Komponentenbasierte dynamische Modellierung von Energiesystemen und Energiemanagement-Strategien für ein intelligentes Stromnetz im Heimbereich

The motivation of this work is to present an energy cost reduction concept in a home area power network (HAPN) with intelligent generation and flexible load demands. This study endeavors to address the energy management system (EMS) and layout-design challenges faced by HAPN through a systematic design approach. The growing demand for electricity has become a significant burden on traditional power networks, prompting power engineers to seek ways to improve their efficiency. One such solution is to integrate dispersed generation sources, such as photovoltaic (PV) and storage systems, with an appropriate control mechanism at the distribution level. In recent years, there has been a significant increase in interest in the installation of PV-Battery systems, due to their potential to reduce carbon emissions and lower energy costs. This research proposes an optimal economic power dispatch strategy using Model Predictive Control (MPC) to enhance the overall performance of HAPN. A hybrid AC/DC microgrid concept is proposed to address the control choices made by the appliance scheduling and hybrid switching approaches based on a linear programming optimization framework. The suggested optimization criteria improve consumer satisfaction, minimize grid disconnections, and lower overall energy costs by deploying inexpensive clean energy generation and control. Various examples from actual case study demonstrate the use of the established EMS and design methodology.Die Motivation dieser Arbeit besteht darin, ein Konzept zur Senkung der Energiekosten in einem Heimnetzwerk (HAPN) mit intelligenter Erzeugung und exiblen Lastanforderungen vorzustellen. Im Rahmen dieser Forschungsarbeit wird ein Entwurf für ein HAPN entwickelt, indem das Energiemanagementsystem (EMS) und der Entwurf des Layouts auf der Grundlage des Systemmodells und der betrieblichen Anforderungen gelöst werden. Die steigende Nachfrage nach Elektrizität ist für traditionelle Stromnetze kostspielig und infrastrukturintensiv. Daher konzentrieren sich Energietechniker darauf, die Effizienz der derzeitigen Netze zu erhöhen. Dies kann durch die Integration verteilter Erzeugungsanlagen (z. B. Photovoltaik (PV), Speicher) mit einem geeigneten Kontrollmechanismus für das Energiemanagement auf der Verteilungsseite erreicht werden. Darüber hinaus hat das Interesse an der Installation von PV-Batterie-basierten Systemen aufgrund der Reduzierung der CO2-Emissionen und der Senkung der Energiekosten erheblich zugenommen. Es wird eine optimale wirtschaftliche Strategie für den Energieeinsatz unter Verwendung einer modellprädiktiven Steuerung (MPC) entwickelt. Es wird zudem ein hybrides AC/DC-Microgrid-Konzept vorgeschlagen, um die Steuerungsentscheidungen, die von den Ansätzen der Geräteplanung und der hybriden Umschaltung getroffen werden, auf der Grundlage eines linearen Programmierungsoptimierungsrahmens zu berücksichtigen. Die vorgeschlagenen Optimierungskriterien verbessern die Zufriedenheit der Verbraucher, minimieren Netzabschaltungen und senken die Gesamtenergiekosten durch den Einsatz von kostengünstiger und sauberer Energieerzeugung. Verschiedene Beispiele aus einer Fallstudie demonstrieren den Einsatz des entwickelten EMS und der Entwurfsmethodik

Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)

Internet of energy based smart power grids demonstrate high in-feed from renewable energy resources (RESs) and lofty out-feed to energy consumers. Uncertainties evolved by incorporating RESs and time-varying energy consumption present immense challenges to the optimal control of smart power networks. To deal with these challenges, it is important to make the system deterministic by making time-ahead prediction and scheduling of power supply and demand. The present work confers a model of a co-scheduling framework, organizing cost-efficient activation of energy supply entities (ESEs) and load demands in a home area power network (HAPN). It integrates roof-top photovoltaic (PV) panels, diesel energy generator (DE), energy storage devices (ESDs), and smart load demands (SLDs) along with grid-supplied power. The scheduling model is based on mixed-integer linear programming (MILP) framework, incorporates a “min-max” optimization algorithm that reduces the daily energy bills, maintains high comfort level for the energy consumers, and increases the self-sufficiency of the home. The proposed strategy exploits the flexibility in dynamic energy price signals and SLDs of various classes, providing day-ahead cost-optimal scheduling decisions for incorporated energy entities. A linearized component-based model is developed, considering inefficiencies, taking various power phase modes of the SLDs along with the cost of operation, maintenance, and degradation of the equipment. A case study based on numerical analysis determines the particular features of the proposed HAPN model. Simulation results demonstrate the real prospect of our implemented strategy, utilizing a cost-effective optimal blend of distinct energy entities in a smart home

PFC Topologies for AC to DC Converters in DC Micro-Grid

With increasing dominance of renewable energy resources and DC household appliances, the novelty of DC micro grid is attracting significant attention. The key interface between the main supply grid and DC micro grid is AC to DC converter. The conventional AC to DC converter with large output capacitor introduces undesirable power quality problems in the main supply current. It reduces system efficiency due to low power factor and high harmonic distortion. Power Factor Correction (PFC) circuits are used to make supply currents sinusoidal and in-phase with supply voltages. This paper presents different PFC topologies for single phase AC to DC converters which are analyzed for power factor (PF), total harmonic distortion (THD) and system efficiency by varying output power. Two-quadrant shunt active filter topology attains a power factor of 0.999, 3.03% THD and 98% system efficiency. Output voltage regulation of the presented active PFC topologies is simulated by applying a step load. Two-quadrant shunt active filter achieves better output voltage regulation compared to other topologies and can be used as grid interface

Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets

To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to 0.013%

Cost Effective Bidirectional Power Transactions for Queueing Energy Requests in Smart Micro-Grids

This paper investigates real time problem of cost efficient energy distribution within smart micro-grids (SMG). The aggregator announces a day-ahead price of electricity, and is most often not fully aware of on spot availability of renewable resources. Sometimes, users also encounter estimation errors in their day-ahead energy procurement. In both situations an extra cost is incurred to aggregator or the users to fulfill their needs. This cost could be minimized by intelligently balancing the real time renewable generations with users load demands. The problem is more complex when there are a number of users communicating with each other and with the aggregator at the same time through a Digital Energy Management System (DEMS) for their demand requirements. It is very challenging for DEMS to ensure comfort level for its consumers while providing low cost electricity. Hence, we establish an optimization problem of curtailing the time average cost of electricity, under certain bounds of consumers satisfactions. We introduce load scheduling and energy transaction (LSET) control policy based on Lyapunov optimization theory to develop our proposed solution

Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets

To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to 0.013%

Mitigation of Circulating Currents for Parallel Connected Sources in a Standalone DC Microgrid

In a standalone DC microgrid, sources are interconnected in a parallel configuration. When sources of different power ratings are parallel connected, there arises a major issue of circulating currents which disturb current sharing by sources as per their capacity. Consequently, the voltage regulation becomes poorer. Additionally, connecting line resistances also play their part to contribute to abnormal current sharing. Droop controllers are normally preferred for the mitigation of circulating currents among parallel-connected sources. However, droop controllers cannot eliminate circulating currents for different rating sources. Hence, current sharing and voltage regulation cannot be ensured simultaneously. To address the issues, a distributed architecture-based Sliding Mode Control (SMC) technique is proposed in this paper. An analysis of the circulating currents for a two-source system is presented. Simulation results are presented to show the effectiveness and fail-safe operation of the proposed technique in a steady-state condition

Optimal Control of Power Cost and Consumer Satisfaction Using Smart Grid Intelligent Energy Management System

This paper investigates the trade off between consumer satisfaction and the electricity cost. Due to forecast error in day ahead low price energy procurement and random realization of real time renewable dc power, it is difficult for an Intelligent Energy Management (IEM) operator to guarantee a satisfaction level to its users. The problem is more complex in the context of hybrid AC/DC smart grid, where AC to DC and DC to AC conversions result in converter losses. Moreover, during peak hours, increased power is required to serve increased load demands which is purchased at higher market rates by the utilities. We demonstrate the optimization problem of minimizing the time average cost of electricity, under the constraints of providing consumers with high satisfaction level. The solution is proposed by introducing load scheduling and hybrid switching control (LSHS) algorithm based on Lyapunov optimization, which is responsible for delivering cost efficient electricity and serving shiftable loads within certain time bound. Our algorithm requires only electricity price values and works without any prior or future knowledge of supply and demand statistics of power