We formulated a problem faced by a power producer who owns a combined-cycle gas turbine (CCGT) and desires to maximize its expected profit in a medium-term planning horizon. We assumed that this producer can participate in the spot and over-the-counter markets to buy and sell natural gas and electricity. We also considered that the power producer has gas storage available that can be used for handling the availability of gas and the uncertainty of gas prices. A stochastic programming model was used to formulate this problem, where the electricity and gas prices were characterized as stochastic processes using a set of scenarios. The proposed model includes the technical constraints resulting from the operation of the combined cycle power plant and the gas storage and a detailed description of the different markets in which the power producer can participate. Finally, the performance of the proposed model is tested in a realistic case study. The numerical results show that the usage of... [more]

This paper addresses the planning and deployment of wireless heterogeneous networks (WHNs) for smart metering, based on a cross-layer solution. We combine the constraints of the network layer that considers routing and flow demands at each link in the WHN, while at the same time, we account for the restrictions of the physical layer referred to the capacity of a short range technology when used in a multi-hop fashion. We propose a model based on a column generation approach to solve the capacitated multicommodity flow problem (CMCF); the model includes wireless links capacities, coverage, and cost. The work integrates the multi-hop routing of packets in a mesh network formed by smart meters and concentrators connected to a cellular network via base stations. The traffic of each link is represented in a multigraph with the occupation percentage, and we build a scalable routing tree on a georeferenced map to represent a real deployment. The results describe the behavior of the proposed m... [more]

Considering that the world transport sector is the second largest contributor of global greenhouse gas (GHG) emissions due to energy use and the least decarbonized sector, it is highly recommended that all countries implement ambitious public policies to decarbonize this sector. In Mexico the transport sector generates the largest share of greenhouse gas emissions, in 2014 it contributed with 31.3% of net emissions. Two original scenarios for the Mexican transport sector, a no-policy baseline scenario (BLS) and a low carbon scenario (LCS) were constructed. In the LCS were applied 21 GHG mitigation measures, which far exceeds the proposals for reducing transport sector GHG emissions that Mexico submitted in its National Determined Contributions (NDC). As a result, the proposed LCS describes a sector transformation path characterized by structural changes in freight and passenger mobility, new motor technologies for mobility, introduction of biofuels, price signals, transportation practi... [more]

Proper planning of the installation of Battery Energy Storage Systems (BESSs) in distribution networks is needed to maximize the overall technical and economic benefits. The limited lifetime and relatively high cost of BESSs require appropriate decisions on their installation and deployment, in order to make the best investment. This paper proposes a comprehensive method to fully support the BESS location and sizing in a low-voltage (LV) network, taking into account the characteristics of the local generation and demand connected at the network nodes, and the time-variable generation and demand patterns. The proposed procedure aims to improve the overall network conditions, by considering both technical and economic aspects. An original approach is presented to consider both the planning and scheduling of BESSs in an LV system. This approach combines the properties of metaheuristics for BESS sizing and placement with a greedy algorithm to find viable BESS scheduling in a relatively sho... [more]

The optimal planning of electric vehicle charging infrastructure has attracted extensive research interest in recent years. Most of the optimization problems were formulated by assuming that the configurations will be fixed at the optimal solution while overlooking the fact that the charging stations and the electric vehicles are “evolving” over time and have mutual impacts. On the other hand, little attention has been paid to evaluate the performance of the solutions in such a dynamic environment. Motivated by these gaps, this work develops a simulation model that captures the interactions between charging station configurations and electric vehicle population (and the preference of electric vehicles when choosing charging station). This modeling framework is then implemented to evaluate the performance of planned charging infrastructure in providing services to electric vehicles. Two indicators are calculated, i.e., usage rate and rejection rate. The former measures the “waste” due t... [more]

Power transmission (PT) projects are vital for the power sector. However, worldwide PT projects experience delay. There is an urgent need to understand the unique causes of delays in PT projects. This paper presents the first empirical study on causes of delays in PT projects via a comprehensive literature review. Based on this literature review, 63 potential delay factors are identified and divided into ten major groups. These include two new groups of delay attributes, comprising sector-specific factors (SSF) and general factors (GF), where SSF pertains solely to PT projects and GF contributes to minimizing the bias of project participants. A questionnaire survey of 311 PT stakeholders is conducted to determine the overall ranking of the delay factors using the relative importance index. The results indicate that SSF, GF, and external/unavoidable factors are the most critical groups of delay factors, with the top-ranked factors being right of way problems of transmission line (TL), f... [more]

The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewa... [more]

Exposure to extreme weather conditions increases power systems’ vulnerability in front of high impact, low probability contingency occurrence. In the post-restructuring years, due to the increasing demand for energy, competition between electricity market players and increasing penetration of renewable resources, the provision of effective resiliency-based approaches has received more attention. In this paper, as the major contribution to current literature, a novel approach is proposed for resiliency improvement in a way that enables power system planners to manage several resilience metrics efficiently in a bi-objective optimization planning model simultaneously. For demonstration purposes, the proposed method is applied for optimal placement of the thyristor controlled series compensator (TCSC). Improvement of all considered resilience metrics regardless of their amount in a multi-criteria decision-making framework is novel in comparison to the other previous TCSC placement approach... [more]

The optimal location and size of charging stations are important considerations in relation to the large-scale application of electric vehicles (EVs). In this context, considering that charging stations are both traffic service facilities and common electric facilities, a multi-objective model is built, with the objectives of maximizing the captured traffic flow in traffic networks and minimizing the power loss in distribution networks. There are two kinds of charging stations that are considered in this paper, and the planning of EV charge stations and distribution networks is jointly modelled. The formulated multi-objective optimization problem is handled by a fuzzy membership function. The genetic algorithm (GA) is used to solve the objective function. In case studies, a 33-node distribution system and a 25-node traffic network are used to verify the effectiveness of the proposed model. The location and capacity of two kinds of charging stations are designed in the case studies, aft... [more]

In generation expansion planning, sustainable generation expansion planning is gaining more and more attention. Based on the comprehensive consideration of generation expansion planning economics, technology, environment, and other fields, this paper analyzes the sustainable development of power supply planning evaluation indicators and builds a multi-objective generation expansion planning decision model considering sustainable development. According to the target variables in the model, the variables such as attribute variables are divided into different subsets, and the logical relationship analysis method between different nodes is obtained based on Dynamic Bayesian network theory, which reduces the complexity of the planning model problem. The application examples show the feasibility and effectiveness of the proposed model and the solution method.

To change an increasing trend of energy consumption, many counties have turned to solar thermal energy as a solution. Without greenhouse gas emissions, solar thermal power stations may play a vital role in the energy industry because they have a potential to produce electricity for 24 h per day. The goal of this study is to select solar thermal power stations from three regions (i.e., the United States, Spain and the other nations) throughout the world and to identify which region most efficiently produces solar thermal power energy. To measure their efficiencies, we use data envelopment analysis as a method to examine the performance of these power stations. Our empirical results show that the United States currently fields the most efficient solar thermal power stations. This study also finds that parabolic trough technology slightly outperforms the other two technologies (i.e., heliostat power tower and linear Fresnel reflector), but not at the level of statistical significance. In... [more]

Traditional energy systems were planned and operated independently, but the diffusion of distributed and renewable energy systems led to the development of new modeling concepts, such as the energy hub. The energy hub is an integrated paradigm, based on the challenging idea of multi-carrier energy systems, in which multiple inputs are conditioned, converted and stored in order to satisfy different types of energy demand. To solve the energy hub optimal scheduling problem, uncertainty sources, such as renewable energy production, price volatility and load demand, must be properly considered. This paper proposes a novel methodology, based on extended Affine Arithmetic, which enables the solving of the optimal scheduling problem in the presence of multiple and heterogeneous uncertainty sources. Realistic case studies are presented and discussed in order to show the effectiveness of the proposed methodology.

Liner shipping is a vital component of the world trade. Liner shipping companies usually operate fixed routes and announce their schedules. However, disruptions in sea and/or at ports affect the planned vessel schedules. Moreover, some liner shipping routes pass through the areas, designated by the International Maritime Organization (IMO) as emission control areas (ECAs). IMO imposes restrictions on the type of fuel that can be used by vessels within ECAs. The vessel schedule recovery problem becomes more complex when disruptions occur at such liner shipping routes, as liner shipping companies must comply with the IMO regulations. This study presents a novel mixed-integer nonlinear mathematical model for the green vessel schedule recovery problem, which considers two recovery strategies, including vessel sailing speed adjustment and port skipping. The objective aims to minimize the total profit loss, endured by a given liner shipping company due to disruptions in the planned operation... [more]

Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt. Finally, based on the improved artificial potential field, a collision-free path for autonomous driving vehicles is generated. To verify the performance of the proposed algorithm, co-simulation and real vehicle tests are carried out. Results show that the generated path satisfies the constraints of roads, dynamics, and kinematics. The real time performance, effectiveness, and feasibility of the proposed path planning approach for obstacle avoidance scenarios are a... [more]

Fossil fuels have been heavily exploited since the Industrial Revolution. The resulting carbon emissions are widely regarded as being the main cause of global warming and climate change. Key mitigation technologies for reducing carbon emissions include carbon capture and storage (CCS) and renewables. According to recent analysis of the International Energy Agency, renewables and CCS will contribute more than 50% of the cumulative emissions reductions by 2050. This paper presents a new mathematical programming model for multi-footprint energy sector planning with CCS and renewables deployment. The model is generic and considers a variety of carbon capture (CC) options for the retrofit of individual thermal power generation units. For comprehensive planning, the Integrated Environmental Control Model is employed in this work to assess the performance and costs of different types of power generation units before and after CC retrofits. A case study of Taiwan’s energy sector is presented t... [more]

This paper presents a mixed receding horizon control (RHC) strategy for the optimal scheduling of a battery energy storage system (BESS) in a hybrid PV and wind power plant while satisfying multiple operational constraints. The overall optimisation problem was reformulated as a mixed-integer linear programming (MILP) problem, aimed at minimising the total operating cost of the entire system. The cost function of this MILP is composed of the profits of selling electricity, the cost of purchasing ancillary services for undersupply and oversupply, and the operation and maintenance cost of each component. To investigate the impacts of day-ahead and hour-ahead forecasting for battery optimisation, four forecasting methods, including persistence, Elman neural network, wavelet neural network and autoregressive integrated moving average (ARIMA), were applied for both day-ahead and hour-ahead forecasting. Numerical simulations demonstrated the significant increased efficiency of the proposed mi... [more]

As the penetration of intermittent renewable energy increases and unexpected market behaviors continue to occur, new challenges arise for system operators to ensure cost effectiveness while maintaining system reliability under uncertainties. To systematically address these uncertainties and challenges, innovative advanced methods and approaches are needed. Motivated by these, in this paper, we consider an energy integrated system with renewable energy and pumped-storage units involved. In addition, we propose a data-driven risk-averse two-stage stochastic model that considers the features of forbidden zones and dynamic ramping rate limits. This model minimizes the total cost against the worst-case distribution in the confidence set built for an unknown distribution and constructed based on data. Our numerical experiments show how pumped-storage units contribute to the system, how inclusions of the aforementioned two features improve the reliability of the system, and how our proposed d... [more]

Energy systems in cities need to be decarbonized and are becoming more integrated via energy sector coupling. Today, cities often use simple methods to assess their low carbon targets, e.g., carbon calculators, and these methods use annualized carbon reduction potentials. For example, reductions from heat savings in buildings or fuel demand in transport. This is done because it is simple and fast. This paper describes a methodology that goes beyond carbon calculators and assesses highly renewable energy systems. The methodology is carried out for a case city—Sønderborg, Denmark. Using a national 100% renewable energy study and a suitable energy system analysis tool (EnergyPLAN), the method accounts for inter-sector coupling and energy system dynamics. The energy system is assessed by comparing the results from the analysis tool against numerous key sustainability factors for a Smart Energy System. The paper illustrates how the method delivers a sustainable 100% renewable Smart Energy S... [more]

The optimal scheduling of multi-energy hub systems plays an important role in the safety, stability, and economic operation of the system. However, due to the strong uncertainty of renewable energy access, serious coupling, and the interaction among energy hubs of multi-energy hub systems, it is difficult for the traditional optimal scheduling method to solve these problems. Therefore, game theory was used to solve the optimal scheduling problem of multi-energy hub systems. According to the internal connection mode and energy conversion relationship of energy hubs, along with the competitive and cooperative relationship between multi-energy hubs, the game theoretic optimal scheduling model of the multi-energy hub system was established. Then, two cases and 50 groups of wind speed series were used to test the robustness of the proposed method. Simulation results show that the total power injection is −16,805.8, 104.1847, and −865.561 and the natural gas injection is 46,046.81, 27,727.65... [more]

The large-scale cascade hydropower plants in southwestern China now challenge a multi-market environment in the new round of electricity market reform. They not only have to supply the load for the local provincial market, but also need to deliver electricity to the central and eastern load centers in external markets, which makes the generation scheduling much more complicated, with a correlated uncertain market environment. Considering the uncertainty of prices and correlation between multiple markets, this paper has proposed a novel optimization model of long-term generation scheduling for cascade hydropower plants in multiple markets to seek for the maximization of overall benefits. The Copula function is introduced to describe the correlation of stochastic prices between multiple markets. The price scenarios that obey the Copula fitting function are then generated and further reduced by using a scenario reduction strategy that combines hierarchical clustering and inconsistent valu... [more]

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is th... [more]

This study examined the association between digital transformation (DT), supply chain integration (SCI), and overall sustainable supply chain performance (OSSCP). The current literature has preliminarily explored the concepts of DT and SCI and their relationship with sustainable supply chain performance. However, real empirical evidence of the direct impact of DT and SCI on OSSCP has been missing so far. To fill this gap, data were collected from 134 professionals working in international manufacturing companies operating in Morocco through a questionnaire-based survey from August 2022 to November 2022. A conceptual framework was developed based on DT, SCI, and OSSCP and analyzed by partial least squares structural equation modeling (PLS-SEM) with the assistance of SmartPLS 4.0 software. The findings revealed that DT has a significant positive influence on SCI and OSSCP. Furthermore, SCI directly and positively impacts OSSCP with a partial mediation effect on the relationship between D... [more]

The aim of the article is the development of methods for optimal overhaul planning of compressor station equipment. Nowadays, due to uncertainties in the forecast of gas supply flow rates, increasing the reliability and energy efficiency of main gas pipelines is an urgent problem. The dependence of operating costs for major repairs on the maintenance periodicity is extreme. Reducing equipment’s maintenance period leads to an increase in repair costs. It also increases the reliability of equipment operation. Overall, all these facts reduce the probability of emergency failures and related expenses for emergency recovery, gas losses, and undersupply to consumers. Therefore, an optimal maintenance frequency exists, at which the total operating costs will be minimal. A procedure for optimizing the periodicity of repairs and equipment replacement is proposed. It was realized by constructing an objective function as a dependence of exploitation costs on the inter-repair period of major repai... [more]

The policy decision of extending electric power transmission lines to connect a remote area to a primary grid vs. developing local electricity generation resources must be informed by studies considering both alternatives’ economic and environmental outcomes. Such analysis must also consider the uncertainty of several factors such as fuel prices, the cost and performance of renewable and conventional power generation technologies, and the value of environmental benefits. This paper presents a method for this analysis, making two main contributions to the literature. First, it shows how to characterize the two alternatives (i.e., main-grid extension vs. local power generation) in detail for precise quantification of their capital and operating costs while guaranteeing that they are both adequate to meet forecast demand and operating reserves. Second, it shows how to properly account for the economic and environmental implications of renewable energy intermittency and uncertainty through... [more]

This article aims to determine the specificity of spatial conflicts related to spatial plans concerning wind power plants. To achieve the aim of the article, all spatial plans in force in Poland were analysed, distinguishing those which determine the possibility of realisation of wind power plants. The research concerns the whole country. The literature review carried out for this article verifies approaches to spatial conflicts and identifies how planning barriers to the implementation of wind power investments are defined. The results identified Polish municipalities where spatial plans containing provisions for implementing wind power plants have been enacted. Then, through survey research, an attempt was made to identify critical spatial conflicts occurring in these municipalities. The last part of the research involved identifying and analysing Polish court decisions concerning spatial plans permitting wind power plants. These were recognised as a particular stage of spatial confl... [more]