Abstract
End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH's participation in a real-time pricing scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system's flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.

Abstract
Due to the fact that distributed generation (DG) has many advantages in the power systems, DG implementation is indispensable. However, it could introduce some problems in the system such as changing the voltage profile. In this paper, an optimum voltage control model based on photovoltaic (PV) inverters is proposed. In the daytime, the PVs inject current to the distribution network, and therefore, in that time there will be a potentially high voltage profile. In contrast, in the evening, the customers consume more power and PV has nothing to compensate, and a low voltage profile is seen. This work seeks to provide a power control scheme for the active and reactive power of the inverter and integrates it with a night mode control of PVs, by a modified hysteresis controller. To gain a suitable voltage profile, all voltages of the buses should get close to the reference voltage. For preventing the interference between different inverters in the network, this control scheme is applied to the whole network coordinately. The 33-bus IEEE system is used to test the performance of the control model, and the results show the effectiveness of the proposed model.

Abstract
The energy system is undergoing a drastic transition towards a system where previously passive consumers will play important roles. These consumers who actively participate in the energy system with a variety of distributed energy resources, such as electric vehicles, solar panels, and battery energy storage systems, become so-called prosumers as they can also generate electricity. This electricity can then be self-consumed, sold to the existing grid, or be sold to other consumers connected to the same electric network through Peer-to-Peer (P2P) trading schemes. This P2P energy trading may offer significant advantages to consumers involved as well as the wider electric system. The use of Agent-Based Modelling (ABM) can help address these problems. ABM models allow to understand complex and dynamic systems by incorporating the behavior of individual agents into the model as the individual behavior of the agents has a direct influence on the outcomes of the systems. In this paper, an ABM model is developed to examine the effects of increased consumer participation within a local energy system. This model utilizes a diverse set of consumers based on real-world data to model and provide insight into the interactions within a P2P energy trading system. The effects of P2P trading on financial outcomes as well as the share of renewable energy utilized within the local energy system is investigated. Results show that ABM models can accurately model P2P energy trading systems and can capture the effects of individual behavior of many active consumers within electrical systems. Also, it is shown that there may be a tradeoff between maximizing P2P energy trades within a community and maximizing the revenues of the prosumers.

Abstract
This paper presents a deep generative model for capturing the conditional probability distribution of future wind power given its history by modeling and pattern recognition in a dynamic graph. The dynamic nodes show the wind sites while the dynamic edges reflect the correlation between the nodes. We propose a scalable optimization model, which is theoretically proved to catch distributions at nodes of the graph, contrary with all learning formulations in the sector of discriminatory pattern recognition. The density of probabilities for each node can be used as samples in our framework. This probabilistic deep convolutional Auto-encoder (PDCA), is based on the deep learning of localized first-order approximation of spectral graph convolutions, a novel evolutionary algorithm and the Bayesian variational inference concepts. The presented generative model is used for the spatiotemporal probabilistic wind power problem in a wide 25 wind sites located in California, the USA for up to 24 hr ahead prediction. The experimental findings reveal that our proposed model outperforms other competitive temporal and spatio-temporal algorithms in terms of reliability, sharpness, and continuous ranked probability score.

Abstract
With the continued growth of wind power penetration into conventional power grid systems, wind power forecasting plays an increasingly competitive role in organizing and deploying electrical and energy systems. The wind power time series, though, often present non-linear and non-stationary characteristics, allowing them quite challenging to estimate precisely. The aim of this paper is in proposing a novel hybrid model named EvolCNN in order to predict the short-term wind power at 10-min interval up to 3-hr based on deep convolutional neural network (CNN) and evolutionary search optimizer. Specifically, we develop an improved version of Grey Wolf Optimization (GWO) algorithm by incorporating two effective modifications in its original structure. The proposed GWO algorithm is more effective than the original version due to performing in a faster way and the ability to escape from local optima. The proposed GWO algorithm is utilized to find the optimal values of hyperparameters for deep CNN model. Moreover, the optimal CNN model is employed to predict wind power time series. The main advantage of the proposed Evol-CNN model is to enhance the capability of time series forecasting models in obtaining more accurate predictions. Several forecasting benchmarks are compared with the Evol-CNN model to address its effectiveness. The simulation results indicate that the Evol-CNN has a significant advantage over the competitive benchmarks and also, has the minimum error regarding of 10-min, 1-hr and 3-hr ahead forecasting.

Abstract
Anomaly detection in electricity consumption data is one of the most important methods to identify anomalous events in buildings and electric assets, such as energy theft, metering defect, cyber attacks and technical losses. In this paper, a novel deep learning based approach is presented to detect anomalies in electricity consumption data one hour ahead of time. We address this challenge in two stages. First, we build an Long Short-Term Memory (LSTM) based neural network model to predict the next hour sample. Second, we use another LSTM autoencoder to learn the features of normal consumption. The output of the first stage is used as an input to the LSTM autoencoder. The LSTM autoencoder will learn the features of normal consumption and the input will be similar to output when applied. For anomalies, the input and output will be significantly different. The Exponential Moving Average (EMA) is used as a threshold and two types of anomalies are distinguished, local and global anomalies. Several weather features are considered in this study, such as pressure, cloud cover, humidity, temperature, wind direction and wind speed in addition to temporal and lag features. A feature selection method to find the optimal features that achieve good results is also implemented. We validate the proposed approach by comparing the detected anomalous consumption and the normal consumption within the same period, and the results demonstrate a drastic increase in electricity consumption during the anomalous periods. The results also show that the temporal and lag features have improved the efficiency and performance of the proposed method.