scholarly journals A Hybrid Energy System Workflow for Energy Portfolio Optimization

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4392
Author(s):  
Jia Zhou ◽  
Hany Abdel-Khalik ◽  
Paul Talbot ◽  
Cristian Rabiti
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Electricity Market ◽  
Process Model ◽  
Net Present Value ◽  
Historical Data ◽  
Energy System ◽  
Initial Guess ◽  
Reduced Order ◽  
The Impact

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

scholarly journals Towards the Design of Resilient Waste-to-Energy Systems Using Bayesian Networks

2018 ◽  
Author(s):  
W. H. Jonathan Mak ◽  
Michel-Alexandre Cardin ◽  
Liu Ziqi ◽  
P. John Clarkson
Keyword(s):  
Bayesian Networks ◽  
Net Present Value ◽  
Energy System ◽  
Waste To Energy ◽  
Engineering System ◽  
Design Strategies ◽  
Expansion Strategy ◽  
Fixed Design ◽  
The Impact ◽  
Initial Capacity

The concept of resilience has emerged from various domains to address how systems, people and organizations can handle uncertainty. This paper presents a method to improve the resilience of an engineering system by maximizing the system economic lifecycle value, as measured by Net Present Value, under uncertainty. The method is applied to a Waste-to-Energy system based in Singapore and the impact of combining robust and flexible design strategies to improve resilience are discussed. Robust strategies involve optimizing the initial capacity of the system while Bayesian Networks are implemented to choose the flexible expansion strategy that should be deployed given the current observations of demand uncertainties. The Bayesian Network shows promise and should be considered further where decisions are more complex. Resilience is further assessed by varying the volatility of the stochastic demand in the simulation. Increasing volatility generally made the system perform worse since not all demand could be converted to revenue due to capacity constraints. Flexibility shows increased value compared to a fixed design. However, when the system is allowed to upgrade too often, the costs of implementation negates the revenue increase. The better design is to have a high initial capacity, such that there is less restriction on the demand with two or three expansions.

scholarly journals The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

2019 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
Lu Qin ◽  
Kyle Shanks ◽  
Glenn Allen Phillips ◽  
Daphne Bernard
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Enrollment Forecasting ◽  
Moving Average Model ◽  
The Impact ◽  
Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

scholarly journals Demand Side Management using Grid-Tied Photovoltaic Energy System

2019 ◽  
Vol 9 (1) ◽  
pp. 4190-4194 ◽  
Keyword(s):  
Energy Management ◽  
Electricity Market ◽  
Low Cost ◽  
Cost Savings ◽  
Energy System ◽  
Electrical Network ◽  
Solar Pv ◽  
Pv System ◽  
Great Cost ◽  
The Impact

This paper investigates the impact of investments in DSM technologies in Palestinian electricity market in order to solve the problem of supply shortages in electrical network, especially in peak demand periods. Renewable hybrid system, which can explore solar PV source at low cost, is a popular choice for this purpose nowadays, optimal energy management solutions can be obtained with great cost savings and active control performance. This paper analyzes the performance and feasibility of implementation DSM system in Palestinian distribution network, using on-grid PV system and energy management system.

scholarly journals Minimum Mode Saddle Point Searches Using Gaussian Process Regression with Inverse-Distance Covariance Function

2019 ◽  
Author(s):  
Olli-Pekka Koistinen ◽  
Vilhjálmur Ásgeirsson ◽  
Aki Vehtari ◽  
Hannes Jónsson
Keyword(s):  
Saddle Point ◽  
Gaussian Process ◽  
Process Model ◽  
Energy Surface ◽  
Saddle Points ◽  
Gaussian Process Regression ◽  
Dissociative Adsorption ◽  
Initial Guess ◽  
Initial State ◽  
Model Based

The minimum mode following method can be used to find saddle points on an energy surface by following a direction guided by the lowest curvature mode. Such calculations are often started close to a minimum on the energy surface to find out which transitions can occur from an initial state of the system, but it is also common to start from the vicinity of a first order saddle point making use of an initial guess based on intuition or more approximate calculations. In systems where accurate evaluations of the energy and its gradient are computationally intensive, it is important to exploit the information of the previous evaluations to enhance the performance. Here, we show that the number of evaluations required for convergence to the saddle point can be significantly reduced by making use of an approximate energy surface obtained by a Gaussian process model based on inverse inter-atomic distances, evaluating accurate energy and gradient at the saddle point of the approximate surface and then correcting the model based on the new information. The performance of the method is tested with start points chosen randomly in the vicinity of saddle points for dissociative adsorption of an H2 molecule on the Cu(110) Surface and three gas phase chemical reactions.<br>

scholarly journals Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine

2021 ◽  
Vol 11 (18) ◽  
pp. 8333
Author(s):  
Xuejun Liu ◽  
Hailong Tang ◽  
Xin Zhang ◽  
Min Chen
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Process Model ◽  
Engine Performance ◽  
Latin Hypercube Sampling ◽  
Sampling Errors ◽  
Gaussian Process Model ◽  
Turboshaft Engine ◽  
The Impact ◽  
Performance Uncertainty

The gas turbine engine is a widely used thermodynamic system for aircraft. The demand for quantifying the uncertainty of engine performance is increasing due to the expectation of reliable engine performance design. In this paper, a fast, accurate, and robust uncertainty quantification method is proposed to investigate the impact of component performance uncertainty on the performance of a classical turboshaft engine. The Gaussian process model is firstly utilized to accurately approximate the relationships between inputs and outputs of the engine performance simulation model. Latin hypercube sampling is subsequently employed to perform uncertainty analysis of the engine performance. The accuracy, robustness, and convergence rate of the proposed method are validated by comparing with the Monte Carlo sampling method. Two main scenarios are investigated, where uncertain parameters are considered to be mutually independent and partially correlated, respectively. Finally, the variance-based sensitivity analysis is used to determine the main contributors to the engine performance uncertainty. Both approximation and sampling errors are explained in the uncertainty quantification to give more accurate results. The final results yield new insights about the engine performance uncertainty and the important component performance parameters.

scholarly journals ANALYSIS OF THE IMPACT OF THE NEW ELECTRICITY MARKET ON LEVELING THE LOAD SCHEDULE OF THE UNIFIED ENERGY SYSTEM OF UKRAINE

World Science ◽  
2020 ◽  
Vol 1 (2(54)) ◽  
pp. 4-10
Author(s):  
Rozen Viktor ◽  
Velykyi Serhii
Keyword(s):  
Electricity Market ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Energy System ◽  
Energy Market ◽  
Service Companies ◽  
Industrial Enterprises ◽  
Load Schedule ◽  
Energy Service ◽  
The Impact

The article discusses methods of regulating the power consumption regime of the schedule of the unified energy system of Ukraine, which can reduce the irregularity load schedule by using stimulating tariffs for electricity charges. A scheme of the equipment operation principle is shown, which can operate in a mode of consumer-power regulator according to the criterion of reducing electricity charges for industrial enterprises. The result of the energy reform in Ukraine led to the rejection of differentiated electricity tariffs, and the transition to market relations between enterprises that are consumers of electric energy and energy service companies that are responsible for working in the electric energy market. The objective of the article is to demonstrate the practical formation of prices for enterprises and the work of electricity suppliers, which boils down to the ongoing planning of hourly volumes for consumers of electricity and the timely purchase of the said volumes in different segments of the electricity market The aim of the article is to demonstrate the formation of prices for enterprises. The work of energy service companies, which consists in the constant planning of hourly volumes of consumers of electric energy and the timely purchase of these volumes in different segments of the electric energy market. The problem of this formation is that enterprises do not have an incentive to regulating the schedule of the unified energy system of Ukraine, as the new tariffs do not differ in terms of electricity consumption in intraday and а reducе in electricity consumption by the enterprise during peak hours. The authors propose measures that are aimed at solving this problem. The proposed measures are mainly aimed at changes in the day-ahead electricity market, which will entail changes in its other segments.

scholarly journals Bottom-up Modeling of Local Energy Markets within a Pan-European Wholesale Electricity Market Model

2020 ◽  
Author(s):  
Carlo Schmitt ◽  
Kenneth Samaan ◽  
Henrik Schwaeppe ◽  
Albert Moser
Keyword(s):  
Electricity Market ◽  
Energy System ◽  
Energy Markets ◽  
Computational Effort ◽  
Market Model ◽  
Local Energy ◽  
Wholesale Market ◽  
Bottom Up ◽  
Local Markets ◽  
The Impact

The energy system decarbonization and decentralization<br>require coordination schemes for distributed generators<br>and flexibilities. One coordination approach is local energy markets for trading energy among local producers and consumers. The resulting local coordination leads to the questions of how the interaction between local and wholesale markets will be designed and of how the introduction of local energy markets influences the wholesale market system. Therefore, this paper proposes a bottom-up modeling method for local markets within a pan- European wholesale market model. Furthermore, an aggregation-disaggregation method for local markets is developed to reduce computational effort. A case study for local markets in Germany shows the computational advantages of the aggregation-disaggregation method. Preliminary results indicate the impact of different interaction designs between local and wholesale markets on the wholesale market and show the need for further research.

ECONOMICAL EVALUATION AND POTENTIAL ENVIRONMENTAL EFFECTS OF HYDROKINETIC ENERGY TECHNOLOGIES

2015 ◽  
Author(s):  
Inga ADAMONYTĖ ◽  
Algis KVARACIEJUS ◽  
Gitana VYČIENĖ
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Aquatic Vegetation ◽  
Net Present Value ◽  
Present Value ◽  
Hydrokinetic Energy ◽  
Energy Technologies ◽  
Economical Evaluation ◽  
The Impact ◽  
Very High

An analysis of the impact of hydrokinetic energy technology schemes has been carried out on the following river parameters: water quality, the riverbed and bank stability, sediment dynamics, coastal and aquatic vegetation, fish communities, noise, aesthetics, fishing and riverbed practicability (kayaks and barges). Hydrokinetic energy generation technologies are compared to conventional tidal technologies. Each parameter assessed was evaluated for minor, notable, high, and very high likelihood of constant and temporary exposure. Subordinate elements, such as aesthetics, fishing, and river practicability were determined to be the greatest possible use of hydrokinetic energy schemes in the world rather than river ecosystem elements. The researchers carried out an approximate assessment of the economic indicators because Lithuania does not operate hydrokinetic power plants. An assessment of reduced investment and electricity market energy purchase price indicates that the approximate payback period is six years and the net present value in the seventh year of operation is EUR 7,450.

scholarly journals A Review on Time Series Aggregation Methods for Energy System Models

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 641 ◽  
Author(s):  
Maximilian Hoffmann ◽  
Leander Kotzur ◽  
Detlef Stolten ◽  
Martin Robinius
Keyword(s):  
Time Series ◽  
Input Data ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Energy System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
System Models ◽  
Number Of Publications ◽  
The Impact

Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

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