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Deregulation of the retail electricity market has led to the emergence of an increasing number of electricity plans with competitive rates. Electricity customers now have more flexibility in choosing an electricity provider and electricity plan based on individual consumption needs. In this paper, a feature engineering hybrid collaborative filtering-based electricity plan recommender system (FECF-EPRS) is proposed for helping the customer get the right electricity plan. This system is composed of three-segment models for missing feature estimation, feature crosses construction, and electricity plan recommendation. It only takes easy-to-obtain household appliance usage features as inputs and outputs ratings for different plans. Through the test of real electricity market data, the FECF-EPRS shows a greater improvement in terms of recommendation accuracy, which can provide more accurate recommendations to customers and more reasonable pricing references for retailers. • Developing an electricity tariff recommender system for customers with different household energy consumption patterns. • Proposing a missing feature value filling method based the link between different appliances. • Introducing a Feature Crosses method to enhance recommendation accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Power system real time security assessment is one of the fundamental modules of the electricity markets. Typically, when a contingency occurs, it is required that security assessment and enhancement module shall be ready for action within about 20min time to meet the real time requirement. The recent California black out again highlighted the importance of system security. This paper proposed an approach for power system security assessment and enhancement based on the information provided from the pre-defined system parameter space. The proposed scheme opens up an efficient way for real time security assessment and enhancement in a competitive electricity market for single contingency case. [Copyright &y& Elsevier]

Abstract: Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes, which may be tens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far, most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply–demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour, electricity demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. [Copyright &y& Elsevier]

Levelized cost of electricity (LCOE) is widely used to compare the cost of different electricity generation technologies. However, with the increasing penetration of variable renewable energy (VRE), it is inappropriate to use traditional equations to calculate the LCOE for non-dispatchable VRE due to its intermittent nature. Therefore, this paper reviews LCOE for renewable generation considering aspects such as improving traditional methods for evaluating and reducing the LCOE of VRE. The existing methods for improving the accuracy of the traditional LCOE have been divided into four aspects: investment-related cost, operating-related cost, expressions for plant performance and the uncertainty and risk-related costs. The review summarizes the existing studies from academic articles and technical reports and proposes suggestions for future research studies. • Comprehensive Levelized cost of electricity (LCOE) methods review. • LCOE evaluation from cost, plant performance, uncertainty and risk. • Summary of existing methods for improving the LCOE estimation accuracy of variable renewable energy (VRE). • Compare and summarize the existing VRE LCOE methods in academic papers and technical reports. [ABSTRACT FROM AUTHOR]

Several electricity tariffs have emerged for Demand Side Management (DSM) and residential customers are faced with challenges to choose the plan satisfying their personal needs. Electricity Plan Recommender System (EPRS) can alleviate the problem. This paper proposes a novel EPRS model named EPRS with Electrical Instruction-based Recovery (EPRS-EI), which is a dual-stage model consisting of feature formulation stage and recommender stage. In the feature formulation stage, matrix recovery with electrical instructions is applied to recover appliance usages, and the recovered data is set as features representing customers' living patterns. In the recommender stage, Collaborative Filtering Recommender System (CFRS) based on K-Nearest Neighbors (KNN) and adjusted similarity is applied to recommend personal electricity plans to customers based on the above features. Different from other EPRS models, EPRS-EI is the first model utilizing matrix recovery methods and similarity computation with electrical instructions. With these electrical instructions, the proposed model is able to utilize more explicit features and recommend more personalized plans. We then apply EPRS-EI to predict the testing customers' preference for electricity plans. Simulation results on recovering electricity data and their applications in EPRS confirm the effectiveness of the proposed methods in comparison to state-of-the-art methods, with 93.56%–94.85% customers correctly recommended. • Proposing an electricity plan recommender system (EPRS) with matrix recovery. • Appliance classification avoids trivial changes in recovering appliance usages. • Total electricity usages are set as a recovery instruction in recovering data. • Novel matrix recovery could recover up to 38.15% more data. • 93.56%–94.85% of users can obtain effective electricity plans with novel EPRS. [ABSTRACT FROM AUTHOR]