• Energy Research

Abstract Active Power/Voltage (P/V) droop control is a method that is implemented in distributed photovoltaic (PV) units for the mitigation of overvoltage problems. This control does not require inter-unit communication and its benefit with respect to the on–off oscillations, the voltage level and the captured PV energy has already been demonstrated in previous studies. However, previous studies of P/V droop controllers only involved a deterministic “worst-case” approach on small networks and for restricted time periods, which often lead to oversized and costly technical solutions. In this paper, P/V droop control is for the first time evaluated with a probabilistic framework based on smart metering (SM) recordings in an existing Low Voltage (LV) network. Thanks to this approach, the uncertainty of PV energy injection, the randomness of the consumption loads and the fluctuations of voltage at the MV/LV transformer can be taken into consideration in the evaluation of the benefits related to the proposed control. The first objective of this paper is therefore to evaluate droop control in a model that is more faithful to the real operation of a LV network. Within this evaluation, the paper aims at doing a realistic parameter tuning of the control based on detailed probabilistic analysis. Practically, the evaluation model is based on a probabilistic framework previously developed by the authors but which up to now did not consider any voltage based droop (VBD) control. Thus, the second objective of this paper is to present a way for including time-based control strategies (here explained by means of droop control) in the probabilistic framework. The newly developed model is used to simulate an existing LV network and the results (EN50160 voltage requirements, curtailed PV generation, etc.) are compared to the scenario in which P/V droop control is not applied.

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 This paper presents a new approach for the improvement of the operation in Medium Voltage (MV) networks. The developed methodology is based on an innovative use of the experimental design method, the aim of which being the reduction of the total line losses while ensuring the best voltage profile possible along the feeders of the grid. Practically, it consists in the optimal positioning, sizing and real-time use of the different devices and processes that can currently be employed by the Distribution System Operators (DSOs) to operate efficiently and securely the network: the on-load tap changer (OLTC) of transformer, the reactive power compensation devices, the curtailment of distributed generation (DG) and the load-shedding. This work is divided into two complementary studies. Firstly, a planning procedure is implemented. This stage includes a screening process of the optimal positioning of the network regulation devices followed by a sizing procedure of those devices that takes technical as well as economic constraints into account. Secondly, an optimization process of the values of these parameters is implemented in order to be used in the context of a real-time management of distribution systems. The effectiveness of the proposed approach is then tested on a 28-bus radial MV network with several DG units. The simulations are carried out under various load and generation profiles over an extensive period of time and the evaluation criteria of the methodology are the costs related to the line losses and the probabilities of voltage violation.

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.

This paper 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. This work has been accepted for publication (11/19/2020) in IEEE Transactions on Smart Grid