Improving frequency response for AC interconnected microgrids containing renewable energy resources

Interconnecting two or more microgrids can help improve power system performance under changing operational circumstances by providing mutual and bidirectional power assistance. This study proposes two interconnected AC microgrids based on three renewable energy sources (wind, solar, and biogas). The wind turbine powers a permanent magnet synchronous generator. A solar photovoltaic system with an appropriate inverter has been installed. In the biogas generator, a biogas engine is connected to a synchronous generator. M1 and M2, two interconnected AC microgrids, are investigated in this study. M2 is connected to a hydro turbine, which provides constant power. The distribution power loss, frequency, and voltage of interconnected AC microgrids are modeled as a multi-objective function (OF). Minimizing this OF will result in optimal power flow and frequency enhancement in interconnected AC microgrids. This research is different from the rest of the research works that talk about the virtual inertia control (VIC) method, as it not only improves frequency using an optimal controller but also achieves optimal power flow in microgrids. In this paper, the following five controllers have been studied: proportional integral controller (PI), fractional-order PI controller (FOPI), fuzzy PI controller (FPI), fuzzy fractional-order PI controller (FFOPI), and VIC based on FFOPI controller. The five controllers are tuned using particle swarm optimization (PSO) to minimize the (OF). The main contribution of this paper is the comprehensive study of the performance of interconnected AC microgrids under step load disturbances, the eventual grid following/forming contingencies, and the virtual inertia control of renewable energy resources used in the structure of the microgrids, and simulation results are recorded using the MATLAB™ platform. The voltages and frequencies of both microgrids settle with zero steady-state error following a disturbance within 0.5 s with less overshoots/undershoots (3.7e-5/-0.12e-3) using VIC. Moreover, the total power losses of two interconnected microgrids must be considered for the different controllers to identify which one provides the best optimal power flow

Optimal Power Management of Interconnected Microgrids Using Virtual Inertia Control Technique

Two interconnected AC microgrids are proposed based on three renewable energy sources (RESs): wind, solar, and biogas. The wind turbine drives a permanent magnet synchronous generator (PMSG). A solar photovoltaic system (SPVS) with an appropriate inverter was incorporated. The biogas genset (BG) consists of a biogas engine coupled with a synchronous generator. Two interconnected AC microgrids, M1 and M2, were considered for study in this work. The microgrid M2 is connected to a diesel engine (DE) characterized by a continuous power supply. The distribution power loss of the interconnected AC microgrids comprises in line loss. The M1 and M2 losses are modeled as an objective function (OF). The power quality enhancement of the interconnected microgrids will be achieved by minimizing this OF. This research also created a unique frequency control method called virtual inertia control (VIC), which stabilizes the microgrid frequency using an optimal controller. In this paper, the following five controllers are studied: a proportional integral controller (PI), a fractional order PI controller (FOPI), a fuzzy PI controller (FPI), a fuzzy fractional order PI controller (FFOPI), and a VIC based on FFOPI controller. The five controllers were tuned using particle swarm optimization (PSO) to minimize the (OF). The main contribution of this paper is the comprehensive study of the performance of interconnected AC microgrids under step load disturbances, stepn changes in wind/solar input power, and eventually grid following/forming contingencies as well as the virtual inertia control of renewable energy resources used in the structure of the microgrid

Microgripper design and evaluation for automated µ-wire assembly: a survey

Microgrippers are commonly used for micromanipulation of micro-objects from 1 to 100 µm and attain features of reliable accuracy, low cost, wide jaw aperture and variable applied force. This paper aim is to review the design of different microgrippers which can manipulate and assemble µ-wire to PCB connectors. A review was conducted on microgrippers’ technologies, comparing fundamental components of structure and actuators’ types, which determined the most suitable design for the required micromanipulation task. Various microgrippers’ design was explored to examine the suitability and the execution of requirements needed for successful micromanipulation

Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management

A predictive real-time Energy Management System (EMS) is proposed which improves PV self-consumption and operating costs using a novel rule-based battery scheduling algorithm. The proposed EMS uses the day-ahead demand and PV generation forecasting to determine the best battery scheduling for the next day. The proposed method optimizes the use of the battery storage and extends battery lifetime by only storing the required energy by considering the forecasted day-ahead energy at peak time. The proposed EMS has been implemented in MATLAB software and using Active Office Building on the Swansea University campus as a case study. Results are compared favorably with published state-of-the-arts algorithms to demonstrate its effectiveness. Results show a saving of 20% and 41% in total energy cost over six months compared to a forecast-based EMS and to a conventional EMS, respectively. Furthermore, a reduction of 54% in the net energy exchanged with the utility by avoiding the unnecessary charge/discharge cycles

Investigation of Electric Vehicles Contributions in an Optimized Peer-to-Peer Energy Trading System

The rapid increase in integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) at the consumption level poses many challenges for network operators. Recently, Peer-to-Peer (P2P) energy trading has been considered as an effective approach for managing RESs, EVs, and providing market solutions. This paper investigates the effect of EVs and shiftable loads on P2P energy trading with enhanced Vehicle to Home (V2H) mode, and proposes an optimized Energy Management Systems aimed to reduce the net energy exchange with the grid. Mixed-integer linear programming (MILP) is used to find optimal energy scheduling for smart houses in a community. Results show that the V2H mode reduces the overall energy costs of each prosumer by up to 23% compared to operating without V2H mode (i.e., EVs act as a load only). It also reduces the overall energy costs of the community by 15% compared to the houses operating without the V2H mode. Moreover, it reduces the absolute net energy exchanged between the community and the grid by 3%, which enhances the energy independence of the community

Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation

Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study

This paper presents a predictive Energy Management System (EMS), aimed to improve the per-formance of a domestic PV-battery system and maximize self-consumption by minimizing energy exchange with the utility grid. The proposed algorithm facilitates a self-consumption approach, which reduces electricity bills, transmission losses, and the required central generation/storage systems. The proposed EMS uses a com-bination of Fuzzy Logic (FL) and a rule based-algorithm to optimally control the PV-battery system while con-sidering the day-ahead energy forecast including forecast error and the battery State of Health (SOH). The FL maximizes the lifetime of the battery by using SOH and State of Charge (SOC) in decision making algorithm to charge/discharge the battery. The proposed Battery Management System (BMS) has been tested using Active Office Building (AOB) located in Swansea University, UK. Furthermore, it is compared with three recently published methods and with the current BMS utilized in the AOB to show the effectiveness of the proposed technique. The results show that the proposed BMS achieves a saving of 18% in the total energy cost over six months compared to a similar day-ahead forecast-based work. It also achieves a saving up to 95% compared to other methods (with a similar structure) but without a day-ahead forecast-based management. The proposed BMS enhances the battery's lifetime by reducing the average SOC up to 47% compared to the previous methods through avoiding unnecessary charge and discharge cycles. The impact of the PV system size and the battery capacity on the net exchanged energy with the utility grid is also investigated in this study

Survey of Energy Harvesting Technologies for Wireless Sensor Networks

Energy harvesting (EH) technologies could lead to self-sustaining wireless sensor networks (WSNs) which are set to be a key technology in Industry 4.0. There are numerous methods for small-scale EH but these methods differ greatly in their environmental applicability, energy conversion characteristics, and physical form which makes choosing a suitable EH method for a particular WSN application challenging due to the specific application-dependency. Furthermore, the choice of EH technology is intrinsically linked to non-trivial decisions on energy storage technologies and combinatorial architectures for a given WSN application. In this paper we survey the current state of EH technology for small-scale WSNs in terms of EH methods, energy storage technologies, and EH system architectures for combining methods and storage including multi-source and multi-storage architectures, as well as highlighting a number of other optimisation considerations. This work is intended to provide an introduction to EH technologies in terms of their general working principle, application potential, and other implementation considerations with the aim of accelerating the development of sustainable WSN applications in industry

MILP Optimized Management of Domestic PV-Battery Using Two Days-Ahead Forecasts

This paper proposes an Energy Management System (EMS) for domestic PV-battery applications with the aim of reducing the absolute net energy exchange with the utility grid by utilizing the two days-ahead energy forecasts in the optimization process. A Mixed-Integer Linear Programming (MILP) exploits two days-ahead energy demand and PV generation forecasts to schedule the day-ahead battery energy exchange with both the utility grid and the PV generator. The proposed scheme is tested using the real data of the Active Office Building (AOB) located in Swansea University, UK. Performance comparisons with state-of-the-art and the commercial EMS currently running at the AOB reveal that the proposed EMS increases the self-consumption of PV energy and at the same time reduces the total energy cost. The absolute net energy exchange with the grid and the total operating costs are reduced by 121% and 54% compared to the state-of-the-art and 194% and 8% when compared to the commercial EMS over a six-month period. Furthermore, the results show that the pro-posed method can reduce the energy bill by up to 46%for the same period compared to the state-of-the-art. The paper also investigates the effect of using different objective functions on the performance of the EMS and shows that the proposed EMS operate more efficiently when it is compared with another cost function that directly promotes reducing the absolute net energy exchange

Enhancing PV Self-consumption within an Energy Community using MILP-based P2P Trading

The high penetration of Distributed Energy Resources (DERs) into the demand side has led to an increase in the number of consumers becoming prosumers. Recently, Peer-to-Peer (P2P) energy trading has gained increased popularity as it is considered an effective approach for managing DERs and offering local market solutions. This paper presents a P2P Energy Management System (EMS) that aims to reduce the absolute net energy exchange with the utility by exploiting two days-ahead energy forecast and allowing the exchange of the surplus energy among prosumers. Mixed-Integer Linear Programming (MILP) is used to schedule the day-ahead household battery energy exchange with the utility and other prosumers. The proposed system is tested using the measured data for a community of six houses located in London, UK. The proposed P2P EMS enhanced the energy independency of the community by reducing the exchanged energy with the utility. The results show that the proposed P2P EMS reduced the household operating costs by up to 18.8% when it is operated as part of the community over four months compared to operating individually. In addition, it reduced the community’s total absolute net energy exchange with the utility by nearly 25.4% compared to a previous state-of-the-art energy management method