Providing Flexibility via Residential Batteries

Microgrids (MGs) are power systems consisting of an electrical network composed by distributed loads and generation units that may include a communication network for improved operation. The considered MG in islanded mode is driven by voltage source inverters implementing decentralized droop control for active power sharing together with a communication-based consensus algorithm for frequency regulation. This paper analyzes the MG performance subject to network failures that provoke network partitions. It is considered that the electrical partition leads to several sub-MGs working in parallel where the power demand can be always guaranteed by the generation units, and the communication partition leads to several consensus algorithms also working in parallel. The double partitioning is analyzed through a closed-loop system model derived using the power flow equations that includes the electrical and communication connectivity. Analytical expressions for the steady-state values for both frequency and active power depending on the partitioning are derived. Selected experimental results on a low-scale laboratory MG illustrate the (undesirable) impact that unexpected partitions have in system performance.

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO2 emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios.

When dealing with interconnected power systems, any sudden load changes leads to the deviation of active power and frequency in the tie-line. The daily management of the power system is also conditioned by the control of the active power and the frequency. It is a very important task in the conduct of the any electrical network to supply sufficiently and with high reliability the active power to be transmitted to the customers. Maintaining the frequency of each area and tie-line active power flow variation within prescribed value by adjusting the generator active outputs remain one of the main tasks of the network operators. This paper presents a model predictive regulator in controlling the power flow in tie-lines and frequency deviations in the microgrid, which will lead to power balance between the total active power generated and active power demand of the system. The system being studied consists of two microgrids, each made up of a wind farm, conventional thermal and hydro plants generator, photovoltaic (PV) system, storage system and active power demand. Predictive control algorithm is applied to control the power flow between two microgrids.

The stability control of nominal frequency and terminal voltage in an interconnected power system (IPS) is always a challenging task for researchers. The load variation or any disturbance affects the active and reactive power demands, which badly influence the normal working of IPS. In order to maintain frequency and terminal voltage at rated values, controllers are installed at generating stations to keep these parameters within the prescribed limits by varying the active and reactive power demands. This is accomplished by load frequency control (LFC) and automatic voltage regulator (AVR) loops, which are coupled to each other. Due to the complexity of the combined AVR-LFC model, the simultaneous control of frequency and terminal voltage in an IPS requires an intelligent control strategy. The performance of IPS solely depends upon the working of the controllers. This work presents the exploration of control methodology based on a proportional integral–proportional derivative (PI-PD) controller with combined LFC-AVR in a multi-area IPS. The PI-PD controller was tuned with recently developed nature-inspired computation algorithms including the Archimedes optimization algorithm (AOA), learner performance-based behavior optimization (LPBO), and modified particle swarm optimization (MPSO). In the earlier part of this work, the proposed methodology was applied to a two-area IPS, and the output responses of LPBO-PI-PD, AOA-PI-PD, and MPSO-PI-PD control schemes were compared with an existing nonlinear threshold-accepting algorithm-based PID (NLTA-PID) controller. After achieving satisfactory results in the two-area IPS, the proposed scheme was examined in a three-area IPS with combined AVR and LFC. Finally, the reliability and efficacy of the proposed methodology was investigated on a three-area IPS with LFC-AVR with variations in the system parameters over a range of  ± 50%. The simulation results and a comprehensive comparison between the controllers clearly demonstrates that the proposed control schemes including LPBO-PI-PD, AOA-PI-PD, and MPSO-PI-PD are very reliable, and they can effectively stabilize the frequency and terminal voltage in a multi-area IPS with combined LFC and AVR.

In this paper, a multi-microgrid (MMG) system consisting of three microgrids (MGs), each with three nano grids (NGs) and one central battery storage unit, is modeled to pursue multiple objectives in on-grid and off-grid modes by implementing control strategies in the d-q frame. When the MMG is connected to the upstream grid, all three MGs are responsible for injecting active power into the grid while supplying their internal loads. On the other hand, MG 1 can inject reactive power into the grid to mitigate voltage sags, while MG 2 can absorb reactive power from the grid to address voltage swells. Additionally, MG 3 focuses on recovering fault voltage by improving its low voltage ride-through (LVRT) capability during a single-line-to-ground (SLG) fault. In off-grid mode, the MGs pursue additional objectives to ensure that the load of MG 2, considered the critical MG, remains powered under all operating conditions. If the central battery of MG 2 becomes disconnected, the central batteries of MGs 1 and 3 will provide support, thereby improving the resilience of the DC bus. Furthermore, the AC bus in MG 2 is designed to ensure that if the inverter supplying the internal loads of any of NGs 1 to 3 encounters an outage, the inverters of NGs 1 to 3 in MGs 1 and 3 will provide support. Results obtained from nonlinear simulations in MATLAB/SIMULINK show that the proposed control strategies in on-grid mode successfully injected active powers of 15 kW, 20 kW, and 20 kW for MGs 1, 2, and 3, respectively. During a voltage sag, MG 1 injected 65 kVAr of reactive power into the grid, while MG 2 absorbed 60 kVAr of reactive power to stabilize the voltage profile during a voltage swell. In the event of an SLG fault, MG 3 recovered the voltage by injecting 90 kVAr of reactive power, with a maximum three-phase current of 200 A into the fault location. When an SLG fault coincided with voltage sags, the voltage profile improved from 0.6 per unit (pu) to over 0.9 pu due to the simultaneous operation of MGs 1 and 3. Also, in off-grid mode, the proposed control strategies successfully injected 20 kW through MG 1 and 25 kW through MG 3 to support MG 2 when its central battery went out of circuit. Additionally, when the inverter of NG 1 in MG 2 was disconnected, 13 kW of auxiliary power was transferred from NG 1 of MG 1 to NG 1 of MG 2, ensuring that the three-phase load remained powered.

This paper gives an intelligent MPPT controller based boost PFC converter topology for AC to DC conversion in the wind energy system. The single stage boost PFC circuit topology comprises of bridge rectifier plus boost converter, which is employed as AC to DC converter with wide conversion ratio, continuous input current, and low input current ripple. It can be widely used for AC to DC conversion with high efficiency and improved power factor. And a grid side converter is engaged for DC to AC conversion in the grid-connected mode, which includes the DC-link voltage control and grid side power control of the proposed system. In this paper, RBFN controller based single-stage PFC converter is designed for the grid-tied wind energy conversion system to supply the load demand of 1kW active power and 900VAr reactive power. The result analysis of both off-grid and grid-tied configurations is validated in MATLAB-Simulink.

A través de este artículo se propone y valida modelos de dispositivos de tecnología FACTS de tipo Compensador Estático Sincrónico para Distribución (DSTATCOM). En primera instancia, se presenta el modelado de un DSTATCOM simplificado realizado a través de una fuente de corriente controlada. Posteriormente, se expone un DSTATCOM modelado a través de estrategias de control considerando un conversor PWM (Pulse-Width Modulation - Modulación por ancho de pulsos). Los modelos propuestos de dispositivos DSTATCOM se validan en un sistema de prueba de Microrred Eléctrica (MRE) de la CIGRÉ. La presencia de cargas de tipo motor de inducción en la MRE requiere una considerable demanda de potencia reactiva, esto ocasiona inconvenientes para mantener la Estabilidad Dinámica de Tensión (EDT). Ésta situación se agrava ante la ocurrencia de fallas que desencadenen una operación aislada de la MRE. Para solucionar estos inconvenientes se hará uso de un dispositivo DSTATCOM. La conexión y ubicación óptima del DSTATCOM en la MRE se realiza a través de un algoritmo de búsqueda exhaustiva, considerando la premisa del mejor rendimiento en términos de EDT realizado a través de simulaciones dinámicas en PowerFactory DIgSILENT. Comparativamente se presentan los resultados obtenidos para los dos modelos realizados, demostrándose la mejora de la EDT en la operación aislada de la MRE con ambos modelos. Dependiendo del detalle y alcance de los estudios en el sistema de MRE, se puede elegir entre los modelos DSTATCOM desarrollados. Todos los detalles de modelos, esquemas de control y datos de los dispositivos DSTATCOM son proporcionados en este documento.

Study of Identification for the Inertia Constant in a Small Power System for LFC

A simple neural network state-of-charge predictor trained on one-year of energy storage system data is presented. The model uses the active power command and the state-of-charge for the current time-step, and implements a nonlinear auto-regressive network with exogenous inputs to predict the state-of-charge at the subsequent time-step. The neural network training algorithm is written in the Julia programming language, independent of any existing machine learning platforms; the resulting model is compared to one developed using Python/TensorFlow. The simulation performance was validated with data collected from the energy storage system that was dispatched to follow a standard frequency regulation duty cycle not used as part of the training data. The mean-absolute-error between the predicted state of charge and the validation data is shown to be less then 1%, despite the limited data and lack of physical information about the system.

Energy management plays a critical role to accomplish a reliable and efficient operation of a Microgrid (MG). As the power from renewable energy sources and load demand is uncertain energy management problem (EMP) of a MG becomes complex. This paper studies the EMP for a grid connected MG consisting of dispatchable distributed generators (DDGs), battery, and renewable generation such as wind power, respectively. In order to handle the uncertainties ascending from wind power and load demand, information gap decision theory (IGDT) is adopted in this paper. An illustrative case study is presented to demonstrate the applicability of the proposed method. Results show that least robustness is achieved on modeling uncertainty in both wind power and load demand in comparison to the modeling of uncertainty in either parameter.

Chapter 15 - Optimal Management of Hydrothermal-Based Micro-Grids Employing Robust Optimization Method

Commercial buildings and shopping malls face rising electricity costs and increasing pressure to adopt sustainable practices. This paper presents the first long-term, multi-site empirical validation of Automatic Voltage Regulator (AVR) deployment in Thai retail facilities, providing robust evidence for tropical, motor-heavy load contexts. The study evaluates the engineering, economic, and environmental performance of an AVR with an autotransformer core under real operating conditions. High-resolution measurements were collected before and after AVR installation, using Class 0.2s analyzers and a Building Energy Management System (BEMS) across multiple branches during a four-month monitoring campaign (February–May). Results indicate that a modest voltage reduction of 8.06% yielded a 12.02% decrease in active power demand, a 6.22% current reduction, and a 2.26% improvement in power factor. The greatest savings occurred in HVAC (8.19%) and refrigeration loads (8.20%), while lighting loads remained nearly unchanged. Economically, the system delivered ~177 kWh/day savings, equivalent to 262,212 THB/year, with a simple payback of 2.67 years and an ROI of 37.5%. Environmentally, the AVR reduced 36.6 tCO2/year (±5%), aligning with Thailand’s Energy Efficiency Plan (EEP) 2018–2037 and Carbon Neutrality Roadmap and offering additional potential for T-VER monetization. These findings confirm AVR technology as a scalable, standards-compliant, and high-return retrofit solution for commercial facilities in tropical climates.

The escalated use of renewable sources especially Solar PV calls for control methods, the aim being increasing its flexibility toward changing conditions of load and irradiances due to intermittent nature of PV. Thus a standalone PV system is presented suitable 3-phase low voltage applications. The DC-DC converters are connected in forward power stage for boosting PV voltage and as bidirectional interface for battery backup. The system control is such that it avoids battery overcharging or deep discharging by maintaining the battery SOC levels. This is facilitated by the Desired Power Control (DPC) of system algorithm which tracks the PV power according to the load requirements. Major drawback of control of the standalone system is management of excess PV power which is solved by using DPC without the use of dump loads which are difficult to manage. The control of the DC-DC converters is done analytically for accurate tracking the changing references according to the prevailing conditions. The ac voltage regulation of the inverter is enhanced by using the abc-dq transformation and its control is also done analytically. The active power demand is controlled by regulating DC link voltage. The system is simulated in MATLAB/Simulink for all operations and the results are presented along with analysis modelling and control design.

CHAPTER 4 - THE ESTIMATION OF DEMAND AND TOTAL GENERATION REQUIREMENT

This paper presents an overview of the current status of the development of the smart grid in Great Britain (GB). The definition, policy and technical drivers, incentive mechanisms, technological focus, and the industry's progress in developing the smart grid are described. In particular, the Low Carbon Networks Fund and Electricity Network Innovation Competition projects, together with the rollout of smart metering, are detailed. A more observable, controllable, automated, and integrated electricity network will be supported by these investments in conjunction with smart meter installation. It is found that the focus has mainly been on distribution networks as well as on real-time flows of information and interaction between suppliers and consumers facilitated by improved information and communications technology, active power flow management, demand management, and energy storage. The learning from the GB smart grid initiatives will provide valuable guidelines for future smart grid development in GB and other countries.