When energy trading meets blockchain in electrical power system: The state of the art

With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system.This research was funded by Beijing Natural Science Foundation (grant number 4182060).Scopu

Cyberphysical Blockchain-Enabled Peer-to-Peer Energy Trading

Scalability and security problems with centralized architecture models in cyberphysical systems have provided opportunities for blockchain-based distributed models. A decentralized energy-trading system takes advantage of various sources and effectively coordinates the energy to ensure the optimal utilization of available resources. Three blockchain-based energy-trading models are proposed to overcome the technical challenges and market barriers as well as enhance the adoption of this disruptive technology

Virtual Power Plant Day Ahead Energy Unit Commitment

In this article we present a model for the interaction of distributed energy resources (DER) with the electricity system, using reinforcement learning. Our method relaxes the requirements for information necessary to train and engage in Pareto improving trading, and can directly incorporate the inherent intermittency of variable renewable energy sources. The distributed resources include consumers of electricity, energy storage systems, and variable renewable energy. We modify the algorithms to improve the scheduling of the resources. In our empirical application, we use data from Colombia subject to large variability due to El Niño Southern Oscillation and illustrate the use of the model under large variations in the data used to train the model

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid

This paper investigates the feasibility of using a discriminate pricing scheme to offset the inconvenience that is experienced by an energy user (EU) in trading its energy with an energy controller in smart grid. The main objective is to encourage EUs with small distributed energy resources (DERs), or with high sensitivity to their inconvenience, to take part in the energy trading via providing incentive to them with relatively higher payment at the same time as reducing the total cost to the energy controller. The proposed scheme is modeled through a two-stage Stackelberg game that describes the energy trading between a shared facility authority (SFA) and EUs in a smart community. A suitable cost function is proposed for the SFA to leverage the generation of discriminate pricing according to the inconvenience experienced by each EU. It is shown that the game has a unique sub-game perfect equilibrium (SPE), under the certain condition at which the SFA's total cost is minimized, and that each EU receives its best utility according to its associated inconvenience for the given price. A backward induction technique is used to derive a closed form expression for the price function at SPE, and thus the dependency of price on an EU's different decision parameters is explained for the studied system. Numerical examples are provided to show the beneficial properties of the proposed scheme.Comment: 7 pages, 4 figures, 3 tables, conference pape

Computational Intelligence Approaches for Energy Optimization in Microgrids

The future electrical system termed as smart grid represents a significant paradigm shift for power industry. Nowadays, microgrids are becoming smarter with the integration of renewable energy resources (RESs) , diesel generators , energy storage systems (ESS), and plug-in electric vehicles (PEV or EV) . However, these integration bring with new challenges for intelligent management systems. The classical power generation approaches can no longer be applied to a microgrid with unpredictable renewable energy resources. To relive these problem, a proper power system optimization and a suitable coordination strategy are needed to balance the supply and demand. This thesis presents three projects to study the optimization and control for smart community and to investigate the strategic impact and the energy trading techniques for interconnected microgrids. The first goal of this thesis is to propose a new game-theoretic framework to study the optimization and decision making of multi-players in the distributed power system. The proposed game theoretic special concept-rational reaction set (RRS) is capable to model the game of the distributed energy providers and the large residential consumers. Meanwhile, the residential consumers are able to participate in the retail electricity market to control the market price. Case studies are conducted to validate the system framework using the proposed game theoretic method. The simulation results show the effectiveness and the accuracy of the proposed strategic framework for obtaining the optimum profits for players participating in this market. The second goal of the thesis is to study a distributed convex optimization framework for energy trading of interconnected microgrids to improve the reliability of system operation. In this work, a distributed energy trading approach for interconnected operation of islanded microgrids is studied. Specifically, the system includes several islanded microgrids that can trade energy in a given topology. A distributed iterative deep cut ellipsoid (DCE) algorithm is implemented with limited information exchange. This approach will address the scalability issue and also secure local information on cost functions. During the iterative process, the information exchange among interconnected microgrids is restricted to electricity prices and expected trading energy. Numerical results are presented in terms of the convergent rate of the algorithm for different topologies, and the performance of the DCE algorithm is compared with sub-gradient algorithm. The third goal of this thesis is to use proper optimization approaches to motivate the household consumers to either shift their loads from peaking periods or reduce their consumption. Genetic algorithm (GA) and dynamic programming (DP) based smart appliance scheduling schemes and time-of-use pricing are investigated for comparative studies with demand response