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This article presents a new spatio-temporal framework for the day-ahead probabilistic forecasting of Distribution Locational Marginal Prices (DLMPs). The approach relies on a recurrent neural network, whose architecture is enriched by introducing a deep bidirectional variant designed to capture the complex time dynamics in multi-step forecasts. In order to account for nodal price differentiation (arising from grid constraints) within a procedure that is scalable to large distribution systems, nodal DLMPs are predicted individually by a single model guided by a generic representation of the grid. This strategy offers the additional benefit to enable cold-start forecasting for new nodes with no history. Indeed, in case of topological changes, e.g., building of a new home or installation of photovoltaic panels, the forecaster intrinsically leverages the statistical information learned from neighbouring nodes to predict the new DLMP, without needing any modification of the tool. The approach is evaluated, along with several other methods, on a radial low voltage network. Outcomes highlight that relying on a compact model is a key component to boost its generalization capabilities in high-dimensionality, while indicating that the proposed tool is effective for both temporal and spatial learning. [ABSTRACT FROM AUTHOR]

This article presents an original collaborative framework for power exchanges inside a low voltage community. The community seeks to minimize its total costs by scheduling on a daily basis the resources of its members. In this respect, their flexibility such as excess storage capacity, unused local generation or shiftable load are exploited. Total costs include not only the energy commodity, but also grid fees associated to the community operation, through the integration of power flow constraints. In order to share the community costs in a fair manner, two different cost distributions are proposed. The first one adopts a distribution key based on the Shapley value, while the other relies on a natural consensus defined by a Nash equilibrium. Outcomes show that both collaboration schemes lead to important savings for all individual members. In particular, it is observed that the Shapley-based solution gives more value to mobilized flexible resources, whereas the Nash equilibrium rewards the potential flexibility consent of end-users. [ABSTRACT FROM AUTHOR]

Abandoned underground quarries or mines may be rehabilitated as natural reservoirs for underground pumped hydro energy storage (UPHES). In addition to the inherent modeling inaccuracies of the traditional PHES that arise from, e.g., approximating the nonlinear pump/turbine head-dependent performance curves, the optimal operation of these underground plants is also affected by endogenous model uncertainties. The latter typically arise from a limited knowledge of the physical characteristics of the system such as the geometry and hydraulic properties of the underground cavity. In this paper, chance-constrained programming is leveraged to immunize the day-ahead scheduling of an UPHES owner against both these model uncertainties and the modeling approximations. The proposed method is tested on a fictitious UPHES system using an existing underground quarry as lower reservoir. Results demonstrate that the methodology allows finding a compromise between conservativeness and economic performance, while being computationally efficient. This model may thus be integrated in the daily scheduling routine of UPHES owners, or may help regulators and system operators to better estimate the available flexibility of such resources. [ABSTRACT FROM AUTHOR]

The single imbalance pricing is an emerging mechanism in European electricity markets where all positive and negative imbalances are settled at a unique price. This real-time scheme thereby stimulates market participants to deviate from their schedule to restore the power system balance. However, exploiting this market opportunity is very risky due to the extreme volatility of the real-time power system conditions. In order to address this issue, we implement a new tailored deep-learning model, named encoder-decoder, to generate improved probabilistic forecasts of the imbalance signal, by efficiently capturing its complex spatio-temporal dynamics. The predicted distributions are then used to quantify and optimize the risk associated with the real-time participation of market players, acting as price-makers, in the imbalance settlement. This leads to an integrated forecast-driven strategy, modeled as a robust bi-level optimization. Results show that our probabilistic forecaster achieves better performance than other state of the art tools, and that the subsequent risk-aware robust dispatch tool allows finding a tradeoff between conservative and risk-seeking policies, leading to improved economic benefits. Moreover, we show that the model is computationally efficient and can thus be incorporated in the very-short-term dispatch of market players with flexible resources. [ABSTRACT FROM AUTHOR]

In this article, a method resorting to active rectification for the optimal control of a bidirectional resonant wireless power transfer system is proposed. Benefiting from a dual active bridge topology and without requiring any additional dc–dc converter, the real and the imaginary parts of the equivalent load impedance are modulated simultaneously and independently, for achieving optimal operating conditions despite circuit parameters deviations. Based on a first harmonic analysis, the methodology and its beneficial impacts against variations in the windings mutual inductance or against mistuned resonant capacitor are presented and illustrated. By an argued discussion, the proposed method extent is enlarged to changes in any circuit parameters. Time-domain simulations of the converters topology and command demonstrates the method effectiveness by achieving the system maximum efficiency. Controllability restrictions preventing the compensation of the most severe parameters variations are highlighted. Associated with the limited voltage producible by conventional full-bridge converters, the latter restrictions are surmounted with success by considering Z-source topologies for the front-end and back-end converters. [ABSTRACT FROM AUTHOR]

A major restructuring of electricity markets takes place worldwide, pursuing maximum economic efficiency. In most modern electricity markets, including the widely adapted Locational Marginal Price (LMP) market, efficiency is only guaranteed under the assumption of perfect competition. Moreover, market design is heavily focused on deterministic conventional generation. Electricity markets, though, are vulnerable to strategic behaviors and challenged by the increased penetration of renewable energy generation. In this paper, we cope with the aforementioned bottlenecks by investigating the application of Vickrey–Clarke–Groves (VCG) auction in a two-stage stochastic electricity market. The VCG mechanism achieves incentive-compatibility by rewarding market participants for their contribution towards market efficiency, being attractive from both market operation and participants perspectives. Both traditional and VCG market-clearing approaches are explored and compared, investigating as well the impact of increasing wind power penetration. The main shortcoming of VCG, i.e., not ensuring revenue adequacy, is quantified in terms of market budget imbalance for various levels of wind power penetration. To this end, a novel ex-post budget redistribution scheme is proposed, which achieves to partially recover budget deficit. [ABSTRACT FROM AUTHOR]

In the current competition framework governing the electricity sector, complex dependencies exist between electrical and market data, which complicates the decision-making procedure of energy actors. These must indeed operate within a complex, uncertain environment, and consequently need to rely on accurate multivariate, multi-step ahead probabilistic predictions. This paper aims to take advantage of recent breakthroughs in deep learning, while exploiting the structure of the problem to design prediction tools with tailored architectural alterations that improve their performance. The method can provide prediction intervals and densities, but is here extended with the objective to generate predictive scenarios. It is achieved by sampling the predicted multivariate distribution with a copula-based strategy so as to embody both temporal information and cross-variable dependencies. The effectiveness of the proposed methodology is emphasized and compared with several other architectures in terms of both statistical performance and impact on the quality of decisions optimized within a dedicated stochastic optimization tool of an electricity retailer participating in short-term electricity markets. [ABSTRACT FROM AUTHOR]

In this paper, a tool for the long-term (LT) planning, i.e., up to 20 years, of industrial microgrids (IMGs) connected to the distribution network and made of industrial consumers, prosumers, and of the distribution system operator (DSO), is proposed. The DSO assumes here the new role of microgrid energy manager. In order to realize the proper choice of LT investments (e.g., in renewable energy system and energy storage system), a short-term (ST) energy management is performed each day of the planning period. For that purpose, a new system of daily operation including industrial load management and allowing peer-to-microgrid as well as external energy exchanges is implemented. The LT investments and ST operational decisions are coupled via two game theoretical frameworks, which also allow the modeling of the different, even conflicting, objectives of the stakeholders. Different LT and ST pricing schemes are also considered in order to provide general advices concerning the creation of new IMGs. The developed tool is tested on a virtual IMG and the technical and economical outputs are presented. [ABSTRACT FROM AUTHOR]

In this paper,  we propose an optimal dispatch scheme for a cascade hydroelectric power system that maximizes the head levels of each dam, and minimizes the spillage effects taking into account uncertainty in the net load variations, i.e., the difference between the load and the renewable resources, and inflows to the cascade. By maximizing the head levels of each dam the volume of water stored, which is a metric of system resiliency, is maximized. In this regard, the operation of the cascade hydroelectric power system is robust to the variability and intermittency of renewable resources and increases system resilience to variations in climatic conditions. Thus, we demonstrate the benefits of coupling hydroelectric and photovoltaic resources. To this end, we introduce an approximate model for a cascade hydroelectric power system. We then develop correlated probabilistic forecasts for the uncertain output of renewable resources, e.g., solar generation, using historical data based on clustering and Markov chain techniques. We incorporate the generated forecast scenarios in the optimal dispatch of the cascade hydroelectric power system, and define a robust variant of the developed system. However, the robust variant is intractable due to the infinite number of constraints. Using tools from robust optimization, we reformulate the resulting problem in a tractable form that is amenable to existing numerical tools and show that the computed dispatch is immunized against uncertainty. The efficacy of the proposed approach is demonstrated by means of an actual case study involving the Seven Forks system located in Kenya, which consists of five cascaded hydroelectric power systems. With the case study, we demonstrate that the Seven Forks system may be coupled with solar generation since the “price of robustness” is small; thus, demonstrating the benefits of coupling hydroelectric systems with solar generation. [ABSTRACT FROM PUBLISHER]

This paper presents a decision-making tool tailored for a portfolio manager aiming at maximizing its profit by participating in both energy and balancing services markets. The proposed formulation is modular and flexible so as to comply with any portfolio configuration and to follow evolutions of the market regulation policy. Detailed formulations of both medium-term (i.e., typically from one week up to one year-ahead) and short-term (i.e., day-ahead) perspectives are jointly considered and solved using surrogate-based optimization. The objective is to adequately account for the interdependencies and possible conflicting objectives between these time horizons. Then, in order to overcome the resulting computational burden associated with the different sources of uncertainty, an innovative method for generating time and space-dependent scenarios is developed. The approach is based on nonparametric copulas and, in contrast to traditional methods, allows including a large number of uncertain parameters into the formulation. Finally, the procedure is tested and illustrated for a portfolio manager with diversified assets. The case study is developed to emphasize the advantages of the proposed optimization tool in terms of accuracy and computational burden of the proposed models as well as subsequent generated profit. [ABSTRACT FROM AUTHOR]

Following a call to foster a transparent and more competitive market, member states of the European transmission system operator are required to publish, among other information, aggregate wind power forecasts. The publication of the latter information is expected to benefit market participants by offering better knowledge of the market operation, leading subsequently to a more competitive energy market. Driven by the above regulation, we consider an equilibrium study to address how public information of aggregate wind power forecasts can potentially affect market results, social welfare, as well as the profits of participating power producers. We investigate, therefore, a joint day-ahead energy and reserve auction, where producers offer their conventional power strategically based on a complementarity approach and their wind power at generation cost based on a forecast. In parallel, an iterative game-theoretic approach (diagonalization) is incorporated in order to investigate the existence of an equilibrium for various values of aggregate forecast. As anticipated, variations in public forecasts will affect market results and, more precisely, underforecasts can mislead power producers to make decisions that favor social welfare, while overforecasts will cause the opposite effect. Furthermore, energy and reserve market prices can also be affected by deviations in aggregate wind forecasts altering, inevitably, the profits of all power producers. [ABSTRACT FROM PUBLISHER]

A light 3-D finite-element magnetodynamic a- $v$ model of resonant wireless power transfer (WPT) coils using a 3-D surface impedance boundary condition (SIBC) strongly coupled with an external circuit is proposed, reflecting the importance of external circuit elements (notably capacitances) in the resonance phenomena at circuit and field levels. The computational gain ensuing from the use of SIBC instead of massive conductor formulations is demonstrated on an academic example. The method is validated by comparing the simulated and experimentally measured input impedance of a complete resonant WPT system, attesting the correct behavior of the model while saving important computational resources. [ABSTRACT FROM AUTHOR]

In this paper, we highlight the influence of geometrical uncertainties (winding pattern and wire diameter) on the $RLC$ parameters of wound magnetic components. To that end, the finite-element method is embedded in a Monte Carlo simulation in order to compute probability distributions of the parameters. An algorithm to randomly generate realistic winding configurations is also proposed. [ABSTRACT FROM AUTHOR]

An original integral formulation dedicated to the high-frequency modeling of electromagnetic systems without magnetic materials is presented. The total current density (i.e., conduction plus displacement currents) is approached by facet elements so that resistive, inductive, and capacitive effects are all modeled. Furthermore, the method avoids the volume mesh of the conductors, which is too dense at high frequencies, due to the skin and proximity effects appearing, e.g., in wound inductors. Surface impedance boundary conditions are employed to that end. The formulation is general and suitable for nonsimply connected domains. It is first compared with the finite-element method on an academic test case, and is then experimentally validated on a coreless wound inductor, using an impedance analyzer. [ABSTRACT FROM AUTHOR]

Recently, a homogenized model in 2-D electrostatics has been proposed in order to avoid the meshing of the thin dielectric layers surrounding the conductors of transformers. The model performs a smoothing of the physical fields, which leads to an incorrect estimation of the peak values of the electric field between the conductors. This issue is critical for a correct design of the transformer insulation. Therefore, in this paper, the subproblem finite-element method is employed, so as to refine the homogenized model on selected areas, and recover the small scale variations of the fields. [ABSTRACT FROM PUBLISHER]

In this paper, a methodology for refining the large-scale solutions obtained by 2-D magnetodynamic homogenized models of high-frequency inductor windings is proposed, based on the subproblem finite-element method. Two approaches are developed, depending on the type of source terms, which are employed to compute the corrections (namely, volumic or surfacic). An analysis of the error committed by the homogenized model, on the local (current densities in the conductors) as well as the global (resistance of the winding) level, is conducted, and appropriate correction strategies are proposed. For instance, it is shown that correcting some conductors in the vicinity of the air gap of an inductor is sufficient to get an acceptable value for the resistance. [ABSTRACT FROM PUBLISHER]

In this paper, mathematically rigorous RL circuits of high-frequency multi-winding transformers are identified using an original three-step numerical procedure. They are composed of frequency-independent coefficients and can therefore be introduced in classical circuit simulators. First, the finite-element method in 3-D magnetodynamics, together with homogenization techniques which reduce the overall computational burden, are employed to extract frequency-dependent impedances and voltage gains. Then, the identified equivalent circuit is modified by removing the static resistances from the impedances, so as to obtain frequency-independent voltage gains, or coupling coefficients. An algorithm which is valid for any number of windings is proposed to that end. Finally, the frequency-dependent impedances are identified to Foster networks with constant elements using an optimization algorithm. An experimental validation with an impedance analyzer is proposed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a homogenization procedure for the thin insulation layers that surround the conductors of wounded magnetic components. The dielectrics are replaced by a homogeneous medium with an equivalent permittivity, previously computed by solving two electrostatic problems on the cell scale, using the finite element method (FEM). The homogenized model is compared with the fine one (i.e., for which each layer is finely discretized), in terms of accuracy and computational burden. [ABSTRACT FROM AUTHOR]

This paper is the second part of a work which aims at proposing a global approach for the numerical modeling of parasitic capacitances in high-frequency multiwinding transformers. In this part, we focus on the numerical identification using the finite-element method (FEM) of the capacitive circuits which have been introduced in a companion paper in the same issue. An original procedure based on the FEM in 2-D electrostatics is first presented. The validity of the 2-D approximation is thereafter discussed, and a new method which consists in solving two successive 3-D models (an electrokinetic problem in the windings and an electrostatic one in the dielectrics) is then exposed. The two methods are illustrated and experimentally validated on a three-winding transformer. [ABSTRACT FROM AUTHOR]

This paper is the first part of a work which aims at proposing a global approach for the numerical modeling of parasitic capacitances in high-frequency multiwinding transformers. In this first part, various capacitive circuits of n-winding magnetic components are presented. Careful attention is paid to the definition of a rigorous mathematical formalism based on the electrostatic equations, valid for any number of windings. The numerical identification of the proposed circuits is discussed in a companion paper. [ABSTRACT FROM PUBLISHER]