P LANTILLAS :

As a potential solution for the design of control algorithms specifically applied to ADNs, several efforts in the literature have proposed to take advantage of the increasing availability of communication technologies, and engage distributed energy resources, such as DG, elastic demand and energy storage systems for providing grid ancillary services (e.g.,[9, 131, 132, 128, 129]).

In the case of demand response (DR), all intentional modifications to the con- sumption patterns of end-use electrical grid customers are included, which result in altering the time, the level of instantaneous demand, or the total electricity con- sumption. The majority of existing DR schemes target peak-shaving and, in general, alter the total electricity consumption on a time scale of minutes up to several hours (e.g., [133]). However, with the increasing availability of advanced monitoring and communication technologies, it is also possible to envision using real-time DR mech- anisms in order to engage large populations of small electrical loads to provide grid

ancillary services (e.g., [134]). In this direction, in [135] DR is deployed to mitigate forecast errors due to the integration of renewable ressources, whereas in [136] DR is considered in the context of islanded microgrids where it aims at providing a form of reserve. Furthermore, inspired by traditional frequency droop controls, there has already been an effort to investigate DR schemes as a way to provide primary and secondary frequency-control to the grid. In particular, in [137] electric vehicles are considered for providing frequency-control, whereas in [138] domestic loads are inves- tigated for primary frequency-control. In this respect, it is worth noting that this type of DR contribution to frequency-control appears interesting in the case of islanded grids but, as it was recently requested by the European Network of Transmission System Operators for Electricity (ENTSO-E), it might be extended to distribution networks that will be requested to provide grid ancillary services (e.g., [2]).

Compared to the existing literature, the purpose of our work is to develop a new DR control mechanism in order to investigate the potential of a large aggregation of small electrical loads for providing a different ancillary service, specifically the primary voltage control of active distribution networks. Contrary to classic DR approaches, GECN acts on a fast time-scale (in the order of few seconds) without significantly impacting the end-customers. Under normal grid operation, the proposed scheme can be used similarly to classic demand response schemes for peak shaving or for maintaining the balance between generation and consumption in the network (when there is not enough capacity or when there are renewable resources whose generation cannot be fully predicted).

The second most likely candidate to be used for ADNs ancillary services is ESSs. ESSs are expected to cover a wide spectrum of applications in distribution networks. They are characterized by charge/discharge cycles that could range from seconds (typically in high-power applications) to hours or even days (in high-energy appli- cations) [125, 126, 127]. Due to their wide range of applications, the use of electro- chemical storage systems within the context of ancillary services provided to power distribution networks has been addressed by several contributions to the literature (e.g. [129, 139]). A typical application of ESSs is the compensation of the short-term volatility in the production of renewable resources (e.g., [131]). Within the context of ADN ancillary services provided by distributed storage systems, in [129, 128] the capability of these systems to provide voltage support to distribution networks is illustrated.

In general, the concerned storage technologies for grid ancillary services are represented by battery storage systems. Within the context of ADNs primary voltage control, we propose the use of supercapacitors as the targeted ESS. Due to their high power density, short charge time and long life duration, these devices are particularly interesting in the ESS applications that require rapid cycles (e.g., primary voltage control via fast compensation of renewable DG, fast charging of electric vehicles) [140]. Furthermore, compared to the existing literature, we specifically model the SCs via an

equivalent circuit model that enables us to correctly represent both the quasi-static and dynamic behavior of a SC, accounting for the so-called “charge redistribution- effect” that plays a major role in its dynamic behavior. Also, we specifically provide a method for estimating the energy reserve required for successfully performing voltage control using GECN.

Despite their differences, most DR and ESSs control schemes found in the lit- erature rely on two-way communication between the controllable entity and the distribution network operator (DNO) (e.g., [116, 117]). However, the distributed nature of the controllable resources, as well as their large number and small individual impact motivates the use of a control mechanism based on one-way communication. In this direction, in [118], the charging rate of electric vehicles is controlled via broadcast signals so as to avoid overloading the distribution feeders. Furthermore, the authors in [119] propose the use of a universal broadcast signal to control the charge rate of a fleet of electric vehicles for the local compensation of renewable production volatility. Additionally, a decentralised control scheme of micro-storage systems via broadcast pricing signals is presented in [141].

Compared to the existing literature on broadcast-based control schemes, we go one step further and we show that heterogeneous controllable resources in the network can contribute to primary voltage control, by responding to the same broadcast signal. We show that with GECN this is indeed possible, without any change to the control architecture. The same GECN signals are broadcasted to the different buses of the network and it is the local controller of each elastic appliance or storage system that decides the system’s response to the received signal. Furthermore, we design GECN is such a way that, in case the DNO seeks to use traditional solutions, the proposed mechanism can be used to provide further support to the network, in addition to the DNO’s own resources. It is for this reason that the proposed GECN algorithm was initially conceived and designed to coexist with traditional solutions such as OLTCs and reactive power compensators.