Experimental Assessment of the Prediction Performance of Dynamic Equivalent Circuit Models of Grid-connected Battery Energy Storage Systems

The paper discusses the model identification, validation and experimental testing of current-to-voltage dynamic circuit models for a grid-connected MW-class battery. The model refers to an utility-scale 720 kVA/560 kWh battery energy storage system (BESS) and is used in a model predictive control framework to forecast the evolution of the battery DC voltage as a function of the current trajectory. The model is identified using measurements from a dedicated experimental session where the BESS is controlled with a pseudo random binary signal (PRBS) to excite the system on a broad spectrum. The identified model relies on the assumption that the battery is a single cell. To test this assumption and assess the quality of predictions, we test the model performance by using a second data set coming from a real-life power system application, where the BESS is used to dispatch the operation of a group of stochastic prosumers (demand and PV generation). Experimental results show that the root mean square voltage prediction error of the best performing model (i.e. two time constant model, TTC) is less than 0.55% for look-ahead times in the range 10 seconds-10 minutes and better than persistence for all considered forecasting horizons

Protection systems and stability of distribution networks and microgrids with distributed energy resources

The large-scale integration of Distributed Energy Resources in distribution networks has several technical implications and consequences, which increase in complexity when energy sources are of renewable type. Renewable Energy Sources are characterized by intermittent/unpredictable availability and are connected to the grid through converters, often close to the final users, which means that they are more prone to cause instability issues and potential mis-operation of protection schemes. These effects are the objects of this thesis. A protection system against earth faults in radial and meshed distribution networks with unearthed and compensated neutral is proposed and assessed. The faulty feeder identification algorithm is based on the angle between the zero-sequence voltage and current phasors, estimated at the dominant transient frequency inferred from the transient response of the network within the first milliseconds after the fault. The performances of the protection system algorithm are assessed through a Monte Carlo method that considers the fault resistance, incidence angle and fault location variations. The power system is simulated within the EMTP-RV environment, while the protection algorithm is developed in Matlab. Results of a real-time simulation obtained in the Opal-RT environment further support the applicability of the algorithm. Another important aspect of the large deployment of distributed resources are the diffusion of Microgrids (MGs) which are characterized by faster dynamics than conventional distributions systems. In this context, load dynamics considerably affect the transient stability performance of MGs. The transient stability of a medium voltage MG is analyzed in two different cases: an islanding transition and a fault when the MG is standalone. The exclusion of any rotating generator is expected to heighten the load influence on the system dynamics. Modern controllable loads are also included. The system is implemented in the EMTP-RV simulation environment, in Simulink and real-time simulations are carried out in the Opal-RT environment

Advanced models and algorithms to provide multiple grid services with battery storage systems

Battery energy storage systems (BESSs) are expected to play a major role in the power grid of the near future. These devices, capable of storing and returning electrical energy, are valuable assets to a grid that integrates more and more distributed, intermittent and renewable generation. Compared to renewable energy sources, however, battery storage systems are still in an early stage of deployment and the way to exploit them in an optimal way is still subject of research. In this respect, this thesis develops two lines of investigation to reach an optimal utilisation of these devices. In its first part, the thesis proposes a control framework to operate a utility-scale BESS connected to a distribution feeder. This control framework allows to provide a set of services: dispatch of the operation of such feeder, load levelling, frequency response. It is structured in a period-ahead and a real-time phase. The former plans the BESS operation for a given time horizon through the solution of optimization problems. These take into account the BESS state of energy as well as forecast scenarios of quantities such as the feeder prosumption and of the BESS energy needs due to the frequency response service. The real-time phase determines the BESS power injections a resolution as fast as 1 second and, in the case of the dispatch, relies on model predictive control. Moreover, the thesis proposes the formulation of a framework for the simultaneous deployment of multiple services. The objective of this is to maximise the BESS exploitation in the presence of uncertainty. All the proposed methods are validated experimentally, on the 560 kWh/720 kVA BESS installed on EPFL campus. This extensive validation demonstrates their effectiveness and deployability. In its second part, the thesis discusses the integration of electrochemical models in the control of BESSs. Such models, compared to more conventional equivalent circuits or empirical ones, can provide deeper insight in the processes occurring within Li-ion cells - the founding elements of BESSs - and by consequence a more effective operation of BESSs. The thesis proposes a method to identify the parameters of one of such models - the single particle model - and, again, validates it experimentally. Moreover, in its final chapter, the thesis provides a proof-of-concept by simulations of the advantages of the integration of electrochemical models in the control framework proposed in its first part and, in general, in BESS control

Modelling and Forecasting of Photovoltaic Generation for Microgrid Applications: from Theory to Validation

The penetration of stochastic renewable generation in modern power systems requires to reconsider conventional practices to ensure the reliable functioning of the electrical network. Decentralized control schemes for distributed energy resources (DERs) have gained attention to support the grid operation. In order to cope with the uncertainties of the DERs, predictive schemes that leverage on forecast of renewable generation recently came into prominence. The period of the control action typically depends on the availability of the reserve in the grid. For the case of microgrids, their limited physical extension and the lack of spatial smoothing imply fast power fluctuations and the necessity of coupling energy management strategies with real-time control. Among the DERs, small-scale photovoltaic (PV) systems are expected to represent most of the future available capacity, and consequently, solar resource assessment and power forecasting are of fundamental importance. This thesis focuses on developing forecasting methods and generation models to support the integration of photovoltaic systems in microgrids, considering short-term temporal horizons (below one hour) and fine spatial resolution (single site installations). In particular, we aim at computing probabilistic prediction intervals (PIs), i.e. we include information accounting for the intrinsic uncertainty of the prediction. In this respect, nonparametric tools to deliver PIs from sub-second to intra-hour forecasting horizons are proposed and benchmarked. They forecast the AC power and/or the global horizontal irradiance (GHI) by extracting selected endogenous influential variables from historical time series. The methods are shown to outperform available state-of-the-art techniques, and are able to capture the fastest fluctuations of small-scale PV plants. Then, we investigate how the inclusion of features from ground all-sky images can be used to improve time-series-based forecasting tools, thanks to identifying clouds movement. In this respect, we define a toolchain that allows predicting the cloud cover of the sun disk, through image processing and cloud motion identification. Furthermore, a methodology to estimate the irradiance from all-sky images is proposed, investigating the possibility of using an all-sky camera as an irradiance sensor. Next, we consider the problem of having power measurements that are corrupted by exogenous control actions (e.g. curtailment) and, therefore, not representative of the true potential of the PV plant. We propose a model-based strategy to reconstruct the maximum power production of a PV power plant thanks to integrating measurements of the PV cell temperature, system DC voltage and current. The strategy can improve time series-based direct power forecasting techniques when the production of the PV system is curtailed and thus the measured power does not correspond to the maximum available. The proposed methods to model and forecast the PV generation are then integrated in a single chain that allows to deliver power PIs that are able to account for the overall uncertainty of a PV system at a predefined confidence level. In the last part of the thesis, the proposed methods are experimentally validated in a real microgrid by considering possible applications in modern power systems