Abstract-We describe the real-time monitoring infrastructure of the smart-grid pilot on the EPFL campus. We experimentally validate the concept of a real-time state-estimation for a 20 kV active distribution network. We designed and put into operation the whole infrastructure composed by the following main elements: (1) dedicated PMUs connected on the medium-voltage side of the network secondary substations by means of specific current/voltage transducers; (2) a dedicated communication network engineered to support stringent time limits and (3) an innovative state estimation process for real-time monitoring that incorporates phasor-data concentration and state estimation processes. Special care was taken to make the whole chain resilient to cyber-attacks, equipment failures and power outages. The achieved latency is within 65ms. The refresh rate of the estimated state is 20ms. The real-time visualization of the state estimator output is made publicly available, as well as the historical data (PMU measurements and estimated states). To the best of our knowledge, the work presented here is the first operational system that provides low-latency real-time stateestimation by using PMU measurements of a real active distribution network.

Demand Response (DR) has traditionally targeted peak shaving for the optimal allocation of the electricity consumption on a time scale that ranges from minutes to hours. However, with the availability of advanced monitoring and communication infrastructure, the potential of real-time DR for providing ancillary services to the grid has not yet been adequately explored. In this work, we propose a low-overhead decentralized DR control mechanism, henceforth called Grid Explicit Congestion Notification (GECN), intended for deployment by Distribution Network Operators (DNOs) to provide ancillary services to the grid by a seamless control of a large population of elastic appliances. Contrary to classic DR approaches, the proposed scheme aims to continuously support the grid needs in terms of voltage control by broadcasting low-bit rate control signals on a fast time scale (i.e., every few seconds). Overall, the proposed DR mechanism is designed to (i) indirectly reveal storage capabilities of end-customers and (ii) have a negligible impact on the end-customer. In order to estimate the benefits of the proposed mechanism, the evaluation of the algorithm is carried out by using the IEEE 13 nodes test feeder in combination with realistic load profiles mixed with non-controllable demand and non-dispatchable generation from photovoltaic distributed generation.

Abstract-The optimal power-flow problem (OPF) has always played a key role in the planning and operation of power systems. Due to the non-linear nature of the AC power-flow equations, the OPF problem is known to be non-convex, therefore hard to solve. Most proposed methods for solving the OPF rely on approximations (e.g., of the network model) that render the problem convex, but that consequently yield inexact solutions. Recently, Farivar and Low proposed in [1,2] a method that is claimed to be exact for the case of radial distribution systems under specific assumptions, despite no apparent approximations. In our work, we show that it is, in fact, not exact. On one hand, there is a misinterpretation of the physical network model related to the ampacity constraint of the lines' current flows and, on the other hand, the proof of the exactness of the proposed relaxation requires unrealistic assumptions related to the unboundedness of specific control variables. Therefore, there is a need to develop algorithms for the solution of the non-appproximated OPF problem that remains inherently nonconvex. Recently, several contributions have proposed OPF algorithms that rely on the use of the alternating-direction method of multipliers (ADMM). However, as we show in this work, there are cases for which the ADMM-based solution of the non-relaxed OPF problem fails to converge. To overcome the aforementioned limitations, we propose a specific algorithm for the solution of a non-approximated, non-convex OPF problem in radial distribution systems. In view of the complexity of the contribution, this work is divided in two parts. In this first part, we specifically discuss the limitations of both BFM and ADMM to solve the OPF problem.

Abstract-Renewable energy sources, such as wind, are characterized by non-dispatchability, high volatility, and non-perfect forecasts. These undesirable features can lead to energy loss and/or can necessitate a large reserve in the form of fast-ramping fuel-based generators. Energy storage can be used to mitigate these effects. In this paper, we are interested in the tradeoff between the use of the reserves and the energy loss. Energy loss includes energy that is either wasted, due to the inefficiency of the storage cycle and the inevitable forecasting errors, or lost when the storage capacity is insufficient. We base our analysis on an initial model proposed by Bejan, Gibbens, and Kelly. We first provide theoretical bounds on the trade-off between energy loss and the use of reserves. For a large storage capacity, we show that this bound is tight, and we develop an algorithm that computes the optimal schedule. Second, we develop a scheduling strategy that is efficient for small or moderate storage. We evaluate these policies on real data from the UK grid and show that they outperform existing heuristics. In addition, we provide guidelines for computing the optimal storage characteristics and the reserve size for a given penetration of wind in the energy mix. Missmatch between production and demand P f t−n (t)Base load production for time t, set at t − n W (t)Wind production at time t W

The increase in penetration of wind in the current energy mix is hindered by its high volatility and poor predictability. These shortcomings lead to energy loss and increased deployment of fast ramping generation. The use of energy storage compensates to some extent these negative effects; it plays a buffer role between demand and production. We revisit a model of real storage proposed by Bejan et al. [1]. We study the impact on performance of energy conversion efficiency and of wind prediction quality. Specifically, we provide theoretical bounds on the trade-off between energy loss and fast ramping generation, which we show to be tight for large capacity of the available storage. Moreover, we develop strategies that outperform the proposed fixed level policies when evaluated on real data from the UK grid.