Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

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This paper presents a multiagent system (MAS) for real-time operation of a microgrid. The proposed operational strategy is mainly focused on generation scheduling and demand side management. In generation scheduling, schedule coordinator agent executes a two-stage scheduling: day-ahead and real-time scheduling. The day-ahead scheduling finds out hourly power settings of distributed energy resources (DERs) from a day-ahead energy market. The real-time scheduling updates the power settings of the distributed energy resources by considering the results of the day-ahead scheduling and feedback from real-time operation of the microgrid in real-time digital simulator (RTDS). A demand side management agent performs load shifting before the day-ahead scheduling, and does load curtailing in real-time whenever it is necessary and possible. The distributed multiagent model proposed in this paper provides a common communication interface for all components of the microgrid to interact with one another for autonomous intelligent control actions. Furthermore, the multiagent system maximizes the power production of local distributed generators, minimizes the operational cost of the microgrid, and optimizes the power exchange between the main power grid and the microgrid subject to system constraints and constraints of distributed energy resources. Outcome of simulation studies demonstrates the effectiveness of the proposed multiagent approach for real-time operation of a microgrid.

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In the modern smart home, smart meters and Internet of Things (IoT) have been massively deployed to replace traditional analogue meters. It digitalises the data collection and the meter readings. The data can be wirelessly transmitted that significantly reduces manual works. However, the community of smart home network is vulnerable to energy theft. Such attacks cannot be effectively detected since the existing techniques require certain devices to be installed to work. This imposes a challenge for energy theft detection systems to be implemented despite the lack of energy monitoring devices. This paper develops an energy detection system called Smart Energy Theft System (SETS) based on machine learning and statistical models. There are 3 stages of decision-making modules, the first stage is the prediction model which uses multi-model forecasting System. This system integrates various machine learning models into a single forecast system for predicting the power consumption. The second stage is the primary decision making model that uses Simple Moving Average (SMA) for filtering abnormally. The third stage is the secondary decision making model that makes the final stage of the decision on energy theft. The simulation results demonstrate that the proposed system can successfully detect 99.96% accuracy that enhances the security of the IoT based smart home.

Multi-agent system (MAS) is one of the most exciting and the fastest growing domains in agent oriented technology which deals with modeling of autonomous decision making entities. This paper presents an application of MAS for distributed energy resource (DER) management in a MicroGrid. MicroGrid can be defined as low voltage distributed power networks comprising various distributed generators (DG), storage and controllable loads, which can be operated as interconnected or as islands from the main power grid. By representing each element in MicroGrid as an autonomous intelligent agent, multi agent modeling of a MicroGrid is designed and implemented. JADE framework is proposed for the modeling and reliability of the MicroGrid is confirmed with PowerWorldSimulator. Further, the FIPA contract net coordination between the agents is demonstrated through software simulation. As a result, this paper provides a MicroGrid modeling which has the necessary communication and coordination structure to create a scalable system. The optimized MicroGrid management and operations can be developed on it in future.

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