scholarly journals Analyzing the Impact of Electricity Market Structure Changes and Mergers: The Importance of Forward Commitments

2016 ◽  
Author(s):  
David Brown ◽  
Andrew Eckert
Keyword(s):  
Market Structure ◽  
Electricity Market ◽  
Structure Changes ◽  
The Impact ◽  
Forward Commitments

Detecting the impact of fundamentals and regulatory reforms on the Greek wholesale electricity market using a SARMAX/GARCH model

Energy ◽  
2018 ◽  
Vol 142 ◽  
pp. 1083-1103 ◽  
Author(s):  
George P. Papaioannou ◽  
Christos Dikaiakos ◽  
Athanasios S. Dagoumas ◽  
Anargyros Dramountanis ◽  
Panagiotis G. Papaioannou
Keyword(s):  
Electricity Market ◽  
Garch Model ◽  
Wholesale Electricity Market ◽  
The Impact ◽  
Regulatory Reforms

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 Competition in Power Generation: Ex-ante Analysis of Indonesia’s Electricity Market

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6741
Author(s):  
Dzikri Firmansyah Hakam ◽  
Sudarso Kaderi Wiyono ◽  
Nanang Hariyanto
Keyword(s):  
Power System ◽  
Market Structure ◽  
Market Power ◽  
Electricity Markets ◽  
Electricity Market ◽  
Regulatory Reform ◽  
Optimal Number ◽  
Load Flow ◽  
Utility Company ◽  
Competitive Electricity Markets

This research optimises the mix and structure of Generation Companies (GenCos) in the Sumatra power system, Indonesia. Market power, indicating the ability to raise prices profitably above the competitive level, tends to be a significant problem in the aftermath of electricity market restructuring. In the process of regulatory reform and the development of competitive electricity markets, it is desirable and practical to establish an efficient number of competitor GenCos. Simulations of a power system account for multi-plant mergers of GenCos subject to a regulatory measure of the Residual Supply Index and the influence of direct current load flow and the topology of the system. This study simulates the Sumatra power system in order to determine the following: optimal market structure, efficient GenCo generation mix, and the optimal number of competitive GenCos. Further, this study seeks to empirically optimise the electricity generation mix and electricity market structure of the Sumatra power system using DC load flow optimisation, market power index, and multi-plant monopoly analysis. The simulations include generation and transmission constraints to represent network constraints. This research is the first to analyse the Sumatra power system using imperfect (Cournot) competition modelling. Furthermore, this study is the first kind to optimise the mix and structure of the Sumatra generation power market. The guidelines and methodology in this research can be implemented in other countries characterised by a monopoly electricity utility company.

The duck curve in a drying pond: The impact of rooftop PV on the Western Australian electricity market transition

2021 ◽  
Vol 71 ◽  
pp. 101232
Author(s):  
Sam Wilkinson ◽  
Martin J. Maticka ◽  
Yue Liu ◽  
Michele John
Keyword(s):  
Electricity Market ◽  
Market Transition ◽  
The Impact ◽  
Western Australian

scholarly journals Investment Incentives in Competitive Electricity Markets

2018 ◽  
Vol 8 (10) ◽  
pp. 1978 ◽  
Author(s):  
Jaber Valinejad ◽  
Taghi Barforoshi ◽  
Mousa Marzband ◽  
Edris Pouresmaeil ◽  
Radu Godina ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Planning Horizon ◽  
Optimization Method ◽  
Design Criterion ◽  
Mixed Integer ◽  
Investment Incentives ◽  
Mixed Integer Linear Program ◽  
Interconnected Power System ◽  
The Impact

This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.

The Impact of Structural Reforms and Regulations on the Demand Side in the Railway Industry

2019 ◽  
Vol 18 (1) ◽  
pp. 1-33
Author(s):  
Fumitoshi Mizutani
Keyword(s):  
Market Structure ◽  
Vertical Integration ◽  
High Speed ◽  
Fixed Effect Model ◽  
Structural Reform ◽  
Estimation Methods ◽  
Public Ownership ◽  
Estimation Model ◽  
Passenger Demand ◽  
The Impact

Abstract The main purpose of this study is to evaluate factors affecting passenger rail demand, with special attention to the effects of structural reform/regulation and competition. In order to do this, we use data obtained from 30 OECD countries for the 24 years from 1990 to 2013. As structural reform/regulation and competition variables, we take the OECD’s five kinds of regulatory indices: (i) overall, (ii) entry, (iii) public ownership, (iv) vertical integration, and (v) market structure; and for competition variables, we take (vi) rail passenger-freight ratio, (vii) rail share, and (viii) high-speed train ratio. As estimation methods, both the fixed effect model and the Hausman-Taylor estimation model are used. The major findings are as follows. First, competition as competitiveness (i.e. the share of rail, passenger over freight ratio) increases passenger demand. And the existence of high-speed trains increases passenger demand. Second, overall, entry regulation, and market structure have no significant effect on demand. Third, public ownership affects passenger demand positively. Last, vertical integration reduces passenger demand.

Sign in / Sign up

Export Citation Format

Share Document