Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications

ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration

An Adaptive Neuro Fuzzy Inference System (ANFIS) based Extreme Learning Machine (ELM) theory is utilised in this research work. In particular, the proposed algorithm is applied for designing a controller for electric vehicle to grid (V2G) integration in smart grid scenario. Initially, learning speed and accuracy of this proposed approach are continuously monitored and then, the performance of ELM-ANFIS (e-ANFIS) based controller is examined for its transient response. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Hence, a control practice for their charge and discharge patterns can be easily calculated even with the presence of large numbers of Plug-in Hybrid Electric Vehicles (PHEV). To examine the computational performance and transient response of the e-ANFIS based controller, it is evaluated with the usual ANFIS supported controller. The IEEE 33 bus radial distribution system based approach is implemented to ensure the sturdiness of this prescribed approach

Solar Energy Dependent Supercapacitor System with ANFIS Controller for Auxiliary Load of Electric Vehicles

Innovations are required for electric vehicles (EVs) to be lighter and more energy efficient due to the range anxiety issue. This article introduces an intelligent control of an organic structure solar supercapacitor (OSSC) for EVs to meet electrical load demands with solar renewable energy. A carbon fibreȬreinforced polymer, nano zinc oxide (ZnO), and copper oxide (CuO) fillers have been used in the development of OSSC prototypes. The organic solar cell, electrical circuits, converter, controller, circuit breaker switch, and batteries were all integrated for the modelling of OSSCs. A carbon fibre (CF)Ȭreinforced CuOȬdoped polymer was utilised to improve the concentration of elecȬ trons. The negative electrodes of the CF were strengthened with nano ZnO epoxy to increase the mobility of electrons as an nȬtype semiconductor (energy band gap 3.2–3.4 eV) and subsequently increased to 3.5 eV by adding 6%ȱΔȬcarbon. The electrodes of the CF were strengthened with epoxyȬ filled nanoȬCuO as a pȬtype semiconductor to facilitate bore/positive charging. They improve the conductivity of the OSSC. The OSSC power storage was controlled by an adaptive neuroȬfuzzy inȬ telligent system controller to meet the load demand of EVs and auxiliary battery charging. MoreoȬ ver, a fully charged OSSC (solar irradiance = 1000 W/m2) produced 561 Wȉh/m2 to meet the vehicle load demand with 45 A of auxiliary battery charging current. Therefore, the OSSC can save 15% in energy efficiency and contribute to emission control. The integration of an OSSC with an EV battery can minimise the weight and capacity of the battery by 7.5% and 10%, respectively

Forecast-based Energy Management Systems

The high integration of distributed energy resources into the domestic level has led to an increase in the number of consumers becoming prosumers (producer + customer), which creates several challenges for network operators, such as controlling renewable energy sources over-generation. Recently, self-consumption as a new approach is encouraged by several countries to reduce the dependency on the national grid. This work presents two different Energy Management System (EMS) algorithms for a domestic Photovoltaic (PV) system: (a) real-time Fuzzy Logic-based EMS (FL-EMS) and (b) day-ahead Mixed Integer Linear Programming-based EMS (MILP-EMS). Both methods are tested using the data from the Active Office Building (AOB) located in Swansea University, Bay Campus, UK, as a case study to demonstrate the developed EMSs. AOB comprises a PV system and a Li-ion Battery Storage System (BSS) connected to the grid. The MILP-EMS is used to develop a Community Energy Management System (CEMS) to facilitate local energy exchange. CEMS is tested using the data from six houses located in London, UK, to form a community. Each household comprises a PV system and BSS connected to the grid. It is assumed that all six households use an EV and are equipped with a bidirectional charger to facilitate the Vehicle to House (V2H) mode. In addition, two shiftable appliances are considered to shift the demand to the times when PV generation is maximum to maximise community local consumption. MATLAB software is used to code the proposed systems. The FL-EMS exploits day-ahead energy forecast (assumed it is available from a third party) to control the BSS with the aim of reducing the net energy exchange with the grid by enhancing PV self-consumption. The FL-EMS determines the optimal settings for the BSS, taking into consideration the BSS's state of health to maximise its lifetime. The results are compared with recently published works to demonstrate the effectiveness of the proposed method. The proposed FL-EMS saves 18% on total energy costs in six months compared to a similar system that utilises a day-ahead energy forecast. In addition, the method shows a considerable reduction in the net energy exchanged between the AOB and the grid. The main objective of the MILP-EMS is to reduce the net energy exchange with the grid by including a two days-ahead energy forecast in the optimisation process. The proposed method reduces the total operating costs (energy cost + BSS degradation cost) by up to 35% over six months and reduces net energy exchanged with the grid compared to similar energy optimisation technique. The proposed cost function in MILP-EMS shows that it can outperform the performance of alternative cost function that directly reduce the net energy exchange. CEMS uses two days-ahead energy forecast to reduce the net energy exchange with the grid by coordinating the distributed BSSs. The proposed CEMS reduces the total operating costs (energy costs + BSSs degradation costs) of the community by 7.6% when compared to the six houses being operated individually. In addition, the proposed CEMS enhances community self-consumption by reducing the net energy exchange with the grid by 25.3% over four months compared to similar community energy optimisation technique. A further reduction in operating costs is achieved using V2H mode and including shiftable appliances. Results show that introducing the V2H mode reduces both the total operating costs of the community and the net energy exchange with the grid

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Modelling and Performance Evaluation of ANFIS Controller-Based Bidirectional Power Management Scheme in Plug-in Electric Vehicles Integrated with Electric Grid

A bi-directional power exchange between the plug-in electric vehicle (PEV) and the AC electrical grid is necessary to perform the Vehicle to Grid (V2G) and Grid to Vehicle (G2V) operations. While performing these operations, different power converters and controllers play an important role as mediators between the PEV and electric grid. Various works have demonstrated the utilization of controllers for PEV’s battery power management. However, the existing conventional controllers have technical shortcomings about vulnerability to controller gain, accurate mathematical modelling, poor adaptability, sluggish response to a sudden outburst and lengthy interval execution processing. Therefore, this paper develops an adaptive neuro-fuzzy inference system (ANFIS) control strategy based bidirectional power management scheme to ensure the optimal electrical power flow exchange between the AC electrical grid and battery storage system in PEVs. This paper aims to reduce the stress on the grid power side and utilize the unused power properly. The performance of the ANFIS model is varied using two PEVs based on real-life power consumptions by different loads at home based on five operational modes. Besides, a comparative analysis between the ANFIS controller and the PI controller is carried out to demonstrate the effectiveness of the proposed control scheme. The results illustrate that the proposed ANFIS controller delivers a smoother power injection from the PEV to the AC power grid with the least harmonics as well as achieves a smoother battery profile and less distortion when power is absorbed by PEV battery.publishedVersio

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

The operation of the smart grid is expected to be heavily reliant on microprocessor-based control. Thus, there is a strong need for interoperability standards to address the heterogeneous nature of the data in the smart grid. In this research, we analyzed in detail the security threats of the Generic Object Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV) protocol mappings of the IEC 61850 data modeling standard, which is the most widely industry-accepted standard for power system automation and control. We found that there is a strong need for security solutions that are capable of defending the grid against cyber-attacks, minimizing the damage in case a cyber-incident occurs, and restoring services within minimal time. To address these risks, we focused on correlating cyber security algorithms with physical characteristics of the power system by developing intelligent agents that use this knowledge as an important second line of defense in detecting malicious activity. This will complement the cyber security methods, including encryption and authentication. Firstly, we developed a physical-model-checking algorithm, which uses artificial neural networks to identify switching-related attacks on power systems based on load flow characteristics. Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofed-data-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together

Applications of Probabilistic Forecasting in Smart Grids : A Review

This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed