Energy Trading In Microgrids Research Articles

Integrating Artificial Intelligence I and Blockchain for Secure Peer-to-Peer Energy Trading in Microgrids

Integrating Artificial Intelligence I and Blockchain for Secure Peer-to-Peer Energy Trading in Microgrids

Decentralized energy trading in microgrids: a blockchain-integrated model for efficient power flow with social welfare optimization

Decentralized energy trading in microgrids: a blockchain-integrated model for efficient power flow with social welfare optimization

Enhancing local energy sharing reliability within peer-to-peer prosumer communities: A cellular automata and deep learning approach

This study introduces a significant advancement in peer-to-peer (P2P) energy trading systems within smart grids, addressing a crucial gap in existing research by incorporating optimal energy storage capacities to accommodate varying energy demands resulting from lifestyle changes. Through a two-level optimization approach, aimed at maximizing self consumption and optimizing energy flow within the grid, we propose a novel energy management strategy. Our contribution lies in the introduction of a new layer of deep learning and rules control, forming a self-energy sharing system for each prosumer. This architecture, termed the smart node, integrates deep learning techniques, to predict and customize energy services through dynamic adjustment of lower and upper bounds of battery capacities. Additionally, we leverage cellular automaton (CA) approaches to establish sustainable consensus among P2P network users, enhancing the adaptability and efficiency of the energy management system. The results show that the proposed algorithm could reduce the energy consumed by the P2P community from the utility by around 20% and maximize the collective self-consumption by around 8% compared to conventional energy trading in microgrids.

Microgrid trading mechanism enhancement for smart contract considering reputation values

ABSTRACT In order to reduce the system instability caused by credit risk in microgrid transactions in the blockchain, we propose a smart contract microgrid transaction model considering reputation value. Considering the instability caused by credit risk, the reputation factor is introduced to ensure the secure and stable operation of the microgrid energy trading system. The effectiveness of the scheme is verified by comparing traditional electricity trading and the trading with the introduced credit value scheme through simulation experiments.

A Federated Learning and Blockchain-Enabled Sustainable Energy Trade at the Edge: A Framework for Industry 4.0

Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy-trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and artificial intelligence, enables smart and secure microgrid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and federated learning-enabled solution provide secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring, and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario.

A Blockchain Based Secure Decentralized Transaction System for Energy Trading in Microgrids

A Blockchain Based Secure Decentralized Transaction System for Energy Trading in Microgrids

Enabling Energy Trading in Cooperative Microgrids: A Scalable Blockchain-Based Approach With Redundant Data Exchange

Blockchain has recently been regarded as an important enabler for building secure energy trading in microgrid systems because of its inherent features of distributively providing immutable data record, storage, and sharing across networks in a peer-to-peer (P2P) manner. However, designing highly efficient and scalable blockchain-enabled energy trading mechanisms is extremely challenging because of the unique features of microgrid systems, e.g., bandwidth-constrained and high-latency communications and large-scale renewable energy source (RES) integration. To address this challenge, in this article, we propose a novel scalable blockchain-based energy trading framework for cooperative microgrid systems, which include four planes, i.e., data plane, consensus plane, smart plane, and application plane. Different from the existing solutions without consideration of network transmission, these four planes are designed with the capability of perceiving the status of block generation and transmission over interrupted P2P networks, and thus proactively improving the consensus process to guarantee the reliability of energy trading in cooperative microgrids. Meanwhile, built on this framework, a novel redundant data exchange strategy is proposed to improve the scalability of block creation with the presence of large-scale RES penetration and interrupted and dynamic communication links. Simulation results show that the proposed system framework outperforms the benchmark blockchain solutions. Furthermore, we investigate the potential applications of the proposed solutions in the practical microgrid systems to facilitate a clear understanding of the mechanisms of the proposed solutions.

Robust microgrid energy trading and scheduling under budgeted uncertainty

The advent of smart grids came with several technological developments including new electricity market rules and regulation mechanisms. Microgrids can trade energy with the main grid to either sell its production surplus (from renewable energy sources) or buy an additional amount to support local consumers’ demand, which includes flexible loads, such as smart appliances and electric vehicles. In this scenario, smart control devices are important elements, executing real-time energy scheduling according to fluctuations in production and consumption. As we might expect, the main grid’s power generation and supply becomes more unscheduled and risky as energy trading quantities oscillate over time. This work studies a flexible energy contract subscription framework, coupled with a real-time command strategy, suited for energy scheduling of microgrids with uncertainty in both production and consumption. Our main contributions are a Robust Optimization model under budgeted uncertainty for contract subscription and a set of heuristic control strategies for the real-time energy scheduling. The robust model is capable of providing solutions for multi-period-ahead trading of energy, while minimizing the worst-case cost. We run an extensive computational case study on a real microgrid instance to confirm the efficacy of our solution approach.

Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning

To reduce global greenhouse gas emissions, the world must find intelligent solutions to maximise the utilisation of carbon-free renewable energy sources. In this paper, multi-agent reinforcement learning is used to control a microgrid in a mixed cooperative and competitive setting. The agents observe fluctuating energy demand, dynamic wholesale energy prices, and intermittent renewable energy sources to control a hybrid energy storage system to maximise the utilisation of the renewables to reduce the energy costs of the grid. In addition, an aggregator agent trades with external microgrids competing against one another and the aggregator to reduce their own energy bills. For this, the algorithm deep deterministic policy gradients (DDPG) and multi-agent DDPG (MADDPG) are used to compare the use of a single global controller versus multiple distributed agents, along with the single and multi-agent variants of distributional DDPG (D3PG) and twin delayed DDPG (TD3). The research found it is significantly more profitable for the primary microgrid to sell energy on its own terms rather than selling back to the utility grid, and is also beneficial for the external microgrids as they also reduce their own energy bills. The methods that produced the greatest profits were the multi-agent approaches where each agent has its own reward function based on the principle of marginal contribution from game theory. The multi-agent approaches were better able to evaluate their performance controlling individual components of the environment which allowed them to develop their own unique policies for the different types of energy storage system.

SynergyGrids: blockchain-supported distributed microgrid energy trading

Growing intelligent cities is witnessing an increasing amount of local energy generation through renewable energy resources. Energy trade among the local energy generators (aka prosumers) and consumers can reduce the energy consumption cost and also reduce the dependency on conventional energy resources, not to mention the environmental, economic, and societal benefits. However, these local energy sources might not be enough to fulfill energy consumption demands. A hybrid approach, where consumers can buy energy from both prosumers (that generate energy) and also from prosumer of other locations, is essential. A centralized system can be used to manage this energy trading that faces several security issues and increase centralized development cost. In this paper, a hybrid energy trading system coupled with a smart contract named SynergyGrids has been proposed as a solution, that reduces the average cost of energy and load over the utility grids. To the best of our knowledge, this work is the first attempt to create a hybrid energy trading platform over the smart contract for energy demand prediction. An hourly energy data set has been utilized for testing and validation purposes. The trading system shows 17.8% decrease in energy cost for consumers and 76.4% decrease in load over utility grids when compared with its counterparts.

Peer-to-Peer energy trading in a microgrid based on iterative double auction and blockchain

The research and construction of microgrids provide the possibility of effective use of distributed energy resources (DERs), where electricity is generated and consumed locally. Peer-to-Peer (P2P) energy trading in microgrids helps to create a totally competitive and autonomous market and brings benefits to both the market participants and utility companies. A P2P energy trading model based on iterative double auction and blockchain is proposed in this paper. The iterative double auction mechanism is used to elicit hidden information from all participants to achieve maximum social welfare, where buyers and sellers adjust quotes to better individual profits according to previous individual transaction volumes and the market equilibrium price, without much knowledge about the market itself. The optimal allocation of the energy and market equilibrium is obtained and verified. The autonomous realization of this P2P energy trading model on blockchain is further presented and discussed. Numerical examples are used to illustrate the feasibility and validity of the proposed iterative double auction algorithm. It is found that the average hourly social welfare for the proposed iterative double auction algorithm is − $29.06, which is a 22.3% increase over the zero-intelligence strategy ( − $37.41).

Complex Problems Solution as a Service Based on Predictive Optimization and Tasks Orchestration in Smart Cities

Smart cities have different contradicting goals having no apparent solution. The selection of the appropriate solution, which is considered the best compromise among the candidates, is known as complex problem-solving. Smart city administrators face different problems of complex nature, such as optimal energy trading in microgrids and optimal comfort index in smart homes, to mention a few. This paper proposes a novel architecture to offer complex problem solutions as a service (CPSaaS) based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city. Predictive model optimization uses a machine learning module and optimization objective to compute the given problem's solutions. The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators. The proposed architecture is hierarchical and modular, making it robust against faults and easy to maintain. The proposed architecture's evaluation results highlight its strengths in fault tolerance, accuracy, and processing speed.

Multi-Agent Based Optimal Scheduling and Trading for Multi-Microgrids Integrated With Urban Transportation Networks

This paper investigates multi-period optimal energy scheduling and trading for multi-microgrids (MMGs) integrated with an urban transportation network (UTN). Specifically, an optimization based multi-period traffic assignment model is built to characterize the vehicular flows considering rational drivers with time-flexible travel demand. Meanwhile, each microgrid (MG) is modeled to independently schedule its operation by trading energy with other MGs and charging electric vehicles (EVs) with its fast charging stations (FCSs). The EV charging prices could further influence traffic flow distribution of the UTN. To co-optimize the coupled system, a multi-agent based optimal scheduling and trading scheme is proposed, which is facilitated by peer-to-peer communication of transactive energy information among the agents, thus can preserve privacy of the market participants. Within the scheme, the MGs and EVs take their optimal decisions in response to MG energy trading and EV charging prices updated via multi-bilateral negotiation based on the demand-supply relationship. When an equilibrium of the coupled system is reached, each MG can minimize its operation cost, and the traffic condition could also be improved. Case studies of a MMG system coupled with a double-ring UTN are conducted to validate the effectiveness of the scheme.

Design of a Management Algorithm for Energy Trading in Microgrids

Background: The scope of this paper is to study the energy trading in microgrids. Microgrids are low voltage or medium voltage distribution networks, which consist of energy storage systems, electric loads, e.g. electric vehicles and Renewable Energy Sources (RES). Methods: Legacy energy grids are being transformed by the introduction of small to medium sized individual or cooperative, mostly RES invested energy producers and prosumers. Electric vehicles penetrate the market and modern power grids integrate them as ancillary services providers when there are peak domestic loads, as well as in order to balance grid voltage aiming to increase system reliability, compensating for renewable energy sources’ intermittency and volatility in energy production. Results: An elaborate management algorithm is proposed in this paper, to balance demand and local renewable energy sources microgrid supply. Conclusion: Finally, the results of simulations of different scenarios, including economic parameters and proposals for future research are presented.

Energy trading in microgrids for synergies among electricity, hydrogen and heat networks

The emerging paradigm of interconnected microgrids advocates energy trading or sharing among multiple microgrids. It helps make full use of the temporal availability of energy and diversity in operational costs when meeting various energy loads. However, energy trading might not completely absorb excess renewable energy. A multi-energy management framework including fuel cell vehicles, energy storage, combined heat and power system, and renewable energy is proposed, and the characteristics and scheduling arrangements of fuel cell vehicles are considered to further improve the local absorption of the renewable energy and enhance the economic benefits of microgrids. While intensive research has been conducted on energy scheduling and trading problem, a fundamental question still remains unanswered on microgrid economics. Namely, due to multi-energy coupling, stochastic renewable energy generation and demands, when and how a microgrid should schedule and trade energy with others, which maximizes its long-term benefit. This paper designs a joint energy scheduling and trading algorithm based on Lyapunov optimization and a double-auction mechanism. Its purpose is to determine the valuations of energy in the auction, optimally schedule energy distribution, and strategically purchase and sell energy with the current electricity prices. Simulations based on real data show that each individual microgrid, under the management of the proposed algorithm, can achieve a time-averaged profit that is arbitrarily close to an optimum value, while avoiding compromising its own comfort.

Distributed Nash Equilibrium Seeking for a Dynamic Micro-grid Energy Trading Game with Non-quadratic Payoffs

To model energy trading interactions among prosumers, a generic non-quadratic dynamic game model is proposed and solved for a stable Nash equilibrium. The framework is modeled as a non-cooperative, infinite strategy, multiplayer game where the participating players can possess a non-quadratic payoff function. Moreover, the market framework realizes a dynamic mapping from strategy set to payoffs of the participating players. It also carries an embedded notion of reliability and fairness by utilizing the concept of market reputation index. The existence of multiple Nash equilibria is discussed as well as the criteria for stability of Nash equilibrium. It is demonstrated that when an extremum seeking approach is used to model Nash seeking behavior of the players, the system converges to one of the stable Nash equilibriums depending on the initial condition. Simulation results are presented to verify the theoretical results, and a complete day with multiple prosumers is simulated to demonstrate effectiveness of the approach. The results are evaluated using several reliability matrices to show that the proposed framework results in an increased local generation, increased payoffs for prosumers, lower market clearing prices, and higher reliability.

Decentralized Local Energy Trading in Microgrids With Voltage Management

Local energy trading in microgrids is one of the emerging concepts in the area of distribution networks. A proper business model is required to manage local energy trading. The pricing mechanism is crucial because the agreed energy price determines the benefits of local energy trading. Designing a proper pricing mechanism with a specific objective considering the privacy of agents and respecting physical network constraints is a challenging task. This article proposes a decentralized algorithm for local energy trading in microgrids with an integrated pricing mechanism considering welfare maximization and network voltage management through local information exchange among neighbors. The proposed algorithm guarantees that the energy transactions do not violate voltage constraints in a physical network and agents' privacy is preserved. A two-stage approach is proposed to achieve fast convergence and increase the practicability of the algorithm. The simulation results are presented to verify the effectiveness of the proposed approach.

Time Series Forecasting Based Day-Ahead Energy Trading in Microgrids: Mathematical Analysis and Simulation

In this paper, we propose a periodic energy trading system in microgrids based on day-ahead forecasting of energy generation and consumption. In the proposed model, each noncooperative prosumer calculates her reward function under her energy change forecasting based on Gaussian process regression and determines her optimal action. Then, the system establishes the equilibrium trading price when all prosumers execute their optimal actions simultaneously. We prove the existence of the equilibrium trading price and establish an algorithm that leads to the equilibrium. Our numerical example shows that the proposed system outperforms its previous model.

A Game Theoretical Pricing Mechanism for Multi-Microgrid Energy Trading Considering Electric Vehicles Uncertainty

The electricity price mechanism based on game theory is one of the research focuses on microgrids energy trading. The complete information game is based on the certainty of the identity of roles of players and variables. However, there are many uncertain factors that cause the game in the state of incomplete information. In this paper, Microgrids Energy Trading Bayesian Game (METBG) model is proposed. The model was based on the Bayesian game, in which MGs make their decision as an agent of native users to tackle bidirectional energy trading between others. First, The Bayesian game modeled types of roles of players by the uncertainty of information including the stochastic characteristics of PEVs which result in hardness that the game participants determine whether they are sellers or buyers in the utility function that depends on the state of power surplus or lack. Moreover, the utility model of players was established by a Bayesian game with the game equilibrium derived rigorously by obligated to coordinate the sharing of energy with maximization of the players' profit. Finally, the solution of game equilibrium has been rigorously derived, and the effectiveness of the model is verified in terms of seller profit, the utilities of buyers, and the net energy usage in the microgrids. The results of the static pricing model and proposed model were compared to demonstrate the effectiveness.

A Blockchain-Enhanced Transaction Model for Microgrid Energy Trading

In the power sector, microgrids play a supportive role in bridging the adequacy gap in the conventional electricity supply. Trading of the generated energy has recently been improved by blockchain technology which offers a new cheap, secure, and decentralized transaction approach. Its operation is however associated with an undesired inherent delay during energy transactions initiated by the prosumers, thus, failure to timely attend to incidences of urgent demand could end up in catastrophe at the consumer's side. This article thus proposes a cyber-enhanced transactive microgrid model using blockchain technology with optimized participants' permission protocol to ameliorate this challenge. It is demonstrated that the optimized blockchain participants' permission model leads to improved transaction speed and greater convenience. The transaction speed simulation is thereafter performed and it was also demonstrated that the node population has a greater effect than the transaction block size on the transaction speed improvement.