In this paper, we study the interactions among interconnected autonomous microgrids, and develop a joint energy trading and scheduling strategy. Each interconnected microgrid not only schedules its local power supply and demand, but also trades energy with other microgrids in a distribution network. Specifically, microgrids with excessive renewable generations can trade with other microgrids in deficit of power supplies for mutual benefits. Since interconnected microgrids operate autonomously, they aim to optimize their own performance and expect to gain benefits through energy trading. We design an incentive mechanism using Nash bargaining theory to encourage proactive energy trading and fair benefit sharing. We solve the bargaining problem by decomposing it into two sequential problems on social cost minimization and trading benefit sharing, respectively. For practical implementation, we propose a decentralized solution method with minimum information exchange overhead. Numerical studies based on realistic data demonstrate that the total cost of the interconnected-microgrids operation can be reduced by up to 13.2% through energy trading, and an individual participating microgrid can achieve up to 29.4% reduction in its cost through energy trading.

This comprehensive review paper aims to provide an in-depth analysis of the most recent developments in the applications of artificial intelligence (AI) techniques, with an emphasis on their critical role in the demand side of power distribution systems. This paper offers a meticulous examination of various AI models and a pragmatic guide to aid in selecting the suitable techniques for three areas: load forecasting, anomaly detection, and demand response in real-world applications. In the realm of load forecasting, the paper presents a thorough guide for choosing the most fitting machine learning and deep learning models, inclusive of reinforcement learning, in conjunction with the application of hybrid models and learning optimization strategies. This selection process is informed by the properties of load data and the specific scenarios that necessitate forecasting. Concerning anomaly detection, this paper provides an overview of the merits and limitations of disparate learning methods, fostering a discussion on the optimization strategies that can be harnessed to navigate the issue of imbalanced data, a prevalent concern in power system anomaly detection. As for demand response, we delve into the utilization of AI techniques, examining both incentive-based and price-based demand response schemes. We take into account various control targets, input sources, and applications that pertain to their use and effectiveness. In conclusion, this review paper is structured to offer useful insights into the selection and design of AI techniques focusing on the demand-side applications of future energy systems. It provides guidance and future directions for the development of sustainable energy systems, aiming to serve as a cornerstone for ongoing research within this swiftly evolving field.

Energy storage can play an important role in energy management of end users. To promote an efficient utilization of energy storage, we develop a novel business model to enable virtual storage sharing among a group of users. Specifically, a storage aggregator invests and operates the central physical storage unit, by virtualizing it into separable virtual capacities and selling to users. Each user purchases the virtual capacity, and utilize it to reduce the energy cost. We formulate the interaction between the aggregator and users as a two-stage optimization problem. In Stage 1, over the investment horizon, the aggregator determines the investment and pricing decisions. In Stage 2, in each operational horizon, each user decides the virtual capacity to purchase together with the operation of the virtual storage. We characterize a stepwise form of the optimal solution of Stage-2 problem and a piecewise linear structure of the optimal profit of Stage-1 problem, both with respect to the virtual capacity price. Based on the solution structure, we design an algorithm to attain the optimal solution of the two-stage problem. In our simulation results, the proposed storage virtualization model can reduce the physical energy storage investment of the aggregator by 54.3% and reduce the users’ total costs by 34.7%, compared to the case where users acquire their own physical storage.

With the booming of smart grid, the ubiquitously deployed smart meters constitutes an energy Internet of Things (IoT). This article develops a novel blockchain-based transactive energy management (TEM) system for IoT-aided smart homes. We consider a holistic set of options for smart homes to participate in transactive energy. Smart homes can interact with the grid to perform vertical transactions, e.g., feeding in extra solar energy to the grid and providing demand response service to alleviate the grid load. Smart homes can also interact with peer users to perform horizontal transactions, e.g., peer-to-peer energy trading. However, conventional TEM method suffers from the drawbacks of low efficiency, privacy leakage, and single-point failure. To address these challenges, we develop a privacy-preserving distributed algorithm that enables users to optimally manage their energy usages in parallel via the smart contract on the blockchain. Further, we design an efficient blockchain system tailored for IoT devices and develop the smart contract to support the holistic TEM system. Finally, we evaluate the feasibility and performance of the blockchain-based TEM system through extensive simulations and experiments. The results show that the blockchain-based TEM system is feasible on practical IoT devices and reduces the overall cost by 25%.

We study the renewable energy generations in Hong Kong based on realistic meteorological data, and find that different renewable sources exhibit diverse time-varying and location-dependent profiles. To efficiently explore and utilize the diverse renewable energy generations, we propose a theoretical framework for the cooperative planning of renewable generations in a system of interconnected microgrids. The cooperative framework considers the self-interested behaviors of microgrids, and incorporates both their long-term investment costs and short-term operational costs over the planning horizon. Specifically, interconnected microgrids jointly decide where and how much to deploy renewable energy generations, and how to split the associated investment cost. We show that the cooperative framework minimizes the overall system cost. We also design a fair cost sharing method based on Nash bargaining to incentivize cooperative planning, such that all microgrids will benefit from cooperative planning. Using realistic data obtained from the Hong Kong observatory, we validate the cooperative planning framework and demonstrate that all microgrids benefit through the cooperation, and the overall system cost is reduced by 35.9% compared with the noncooperative planning benchmark.