Electricity load and price forecasting with influential factors in a deregulated power industry

With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.

Basic electricity for the petroleum industry.

Mode of access: Internet.

Vertical and horizontal scope economies in the regulated US electricity power industry

This paper investigates vertical economies between generation and distribution of electric power, and horizontal economies between different types of power generation in the U.S. electric utility industry. Our quadratic cost function model includes three generation output measures (hydro, nuclear and fossil fuels), which allows us to analyze the effect that generation mix has on vertical economies. Our results provide (sample mean) estimates of vertical economies of 8.1% and horizontal economies of 5.4%. An extensive sensitivity analysis is used to show how the scope measures vary across alternative model specifications and firm types. © 2012 Blackwell Publishing Ltd and the Editorial Board of The Journal of Industrial Economics.

Predictiuon and automatic control with special reference to the electricity supply industry

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Which industry is greener? An empirical study of nine industries in OECD countries

This study analyzed the relationship between the CO2 emissions of different industries and economic growth in OECD countries from 1970 to 2005. We tested an environmental Kuznets curve (EKC) hypothesis and found that total CO2 emissions from nine industries show an N-shaped trend instead of an inverted U or monotonic increasing trend with increasing income. The EKC hypothesis for sector-level CO2 emissions was supported in the (1) paper, pulp, and printing industry; (2) wood and wood products industry; and (3) construction industry. We also found that emissions from coal and oil increase with economic growth in the steel and construction industries. In addition, the non-metallic minerals, machinery, and transport equipment industries tend to have increased emissions from oil and electricity with economic growth. Finally, the EKC turning point and the relationship between GDP per capita and sectoral CO2 emissions differ among industries according to the fuel type used. Therefore, environmental policies for CO2 reduction must consider these differences in industrial characteristics. © 2013 Elsevier Ltd.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

A design approach to innovation in the Australian energy industry

Electricity businesses across Australia are facing many market disruptions, such as the increasing demand from the rapid uptake of domestic air conditioners and the contrasting problematic generation from solar power connections to the grid. In this context, the opportunity to proactively leverage forthcoming technological advances in battery storage and electric vehicles to address the steeply rising cost of electricity supply has emerged. This research explores a design approach to support a business to navigate such disruptions in the current market.This study examines a design-led approach to innovation conducted over a ten month action research study within a large, risk-averse firm in the Australian energy sector. This article presents results describing a current foresight gap within the business; the response of the business to using design-led innovation to address this issue; and the tools, approaches and processes used. The business responses indicate their perception of the value of qualitative customer engagement as a path to addressing, and potentially benefiting from, disruptive innovation. It is anticipated that these results will further business model development within the company, and assist in leveraging disruptive innovations for this industry participant, thus limiting future increases in the cost of electricity supply for customers in Australia.

Potential of biomass energy for electricity generation in sub-Saharan Africa

The paper explores the biomass based power generation potential of Africa. Access to electricity in sub-Saharan Africa (SSA) is about 26% and falls to less than 1% in the rural areas. On the basis of the agricultural and forest produce of this region, the residues generated after processing are estimated for all the countries. The paper also addresses the use of gasification technology - an efficient thermo-chemical process for distributed power generation - either to replace fossil fuel in an existing diesel engine based power generation system or to generate electricity using a gas engine. This approach enables the implementation of electrification programs in the rural sector and gives access to grid quality power. This study estimates power generation potential at about 5000 MW and 10,000 MW by using 30% of residues generated during agro processing and 10% of forest residues from the wood processing industry, respectively. A power generation potential of 15000 MW could generate 100 terawatt-hours (TWh), about 15% of current generation in SSA. The paper also summarizes some of the experience in using the biomass gasification technology for power generation in Africa and India. The paper also highlights the techno economics and key barriers to promotion of biomass energy in sub-Saharan Africa. (C) 2011 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Managing Information Risks in asset management - Experiences from an in-depth case study in the utility industry

Information is one of the most important resources in our globalized economy. The value of information often exceeds the value of physical assets. Information quality has, in many ways, an impact on asset management organisations and asset managers struggle to understand and to quantify it, which is a prerequisite for effective information quality improvement. Over the past few years, we have developed an innovative management concept that addresses these new asset management challenges: a process for Total Information Risk Management (TIRM), which has been already tested in a number of asset management industries. The TIRM process enables to manage information quality more effectively in asset management organisations as it focuses specifically on the risks that are imposed by information quality. In this paper, we show how we have applied the TIRM process in an in-depth study at a medium-sized European utility provider, the Manx Electricity Authority (MEA), at the Isle of Man.

A strategic review of electricity systems models

Electricity systems models are software tools used to manage electricity demand and the electricity systems, to trade electricity and for generation expansion planning purposes. Various portfolios and scenarios are modelled in order to compare the effects of decision making in policy and on business development plans in electricity systems so as to best advise governments and industry on the least cost economic and environmental approach to electricity supply, while maintaining a secure supply of sufficient quality electricity. The modelling techniques developed to study vertically integrated state monopolies are now applied in liberalised markets where the issues and constraints are more complex. This paper reviews the changing role of electricity systems modelling in a strategic manner, focussing on the modelling response to key developments, the move away from monopoly towards liberalised market regimes and the increasing complexity brought about by policy targets for renewable energy and emissions. The paper provides an overview of electricity systems modelling techniques, discusses a number of key proprietary electricity systems models used in the USA and Europe and provides an information resource to the electricity analyst not currently readily available in the literature on the choice of model to investigate different aspects of the electricity system.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

The role of information systems in business process redesign : a case of Turkish electricity market

This study is specifically concerned with the effect of the Enterprise Resource Planning (ERP) on the Business Process Redesign (BPR). Researcher’s experience and the investigation on previous researches imply that BPR and ERP are deeply related to each other and a study to found the mentioned relation further is necessary. In order to elaborate the hypothesis, a case study, in particular Turkish electricity distribution market and the phase of privatization are investigated. Eight companies that have taken part in privatization process and executed BPR serve as cases in this study. During the research, the cases are evaluated through critical success factors on both BPR and ERP. It was seen that combining the ERP Solution features with business processes lead the companies to be successful in ERP and BPR implementation. When the companies’ success and efficiency were compared before and after the ERP implementation, a considerable change was observed in organizational structure. It was spotted that the team composition is important in the success of ERP projects. Additionally, when the ERP is in driver or enabler role, the companies can be considered successful. On the contrary, when the ERP has a neutral role of business processes, the project fails. In conclusion, it can be said that the companies, which have implemented the ERP successfully, have accomplished the goals of the BPR.