Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services

In this paper, we propose a control framework for a battery energy storage system to provide simultaneously multiple services to the electrical grid. The objective is to maximise the battery exploitation from these services in the presence of uncertainty (load, stochastic distributed generation, grid frequency). The framework is structured in two phases. In a period-ahead phase, we solve an optimization problem that allocates the battery power and energy budgets to the different services. In the subsequent real-time phase the control set-points for the deployment of such services are calculated separately and superimposed. The control framework is first formulated in a general way and then casted in the problem of providing dispatchability of a medium voltage feeder in conjunction to primary frequency control. The performance of the proposed framework are validated by simulations and real-scale experiments, performed with a grid-connected 560 kWh/720 kVA Li-ion battery energy storage system.

A Robust Optimization Framework for the Day-Ahead Scheduling of Active Distribution Networks including Energy Storage Systems

Mokhtar Bozorg, Mostafa Nick, Mario Paolone

In this paper we present an adaptive robust optimization framework for the day-ahead scheduling of Active Distribution Networks (ADNs) where the controlled devices are distributed Energy Storage Systems (ESSs). First, the targeted problem is formulated using a two-stage optimization approach. The first-stage decisions determine the amount of import/export energy from the external grid at each hour, as well as energy exchanges for each ESS. The second stage deals with the intra-day control, given the first stage decisions. In order to effectively consider the impacts of uncertainties, the problem is transformed into a `min-max-min' formulation. Here, we minimize the first (day-ahead scheduling) and second stage (intra-day operation) costs while uncertainties are maximally affecting the cost function of the second stage. The Benders dual cut algorithm is employed for the solution of the optimization problem. IEEE 34 bus standard network is the benchmark grid for assessing the performances and effectiveness of the developed robust optimization process.

IEEE2019

Impact of battery energy storage systems on the dynamic behavior of low-inertia power grids

Yihui Zuo

The extensive deployment of non-synchronous generation determines lower level of grid inertia resulting in deteriorated frequency containment performance and abnormal frequency excursions in case of contingency. This calls for identifying assets, controls, and relaying schemes capable to ensure acceptable grid frequency containment and dynamics satisfying the requirements of existing grid codes. A potential way to counterbalance this lack of inertia is to use large-scale battery energy storage systems (BESSs) since they provide large ramping rates and fast power control. As known, there are generally two main approaches to control converter-interfaced BESSs: grid-following and grid-forming controls. As BESSs may provide significant value to system frequency containment, it is of fundamental importance to quantitatively evaluate the dynamics of low-inertia power grids hosting large-scale BESSs. Within this context, it is also of importance to study the behavior of low-inertia power systems subsequent to large contingencies in order to develop appropriate under-frequency load shedding (UFLS) relaying schemes that may take advantage of nowadays distributed sensing technologies enabled by the Phasor Measurement Units (PMUs).Framed within the EU H2020 project "Optimal System-Mix Of flexibility Solutions for European electricity", the Thesis first characterizes the interplay between converter-interfaced BESSs and low-inertia power grids and, then, provides quantitative assessments of system dynamics and quantifies the benefits associated to different control strategies of BESSs. For this purpose, state-of-the-art detailed dynamic simulation models of power grids, BESS, and controls are implemented on a real-time simulator for detailed numerical analyses. At first, contingency tests are conducted. The results verify the substantial influence of inertia reduction on post-contingency dynamics of power systems and quantitatively prove that converter-interfaced BESSs can effectively limit the frequency decreasing and damp the frequency oscillations. Then, the proposed dynamic models are used for one-day-long simulations to assess the impact of converter-interfaced BESSs on the frequency containment of low-inertia power grids in normal operating conditions. For a practical operative context, a day-ahead schedule layer is considered where reserve levels for frequency containment and restoration are allocated considering the current practice required by European transmission system operators. Numerical analyses on suitably defined metrics applied to grid frequency show that the grid-forming control strategy outperforms the grid-following one, achieving better system frequency containment.As large frequency excursions are more likely to occur due to the decrease of kinetic energy stored in rotating synchronous machines, fast adaptive UFLS schemes are necessary to secure low-inertia power systems under contingency. PMUs provide an effective tool to track the network state in any node of interest with reporting rates in tens of frames per second. In this respect, the Thesis proposes and validates two new UFLS schemes suitable for low-inertia power grids. The first scheme is a centralized UFLS that leverages PMU-fed situational awareness systems and is coupled with an Optimal Power Flow (OPF) problem. The OPF problem is formulated to constrain nodal voltages and branch currents in combination with a model capable of predicting the system response.

EPFL2021

Sizing and siting of a utility scale distributed battery energy storage system

Mostafa Nick, Mario Paolone

The report focuses on the development of a practical and scalable methodology for the planning and operation of Active Distribution Networks (ADNs) with particular reference to the integration of Energy Storage Systems (ESSs) owned, and directly controlled, by the Distribution Network Operators (DNOs). In this respect, an exact convex formulation of the Optimal Power Flow (OPF) problem, called Augmented Relaxed OPF (AR-OPF), is first proposed for the case of radial power networks [1]-[3]. The proposed formulation takes into account the correct model of the lines (i.e., the non-approximated two-port Π model) and, therefore, the full AC load flow equalities. Moreover, the security constraints related to the nodal voltage magnitudes, as well as the lines ampacity limits, are suitably incorporated into the AR-OPF using a set of more conservative constraints. Therefore, the AR-OPF is characterized by a slightly reduced space of feasible solutions where the removed space is in correspondence of the ones close to the technical limits of the grid. Sufficient conditions have been identified to guarantee that the solution of the AR-OPF formulation is feasible and optimal, i.e., the relaxation used in the formulation is exact [1]. Moreover, by analyzing the exactness conditions, it is revealed that they are mild and hold for real distribution networks operating in feasible region. Then, by making use of the AR-OPF method, we formulate a specific optimization problem associated to the optimal resource planning and operation in ADNs with particular reference to the case of Battery Energy Storage Systems (BESSs). In this respect, it is assumed that the ESSs are owned, and directly controlled, by the DNOs. The objective function is augmented aiming at finding the optimal trade-off between technical and economic goals. In particular, the proposed procedures accounts for (i) network voltage deviations, (ii) feeders/lines congestions, (iii) network losses, (iv) cost of supplying loads (from external grid or local producers) together with the cost of ESS investment/maintenance, (v) load curtailment and (vi) stochasticity of loads and renewables production. The use of decomposition methods for solving the targeted optimization problems with discrete variables and probable large size is also investigated (see [3] for further details). More specifically, Benders decomposition and Alternative Direction Method of Multipliers (ADMM) techniques are successfully applied to the solution of the targeted problems. The developed technique is the applied to the siting and sizing problem of the BESS in the electrical distribution feeder of Onnens (medium voltage) that is expected to be used as demonstration sites for the Phase II of the SCCER-FURIES.