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Virtual power plants (VPPs) have emerged as a way to coordinate and control the growing number of distributed energy resources (DERs) within power systems. Typically, VPP models have focused on financial or commercial outcomes and have not considered the technical constraints of the distribution system. The objective of this article is the development of a technical VPP (TVPP) operational model to optimize the scheduling of a diverse set of DERs operating in a day-ahead energy market, considering grid management constraints. The effects on network congestion, voltage profiles, and power losses are presented and analyzed. In addition, the thermal comfort of the consumers is considered and the tradeoffs between comfort, cost, and technical constraints are presented. The model quantifies and allocates the benefits of the DER operation to the owners in a fair and efficient manner using the Vickrey–Clarke–Grove mechanism. This article develops a stochastic mixed-integer linear programming model and various case studies are thoroughly examined on the IEEE 119 bus test system. Results indicate that electric vehicles provide the largest marginal contribution to the TVPP, closely followed by solar photovoltaic (PV) units. Also, the results show that the operations of the TVPP improve financial metrics and increase consumer engagement while improving numerous technical operational metrics. The proposed TVPP model is shown to improve the ability of the system to incorporate DERs, including those from commercial buildings. [ABSTRACT FROM AUTHOR]

Emerging technologies are helping to accelerate the ongoing energy transition. At the forefront of these new technologies is blockchain, which has the potential to disrupt energy trading markets. This article explores this potential by presenting an innovative multilevel transactive energy (TE) optimization model for the scheduling of distributed energy resources (DERs) within connected virtual power plants (VPPs). The model allows for energy transactions within a given VPP as well as between connected VPPs. A blockchain-based smart contract layer is applied on top of the TE optimization model to automate and record energy transactions. The model is formulated to adhere to the new regulations for the self-generation and self-consumption of energy in Portugal. This new set of regulations can ease barriers to entry for consumers and increase their active participation in energy markets. Results show a decrease in energy costs for consumers and increased generation of locally produced electricity. This model shows that blockchain-based smart contracts can be successfully integrated into a hierarchical energy trading model, which respects the novel energy regulation. This combination of technologies can be used to increase consumer participation, lower energy bills, and increase the penetration of locally generated electricity from renewable energy sources. [ABSTRACT FROM AUTHOR]

The use of data from residential smart meters can help in the management and control of distribution grids. This provides significant benefits to electricity retailers as well as distribution system operators but raises important questions related to the privacy of consumers' information. In this article, an innovative differential privacy (DP) compliant algorithm is developed to ensure that the data from consumer's smart meters are protected. The effects of this novel algorithm on the operation of the distribution grid are thoroughly investigated not only from a consumer's electricity bill point of view but also from a power systems point of view. This method allows for an empirical investigation into the losses, power quality issues, and extra costs that such a privacy-preserving mechanism may introduce to the system. In addition, severalcost allocation mechanisms based on the cooperative game theory are used to ensure that the extra costs are divided among the participants in a fair, efficient, and equitable manner. Overall, the comprehensive results show that the approach provides privacy preservation in line with the consumer's preferences and does not lead to significant cost or loss increases for the energy retailer. In addition, the novel algorithm is computationally efficient and performs very well with a large number of consumers, thus demonstrating its scalability. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of recovering the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the fundamental limits of accurately recovering such sets through compressive measurements, while simultaneously providing adaptive support recovery protocols that perform near optimally for these classes. We show that by adaptively designing the sensing matrix, we can attain significant performance gains over non-adaptive protocols. These gains arise from the fact that adaptive sensing can: 1) better mitigate the effects of noise and 2) better capitalize on the structure of the support sets. [ABSTRACT FROM PUBLISHER]

Ocean waves have a high potential to become a reference among the renewable energies with economic viability, in the future. Currently, there is in Portugal a WaveRoller technology experimental facility delivering power to the grid. However, Oscillating Water Column (OWC) is the most used technology to capture the energy from the waves. In this paper, a full integrated model of an OWC installation is presented. This model incorporates dedicated models of its components: pneumatic chamber, Wells turbine and Doubly Fed Induction Generator (DFIG). Data to feed the model is taken from Pico (Azores) power plant. The implementation in MATLAB®/Simulink® environment allows to carry on some simulations, regarding the behavior of the OWC system in the presence of pressure increases in the pneumatic chamber (meaning a higher airflow in the turbine). The results allow the conclusion that the speed control feature is crucial to obtain an optimized operation of the system. [ABSTRACT FROM PUBLISHER]

In many practical settings one can sequentially and adaptively guide the collection of future data, based on information extracted from data collected previously. These sequential data collection procedures are known by different names, such as sequential experimental design, active learning, or adaptive sensing/sampling. The intricate relation between data analysis and acquisition in adaptive sensing paradigms can be extremely powerful, and often allows for reliable signal estimation and detection in situations where nonadaptive sensing would fail dramatically. In this paper, we investigate the problem of estimating the support of a structured sparse signal from coordinate-wise observations under the adaptive sensing paradigm. We present a general procedure for support set estimation that is optimal in a variety of cases and shows that through the use of adaptive sensing one can: 1) mitigate the effect of observation noise when compared with nonadaptive sensing and 2) capitalize on structural information to a much larger extent than possible with nonadaptive sensing. In addition to a general procedure to perform adaptive sensing in structured settings, we present both performance upper bounds, and corresponding lower bounds for both sensing paradigms. [ABSTRACT FROM AUTHOR]

The problem of detecting correlations from samples of a high-dimensional Gaussian vector has recently received a lot of attention. In most existing work, detection procedures are provided with a full sample. However, following common wisdom in experimental design, the experimenter may have the capacity to make targeted measurements in an on-line and adaptive manner. In this paper, we investigate such adaptive sensing procedures for detecting positive correlations. It is shown that, using the same number of measurements, adaptive procedures are able to detect significantly weaker correlations than their nonadaptive counterparts. We also establish minimax lower bounds that show the limitations of any procedure. [ABSTRACT FROM AUTHOR]

Multilevel converters are attractive for unified power-flow controller (UPFC) applications, due to their high-voltage and power capability. Among multilevel topologies, the three-level neutral-point-clamped (NPC) converter allows back-to-back connection as the UPFC shunt and series converters. Besides the pulsewidth-modulated (PWM) multilevel control schemes, UPFCs require constant dc-link voltage and balanced voltages in the NPC multilevel dc capacitors. This paper proposes three main contributions to increase the dc-link voltage steadiness of multilevel UPFCs under line faults: 1) decoupled active and reactive linear power controllers; 2) real-time PWM generation; and 3) double balancing of dc capacitor voltages. A case study using part of the Portuguese transmission network is presented. The results show the effectiveness of the real-time PWM generation and dc-link capacitor voltages balancing included in NPC series and shunt converters to keep the dc-link voltage steadiness under line faults, overall enhancing the UPFC ride-through capability. [ABSTRACT FROM AUTHOR]

The increasing integration of wind energy in power systems can be responsible for the occurrence of over-generation, especially during the off-peak periods. This paper presents a dedicated methodology to identify and quantify the occurrence of this over-generation and to evaluate some of the solutions that can be adopted to mitigate this problem. The methodology is applied to the Portuguese power system, in which the wind energy is expected to represent more than 25% of the installed capacity in a near future. The results show that the pumped-hydro units will not provide enough energy storage capacity and, therefore, wind curtailments are expected to occur in the Portuguese system. Additional energy storage devices can be implemented to offset the wind energy curtailments. However, the investment analysis performed show that they are not economically viable, due to the present high capital costs involved. [ABSTRACT FROM PUBLISHER]

Cognitive radios process their sensed information collectively in order to opportunistically identify and access underutilized spectrum segments (spectrum holes). Due to the transient and rapidly varying nature of the spectrum occupancy, the cognitive radios (secondary users) must be agile in identifying the spectrum holes in order to enhance their spectral efficiency. We propose a novel adaptive procedure to reinforce the agility of the secondary users for identifying multiple spectrum holes simultaneously over a wide spectrum band. This is accomplished by successively exploring the set of potential spectrum holes and progressively allocating the sensing resources to the most promising areas of the spectrum. Such exploration and resource allocation results in conservative spending of the sensing resources and translates into very agile spectrum monitoring. The proposed successive and adaptive sensing procedure is in contrast to the more conventional approaches that distribute the sampling resources equally over the entire spectrum. Besides improved agility, the adaptive procedure requires less-stringent constraints on the power of the primary users to guarantee that they remain distinguishable from the environment noise and renders more reliable spectrum hole detection. [ABSTRACT FROM AUTHOR]