Design Simulation of Improvement of Voltage Profile and Loss Minimization by Efficient Placement of Distributed Generation in Grid Connected System

Electricity consumption is rapidly increasing, and the gap between generation and load is widening. The mismatch between demand and load causes a range of problems, including failure, low power, and, in certain cases, blackout. These issues will be solved by including Distributed Generation (DG) into the system. For maximum dependability, technological and economic benefits, and optimal size and capabilities of distributed generators, the  proper  distribution of  power systems,  kind of generating equipment, number of units, and so on are critical. Among these concerns, the difficulty of placing DG units in the best location and size is critical. Inadequate DG resource distribution to the power system will result in increased power losses. This problem is solved using genetic algorithms. For the conventional 15 bus radial distribution system, the load flow is generated using the backward forward sweep method. Load flow is used to assess the impact of DG size and location on system losses. Machine losses rise as a result of inappropriate DG allocation. As a result, the genetic algorithm (GA), an evolutionary process, is being researched, and an algorithm is being created to discover the appropriate size and position of the distributed generation unit in a radial distribution system. The overall active power losses are reduced, and the voltage profile is improved due to proper DG allocation. Introduces a multi-objective feature that accounts for active power losses, voltage changes, and DG costs, with each variable given a weight. Voltage limits, active power loss constraints, and DG size limitations all affect objective feature minimization. This method is utilized on the conventional 15 bus radial distribution system

A Congestion Forecast Framework for Distribution Systems with High Penetration of PV and PEVs

This paper presents a congestion forecast framework for electrical distribution systems with high penetration of solar photovoltaic and plug-in electric vehicles. The framework is based on probabilistic power flow to account for the uncertainties in photovoltaic production and load demand. The proposed framework has been implemented and tested using the data of the real distribution grid of Chalmers University of Technology campus. Cases studies have been carried out using the framework to analyse the impact of different local production levels and operating modes of solar photovoltaic inverter. The results have shown that cumulative probability for network congestion in branches and transformers would increase by 30% and 20% respectively, when the level of local PV generation, demand and PEVs demand to increase by 100%, 95% and 100% respectively. Also, results have shown that network congestion in branches and transformers is 4% and 8% respectively, more likely to occur in the constant-V mode as compared to constant-pf mode. These results can help distribution system operators to predict any upcoming congestion in their system and subsequently help them in taking suitable actions in order to mitigate congestion

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems

Modeling of Utility Distribution Feeder in OpenDSS with Steady State Impact Analysis of Distributed Generation

With the deregulation of the electric power industry and the advancement of new technologies, the attention of the utilities has been drawn towards adopting Distributed Generation (DG) into their existing infrastructure. The deployment of DG brings ample technological and environmental benefits to the traditional distribution networks. The appropriate sizing and placement of DGs which generate power locally to fulfill consumer demands, helps to reduce power losses and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a utility distribution feeder in OpenDSS. Studies are conducted on the data obtained from American Electric Power utility. This thesis develops models for 12.47 kV (medium voltage) distribution feeders in OpenDSS by utilizing the existing models in CYMDIST. The model conversion is achieved by a detailed one-to-one component matching approach for multi phased lines, conductors, underground cables, loads, regulators and capacitor banks. The power flow results of OpenDSS and CYMDIST are compared to derive important conclusions.;The second major objective is to analyze the impacts of DG on distribution systems and two focus areas are chosen, namely: effect on voltage profiles and losses of the system and the effects on power market operation. To analyze the impacts of DG on the distribution systems, Photovoltaic (PV) system with varying penetration levels are integrated at different locations along the developed feeder model. PV systems are one of the fastest growing DG technologies, with a lot of utilities in North America expressing interest in its implementation. Many utilities either receive incentives or are mandated by green-generation portfolio regulations to install solar PV systems on their feeders. The large number of PV interconnection requests to the utilities has led to typical studies in the areas of power quality, protection and operation of distribution feeders. The high penetration of PV into the system throws up some interesting implications for the utilities. Bidirectional power flow into a distribution system, (which is designed for one way power flow) may impact system voltage profiles and losses. In this thesis, the effects of voltage unbalance and the losses of the feeder are analyzed for different PV location and penetration scenarios.;Further, this thesis tries to assess the impact of DG on power market operations. In a deregulated competitive market, Generation companies (Genco) sell electricity to the Power exchange (PX) from which large customers such as distribution companies (Disco) and aggregators may purchase electricity to meet their needs through a double sided bidding system. The reliable and efficient operation of this new market structure is ensured by an independent body known as the Independent System Operator (ISO). Under such a market structure, a particular type of unit commitment, called the Price Based Unit Commitment (PBUC) is used by the Genco to determine optimal bids in order to maximize its profit. However, the inclusion of intermittent DG resources such as wind farms by the Gencos causes uncertainty in PBUC schedules. In this research, the effects of intermittency in the DG resource availability on the PBUC schedule of a Genco owning a distribution side wind farm are analyzed

A Review of Hybrid Renewable Energy Systems Based on Wind and Solar Energy: Modeling, Design and Optimization

In this chapter, an attempt is made to thoroughly review previous research work conducted on wind energy systems that are hybridized with a PV system. The chapter explores the most technical issues on wind drive hybrid systems and proposes possible solutions that can arise as a result of process integration in off-grid and grid-connected modes. A general introduction to wind energy, including how wind energy can be harvested, as well as recent progress and development of wind energy are discussed. With the special attention given to the issues related to the wind and photovoltaic (Wind-PV) systems. Throughout the chapter emphasis was made on modeling, design, and optimization and sensitivity analysis issues, and control strategies used to minimize risk as well as energy wastage. The reported reviewed results in this chapter will be a valuable researchers and practicing engineers involved in the design and development of wind energy systems

Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH

In recent years, photovoltaic distributed generation (PVDG) has seen rapid growth due to its benefits in supporting the power system network, enhancing the transmission and distribution of power, and minimizing power congestion. PVDGs are connected directly to the load and produce power locally for the users, thus help to relieve the entire grid by reducing the demand especially during the peak load. Due to the random nature of the weather and occurrences of uncertainty, the planning and optimization of PVDG in the power system network with predicted uncertainty in photovoltaic generations and load variations are of crucial importance to minimize power losses. Thus, this research aims to develop a new optimization framework based on Monte Carlo embedded hybrid variant mean – variance mapping optimization (MVMO-SH) for the planning of PVDGs by considering these uncertainties. In this work, the probabilistic method in managing the risk of solar irradiance uncertainty with load variability is prepared. Uncertainty management is focused on the Malaysian tropical climate. Using meteorological data for one reference year, the Monte-Carlo simulation is performed in the Beta probability density function (PDF) to model continuous random variables of solar irradiances. For the load modelling studies, the Monte Carlo simulation is performed in Gaussian PDF to develop a probability model of various types of loads. The urban residential, commercial and industrial load profiles for one reference year are used for the load modelling. The probabilistic values of PV generation and load models are employed as the input data to the load flow analysis for the radial distribution network. The load flow patterns will significantly have affected when uncertain PV generation – load models are considered into the power flow algorithm. A new method of probabilistic backward – forward sweep power flow (BFSPF) based on Monte Carlo – PDF is developed as the fitness evaluation for the PVDG planning. A hybrid population – based stochastic optimization method named MVMO-SH algorithm is proposed to optimize PVDG locations and sizes in the grid system network. The objective function is to minimize the active power loss (APL) index. The proposed algorithm is applied to the standard radial test system to examine the usefulness and effectiveness of the proposed method. The impacts of PVDG on the power system network have been examined. As the results of the study, the uncertainty model of solar irradiance in Monte Carlo – Beta PDF has shown an almost similar pattern with less than 15% deviation as compared to the model from SEDA. The reductions in the power system’s total power losses have been shown with appropriate planning of PVDG in the power system network considering uncertainty in PV generation and load variations based on the Malaysian Tropical climate. When probabilistic BFSPF is optimized by MVMO-SH embedded Monte Carlo – PDF under uncertainties, the results show a better APL index compared to utilizing PSO and GA. The results also revealed that the uncertainties had the greatest influence on the optimal planning of PVDG in the power system network

ZEB Prototype Controlled by a Machine Learning System

This communication concerns a research project by the Interdepartmental Research Centre for Territory Construction Restoration and Environment (CITERA) of Sapienza University of Rome in collaboration with ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) based on the realization of a 1:1 scale demonstrator of a Zero Energy Building that allows continuous experimentation of new technologies for innovative photovoltaic systems, efficient storage systems and high-performance envelope materials. In particular a measurement protocol has been developed for both the overall efficiency of the building and the individual technological components with a view to a comparative critical analysis of the integration of the individual components in the building-system complex. All the technological systems has been used in Solar Decathlon Middle East 2018 competition in Dubai. The project concerns the development of a control and management system for photovoltaic energy production systems for the ZEB prototype, based on an intelligent self-learning system (AI) able to optimize the parameters of self-produced electricity supply based on real consumption of air conditioning equipment, electrical power supply to the equipment, access control and safety equipment. The most immediate result concerns the integrated design of both the hardware systems for the production and use of electricity and the algorithms that continuously measure parameters such as grid load, consumption and electricity production, and which takes into account weather forecasts, energy tariffs, and learns the trend of electricity consumers through the use of artificial intelligence