Jornada reducida

When providing grid services, the main characteristic and challenge that differentiates DERs from conventional generators is represented by the presence of two conflicting objectives - on one side the resource should be able to deliver a high quality service to the grid operator while, on the other side, fulfilling its primary purpose to the level expected by the customers. Thus, only flexible resources have the potential to offer services to the grid. Throughout the years, a number of different resources, such as BESSs, Thermostatically Controlled Loads (TCLs), Plugin Hybrid Electric Vehicles (PHEVs), and commercial buildings, have been identified as being suitable for the provision of grid services both at the distribution as well as at the transmission level. In [105] a comprehensive assessment of the capabilities of these devices is conducted. The reference also provides guidelines, based on technical and economic criteria, to determine the most appropriate type of resource for a given service.

In the following, we start by providing a broad overview of the recent advancements in the control of DERs for the provision of single services either at the transmission or at the distribution level. In particular, few selected contributions are described and their main conclusions are summarized. After that, the focus will shift on a recently introduced paradigm, typically referred to as multi- tasking, where, in order to maximize their exploitation, the controllable resources are employed for the simultaneous provision of multiple services. Finally, a novel model-based control framework that formally characterizes the amount of services that a set of DERs can offer to the grid is presented, and similarities and differences with respect to existing works are highlighted.

Distribution level services

Thanks to their high controllability and the fact that their location can be arbitrarily chosen, BESSs are starting to become a very competitive solution to relieve congestion at the distribution level. Possible applications are: upgrade deferral as in [102], where a rule-based controller for a 1.2 MW battery is used to reduce the peak load on a distribution transformer; peak shaving using optimization-based control methods [72, 124]; absorption of local deviation as in [127] where an MPC controller is designed to dispatch the operation of a distribution feeder characterized by the

4.1 Introduction 49

presence of stochastic prosumers; and voltage regulation using both centralized [99] and distributed approaches [147].

The potential of the demand side to provide services at the distribution level has been the subject of many studies. Despite DR programs have different peculiarities depending on the considered country, they can, in general, be categorized in two groups - price-driven programs, and direct load control programs.

In a price-driven scheme, the general idea is to affect the consumption pattern by appropriately setting energy or power prices. Common strategies being explored are: standard day-night tariffs; critical peak pricing for small business customers [134], large commercial buildings [43] or TCLs [56], where the prices can experience a manifold increment during peak hours; dynamic pricing where the price tag is chosen to reflect the marginal cost of energy provision; peak-power reduction where, beside the consumed energy, the load also pays for the maximal power drawn over a pre- determined period of time (e.g. one month). Regardless of the particular scheme being considered, it is clear that the key to optimize the operation cost of a resource participating in such programs is the capability of adapting to rapidly changing external conditions. Because of this, MPC has emerged in the literature as one of the most promising control techniques thanks to its ability of directly incorporating time-varying prices in the problem formulation [104, 105].

In a direct load control, the loads are incentivized to reduce/modify their consumption at specific times of the day. The remunerations are then based on the amount of reduction the loads can provide. Reference [116] studies the potential using MPC of an office building participating in New York’s DR program. A similar study is represented by [137] where a model-based method is used to provide DR services with a building equipped with an additional thermal storage. The work [138] investigates the usage of TCLs for the provision of voltage regulation.

Transmission level services: frequency control

BESSs clearly represent a potential candidate for services at the transmission level thanks to the high ramp rates they can achieve, which typically exceed the requirements for AS provision. How- ever, the main challenge is represented by the management of the SoC since regulation signals can display significant biases over time. Thus, in most approaches, it is assumed that BESSs are allowed to only track the fast and zero-mean components of the regulation signal, while low-frequency and biased components are passed to slower units [19, 108]. In particular, most applications have been focusing on PFC due to the smaller energy requirements involved.

When dealing with loads, the provision of AS presents specific challenges and, as a consequence, requires more sophisticated controllers. First of all, the participating resource should declare, ahead of time and over the whole regulation period, its baseline consumption as well as the power capacity around the baseline. The first determines the energy the resource commits to consume in absence

of any regulation requests. The second characterizes the maximum deviations around the baseline the resource is willing to sustain. Finally, the last challenge is represented by the strict tracking requirements that are typically imposed during online operation. In the following, we summarize the main contributions appearing in the literature to address these problems. We focus mainly on theoretical/simulation-based works leaving the review of experimental contributions to Chapter 6.

A min-max approach to compute the electrical flexibility and the baseline of a commercial build- ing for the provision of SFC was investigated in [87]. However, the applicability of the method is restricted to a single zone served by a single electrical fan. A robust-based method for the control of multi-zone systems is proposed in [47] where the load’s flexibility is characterized as a virtual battery with power and energy limitation. Again for commercial buildings, a two-stage approximation scheme was designed in [118] and extended in [117] to account also for the non linear models of HVAC systems. Regarding TCLs, most of recent research in the area has focused on the development of aggregated system models to be used in the controller design [54, 90, 138]

Regardless of the type of resource or service considered, one of the shared conclusions of these studies is that a potential obstacle for the exploitation of DERs as providers of grid services is their limited storage capacity, i.e., their inability to store or release energy over extended periods of time. For instance, for the case of a BESS, the energy capacity is not only one of the defining specifications, but it also represents the main driver for its cost. Thus, it would be desirable to keep the energy/power ratio as small as possible. Similarly, for the case of an aggregation of loads (PHEVs, TLCs, or commercial buildings), and for a fixed amount of grid service to be provided, one would like to keep the aggregation as small as possible in order to minimize the dispersion of the economic return among the participants. Nevertheless, due to the worst-case energy requirements and/or the conservative prequalification rules implemented by many TSOs, the sizing of a BESS (respectively loads aggregation) for a particular service is often dictated by few extreme scenarios seldomly encountered in practice. In particular, this could cause an under exploitation of the resources which consequently reduces the return on the invested capital (equivalently an increment of the operating cost). A possible way to improve the economics is to stack multiple services and optimize their provision. Thus, when the available control power of a resource is not used to fulfill the main service (e.g. alleviation of local grid congestions, peak shaving), it could be assigned to provide a secondary service (e.g. frequency control). Such a way of coupling different services and dynamically allocating the available power across them is typically referred to as multi-tasking [38] and it has been the subject of recent intense research activity.