholesale electricity markets, VRE participation tends to reduce marginal prices (MP) because of its low marginal costs. This circumstance, called the “merit-order effect” (MOE), reduces the generators’ profits. Thus, the traditional method is possibly no longer the best and only method to maximize the generators’ profits. Moreover, the VRE support schemes also affect MP, making MOE more severe. VRE curtailment can relieve MOE, but VRE output must be decreased, thereby reducing the generators’ profits. This paper proposes a method to find the optimal VRE generation schedules that maximize VRE generators’ profits while considering the trade-off among the VRE output, MP, and SOCs. The method combines the merit-order model and the unit-commitment model solved by the optimization tools in MATLAB... [more]