• Energy Research

Abstract The need to generate thermal and electrical energy, global warming caused by increased emissions of greenhouse gases, rising fossil fuel prices and demand for energy independence, have created a new industry focused on energy production through the use of renewable sources. Among the different options, biomass is the third most important source for obtaining electricity, and is the main source for the production of thermal energy. However, problems related to the low density of the different types of biomass, and the difficulty of transportation and storage, have led to the need to find solid fuels with higher density and greater hardness, known as pellets and briquettes. This paper seeks to develop an analysis of the current situation of the production of pellets, mainly with mixed biomass types, and the possible uses they have, with the main emphasis on the review of different combustion processes.

The articles in this special section focus on the topic of real time demand response applications.

This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines’ control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise. ; © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; fi=vertaisarvioitu|en=peerReviewed|

This paper is concerned with the protection of wind energy systems against the indirect effects of lightning. As wind energy is gaining increasing importance throughout the world, lightning damages involving wind energy systems have come to be regarded with more attention. Nevertheless, there are still very few studies in Portugal regarding lightning protection of wind energy systems using models of the Electro-Magnetic Transients Program (EMTP). Hence, a new case study is presented in this paper, based on a wind turbine with an interconnecting transformer, considering that lightning strikes the soil near the tower at a distance such that galvanic coupling occurs through the grounding electrode. Computer simulations obtained by using EMTP-RV are presented and conclusions are duly drawn.

Virtual power plants (VPPs) are defined as an aggregator of different types of energy resources and flexibility, coordinated by VPP owner through a smart control system. A correct establishment of a VPP will result in reduced electricity costs for the consumers within the VPP. One of the key aspect of VPP’s success is the consumer engagement in order to manage their flexibilities effectively. Gamification is an efficient way of learning and engagement, which can efficiently change the behavior of consumers towards participating in programs provided by VPPs for energy cost reduction. In this paper, a gamification-based approach for consumer engagement is proposed and a methodology based on Fogg’s behavior model and Kim’s model on player types is developed to examine the suitability of available gamification applications for energy saving/efficiency in the context of a VPP. Seven gamification applications are analyzed and evaluated based on the developed methodology and the results are provided.

This paper presents an algorithm for optimal resilient allocation of Mobile Energy Storage Systems (MESSs) for an active distribution system considering the microgrids coordinated bidding process. The main contribution of this paper is that the impacts of coordinated biddings of microgrids on the allocation of MESSs in the day-ahead and real-time markets are investigated. The proposed optimization framework is another contribution of this paper that decomposes the optimization process into multiple procedures for the day-ahead and real-time optimization horizons. The active distribution system can transact active power, reactive power, spinning reserve, and regulating reserve with the microgrids in the day-ahead horizon. Further, the distribution system can transact active power, reactive power, and ramp services with microgrids on the real-time horizon. The self-healing index and coordinated gain index are introduced to assess the resiliency level and coordination gain of microgrids, respectively. The proposed algorithm was simulated for the 123-bus test system. The method reduced the average value of aggregated operating and interruption costs of the system by about 60.16% considering the coordinated bidding of microgrids for the worst-case external shock. The proposed algorithm successfully increased the self-healing index by about 49.88% for the test system. ; © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; fi=vertaisarvioitu|en=peerReviewed|

This paper analyses the impact of the penetration of electric vehicles (EVs) charging loads on thermal ageing of a distribution transformer of a private industrial client that allows EVs to charge while their owners are at work and at three different working shifts during a day. Furthermore, the system is part of an isolated electric grid in a Portuguese Island. In this paper, a transformer thermal model is used to estimate the hot-spot temperature given the load ratio. Real data were used for the main inputs of the model, i.e. private industrial client load, transformer parameters, the characteristics of the factory and electric vehicle parameters.

The decision making framework in power systems has changed due to presence of distributed energy resources (DERs). These resources are installed in distribution networks to meet demand locally. Therefore, distribution companies (Discos) are able to supply energy through these resources to meet their demand at a minimum operation cost. In this framework, the Disco will change its role in the wholesale energy market from price taker to price maker. DERs can provide reserve in their normal operation; this facilitates the provision of reserves by the Disco in the wholesale reserve market. Therefore, in this paper, the strategic behavior of a Disco in wholesale energy and reserve markets is modeled as a bi-level optimization problem. In the proposed model, the operation problem of the Disco and the independent system operator are modeled in the upper- and lower-level problems, respectively. Karush–Kuhn–Tucker conditions and duality theory are used to transform the proposed nonlinear bi-level problem to a linear single level one. Numerical studies show the effectiveness of the proposed model and its solution methodology.