J.K. Gruber

A Closed-Loop State Estimation Tool for MV Network Monitoring and Operation

This paper discusses the design and simulation of an integrated load forecasting and state estimation tool for distribution system operations. A predictive database is created and applied to forecast future network states in order to allow short-term (e.g., hours/days ahead) planning to be carried out. The predictive database is based on adaptive nonlinear auto-regressive exogenous (NARX) load estimation and forecasting models, which are continuously updated using feedback from the state estimator. This creates a closed-loop information flow designed to continuously monitor and improve the system state estimation performance by updating and retraining models where appropriate. The aim of this methodology is to improve situational awareness and help to provide network operators with early warning of potential issues, in medium voltage (MV) networks where the number of on-line measurements is limited, and state estimation relies heavily on estimates of power injections. The applicability of the approach is demonstrated through simulation using supervisory control and data acquisition (SCADA) and smart meter measurements recorded from an actual MV distribution network.

Residential Energy Load Profile Generation Using a Probabilistic Approach

Energy demand management schemes, applied to individual and aggregated residential demands, have advanced significantly in recent years. In order to improve the demand models used for the management schemes there have been several attempts to create a representative model of a households consumption. This paper presents a novel approach to generation of energy consumption profiles for one or several households based on the energy demand contribution of each household appliance and calculated by using a probabilistic approach. The proposed energy demand model takes into consideration a wide range of household appliances and provides a high degree of flexibility by benefiting from its modular structure. Artificially generated profiles of detailed energy consumption can be readily used for testing and verification of demand control techniques designed for both individual and aggregated household demands. The features of the proposed energy demand model along with its configuration are demonstrated by generating and illustrating the profiles of consumer energy demand.

Nonlinear control of the air feed of a fuel cell

This document presents the design of a hierarchical control to regulate the oxygen excess ratio of a fuel cell. The master controller calculates the necessary air flow to stabilize the oxygen excess ratio at a fixed set point. A nonlinear model based predictive controller (NMPC) using a Volterra series model is used as a master controller. The slave controller, a nonlinear PI, uses the reference of the air flow calculated by the master controller to stabilize the air flow in the compressor and allows reference tracking. The proposed control strategy is applied to full nonlinear model of a fuel cell in which simulations are carried out.

Short-Term Load Forecasting at the local level using smart meter data

Recent developments in active distribution networks, and the availability of smart meter data has led to much interest in Short-Term Load Forecasting (STLF) of electrical demand at the local level, e.g. estimation of loads at substations, feeders, and individual users. Local demand profiles are volatile and noisy, making STLF difficult as we move towards lower levels of load aggregation. This paper examines in detail the correlations between demand and the variables which influence it, at various levels of load disaggregation. The analysis investigates the forecasting capability of both linear and non-linear STLF approaches for forecasting local demands, and quantifies the forecast uncertainty for each level of load aggregation. The results demonstrate the limitations of several of the most commonly-used STLF approaches in this context. It is shown that, at the local level, standard STLF models may not be effective, and that simple load models created from historical smart meter data can give similar prediction accuracies. The analysis in the paper is carried out using two large smart meter data sets recorded at distribution networks in Denmark and in Ireland.

Control Predictivo no Lineal Basado en Modelos de Volterra. Aplicación a una Planta Piloto

The present publication demonstrates the application of a nonlinear predictive control (NMPC) strategy based on Volterra models to a pilot plant in which the temperature of the reactors content is controlled. The control is based on a second-order diagonal Volterra model in order to consider nonlinear effects. To calculate the control action, an iterative method with low computational cost, is used. The behavior of the process and the controller will be presented by means of experimental results. Finally, the experimental results of the NMPC will be compared to the results of a linear model based predictive controller (MPC).

Power-hardware-in-the-loop test beds: evaluation tools for grid integration of distributed energy resources

Recently, the topic of grid integration of distributed energy resources (DERs) has been closely linked with developments in power electronics research. The experimental evaluation of control strategies for power interfaces usually requires a significant allocation of both time and resources, and this is why the power hardware-in-the-loop (PHIL) techniques have been introduced to help accelerate the design and evaluation process. This article presents a PHIL-based microgrid test bed, Smart Energy Integration Lab (SEIL), that has been specifically designed for evaluation studies on the grid integration of DERs, with an emphasis on the system dynamic performance and power dispatch scenarios. The SEIL is equipped with a remotely controlled network configuration, power electronics converters, passive load banks, a battery storage system, and real-time (RT) control and acquisition systems that allow the emulation of several microgrids and connection of several DERs at the same time. To demonstrate the functionality of the SEIL, experimental results for two typical scenarios will be presented: 1) a power dispatch scenario in a microgrid and 2) a wind turbine (WT) operation in grid connection.

Demand-aware price policy synthesis and verification services for Smart Grids

In management tasks for modern electricity networks the stakeholders face typically two conflicting objectives: maximization of income (increasing demand) and reduction of demand peaks (reducing costs). To improve management of electricity distribution networks, an integrated service-based methodology is presented in this paper. Namely, the proposed approach: i) computes the operational constraints in order to improve utilization of the whole network; ii) enforces those constraints by focusing on each network substation separately; iii) verifies that probability of violating those constraints in nonnominal cases is fairly low. The feasibility of the approach has been tested tested by using a realistic scenario taken from an existing medium voltage Danish distribution network. In such scenario, the proposed method improves the network utilization and offers economic benefits for all the principal participants, i.e. DSOs, retailers and end users.

A Power-HIL microgrid testbed: Smart energy integration lab (SEIL)

Grid integration of distributed energy resources (DERs) is gaining importance in the field of power electronics research. Experimental evaluation of control strategies and detailed evaluation of power interfaces usually require significant allocation of time and resources. This is where the power-hardware-in-the-loop (PHIL) techniques help accelerate control design and evaluation processes. The paper presents a PHIL based microgrid testbed: Smart Energy Integration Lab (SEIL); that has been specifically constructed for studying grid integration aspects of DERs with a focus on system dynamic performance and power dispatch scenarios. The SEIL is equipped with a remotely controlled network configuration, power electronics converters, passive loadbanks, a battery storage system and real time control and acquisition systems that allow emulation of several microgrids and connection of several DERs at the same time. To demonstrate the functionality of the SEIL, the experimental results for two typical scenarios were used: a power dispatch scenario in a microgrid and a wind turbine operation in grid connection.

A Glimpse of SmartHG Project Test-bed and Communication Infrastructure

The SmartHG project goal is to develop a suite of integrated software services (the SmartHG Platform) aiming at steering residential users energy demand in order to: keep operating conditions of the electrical grid within given healthy bounds, minimize energy costs, and minimize CO2 emissions. This is achieved by exploiting knowledge (demand awareness) of electrical energy prosumption of residential users as gained from SmartHG sensing and communication infrastructure. This paper describes such an infrastructure along with user demand patterns emerging from the data gathered from ~600 sensors installed in ~40 homes participating in SmartHG test-beds.

Min-Max Model Predictive Control of a pilot plant

This paper shows the application of a Min-Max Model Predictive Control (MMMPC) strategy to a pilot plant in which the temperature of a reactor is controlled. An approximation of the worst case cost is used to obtain the control action. This approximation can be easily computed yielding a solution of the min-max problem very close to the exact one. The complexity of the algorithm allows the real time implementation for typical prediction and control horizons. The behavior of the system and the controller will be illustrated by means of experimental results.