Emil Namor

Achieving the Dispatchability of Distribution Feeders Through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage

We propose and experimentally validate a process to dispatch the operation of a distribution feeder with heterogeneous prosumers according to a trajectory with 5 min resolution, called dispatch plan, established the day before the operation. The controllable element is a utility-scale grid-connected battery energy storage system (BESS) integrated with a minimally pervasive monitoring infrastructure. The process consists of two stages: day-ahead, where the dispatch plan is determined by using forecast of the aggregated consumption and local distributed generation (prosumption), and real-time operation, where the mismatch between the actual prosumption realization and dispatch plan is compensated for thanks to adjusting the real power injections of the BESS with model predictive control (MPC). MPC accounts for BESS operational constraints thank to reduced order dynamic grey-box models identified from online measurements. The experimental validation is performed by using a grid-connected 720 kVA/500 kWh BESS to dispatch the operation of a 20-kV distribution feeder of the École Polytechnique Fédérale de Lausanne campus with both conventional consumption and distributed photo-voltaic generation.

Control of a battery energy storage system accounting for the charge redistribution effect to dispatch the operation of a medium voltage feeder

We describe a process to dispatch the operation of a medium voltage distribution feeder and improve its quality-of-service using a battery energy storage system (BESS). It is organized according to a day-ahead and intra-day structure to allow the integration with electricity markets. In the former stage, the average power transit on a 5-minute resolution is determined for the next day of operation using adaptive black-box forecasting. In the latter stage, the feeder operation is dispatched according to the previously determined trajectory. The resulting tracking problem is accomplished by using the BESS to compensate for deviations from the dispatched plan, which are likely to occur due to forecasting errors. For the first time in the literature, the control strategy is realized using model predictive control (MPC) accounting for the battery charge redistribution effect. This phenomena is described using a state-of-the-art lithium battery model. A simulated proof-of-concept is given.

Assessment of battery ageing and implementation of an ageing aware control strategy for a load leveling application of a lithium titanate battery energy storage system

The manuscript describes a method to embed into a battery energy storage system (BESS) control strategy the performance degradation associated with the battery operation. In particular, the proposed method aims at minimizing the degradation of the BESS electrochemical cells. A load leveling strategy is described as a case study and the ageing effects associated with the battery current extraction are embedded as constraints into the optimization problem. The main contributions of the work, compared to the existing literature are: i) the degradation process is formulated as a weighted energy throughput, thus taking into account the C-rate effect on the degradation phenomena; ii) the performance of the proposed control strategy has been applied to a large scale lithium-titanate BESS of 280 kWh interfaced to a 20 kV active distribution network.

Load leveling and dispatchability of a medium voltage active feeder through battery energy storage systems: Formulation of the control problem and experimental validation

This paper proposes and experimentally validates a control algorithm for a grid-connected battery energy storage system (BESS) to level the consumption of a group of prosumers and dispatch their aggregated operation. The control strategy is layered in a two-stage procedure: day-ahead and intra-day operation. During day-ahead operation, the load leveled power consumption profile is determined by applying a scenario-based robust optimization using historical measurements of the aggregated feeder power consumption. In the intra-day phase, the operation of the group of prosumers is dispatched according to the profile defined during the day-ahead stage, which becomes the so-called dispatch plan. The dispatch plan is tracked in real-time by properly adjusting the injections of the BESS using model predictive control (MPC). The proposed control process is experimentally validated using a 750 kW/567 kWh BESS connected to a 20 kV feeder of the EPFL campus that interfaces loads and PV generation.

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 (BESS) to provide simultaneously multiple services to the electrical grid. The objective is to maximize the battery exploitation from these services in the presence of uncertainty (load, stochastic distributed generation, and 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 BESS.

Battery storage system optimal exploitation through physics-based model predictive control

Traditionally, the safe operation of a battery energy storage system (BESS) is achieved by imposing conservative constraints on its DC bus current and voltage. By using a computationally efficient single particle model (SPM), we propose to replace these constraints with the battery internal ion concentrations and electrical potentials in order to avoid these quantities to exceed hazardous limits. Indeed, the in-depth knowledge of the BESS internal states provided by the SPM, enhances the awareness of a control action and allows for a better exploitation of the BESS energy and power capabilities, while maintaining safe operational conditions. The target application is composed by a model predictive control (MPC) applied to a MW-class grid-connected BESS responsible to dispatch the operation of a medium voltage (20 kV) feeder interfacing heterogeneous loads and distributed generation. The performance of the proposed MPC are assessed and compared with respect to a traditional approach constraining the BESS DC bus current and voltage.

Dispatching active distribution networks through electrochemical storage systems and demand side management

In this paper, the problem of dispatching the operation of a distribution feeder comprising a set of heterogeneous resources is investigated. In particular, the main objective is to track a 5-minute resolution trajectory, called the dispatch plan that is computed one day before the beginning of operation. During real-time operation, due to the stochasticity of part of the resources in the feeder portfolio, tracking errors need to be absorbed in order to track the committed dispatch plan. This is achieved by modulating the power consumption of a grid-connected battery energy storage system (BESS) and of the HVAC system of a commercial controllable building (CB). To this end, a hierarchical multi-time-scale controller is designed to coordinate the two entities while requiring a minimal communication infrastructure. The effectiveness of the proposed control framework is demonstrated by means of a set of full-day experimental results on the 20kV distribution feeder of the EPFL campus that is comprised of: 1) a set of uncontrollable resources represented by 5 office buildings (350kWp) and a roof-top PV installation (90kWp) 2) a set of controllable resources, namely, a grid-connected BESS (720kVA–500kWh), and a fully-occupied multizone office building (45 kWp).