Marco Giuntoli

Bio: Marco Giuntoli is an academic researcher from Ladenburg Thalmann. The author has contributed to research in topics: Electric power system & Grid. The author has an hindex of 8, co-authored 21 publications receiving 328 citations. Previous affiliations of Marco Giuntoli include University of Pisa & Hitachi.

Optimized Thermal and Electrical Scheduling of a Large Scale Virtual Power Plant in the Presence of Energy Storages

Abstract: Smart grids are often analyzed using a top-down approach, i.e., starting from communication and control technologies evolution, to then focus on their effects on active and passive users, in terms of new services, higher efficiency and quality of supply. However, with their bottom-up approach, virtual power plants (VPP) are very promising instruments for promoting an effective integration of distributed generation (DG) and energy storage devices as well as valid means for enabling consumers to respond to load management signals, when operated under the supervision of a scheduling coordinator. These aggregation factors can be very profitable for the distributed energy resources (DERs) economy and for the energy network itself. This paper presents a new algorithm to optimize the day-ahead thermal and electrical scheduling of a large scale VPP (LSVPP) which contains: a) many small-scale producers and consumers (“prosumers”) distributed over a large territory and b) energy storage and cogeneration processes. The algorithm also takes into account the actual location of each DER in the public network and their specific capability. Thermal and electrical generator models, load and storage devices are very detailed and flexible, as are the rates and incentives framework. Several novelties, with respect to the previous literature, are proposed. Case study results are also described and discussed.

Control strategies and real time operation of storage systems integrated with MV photovoltaic plants

Abstract: Nowadays the increasing share of renewable sources requires a direct participation of all form of generation to the secure operation of the electric power system, at least in terms of power balancing and voltage regulation. Resiliency, controllability and flexibility are the key factors that characterize a smart grid, compared to traditional energy systems. In this regard, storage systems can be used to provide the grid with a large range of services, such as frequency and voltage regulation, short-term power reserve and power quality. Nevertheless, persistent power modulation can originate a progressive drift of the battery State of Charge, thus reducing the real exploitability of the storage system for other scheduled energy services, like load levelling. This paper describes in a systematic approach a novel control architecture, in which an external loop aimed to compensate the SOC drift, in addition to the conventional frequency droop regulation, has been implemented and tested on an existing MV photovoltaic plant integrated with energy storage.

On the possible use of probabilistic techniques for purposes of short-term load dispatching

Abstract: The security assessment of the working point set by the energy markets is one of the main tasks of any System Operator (SO). The experience of vertically-integrated utilities in the use of probabilistic methods has proved to be very useful also in the new deregulated environment: an increasing number of SOs is presently enforcing with probabilistic tools the deterministic criteria traditionally employed to validate the ex-ante dispatching. Very challenging, promising and discussed, but not yet well-established, is conversely the use of probabilistic tools also for real-time decision-support and very short-term dispatching. The computational complexity of many time-consuming algorithms has constituted for years a crucial barrier; nowadays, the availability of cheap and fast computers discloses new opportunities for probabilistic techniques also for purposes of on-line security assessment. In this paper a probabilistic technique for real-time security assessment and operational decision-making is proposed and described. The use of a novel power system simulator, based on a Sequential Monte Carlo technique, is applied to very short-term dispatching, in order to alert the SO if the system reliability is about to decrease in the next few hours, thus anticipating critical contingencies and supporting the control room to find the most cost-effective corrective action. A case study relevant to the IEEE Reliability Test System RTS-96 is shown and discussed

Mechanical Behaviour of Multi-Span Overhead Transmission Lines Under Dynamic Thermal Stress of Conductors Due to Power Flow and Weather Conditions

Abstract: Dynamic Thermal Rating (DTR) of overhead transmission lines represents a significant improvement with respect to the traditional criteria used to assess the steady-state ampacity of conductors. In fact DTR uses actual operating conditions of the power line, rather than assumed conservative conditions. This is extremely promising for the secure operation of the power system: with DTR, TSOs can fully exploit the dynamic performances of conductors, i.e. currents significantly higher than the steady-state thermal limits, in the meantime that the system is redispatched. The present paper proposes a novel dynamic model for calculating sags and tensions in a multi-span power line, for purposes of DTR. The model considers not only the mechanical interaction between spans, due to rotation of strings, but also that the temperature of conductors can vary span by span, for different weather conditions. The problem was solved with a fast Newton-Raphson technique, rather than with conventional relaxation methods, in order to comply with the requirements of real-time operation. The developed tool is able to forecast the time trend of conductor temperatures, tensions, sags and clearances at each span, or to indicate which current can be carried for a given time, before a clearance or temperature constraint is violated. A case study compares the results of this novel method with the outcomes of the traditional "ruling span" technique.

On the use of simplified reactive power flow equations for purposes of fast reliability assessment

Abstract: In the present paper, a new method, derived from the linearization of the reactive power flow equations, is presented and discussed. The proposed technique is able to quickly calculate voltages at load busses, as well as reactive power injections at generation busses, using the results of a preliminary ordinary DC load flow. Due to the extremely reduced computational time, this technique can be successfully used for fast reliability assessments, when a huge number of load flow scenarios, generated by a probabilistic approach like Monte Carlo techniques, must be analyzed in terms of voltage violations; the algorithm is also promising for Optimal Reactive Power Flow procedures. Two case studies, based on the IEEE RTS-96 and Poland transmission and sub-transmission network, confirm a speed-up of more than 6 times respect to the Newton-Raphson algorithm, while keeping the mean squared error of voltages within 0.5% and the maximum voltage error below 1.5% of the voltage limits.

Cloud Computing Applications for Smart Grid: A Survey

Abstract: The fast-paced development of power systems necessitates smart grids to facilitate real-time control and monitoring with bidirectional communication and electricity flows. Future smart grids are expected to have reliable, efficient, secured, and cost-effective power management with the implementation of distributed architecture. To focus on these requirements, we provide a comprehensive survey on different cloud computing applications for the smart grid architecture, in three different areas— energy management , information management , and security . In these areas, the utility of cloud computing applications is discussed, while giving directions on future opportunities for the development of the smart grid. We also highlight different challenges existing in the conventional smart grid (without cloud application) that can be overcome using cloud. In this survey, we present a synthesized overview of the current state of research on smart grid development. We also identify the current research problems in the areas of cloud-based energy management, information management, and security in smart grid.

A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems

Abstract: Due to different viewpoints, procedures, limitations, and objectives, the scheduling problem of distributed energy resources (DERs) is a very important issue in power systems. This problem can be solved by considering different frameworks. Microgrids and Virtual Power Plants (VPPs) are two famous and suitable concepts by which this problem is solved within their frameworks. Each of these two solutions has its own special significance and may be employed for different purposes. Therefore, it is necessary to assess and review papers and literature in this field. In this paper, the scheduling problem of DERs is studied from various aspects such as modeling techniques, solving methods, reliability, emission, uncertainty, stability, demand response (DR), and multi-objective standpoint in the microgrid and VPP frameworks. This review enables researchers with different points of view to look for possible applications in the area of microgrid and VPP scheduling.

Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach

Abstract: This paper addresses the optimal bidding strategy problem of a commercial virtual power plant (CVPP), which comprises of distributed energy resources (DERs), battery storage systems (BSS), and electricity consumers, and participates in the day-ahead (DA) electricity market. The ultimate goal of the CVPP is the maximization of the DA profit in conjunction with the minimization of the anticipated real-time production and the consumption of imbalance charges. A three-stage stochastic bi-level optimization model is formulated, where the uncertainty lies in the DA CVPP DER production and load consumption, as well as in the rivals’ offer curves and real-time balancing prices. Demand response schemes are also incorporated into the virtual power plant (VPP) portfolio. The proposed bi-level model consists of an upper level that represents the VPP profit maximization problem and a lower level that represents the independent system operator (ISO) DA market-clearing problem. This bi-level optimization problem is converted into a mixed-integer linear programing model using the Karush–Kuhn–Tucker optimality conditions and the strong duality theory. Finally, the risk associated with the VPP profit variability is explicitly taken into account through the incorporation of the conditional value-at-risk metric. Simulations on the Greek power system demonstrate the applicability and effectiveness of the proposed model.