The definition of multiple slicing types in 5G has created a wide field for service innovation in communications. However, the advantages that network slicing has to offer remain to be fully exploited by today’s applications and users. An important area that can potentially benefit from 5G slicing is emergency communications for First Responders. The latter consists of heterogeneous teams, imposing different requirements on the connectivity network. In this paper, the RESPOND-A platform is presented, which provides First Responders with network-enabled tools on top of 5G on-scene planning, with enhanced service slicing capabilities tailored to emergency communications. Furthermore, a mapping of emergency services and communications to specific slice types is proposed to identify the current challenges in the field. Additionally, the proposed tentative mechanism is evaluated in terms of energy efficiency. Finally, the approach is summarized by discussing future steps in the convergence... [more]

Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multis... [more]

The goal of this paper is to optimally combine day-ahead solar and demand forecasts for the optimal battery schedule of a hybrid solar and battery farm connected to a distribution station. The objective is to achieve the maximum daily peak load reduction and charge battery with maximum solar photovoltaic energy. The innovative part of the paper lies in the treatment for the errors in solar and demand forecasts to then optimize the battery scheduler. To test the effectiveness of the proposed methodology, it was applied in the data science challenge Presumed Open Data 2021. With the historical Numerical Weather Prediction (NWP) data, solar power plant generation and distribution-level demand data provided, the proposed methodology was tested for four different seasons. The evaluation metric used is the peak reduction score (defined in the paper), and our approach has improved this KPI from 82.84 to 89.83. The solution developed achieved a final place of 5th (out of 55 teams) in the chall... [more]

This volume contains the successful invited submissions [...]

The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the schedulin... [more]

Geodesign is a participatory planning approach in which stakeholders use geographic information systems to develop and vet alternative design scenarios in a collaborative and iterative process. This study is based on a 2019 geodesign workshop in which 17 participants from industry, government, university, and non-profit sectors worked together to design an initial network of hydrogen refueling stations in the Hartford, Connecticut, metropolitan area. The workshop involved identifying relevant location factors, rapid prototyping of station network designs, and developing consensus on a final design. The geodesign platform, which was designed specifically for facility location problems, enables breakout groups to add or delete stations with a simple point-and-click operation, view and overlay different map layers, compute performance metrics, and compare their designs to those of other groups. By using these sources of information and their own expert local knowledge, participants recomm... [more]

Data centres are a key infrastructure for the global digital economy, helping enable the EU “Digital Decade” by 2030. In 2015, data centres were estimated to consume 2.5% of EU electricity demand. In Ireland, the concentrated presence of data centres could consume 37% of national electricity demand by 2028. The uncertainty of data centre facility-level energy efficiency paired with the need to achieve a low-carbon economy pose significant challenge for generation and transmission network planning. This is the first paper to apply a model of technology diffusion with a national forecast of changes in Irish data centre electricity demand through more efficient liquid cooling. The methodology serves as a technology-agnostic resource for practitioners performing forecasts under uncertainty with limited information. Results suggest that technology adoption could lower national electricity demand by 0.81% if adopted by new plant from 2019 to 2028. Savings rise to 3.16% over the same period i... [more]

Micro-grids have become the building block of modern energy systems, where distributed resources are the characterizing feature. The charging operation of electric vehicles can be exploited as a flexible load to achieve operational goals of the micro-grid. In the particular case of car-sharing fleets, the degrees of freedom in the charging procedures are reduced when compared to private users. In this work, we illustrate how a car sharing fleet can be incorporated as a flexible load in the micro-grid management system. A linear optimization problem is formulated, where the cost function makes a trade-off between the gain in flexibility in the micro-grid and the loss incurred by the car-sharing service for delaying the recharging procedure of the EV. The proposed approach is evaluated on a data set of charging events generated by a real car-sharing fleet showing that the EMS allows reducing the daily peak demand requested to the public grid and diminishes the operational costs.

Energy efficiency generally implies the efficient use of energy in all sectors of final consumption—industry, services, agriculture, households and transport. Shipping accounts for nearly 3% of global greenhouse gas emissions, making it the sixth largest CO2 producer in the world. This is a result of inefficient ship design, lack of planning and optimal use of resources. As the transport sector expands, so does the pressure for a greener and cleaner maritime industry. Reducing fuel consumption is a major driver of the need for energy efficiency on ships. In this paper, due to the importance of maritime transport, we observed the impact of reducing the dimensions of the main switchboard as a contribution to energy efficiency. This contribution is not of great importance as is the case with the optimization of the navigation route, etc., but it certainly affects the weight and, thus, the fuel consumption, which contributes to energy efficiency in the designed system. The aim of this pape... [more]

In this paper, the study of a sustainable production−inventory model with price and advertisement dependent on demand considering carbon emission reduction technology is investigated. The aim of this paper is to determine the optimal appropriate pricing, advertising, production, inventory, and capital investment decisions under various carbon emission policies to maximize the joint total profit of a multi-stage supply chain system. Various theoretical results and an algorithm are provided to verify and obtain the optimal solution of the problem. Further, the model is verified by numerical examples, and the robustness check of parameter variation is also analyzed. Finally, some management implications for decision makers are drawn from numerical examples. In summary, this study puts forward more realistic modeling hypothesis, which is beneficial to the academic research, and the research results can provide relevant decision makers with a model for managing a sustainable supply chain.

This paper presents a robust optimization model to find out the day-ahead energy and reserve to be scheduled by an electric vehicle (EV) aggregator. Energy can be purchased from, and injected to, the distribution network, while upward and downward reserves can be also provided by the EV aggregator. Although it is an economically driven model, the focus of this work relies on the actual availability of the scheduled reserves in a future real-time. To this end, two main features stand out: on one hand, the uncertainty regarding the EV driven pattern is modeled through a robust approach and, on the other hand, a set of non-anticipativity constraints are included to prevent from unavailable future states. The proposed model is posed as a mixed-integer robust linear problem in which binary variables are used to consider the charging, discharging or idle status of the EV aggregator. Results over a 24-h case study show the capability of the proposed model.

Acid fracturing simulation has been widely used to improve well performance in carbonate reservoirs. In this study, a computational method is presented to optimize acid fracturing treatments. First, fracture geometry parameters are calculated using unified fracture design methods. Then, the controllable design parameters are iterated till the fracture geometry parameters reach their optimal values. The results show higher flow rates are required to achieve optimal fracture geometry parameters with larger acid volumes. Detailed sensitivity analyses are performed on controllable and reservoir parameters. It shows that higher flow rates should be applied for fluids with lower viscosity. Straight acid reaches optimal conditions at higher flow rates and lower volumes. These conditions for retarded acids appear to be only at lower flow rates and higher volumes. The study of the acid concentration for gelled acids shows that both flow rate and volume increase as the concentration increases. F... [more]

Modern multi-megawatt wind turbines are currently designed as pitch-regulated machines, i.e., machines that use the rotation of the blades (pitching) in order to adjust the aerodynamic torque, such that the power is maintained constantly throughout a wide range of wind speeds when they exceed the design value (rated wind speed). Thus, pitch control is essential for optimal performance. However, the pitching activity is not for free. It introduces vibrations to the tower and blades and generates fatigue loads. Hence, pitch control requires a compromise between wind turbine performance and safety. In the past two decades, many approaches have been proposed to achieve different objectives and to overcome the problems of a wind energy converter using pitch control. The present work summarizes control strategies for problem of wind turbines, which are solved by using different approaches of pitch control. The emphasis is placed on the bibliographic information, but the merits and demerits o... [more]

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system... [more]

As the proportion of power generation using renewable energy increases, it is important to improve the operational flexibility of gas turbines (GTs) for the stability of power grids. Increasing the ramp-rate of GTs is a general solution. However, a higher ramp-rate increases the turbine inlet temperature (TIT), its rate of change, and the fluctuation of the frequency of produced electricity, which are negative side effects. This study proposes a method to optimize the set-point schedule for a PID controller to improve the ramp-rate while decreasing the negative impacts. The set-point schedule was optimized for a 170-MW class GT using a genetic algorithm to minimize the difference between the value of the process variable and the set-point value of the conventional control. The advanced control reduced the fluctuation of the rotation speed by 20% at the reference ramp-rates (12 MW/min and 15 MW/min). The maximum TIT decreased by 6.3 °C, and its maximum rate of change decreased from 0.7... [more]

Multi-energy complementary systems (MCSs) are complex multilevel systems. In the process of system planning, many aspects—such as power planning, investment cost, and environmental impact—should be considered. However, different decision makers tend to have different levels of control objectives, and the multilevel problems of the system need to be solved effectively with comprehensive judgment. Therefore, based on the terminal MCS energy structure model, the optimization method of MCS planning and operation coordination, considering the influence of planning and operation in the system’s life cycle, is studied in this paper. Consequently, the research on the collaborative optimization strategy of MCS construction and operation was carried out based on the bi-level multi-objective optimization theory in this paper. Considering the mutual restraint and correlation between system construction and operation in practical engineering, a bi-level optimization model for collaborative optimiza... [more]

The precision of a linear object measurement using satellite techniques is determined by the number and the relative position of the visible satellites by the receiver. The status of the visible constellation is described by the Dilution Of Precision (DOP). The obtained geometric coefficient values are dependent on many variables. When determining these values, field obstacles at the receiver location and satellite positions changing with time must be taken into account. Carrying out a series of surveys as part of a linear object Global Navigation Satellite System (GNSS) measurement campaign requires the optimisation problem to be solved. The manner of the inspection vehicle’s movement should be determined in such a way that the surveys are taken only within the pre-defined time frames and that the geometric coefficient values obtained at subsequent points of the route are as low as possible. The purpose of this article is to develop a software for the planning of a linear object GNSS... [more]

As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, t... [more]

Energy storage (ES) only contributes to a single-scene (peak or frequency modulation (FM)) control of the power grid, resulting in low utilization rate and high economic cost. Herein, a coordinated control method of peak modulation and FM based on the state of ES under different time scales is proposed. Firstly, for monotone peak and FM control scenarios, the ES configuration and scheduling model is constructed with the goal of maximizing net profit. Secondly, to further improve the ES utilization rate and optimize the operating cost of ES, a cooperative control method of peak modulation and FM is proposed. This method can realize the switch between peak modulation and FM control of ES and improve the ES utilization rate and system economy. Finally, the simulation results show that, compared with that of mono-peak and single-FM control, the ES efficiency of the peak-FM multiscenario optimization scheduling method is improved by 16.25% and 37.29%, respectively. The annual net income is... [more]

This article reviews the evolution of local environmental policy in the context of energy transition and particularly the implementation of RES. The study concerns Kraków, whose policy has been compared with other cities and metropolises and was analysed in a timespan of about 30 years. It was hypothesised that, until recently, RES were treated in the city with reserve concerning their feasibility in local environmental and economic conditions, but since RES have been appreciated as a viable means to effectively combat low-stack emissions, the local air quality targets have been integrated with global decarbonisation goals. This launched a dedicated subsidy stream for RES installations and contributed to the sharp increase in the number of installations. Trend analysis techniques have been used to study environmental indicators in relation to the evolution of municipal policies, the expenditures, and their effects. The review confirms that the implementation of RES had not been a prior... [more]

This article presents a case study of a single-family house with several photovoltaic micro-installations oriented in different directions, in which the energy electricity storage systems have been operating for several months. In the house, the heat source is the air−water heat pump cooperating with heat buffers. The first photovoltaic installation was installed in 2016 and, in the subsequent five years, was expanded using microinverters. The final amount of energy from photovoltaics covers 50% of the energy demand of the building. The procedure for dealing with technical and economic aspects was presented, allowing us to determine whether it is profitable to install energy storage in the given conditions of energy prices, equipment efficiency, and prices, as well as government support. This paper presents the effects of the designed and built home energy management system that supervises energy storage in heat and batteries, mainly through its impact on the self-consumption of energy... [more]

The increase in electricity demand is caused by population density, gross domestic product growth and technological conditions. A long-term forecast study on the electricity demand could be a promising alternative to the investment planning of power systems and distribution. In this study, the main aim is to forecast and understand the long-term electricity demand of the Taoussa area for the sustainable development of the regions of northern Mali, by using the Model for Analysis of Energy Demand (MAED) from the International Atomic Energy Agency. To fill such a knowledge gap, the long-term evolution of electricity demand is calculated separately for four consumption sectors: industry, transportation, service and household from 2020 to 2035. The demand for each end-use category of electricity is driven by one or several socioeconomic and technological parameters development of the country, which are given as part of the reference scenario (RS) and two alternative scenarios (Low and High... [more]

With recent developments, smart grids assured for residential customers the opportunity to schedule smart home appliances’ operation times to simultaneously reduce both the electricity bill and the PAR based on demand response, as well as increasing user comfort. It is clear that the multi-objective combinatorial optimization problem involves constraints and the consumer’s preferences, and the solution to the problem is a difficult task. There have been a limited number of investigations carried out so far to solve the indicated problems using metaheuristic techniques like particle swarm optimization, mixed-integer linear programming, and the grey wolf and crow search optimization algorithms, etc. Due to the on/off control of smart home appliances, binary-coded genetic algorithms seem to be a well-fitted approach to obtain an optimal solution. It can be said that the novelty of this work is to represent the on/off state of the smart home appliance with a binary string which undergoes c... [more]

Recent episodes of natural disasters have challenged the resilience of power grids. Adequate distribution grid planning that properly captures the risk aversion of the utility system planner is a key factor to increase the flexibility of distribution networks to circumvent these events. In this paper, we propose a methodology to determine the optimal portfolio of investments in lines and storage devices in order to minimize a convex combination between expected value and CVaR of operational costs, including energy not served, while taking into account the multistage nature of the energy storage management within this context. While the expected value of energy not served has been traditionally employed to tackle routine failures, we also minimize the CVaR of energy not served to address high-impact, low-probability (HILP) events. We illustrate the performance of the proposed methodology with a 54-Bus system test case.

In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive; some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts; (ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for mini... [more]