Dan-Cristian Tomozei

Efficient Computation of Sensitivity Coefficients of Node Voltages and Line Currents in Unbalanced Radial Electrical Distribution Networks

The problem of optimal control of power distribution systems is becoming increasingly compelling due to the progressive penetration of distributed energy resources in this specific layer of the electrical infrastructure. Distribution systems are, indeed, experiencing significant changes in terms of operation philosophies that are often based on optimal control strategies relying on the computation of linearized dependencies between controlled (e.g., voltages, frequency in case of islanding operation) and control variables (e.g., power injections, transformers tap positions). As the implementation of these strategies in real-time controllers imposes stringent time constraints, the derivation of analytical dependency between controlled and control variables becomes a non-trivial task to be solved. With reference to optimal voltage and power flow controls, this paper aims at providing an analytical derivation of node voltages and line currents as a function of the nodal power injections and transformers tap-changers positions. Compared to other approaches presented in the literature, the one proposed here is based on the use of the [Y] compound matrix of a generic multi-phase radial unbalanced network. In order to estimate the computational benefits of the proposed approach, the relevant improvements are also quantified versus traditional methods. The validation of the proposed method is carried out by using both IEEE 13 and 34 nodes test feeders. The paper finally shows the use of the proposed method for the problem of optimal voltage control applied to the IEEE 34 node test feeder.

GECN: Primary Voltage Control for Active Distribution Networks via Real-Time Demand-Response

Demand response (DR) has traditionally targeted peak shaving for the optimal allocation of 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.

Real-time state estimation of the EPFL-campus medium-voltage grid by using PMUs

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 state-estimation by using PMU measurements of a real active distribution network.

Optimal Generation and Storage Scheduling in the Presence of Renewable Forecast Uncertainties

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 U.K. 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.

Optimal storage policies with wind forecast uncertainties

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.

Demand response using service curves

Existing methods for demand response either assume direct control of appliances by supplier, or assume that consumers adapt their load by reacting to pricing signals. The former are intrusive and might not scale well; the latter expose consumers to price volatility and require detailed awareness of time varying prices. We propose an alternative approach, based on “service curves”, which uses the following two ingredients. (1) The rate at which a consumer may draw power from the grid may be controlled by real time signals. Typical concerned devices would be heating systems, air conditioners and e-car batteries. (2) However, consumers are guaranteed that, over any window of time t, the amount of energy that may be drawn is at least equal to b(t), where the function b() (called the service curve) is agreed upon by contract at subscription time. The contract also specifies the maximum power that may be drawn, as well as a fixed price per unit of energy. Hence users are protected from price variability, at the expense of possible, but upper bounded, delays. With a proper service curve definition, an operator may distribute small service reductions in order to alleviate the impact of massive incoming demand onto the power grid. Consumers are able to observe the past service control signals that they received and can compute optimal load schedules from this and their service curve contracts, using only local information. Thus, this provides a distributed, scalable and robust demand response mechanism.

iPRP: Parallel redundancy protocol for IP networks

Reliable packet delivery within stringent delay constraints is of primal importance to industrial processes with hard real-time constraints, such as electrical grid monitoring. Because retransmission and coding techniques counteract the delay requirements, reliability is achieved through replication over multiple fail-independent paths. Existing solutions such as parallel redundancy protocol (PRP) replicate all packets at the MAC layer over parallel paths. PRP works best in local area networks, e.g., sub-station networks. However, it is not viable for IP layer wide area networks which are a part of emerging smart grids. Such a limitation on scalability, coupled with lack of security, and diagnostic inability, renders it unsuitable for reliable data delivery in smart grids. To address this issue, we present a transport-layer design: IP parallel redundancy protocol (iPRP). Designing iPRP poses non-trivial challenges in the form of selective packet replication, soft-state and multicast support. Besides unicast, iPRP supports multicast, which is widely using in smart grid networks. It duplicates only time-critical UDP traffic. iPRP only requires a simple software installation on the end-devices. No other modification to the existing monitoring application, end-device operating system or intermediate network devices is needed. iPRP has a set of diagnostic tools for network debugging. With our implementation of iPRP in Linux, we show that iPRP supports multiple flows with minimal processing and delay overhead. It is being installed in our campus smart grid network and is publicly available.

Attribution Mechanisms for Ancillary Service Costs Induced by Variability in Power Delivery

The increased penetration of renewable energy sources in existing power systems has led to necessary developments in electricity market mechanisms. Most importantly, renewable energy generation is increasingly made accountable for deviations between scheduled and actual energy generation. However, there is no mechanism to enforce accountability for the additional costs induced by power fluctuations. These costs are socialized and eventually supported by electricity customers. We propose some metrics for assessing the contribution of all market participants to power regulation needs, as well as an attribution mechanism for fairly redistributing related power regulation costs. We discuss the effect of various metrics used by the attribution mechanisms, and we illustrate, in a game-theoretical framework, their consequences on the strategic behavior of market participants. We also illustrate, by using the case of Western Denmark, how these mechanisms may affect revenues of the various market participants.

iPRP—The Parallel Redundancy Protocol for IP Networks: Protocol Design and Operation

Reliable packet delivery within stringent delay constraints is of paramount importance to mission-critical computer applications with hard real-time constraints. Because retransmission and coding techniques counteract the delay requirements, reliability may be achieved through replication over multiple fail-independent paths. The existing solutions, such as the parallel redundancy protocol (PRP), replicate all packets at the media access control layer over parallel paths. PRP works best in local area networks; however, it is not viable for IP networks that are a key element of emerging mission-critical systems. This limitation, coupled with diagnostic inability and lack of security, renders PRP unsuitable for reliable data delivery in these IP networks. To address this issue, we present a transport-layer solution: the IP parallel redundancy protocol (iPRP). Designing iPRP poses nontrivial challenges in the form of selective packet-replication, and soft-state and multicast support. iPRP replicates only time-critical unicast or multicast user datagram protocol traffic. iPRP requires no modifications to the existing monitoring application, end-device operating system, or to the intermediate network devices. It only requires a simple software installation on the end devices. iPRP has a set of diagnostic tools for network debugging. With our implementation of iPRP in Linux, we show that iPRP supports multiple flows with minimal processing-and-delay overhead. It is being installed in our campus smart-grid network and is publicly available.

Stability of a Stochastic Model for Demand-Response

We study the stability of a Markovian model of electricity production and consumption that incorporates production volatility due to renewables and uncertainty about actual demand versus planned production. We assume that the energy producer targets a fixed energy reserve, subject to ramp-up and ramp-down constraints, and that appliances are subject to demand-response signals and adjust their consumption to the available production by delaying their demand. When a constant fraction of the delayed demand vanishes over time, we show that the general state Markov chain characterizing the system is positive Harris and ergodic (i.e., delayed demand is bounded with high probability). However, when delayed demand increases by a constant fraction over time, we show that the Markov chain is non-positive (i.e., there exists a non-zero probability that delayed demand becomes unbounded). We exhibit Lyapunov functions to prove our claims. In addition, we provide examples of heating appliances that, when delayed, have energy requirements corresponding to the two considered cases.