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.
We study the effect of energy-storage systems in dynamic real-time electricity markets. We consider that demand and renewable generation are stochastic, that real-time production is affected by ramping constraints, and that market players seek to selfishly maximize their profit. We distinguish three scenarios, depending on the owner of the storage system: (A) the supplier, (B) the consumer, or (C) a stand-alone player. In all cases, we show the existence of a competitive equilibrium when players are price-takers (they do not affect market prices). We further establish that under the equilibrium price process, players' selfish responses coincide with the social welfare-maximizing policy computed by a (hypothetical) social planner. We show that with storage the resulting price process is smoother than without.We determine empirically the storage parameters that maximize the players' revenue in the market. In the case of consumer-owned storage, or a stand-alone storage operator (scenarios B and C), we find that they do not match socially optimal parameters. We conclude that consumers and the stand-alone storage operator (but not suppliers) have an incentive to under-dimension their storage system. In addition, we determine the scaling laws of optimal storage parameters as a function of the volatility of demand and renewables. We show, in particular, that the optimal storage energy capacity scales as the volatility to the fourth power.
Abstract-In this paper, we consider an active distribution network (ADN) that performs primary voltage control using real-time demand response via a broadcast low-rate communication signal. The ADN also owns distributed electrical energy storage. We show that it is possible to use the same broadcast signal deployed for controlling loads to manage the distributed storage. To this end, we propose an appropriate control law to be embedded into the distributed electrical storage controllers that reacts to the defined broadcast signal in order to control both active and reactive power injections. We analyze, in particular, the case where electrical storage systems consist of supercapacitor arrays and where the ADN uses the grid explicit congestion notification (GECN) for real-time demand response that the authors have developed in a previous contribution. We estimate the energy reserve required for successfully performing voltage control depending on the characteristics of the network. The performance of the scheme is numerically evaluated on the IEEE 34-node test feeder. We further evaluate the effect, depending on the line characteristics, of reactive versus active power controlled injections. We find that without altering the demand-response signal, a suitably designed controller implemented in the storage devices enables them to successfully contribute to primary voltage control.Index Terms-Active distribution network (ADN), ancillary services, broadcast signals, demand response, electrical energy storage systems, primary voltage control.
User profiling is a useful primitive for constructing personalised services, such as content recommendation. In the present paper we investigate the feasibility of user profiling in a distributed setting, with no central authority and only local information exchanges between users. We compute a profile vector for each user (i.e., a low-dimensional vector that characterises her taste) via spectral transformation of observed user-produced ratings for items. Our two main contributions follow: i) We consider a low-rank probabilistic model of user taste. More specifically, we consider that users and items are partitioned in a constant number of classes, such that users and items within the same class are statistically identical. We prove that without prior knowledge of the compositions of the classes, based solely on few random observed ratings (namely O(N log N ) such ratings for N users), we can predict user preference with high probability for unrated items by running a local vote among users with similar profile vectors. In addition, we provide empirical evaluations characterising the way in which spectral profiling performance depends on the dimension of the profile space. Such evaluations are performed on a data set of real user ratings provided by Netflix.ii) We develop distributed algorithms which provably achieve an embedding of users into a lowdimensional space, based on spectral transformation. These involve simple message passing among users, and provably converge to the desired embedding. Our method essentially relies on a novel combination of gossiping and the algorithm proposed by Oja and Karhunen.
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