The problem of customizing electricity retail prices using data mining techniques is studied in this paper. The density-based spatial clustering of applications with noise (DBSCAN) is firstly applied to load profile analysis, in order to explore end-users’ inherent electricity consumption patterns from their historical load data. Then, statistical analysis of end-users’ historical consumption is conducted to better capture their consumption regularity. After extracting these load features, a mixed integer nonlinear programming (MINLP) model for customizing electricity retail prices is proposed. In the proposed model, both the structure of TOU retail price and the price level are optimized once given the number of price blocks. It is among the first that the optimization of TOU price structure is studied in electricity retail pricing research. The proposed model is mathematically reformulated and solved by online commercial solvers provided by the NEOS (Net-work-Enabled Optimization System) server. Electricity usage data collected by the Smart Grid, Smart City (SGSC) project in Australia is used to demonstrate the feasibility and efficiency of the developed models and algorithms.

With the continuous development of smart grid and further restructuring of power industry, modern power systems have been transformed to complex cyber-physical systems characterized with high renewable energy penetrations, distributed facilities, advanced metering, and communication technologies, as well as ever-increasing customer awareness. These new development trends pose significant challenges for electricity retailers and call for innovative decision-making methods. To help researchers and engineers have a better overview of the state-of-the-art on electricity retail decision-making schemes, this paper aims to survey the latest progress on this subject. Some critical and open issues in this field are also discussed.

The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR). Existing solution methods for DNR are either inaccurate or excessively time-consuming, and are therefore incapable of solving multi-hour SDNRs for large distribution networks. In this regard, a switch opening and exchange (SOE) method is proposed. Starting from a looped network with all switches closed, the SOE consists of three steps. The first step is to sequentially open the switches until all of the loops are opened. The second and third steps modify the status of branches obtained in the first step to obtain better radial topologies. Five test systems are used to validate the accuracy and fast solution speed of the SOE and the superiority of multi-hour SDNR over single-hour deterministic DNR.

False data injection attacks (FDIAs) significantly disrupt the secure operation of smart grids by manipulating the measured values collected by intelligent instruments. Existing studies have utilized deep learning techniques to enhance the detection of FDIAs, however, these studies often overlook the spatial correlation between power grid topology and measurement data. Meanwhile, the high complexity of deep neural network severely impedes the interpretability of detection models, resulting in the incredibility of detection results. To address the above challenges, this paper proposes an interpretable deep learning FDIAs detection method, named dual-attention multi-head graph attention network, DAMGAT. The DAMGAT introduces a dual-attention mechanism that incorporates both node feature attention and spatial topology attention into a multi-head graph attention network. This mechanism efficiently aggregates attack characteristics and spatial topology information by dynamically capturing the potential correlations between FDIAs detection and measurement data. Furthermore, the proposed model can provide clear and credible interpretations for high-accuracy detection results via analyzing features and spatial topology attention weights. Extensive simulations are performed using the IEEE 14-bus and 118-bus test systems. The experimental results demonstrate that the proposed model outperforms state-of-the-art FDIA detection methods in terms of accuracy, while also providing reasonable interpretability for features and spatial dimensions.

The penetration rates of both electric vehicles (EVs) and distributed energy resources (DERs) have been increasing rapidly as appealing options to address the global problems of carbon emissions and fuel supply issues. However, uncoordinated EV charging activities and DER generation result in operational challenges for power distribution systems. Therefore, this article has developed a hierarchical transactive energy (TE) framework to locally induce and coordinate EV charging demand and DER generation in electric distribution networks. Based on a modified version of the alternating direction method of multipliers (ADMMs), two fully decentralized (DEC) peer-to-peer (P2P) trading models are presented, that is, an hour-ahead market and a 5-min-ahead real-time market. Compared to existing P2P electricity markets, this research represents the first attempt to comprehensively incorporate alternating current (ac) power network constraints into P2P electricity trading. The proposed TE framework not only contributes to mitigating operational challenges of distribution systems, but also benefits both EV owners and DER investors through secured local energy transactions. The privacy of market participants is well preserved since the bid data of each participant are not exposed to others. Comprehensive simulations based on the IEEE 33-node distribution system are conducted to demonstrate the feasibility and effectiveness of the proposed method.

A key feature of an electricity distribution market is that it may be dominated by renewable generation with zero marginal cost. Existing market mechanisms are likely to fail in this context since it cannot generate a reasonable price signal to compensate for the investment cost of renewable generators. Given this background, a double-sided auction market mechanism is presented for pricing the zero marginal cost renewable generation in the distribution system. Honesty is proved to be a dominant strategy for participants, which would enable the proposed mechanism to develop into a set-and-forget bidding market. The proposed market mechanism is also shown to be compatible with the nodal pricing system. Finally, case studies are carried out and the results show that under the proposed market mechanism, the problem of always bidding a zero price by renewable generators in some existing markets can be avoided. Even when only renewable generation units with zero marginal costs participate in the bid-ding, the proposed mechanism can still produce a reasonable market clearing price. When adopting the average pricing market (APM) mechanism, merits of nodal pricing can still be retained and contribute to the enhancement of the operating efficiency of the distribution network.