Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System

Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power availability with load reduce the benefits of PV integration. These challenges can be handled by introducing reserve, which however leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. In this thesis, a new solar irradiance forecasting method for remote microgrids based on the Markov Switching Model (MSM) is presented. This method uses locally available data to predict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers the past solar irradiance data, the Clear Sky Irradiance (CSI), and the Fourier basis functions to create linear models for three regimes or states: high, medium, and low energy regimes for a day corresponding to sunny, mildly cloudy, and extremely cloudy days, respectively. The case study for Brookings, SD, discussed in this thesis, resulted in an average Mean Absolute Percentage Error (MAPE) of 31.8% for five years, 2001 to 2005, with higher errors during summer months than during winter months. The solar irradiance forecasting method was implemented in OPAL-RT real-time digital simulator using PV panels as sensors. For forecasting irradiance, the first four hours of irradiance data in the morning are required. These data were measured using the solar panels rather than pyranometers as the sensors . A case study for real-time irradiance forecasting in Brookings on June 9, 2015 showed RMSE and MAPE of 131.08W=m2 and 45.45%, respectively. The improvement of renewable integration is the future and present prospects for power utilization. Microgrids experience several constraints such as integration of intermittent renewable sources, costlier reliability improvements, restricted expansion of the microgrid system, growth in load, etc. Hence, more research in this field of study is required and a complete laboratory scale microgrid testbed is needed for experimenting different types of microgrid topologies and for studying the coordination of individual components with a well-defined energy management scheme. In this thesis, the development of a laboratory scale single-phase microgrid testbed along with the implementation of microgrid’s Energy Management System (EMS) are discussed. The testbed was developed using central controller and Commercial Off-The-Shelf (COTS) equipment. The EMS comprised of double layers: schedule layer and real-time dispatch layer. A case study conducted for the implementation of the EMS showed that the difference in the scheduled and the dispatched powers were handled by the generator and the energy storage system themselves

Optimal household energy management and participation in ancillary services with PV production

The work presented in this paper deals with a project aiming to increase the value of photovoltaic (PV) solar production for residential application. To contribute to the development of the new functionalities for such system and the efficient control system to optimize its operation, this paper defines the possibility for the proposed system to participate to the ancillary services, particularly in active power service provider. This service of PV-based system for housing application, as it does not exist today, has led to a market design proposition in the distribution system. The mathematical model for calculating the optimal operation of system (sources, load, and the exchange power with the grid) results in a linear mix integer optimization problem where the objective is to maximize the profit obtained by participating to electricity market. The approach is illustrated in an example study case. The PV producer could benefit from its intervention on balancing market or ancillary services market despite of the impact on the profit of several kinds of uncertainty, as the intermittence of PV source.energy management ; ancillary services ; PV production ; household application

Intermittency and the Value of Renewable Energy

A key problem with renewable energy is intermittency. This paper develops a method to quantify the social costs of large-scale renewable energy generation. The method is based on a theoretical model of electricity system operations that allows for endogenous choices of generation capacity investment, reserve operations, and demand-side management. We estimate the model using generator characteristics, solar output, electricity demand, and weather forecasts for an electric utility in southeastern Arizona. The estimated welfare loss associated with a 20% solar photovoltaic mandate is 11% higher than the average cost difference between solar generation and natural gas generation. Unforecastable intermittency yields welfare loss equal to 3% of the average cost of solar. Eliminating a mandate provision requiring a minimum percentage of distributed solar generation increases welfare. With a $21/ton social cost of CO2 this mandate is welfare neutral if solar capacity costs decrease by 65%.

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

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Intra-Day Solar Irradiance Forecasting for Remote Microgrids Using Hidden Markov Model

Accurate solar irradiance forecasting is the key to accurate estimation of solar power output at any given time. The accuracy of this information is especially crucial in diesel-PV based remote microgrids with batteries to determine the set points of the batteries and generators for their optimal dispatch. This, in turn, is related directly to the overall operating cost because both an overestimation and an underestimation of the irradiance means additional operating costs for either suddenly ramping up the backup resources or causing under-utilization of the available PV power output. Accurately predicting the solar irradiance is not an easy task because of the sporadic nature of the irradiance that is received at the solar panel surfaces. Handling the dynamic nature of the irradiance pattern requires a strong and flexible model that can precisely capture the irradiance trend in any given location at a given time. Usually, such a robust model requires a lot of input variables like weather data including humidity, temperature, pressure, wind speed, wind direction, etc. and/or large inventory of satellite images of clouds over a long period of time. The expensive sensors and database tools for collecting and storing such huge information may not be installed in remote locations. Therefore, this thesis prioritizes on developing a simple method requiring a minimum input to accurately forecast the solar irradiance for remote microgrids