An Agent-based computational economics model of the National Electricity Market of Singapore.

Master'sMASTER OF ENGINEERIN

Optimal GENCO bidding strategy

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming.;The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed.;A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies\u27 (GENCOs) bidding strategies.;After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

A reinforcement learning algorithm developed to model GenCo strategic bidding behavior in multidimensional and continuous state and action spaces

10.1109/ADPRL.2013.6614997IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL116-12

Proceedings of the WABER 2017 Conference

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Library websites popularity: does Facebook really matter?

The purpose of this paper is to determine whether the utilization of social media (Facebook) is an important factor in increasing the visibility of the library site usage in Malaysian public universities. Nine top ranked Malaysian public universities involved in this research and number of Facebook followers for each library website is listed. Alexa software was used as the approach to study the issue of visibility. Alexa is able to determine web site usage, by showing the percentage of visitors of library related subdomain(s) as listed in the top subdomains for each University website (domain) over a month. It is found that Universiti Utara Malaysia library website scored the highest percentage of visitors based on the library related subdomain(s) as listed in the top subdomains for the University website in Alexa. To check such irregularities in access, this paper use EvalAccess 2.0 and it is found that Universiti Sains Malaysia’s library website scored higher irregularities. In term of number of Facebook followers, Univesity of Malaya library has the highest score. It is showed that the utilization of social media (Facebook) is not yet an important factor in increasing the visibility of the library websites. However, expectedly, top ranked universities’ library web sites, are more visible and popular. This research is limited to the situation in Malaysia where public universities are more noticeable and seldom face financial constraints rather than private universities. It is highly important for those universities’ library web sites that are not highly visible to initiate the necessary measures in improving the development of their web sites as the usage of the website is an indicator of online quality