Due to the inflexibility of cogeneration power plants and the uncertainty of wind power production, the excess power of the distribution network brings challenges to the power grid operation. This paper introduced an improved moth-flame optimization algorithm to meet the challenge of energy complementary dispatching. The proposed algorithm adopts three effective strategies, namely inertia weight, unified initialization, and the spiral position update strategy, which maintains a strong global search ability and a potent compromise between global and local search. The effectiveness of the proposed method was evaluated by benchmark functions. Furthermore, the proposed method was applied to combine heat and power system operation problems and economic dispatch in light load and wind power unpredictability. In order to verify the robustness of the algorithm and solve the complex constraints of power systems under extreme conditions, three different cases had been discussed. The experimental... [more]

A combined heating and power (CHP) microgrid has high flexibility and economy, but the output of renewable energy is uncertain. Meanwhile, excessive flexible load adjustment in the demand response process will increase user dissatisfaction. In order to solve the above problems, this paper quantifies uncertainty with the conditional value-at-risk (CVaR) of relative disturbance. Additionally, a multi-objective optimal scheduling model that takes into account both the operating economy and the demand-side power consumption satisfaction is established. In order to solve the multi-objective mixed-integer nonlinear programming problem well, we propose an improved sparrow search algorithm (ISSA), which solves the problem that the sparrow search algorithm (SSA) is prone to low accuracy, insufficient in population diversity and easy to be trapped in local optimum. Combined with the non-dominated solution ranking method, ISSA has the ability of multi-objective optimization. Finally, simulation o... [more]

Work processes and sociological structures can differ significantly between organizations. These organizational aspects determine user behavior, which in turn exerts considerable influence on the key performance indicators of artificial lighting systems. Accordingly, the use of generalized assumptions about user behavior in the building design phase can lead to large discrepancies between design and operation. In the following work, the possible influences of different occupancy schemes, an essential aspect of user behavior and shaped by the organization, on energy demand and workplace-related daylight dose are evaluated. For this purpose, calculations are made based on real measurement data of an open-plan office with zoned lighting. Multi-level calculation models are used to determine improved user distributions in the room to ensure optimization according to the desired target criteria. The results show that occupancy schemes have a significant impact on energy demand, contributing... [more]

In the context of new power systems, reasonable capacity optimization of multiple power systems can not only reduce carbon emissions, but also improve system safety and stability. This paper proposes a situation awareness-based capacity optimization strategy for wind-photovoltaic-thermal power systems and establishes a bi-level model for system capacity optimization. The upper-level model considers environmental protection and economy, and carries out multi-objective optimization of the system capacity planning solution with the objectives of minimizing carbon emissions and total system cost over the whole life cycle of the system, further obtaining a set of capacity planning solutions based on the Pareto frontier. A Pareto optimal solution set decision method based on grey relativity analysis is proposed to quantitatively assess the comprehensive economic−environmental properties of the system. The capacity planning solutions obtained from the upper model are used as the input to the... [more]

Nowadays the utilization of Electric Vehicles (EVs) has greatly increased. They are attaining greater attention due to their impacts on the grid at the distribution level. However, due to the increased need for electricity, EVs are also used to serve the load in the instance of electrical failure in the distribution systems. This paper presents a new approach to a service restoration method for a low-voltage distribution network at the time of a power outage using existing EVs available in a parking place. The objective function formulated here was a constrained linear optimization model. It aimed to develop priority-based scheduling of the residential user appliances while meeting all the operational constraints if the EV’s power was in a deficit at the hour of the outage. Weight factors were assigned to various residential appliances to decide their priority while scheduling. To substantiate the proposed methodology, a day load profile of a 20 kVA distribution transformer feeding eig... [more]

Following the recent European directives highlighting the need to increase energy efficiency in the European Union, this work aims to show the possibility of using Non-Intrusive Load Monitoring (NILM) techniques to improve energy audits by estimating the compressed air leakage from a dataset of a tertiary building. The first step towards the reduction of energy consumption is performing an energy audit, in which a detailed analysis of the energy performance is executed. This analysis usually uses on-site measured data by the auditors. However, the time available for these measurements is limited and may not include some modes of operation. One example of that is the quantification of compressed air leaks. This task can be performed by estimating the flow rate during a no compressed air consumption period. However, these periods may not coincide with the auditors’ original schedule. This problem could be addressed by using historical data. Nevertheless, historical data from energy manag... [more]

Floating storage and regasification units (FSRU) provide a flexible and competitive energy distribution option when it comes to the regasification of liquefied natural gas (LNG). FSRU projects have become more and more popular, attracting the interest of investors, energy authorities, and governments; therefore, the project feasibility in terms of risks and profitability are a major concern. This paper deals with the appraisal of a greenfield LNG infrastructure project, where usually, decision complexity deals with the high number and different expectation of stakeholders, the capital-intensive financing nature, and the business risks in the project life cycle. Conventional wisdom is to provide a coherent, compact, and well-structured appraisal modelling framework, adjusted to FSRU technical, structural, and operational features on one hand; and business risks, long-term life cycle, and investment attractiveness on the other. Appraisal modelling structure and outputs are considered to... [more]

The ongoing COVID-19 pandemic has disrupted global economic activity in all sectors, including forest industries. Changes in demand for forest products in North America over the course of the pandemic have affected both primary processors and downstream industries reliant on residues, including wood pellet producers. Wood pellets have become an internationally traded good, mostly as a substitute for coal in electricity generation, with a significant proportion of the global supply coming from Canadian producers. To determine the effect of the COVID-19 pandemic on the Canadian wood pellet industry, economic and market data were evaluated, in parallel with a survey of Canadian manufacturers on their experiences during the first three waves of the pandemic (March 2020 to September 2021). Overall, the impact of the pandemic on the Canadian wood pellet industry was relatively small, as prices, exports, and production remained stable. Survey respondents noted some negative impacts, mostly in... [more]

This study examines the long-term energy capacity investment problem of a power generation company (GenCo), considering the drought threat posed by climate change in hydropower resources in Turkey. The mid-term planning decisions such as maintenance and refurbishment scheduling of power plants are also considered in the studied investment planning problem. In the modeled electricity market, it is assumed that GenCos conduct business in uncertain market conditions with both bilateral contracts (BIC) and day-ahead market (DAM) transactions. The problem is modeled as a fuzzy mixed-integer linear programming model with a fuzzy objective and fuzzy constraints to handle the imprecisions regarding both the electricity market (e.g., prices) and environmental factors (e.g., hydroelectric output due to drought). Bellman and Zadeh’s max-min criteria are used to transform the fuzzy capacity investment model into a model with a crisp objective and constraints. The applicability of methodology is il... [more]

Nonlinear characteristics of a battery energy storage system (BESS) may cause errors in the stored energy between the operation plan and the actual operation. These errors may hinder the reliability of the power system especially in environments such as microgrids with limited power generation resources and high uncertainty. This study proposes a method to alleviate the occurrence of such errors in the charging/discharging scheduling process of the BESS by piecewise linearizing its nonlinear characteristics. Specifically, the stored energy in a BESS that changes nonlinearly according to the size of the charging/discharging power was modeled using the special ordered set of the type 2 (SOS2) method. The proposed model and the typical BESS-operation models with constant power conditioning system (PCS) input/output power efficiency were applied to the unit commitment (UC) problem in a microgrid environment, and the results were compared with the actual operation results. The proposed mode... [more]

The coexistence of the need to improve economic conditions and the conscious use of environmental resources plays a central role in today’s sustainable development challenge. In this study, a novel integrated framework to evaluate the impact of new technological interventions is presented and an application to smallholder coffee farms and their supply chains in Kenya is proposed. This methodology is able to combine multiple information through the joint use of three approaches: supply chain analysis, input-output analysis, and energy system modeling. Application to the context of the Kenyan coffee sector enables framework validation: shading management measures, the introduction of eco-pulpers, and the exploitation of coffee waste biomass for power generation were compared within a holistic high-level perspective. The implementation of shading practices, carried out with fruit trees, shows the most relevant effects from the economic point of view, providing farmers with an additional s... [more]

Energy-environmental planning for road transportation involves a vast investigation of vehicles’ technologies and electricity production. However, in developing countries where the public transportation sector is growing quickly, energy-environmental planning is urgently needed. This paper evaluates the future electricity demand, as well as fuel consumption and CO2 emissions reduction, due to the operation of an expected increasing number of electric vehicles (EVs) in Pakistan. The planning of EVs up to 2040 is performed with the ePop simulator that calculates the future EVs’ electricity demand, while EnergyPLAN® assesses the expected new power capacities. Two scenarios are investigated by penetrating 30% and 90% of 2/3 electric wheelers and cars by 2030 and 2040 compared to 2020, respectively. To fulfill the expected energy demand, PV in the daytime and the national electric grid at nighttime are here considered. Finally, a 9 GW of PV capacity is needed to satisfy the EVs’ electricity... [more]

In the present paper, a tool is proposed to optimally schedule the charging requests of a fleet of carsharing Electric Vehicles (EVs) in an urban area, to enable their participation in the Ancillary Service Market. The centralized scheduler minimizes the imbalance of an EV fleet with respect to the power commitment declared in the Day-Ahead Market, providing also tertiary reserve and power balance control to the grid. The regulation is carried out by optimizing the initial charging time of each vehicle, according to a deadline set by the carsharing operator. To this purpose, a nature-inspired optimization is adopted, implementing innovative hybridizations of the Artificial Bee Colony algorithm. The e-mobility usage is simulated through a topology-aware stochastic model based on carsharing usage in Milan (Italy) and the Ancillary Services requests are modeled by real data from the Italian electricity market. The numerical simulations performed confirmed the effectiveness of the approach... [more]

Castor bean (Ricinus communis L.) is a promising industrial crop suitable for cultivation in marginal conditions in the Mediterranean area, but the mechanical harvesting of the seeds is still usually performed manually. In this manuscript, the authors present a preliminary test to assess the effectiveness of equipping a combine harvester with a sunflower header to mechanically harvest castor beans. Machinery performance, seed loss from impact (ISL) and cleaning systems (CSL), and seed cleaning were evaluated and compared with the results obtained from the same combine harvester equipped with a cereal header. According to the results, no statistically significant difference in CSL was found. Values ranged from 162. 41 kg dry matter (DM) ha−1 in the cereal header to 145.56 kg DM ha−1 in the sunflower header, corresponding, respectively, to 8% w/w and 7% w/w of the potential seed yield (PSY). Using the sunflower header significantly lowered ISL (158.16 kg DM ha−1, i.e., 8% w/w of PSY) in... [more]

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load foreca... [more]

The extensive adoption of electric vehicles (EVs) can introduce negative impacts on electric infrastructure in the form of sporadic and excessive charging demands, line overload, and voltage quality. Because EV loads can be movable around the system and time-dependent due to human daily activities, it is therefore proposed in this research to investigate the spatial effects of EV loads and their impacts on a power system. We developed a behavior-based charging profile simulation for daily load profiles of uncontrolled and controlled charging simulations. To mitigate the impact of increased peak demand, we proposed an optimal scheduling method by genetic algorithm (GA) using charging event matrices and EV queuing management. The charging event matrices are generated by capturing charging events and serve as an input of the GA-based scheduling, which optimally defines available charging slots while maximizing the system load factor while maintaining user satisfaction, depending on the we... [more]

This experiment was designed to study the water-weakening effect of artificially fractured chalk caused by the injection of different compositions of brines under reservoir conditions replicating giant hydrocarbon reservoirs at the Norwegian Continental Shelf (NCS). NaCl, synthetic seawater (SSW), and MgCl2, with same ionic strength, were used to flood triaxial cell tests for approximately two months. The chalk cores used in this experiment originate from the Mons basin, close to Obourg, Belgium (Saint Vast Formation, Upper Cretaceous). Three artificially fractured chalk cores had a drilled central hole parallel to the flooding direction to imitate fractured chalk with an aperture of 2.25 (±0.05) mm. Two additional unfractured cores from the same sample set were tested for comparison. The unfractured samples exposed a more rapid onset of the water-weakening effect than the artificially fractured samples, when surface active ions such as Ca2+, Mg2+ and SO42− were introduced. This instan... [more]

The topic of power loss reduction in distribution systems has gained significant attention over recent years. Despite the efforts of the European Union towards the minimization of power losses, the decarbonization of the transport sector has raised several concerns, since charging overlaps of Electric Vehicles (EVs) can cause extensive power losses and power quality issues. Considering these, the present paper proposes a two-stage EV charging planning and Network Reconfiguration (NR) methodology, addressing the problem of power loss minimization in both Low-Voltage (LV) and Medium-Voltage (MV) Distribution Networks (DNs), respectively. In the first stage, considering the key role of the aggregator, the EV charging planning is applied to LV DN. In the second stage, the NR technique is applied to the MV DN, by taking into account the hourly power demand of LV DNs as obtained by the aggregators. The proposed methodology has been applied on a benchmarked MV network for which each node is r... [more]

This article presents an energy-efficient method of controlling unmanned aircraft (fixed-wing UAVs), which consists of three groups of algorithms: aerial vehicle route planning, in-flight control, and algorithms to correct the preplanned flight trajectory. All algorithms shall take into account the existence of obstacles that the UAV must avoid and wind gusts in the UAV’s area of operation. Tests were carried out on the basis of the UAV mathematical model, stabilization and navigation algorithms, and Dryden turbulence model, considering the parameters of the UAV’s propulsion system. The work includes a detailed description of constructing a network of connection that is used to plan a UAV mission. It presents the algorithm for determining the actual distances between the different points in the field of action, which takes into account the existence of obstacles. The algorithm shall be based on methods for determining the flight trajectory on a hexagonal grid. It presents the developed... [more]

In this work, we present a three-stage multiobjective mixed-integer linear programming (MILP) for the optimal expansion planning and operation of isolated multienergy microgrids in remote areas. By selecting the optimal distributed generators (DGs) and energy storage systems (ESSs) mix selection, siting, sizing, and scheduling in the remote microgrid, the proposed model is targeted to minimize the annualized total cost of microgrids while enhancing the performance of the system, i.e., minimizing the voltage deviations and line power loss. To represent the electricity and heat flow between generation resources and various electrical, heating, and cooling loads in the isolated microgrid, linearized power flow, and heat flow constraints are employed in the proposed optimization model. The available capacity of DGs and ESSs are modeled as discrete constants instead of continuous variables for practical purpose. Numerical simulation results on a remote microgrid consisting of DGs, ESSs, and... [more]

The shift towards renewable energy and decreasing battery prices have led to numerous installations of PV and battery systems in industrial and public buildings. Furthermore, the fluctuation of energy costs is increasing since energy sources based on solar and wind power depend on the weather situation. In order to reduce energy costs, it is necessary to plan energy-hungry activities while taking into account private PV production, battery capacity, and energy market prices. This problem was posed in the 2021 “IEEE-CIS Technical Challenge on Predict + Optimize for Renewable Energy Scheduling”. The target was to solve the two subtasks of forecasting the base load and of computing an optimal schedule of a list of energy intensive activities with inter-dependencies. We describe our approach to this challenge, which resulted in the third place of the leaderboard. For the prediction of the base load, we use a combination of a statistical and a machine learning approach. For the optimization... [more]

Advances in communication technologies and protocols among vehicles, charging stations, and controllers have enabled the application of scheduling techniques to prioritize EV fleet charging. From the perspective of users, residential EV charging must particularly address cost-effective solutions to use energy more efficiently and preserve the lifetime of the battery—the most expensive element of an EV. Considering this matter, this research addresses a residential EV charging scheduling model including battery degradation aspects when discharging. Due to the non-linear characteristics of charging and battery degradation, we consider a mixed integer non-linearly constrained formulation with the aim of scheduling the charging and discharging of EVs to satisfy the following goals: prioritizing charging, reducing charging costs and battery degradation, and limiting the power demand requested to the distribution transformer. The results shows that, when EVs are discharged before charging up... [more]

Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems will be on a quarter-hour basis, which will reduce computation time and increase the computational burden. Therefore, a predictive system may not dispose of sufficient time to compute all future forecasts. Prediction systems perform calculations throughout the day, calculating the same forecasts repeatedly as the predicted time approaches. However, there are forecasts that are no more accurate than others that have already been made. If previous forecasts are used preferentially over these, then computational burden will be saved while accuracy increases. In this way, it will be possible to optimize the schedule of future quarter-hour systems... [more]

During supply chain redesign processes, many environmental initiatives at facilities and their surrounding regions are affected by the closing, opening, or even broadening of facilities. These initiatives may be about the surplus renewable energies that the facility generates for its region, the renewable energy sources that are available in the area for the facility, or the ecological relationships between facilities and their surrounding regions in general. The implications of these ecological relationships for the supply chain redesign problem were considered in this study in order to minimize the negative outcomes while minimizing the process’s related expenses. Thus, to minimize the unwanted effects, an objective function is defined in addition to the cost objective, which results in a bi-objective problem. Although small cases can be solved by exact methods such as AUGMECON, for larger scales, a matheuristic algorithm needs to be developed. The efficiency of the developed algorit... [more]

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018−2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to invest... [more]