Impact of weather dependency on planning decisions in the australian national electricity market

A long-term planning method for the electricity market is to simulate market operation into the future. Outputs from market simulation include indicators for transmission augmentation and new generation investment. A key input to market simulations is demand forecasts. For market simulation purposes, regional demand forecasts for each half-hour interval of the forecasting horizon are required, and they must accurately represent realistic demand profiles and interregional demand relationships. In this paper, a demand model is developed to accurately model these relationships. The effects of uncertainty in weather patterns and inherent correlations between regional demands on market simulation results are presented. This work signifies the advantages of probabilistic modeling of demand levels when making market-based planning decisions.

A new methodology for generation investment in the national electricity market of Australia

Market administrators hold the vital role of maintaining sufficient generation capacity in their respective electricity market. However without the jurisdiction to dictate the generator types, locations and timing of new generation, the reliability of the system may be compromised by delayed entry of new generation. This paper illustrates a new generation investment methodology that can effectively present expected returns from the pool market; while concurrently searching for the type and placement of a new generator to fulfil system reliability requirements.

A genetic algorithm based method for deregulated electricity market dispatch

In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.

Probabilistic cost benefit analysis of generation investment in a deregulated electricity market

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the expected energy generated, taking into account system load variations and plant forced outage rates, while the Monte Carlo approach has been applied to model the electricity price variability seen in a realistic network. The model is able to capture the price and hence the profitability uncertainties for generator companies. Seasonal variation in the electricity prices and the system demand are independently modeled. The method is validated on IEEE RTS system, augmented with realistic market and plant data, by using it to compare the financial viability of several generator investments applying either conventional or directly connected generator (powerformer) technologies. The significance of the results is assessed using several financial risk measures.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.

Optimal dispatch of spinning reserve in a competitive electricity market using genetic algorithms

Ancillary service plays a key role in maintaining operation security of the power system in a competitive electricity market. The spinning reserve is one of the most important ancillary services that should be provided effectively. This paper presents the design of an integrated market for energy and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the cost of service while maintaining system security. Genetic algorithms (GA) are used for finding the global optimal solutions for this dispatch problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed method.

High selectivity microporous silica membranes for lactic acid dehydration

Lactic acid (LA) has significant market potential for many industries including food, cosmetics, pharmaceuticals, medical and biodegradable materials. Production of LA usually begins with the fermentation of glucose but subsequent stages for the enrichment of lactic acid are complex and energy intensive and could be minimised using water selective membrane technology. In this work, we trialled a highly selective hydrostable carbonised template molecular sieve silica (CTMSS) membrane for the dehydration of a 15 vol% aqueous lactic acid solution with 0.1 vol% glucose. CTMSS membrane films were developed by dip-coating ceramic substrates with silica sols made using the acid catalysed sol-gel process. Permeation was performed by feeding LA/glucose solution to the membrane cell at 18°C in a standard pervaporation setup. The membrane showed selective transport of water from the aqueous feed to the permeate while glucose was not detected. CTMSS membrane permeate flux stabilised at 0.2 kg.m-2.hr-1 in 3.9 hours, and reduced LA to lower than 0.2 vol%. Flux through the CTMSS micropores was activated, displaying increased initial flux to 1.58 kg.m-2.hr-1 at 60°C. To enrich a 1 l.min-1 stream to 85% LA in a single stage, a minimum membrane area of 324 m2 would be required at 18°C. Increased operating temperature to 80°C significantly reduced this area to 24 m2 but LA levels in the permeate stream increased to 0.5 vol%. The highly selective CTMSS membrane technology is an ideal candidate for LA purification. CTMSS membrane systems operate stably in aqueous systems leading to potential cost reductions in LA processing for future markets.

Correcting physical activity energy expenditure for body size in young children

Being able to compare the energy cost of physical activity across and between populations is important. However, energy expenditure is related to body size, so it is necessary to appropriately adjust for differences in body size when comparisons are made. This study examined the relationship between the daily energy cost of activity and body weight in 47 children aged 6-10 years. Log-log regression showed weight(1.0) to be an inappropriate adjustment for activity energy expenditure in children, with a more valid adjustment being weight(0.3). Clearly, both weight dependent and non-weight dependent activities are part of everyday living in children. This balance influences how energy expenditure is correctly adjusted for body size. Investigators interpreting data of energy expenditure in children from children of different body sizes need to take this into consideration.

Assessing the competitiveness of loss allocation methods in a deregulated electricity market

This paper critically assesses several loss allocation methods based on the type of competition each method promotes. This understanding assists in determining which method will promote more efficient network operations when implemented in deregulated electricity industries. The methods addressed in this paper include the pro rata [1], proportional sharing [2], loss formula [3], incremental [4], and a new method proposed by the authors of this paper, which is loop-based [5]. These methods are tested on a modified Nordic 32-bus network, where different case studies of different operating points are investigated. The varying results obtained for each allocation method at different operating points make it possible to distinguish methods that promote unhealthy competition from those that encourage better system operation.