A control strategy for a distributed power generation microgrid application with voltage and current controlled source converter

This paper presents a pseudodroop control structure integrated within a microgrid system through distributed power generation (DPG) modules capable to function in off-grid islanded, genset-connected, and grid-connected modes of operation. System efficiency has an important role in order to harvest the maximum available renewable energy from dc or ac sources while providing power backup capability. A control strategy is proposed in off-grid islanded mode method based on the microgrid line-frequency control as agent of communication for energy control between the DPG modules. A critical case is where the ac load demand could be lower than the available power from the photovoltaic solar array, where the battery bank can be overcharged with unrecoverable damage consequences. The DPG voltage-forming module controls the battery charge algorithm with a frequency-generator function, and the DPG current source module controls its output current through a frequency-detection function. The physical installation between DPG modules is independent, since no additional communication wiring is needed between power modules, which represent another integration advantage within the microgrid-type application

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

The influence of the DC infrastructure on the control of power-storage flow in micro- and smart grids has gained attention recently, particularly in dynamic vehicle-to-grid charging applications. Principal effects include the potential loss of the charge–discharge synchronization and the subsequent impact on the control stabilization, the increased degradation in batteries’ health/life, and resultant power- and energy-efficiency losses. This paper proposes and tests a candidate solution to compensate for the infrastructure effects in a DC microgrid with a varying number of heterogeneous battery storage systems in the context of a multiagent neighbor-to-neighbor control scheme. Specifically, the scheme regulates the balance of the batteries’ load-demand participation, with adaptive compensation for unknown and/or time-varying DC infrastructure influences. Simulation and hardware-in-the-loop studies in realistic conditions demonstrate the improved precision of the charge–discharge synchronization and the enhanced balance of the output voltage under 24 h excessively continuous variations in the load demand. In addition, immediate real-time compensation for the DC infrastructure influence can be attained with no need for initial estimates of key unknown parameters. The results provide both the validation and verification of the proposals under real operational conditions and expectations, including the dynamic switching of the heterogeneous batteries’ connection (plug-and-play) and the variable infrastructure influences of different dynamically switched branches. Key observed metrics include an average reduced convergence time (0.66–13.366%), enhanced output-voltage balance (2.637–3.24%), power-consumption reduction (3.569–4.93%), and power-flow-balance enhancement (2.755–6.468%), which can be achieved for the proposed scheme over a baseline for the experiments in question.</p

Control algorithms for e-car

Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.The aim of this work is to design and implement energy consumption optimization control algorithms for electric vehicle. The main objective is to optimize the power-split-ratio between the main power source (batteries) and the super-capacitors during the driving cycle. The driving power profile is estimated and predicted using 3D geographic data and vehicle model. In the first part, vehicle components modelling is introduced. Then, moving average based algorithm and dynamic programming algorithm are presented. Simulations and analysis are provided to show algorithms' benefits. In the last part, Java implementation and also Android operating system application are described.

Modeling and analysis of a DC electrical system and controllers for implementation of a grid-interactive building

As the penetration of photovoltaic (PV) systems on building rooftops increases, the accumulated effect of the rooftop PV power outputs on electric network operation is no longer negligible. Energy storage resources (ESRs) have been used to smooth PV power outputs, particularly when building load becomes low. In commercial buildings, the batteries of plug-in electric vehicles (PEVs) can be regarded as distributed ESRs. This paper proposes a DC electrical system in a commercial building that enables PEVs to compensate for rooftop PV power fluctuation and participate in tracking signals for grid frequency regulation (GFR). The proposed building system and associated controllers are modeled considering steady-state and dynamic operations of the PV system and PEV batteries. Simulation case studies are conducted to demonstrate the performance of the proposed building system under various conditions, determined by such factors as the maximum voltage, minimum state-of-charge, and desired charging end-time of PEVs batteries. ? 2017 by the authors; licensee MDPI, Basel, Switzerland.112Ysciescopu

A Modular Multi-level Converter for Energy Management of Hybrid Energy-Storage Systems in Electric Vehicles

Electric vehicles (EVs) are substantial applications of clean energy. Their effectiveness for mainstream transportation is predicated on the efficient use of stored energy within the vehicles’ power pack. Among rechargeable storage solutions, lithium-ion (Li-ion) battery cells have high energy density making them suitable to supply the EVs’ average power. However, the peak power requirements of the vehicles exert stress on the Li-ion cells due to their low pulsating power capabilities. Ultracapacitors can be used instead as the power-pulsating storage elements given their superior power density. Incorporating the two cell types for energy storage signifies a hybrid configuration that leads to challenging tasks in managing the energy between cells due to varying cell dynamics. Therefore, this study investigated the design of an end-to-end hybrid energy-storage and management system. The limitations of existing power electronics and control schemes were identified based on comparative analysis, both on a cell level and on a system level. Subsequently, an energy system was developed that utilized modular multi-level converters to manage the energy between the different cell types. The formulated control strategy accounted for various power modes and added immense flexibility in charge sharing through diverse switching states. Furthermore, the proposed configuration eliminated the conventional need for a system level drive inverter feeding the EV motor. Electro-mechanical modeling results and physical design merits verified the proposed configuration’s effectiveness in improving EV efficiency

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

We propose and experimentally validate a control strategy to dispatch the operation of a distribution feeder interfacing heterogeneous prosumers by using a grid-connected battery energy storage system (BESS) as a controllable element coupled with a minimally invasive monitoring infrastructure. It consists in a two-stage procedure: day-ahead dispatch planning, where the feeder 5-minute average power consumption trajectory for the next day of operation (called \emph{dispatch plan}) is determined, and intra-day/real-time operation, where the mismatch with respect to the \emph{dispatch plan} is corrected by applying receding horizon model predictive control (MPC) to decide the BESS charging/discharging profile while accounting for operational constraints. The consumption forecast necessary to compute the \emph{dispatch plan} and the battery model for the MPC algorithm are built by applying adaptive data driven methodologies. The discussed control framework currently operates on a daily basis to dispatch the operation of a 20~kV feeder of the EPFL university campus using a 750~kW/500~kWh lithium titanate BESS.Comment: Submitted for publication, 201