Explore the vast range of titles available.

Integration of distributed energy resources, such as photovoltaic solar (PV), introduces new opportunities to establish local energy market frameworks to improve renewable energy utilisation in residential sectors. Such peer‐to‐peer (P2P) energy trading refers to a local market structure where customers (and prosumers) interact to share excess PV generation to enhance the individual and community social welfare. In this work, a day‐ahead continuous double auction (CDA)‐based P2P market structure considering network losses and network utilisation fees was designed. Day‐ahead PV energy is modelled using fractional integral polynomials and the output is forecasted using an autoregressive integrated moving average model for each market interval. Based on the customer load and excess PV energy, the CDA market is cleared using a bid/ask matching mechanism. The performance of the P2P market was evaluated by computing different welfare metrics while analysing the effect of network constraints. The results show that the designed CDA‐based P2P market structure increases the social welfare of all participants by an average of 17.75% compared to the baseline for the presented cases. Moreover, the impact of the forecasting error between the day‐ahead and real‐time market was also quantified. This article presents a modified continuous double auction‐based peer‐to‐peer trading market structure incorporating the network constraints. The proposed market framework has been evaluated for different practical test scenarios to evaluate the performance of the market under the varying real‐time parameters. The manuscript contributes to develop a practical energy trading platform where different residential customers can interact autonomously to enhance their welfare.

This paper proposes an architecture of locality electricity market (LEM) for peer-to-peer (P2P) energy trading among a group of residential prosumers (consumers and producers) with renewable energy resources, smart meters, information and communication technologies, and home energy management systems in a smart residential locality. Prosumers may sell(buy) their excess generation(demand) in LEM at a profitable prices compared to the utility prices in P2P fashion. In order to manage the trading in LEM, a common portal named as locality electricity trading system (LETS) is introduced. The purpose of LETS is to prepare a trading agreement between the participants by fixing a price for every deal based on the quoted price and day-ahead power trading schedule given by the participants. An enhanced intelligent residential energy management system (EIREMS) is proposed at the prosumers' premises to enable their participation in the day-ahead energy trading process and in real-time scheduling of schedulable loads and battery for reducing the electricity bill with due consideration to the operational constraints and LETS agreement. The performances of proposed LETS and EIREMS are validated through a few case studies on a locality with ten prosumers. The proposed methodology endorses marginal economic benefit for all the participants.

In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid’s energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches.

Advanced smart grid technologies enable energy prosumers to trade surplus energy from their distributed renewable energy sources with other peer prosumers through peer-to-peer (P2P) energy trading. In many previous works, P2P energy trading was facilitated by blockchain technology through blockchain’s distributive nature and capacity to run smart contracts. However, the feature that all the data and transactions on a blockchain are visible to all blockchain nodes may significantly threaten the privacy of the parties participating in P2P energy trading. There are many previous works that have attempted to mitigate this problem. However, all these works focused on the anonymity of participants but did not protect the data and transactions. To address this issue, we propose a P2P energy trading system on a blockchain where all bids are encrypted and peer matching is performed on the encrypted bids by a functional encryption-based smart contract. The system guarantees that the information encoded in the encrypted bids is protected, but the peer matching transactions are performed by the nodes in a publicly verifiable manner through smart contracts. We verify the feasibility of the proposed system by implementing a prototype composed of smart meters, a distribution system operator (DSO) server, and private Ethereum blockchain.

With the increasing integration of distributed renewable energy, traditional power users are evolving into prosumers capable of both generation and consumption. However, their decentralized nature poses challenges in resource coordination. This study proposes a bi‐level optimization framework for distribution networks integrating peer‐to‐peer (P2P) energy trading and shared energy storage. The upper‐level model minimizes distribution system operator (DSO) operational costs, including network losses and storage management, while ensuring voltage stability. The lower‐level model enables prosumers to maximize P2P market profits through adaptive load adjustments and shared storage utilization. To address the nonlinear, high‐dimensional optimization challenges, an improved Convex‐Soft Actor‐Critic (C‐SAC) algorithm is developed, combining deep reinforcement learning with convex optimization to achieve privacy‐preserving distributed coordination. Case studies on an IEEE 33‐node system demonstrate that the framework increases prosumer profits by 56.9%, reduces DSO costs by 23.6%, and lowers network losses by 21.5% compared to non‐cooperative scenarios. The shared storage system reduces capacity and power requirements by 20% and 14.1%, respectively. The C‐SAC algorithm outperforms traditional methods (DDPG, SAC) in convergence speed and economic metrics, showing scalability across larger systems (IEEE 69/118 nodes). This work provides a model‐free solution for renewable‐rich distribution networks, balancing efficiency and operational security. This study proposes a bi‐level optimization framework for distribution networks integrating P2P energy trading and shared storage. The upper level minimizes DSO costs (network losses, storage management) with voltage stability constraints, while the lower level optimizes prosumer profits via adaptive load/storage adjustments. An improved Convex‐SAC algorithm combining deep reinforcement learning and convex optimization addresses nonlinear, high‐dimensional challenges. Tests on an IEEE 33‐node system show a 56.9% profit rise for prosumers, 23.6% cost reduction for DSOs, and 21.5% lower network losses. Shared storage cuts capacity/power needs by 20% and 14.1%. The framework demonstrates scalability in larger systems (IEEE 69/118 nodes), offering a model‐free solution for renewable‐rich grids that balances efficiency and security.

With the emergence of distributed energy resources (DER) and prosumers in the distribution networks, the traditional centralised operation structure has transformed into the decentralised operation and transaction structure, which is regarded as the transactive energy market. Furthermore, the planning problem in the distribution network is deeply affected by the transactive energy market. First, the literature and studies on distribution network planning with DERs are reviewed. Moreover, the multi‐stakeholder operation and planning framework under the transactive energy market are analysed. The transactive energy market provides a peer‐to‐peer (P2P) transaction for third‐party prosumers. Several techniques for multi‐stakeholders planning and P2P transaction are introduced. Finally, a tri‐layer distribution network expansion planning (DNEP) framework integrated with virtual P2P transactions is proposed and analysed to accommodate uncertainties and postpone investment. The key technology and difficulties are also discussed. The emergence of DERs and integrated planning problem in the distribution network is deeply affected by the transactive energy market. To cope with these problems, the literature and studies on distribution network planning with DERs are reviewed and analysed. Following that, a distribution network expansion planning framework with virtual P2P transactions is proposed and analysed.

A decarbonised future grid should couple technological novelty with innovative market models to efficiently capture the value of grid‐edge decarbonised assets. The transactive energy (TE) concept inverts the centralised grid model by leveraging the evolution of consumers to prosumers to prosumagers. The principal TE market design challenge is transactive control—using market and pricing mechanisms to coordinate autonomous peer interactions, to optimally allocate power and incentivise peers. Peer attraction, incentivisation and retention are all critical for practical TE implementation along three adoption stages, starting from independent peer transactions with the centralised market; to decentralised peer coordination; towards distributed peer‐to‐peer trading. Addressing gaps in related scholarship, the authors investigate the economic positions of distinct peer roles in each adoption stage and two local pricing strategies. Using a real market dataset, trading decisions are simulated over a 1‐year horizon at hourly granularity. Coordinated action achieves better transactive control for the community, with economic superiority over centralised and distributed mechanisms. Distinct peer incentives should equitably align with their contribution to market functionality, such as the value ascribed to prosumagers' flexibility in local pricing and the constrained bargaining power of prosumers in distributed bilateral negotiations. Addressing gaps in related scholarship, the authors use a case study approach to quantify the economic outcomes of distinct peer evolutionary roles (consumers, prosumers, and prosumagers) as they participate in the market designs (centralised, decentralised, and distributed peer‐to‐peer) along the transactive energy maturity roadmap. Decentralised coordinated action is economically superior for the community, over centralised and distributed mechanisms. However, distinct peer incentives should equitably align with their contribution to market functionality, such as the value ascribed to prosumagers' flexibility in local prices and the constrained bargaining power of prosumers in distributed bilateral negotiations.

Microgrids with substantial incorporation of distributed renewable energy sources face challenges such as magnitude voltage rise, frequency variations, and power quality issues. A novel method is proposed to managing and controlling reactive power within microgrids with high integration of photovoltaic panels. The proactive dispatch is carried out for a few minutes in advance, using power forecast and the inverters of the photovoltaic installations as reactive energy providers. The goal is to stabilise voltage levels and reduce overall energy losses. Optimising reactive power poses a non‐convex problem. The presented method tackles this challenge by employing Conic approximations and Wirtinger calculus techniques to convert and frame the issue within a convex framework, simplifying its resolution. It primarily concentrates on fine‐tuning reactive power output from photovoltaic inverters to minimise overall losses and maintain stable magnitude voltage levels. To enable the reactive power dispatch, a Markov switching forecasting model that uses on‐site data, including sky imagery and other external variables, predicting active power generation every 6 minutes. The proposed technique is evaluated in a CIGRE test network, showing an average loss reduction between 14% and 66% for sunny and cloudy conditions with different levels of PV penetration, this while maintaining the steady state voltages in range. A novel method is proposed to manage and control reactive power within microgrids with high integration of photovoltaic panels. A proactive dispatch is carried out for a few minutes in advance, using power forecast and photovoltaic installations as reactive energy providers.

Islanding detection is a critical issue in grid‐connected distributed microgrid systems. Distributed generation in the current power system has caused many challenges. Consequently, detecting quick and effective islanding is the most critical issue to minimise equipment failure, avoid danger, and maintain grid safety. There are various techniques for islanding identification in microgrids. Three classifications have been applied to categorise these strategies, which are: active, passive, and hybrid. This paper proposes and demonstrates an efficient and accurate approach to islanding detection based on the Variance Autocorrelation Function of a Modal Current Envelope (VAMCE) technique. Demodulation techniques including synchronous real demodulation, square law demodulation, asynchronous complex square law demodulation, and the quadrature demodulation technique are employed to detect the envelope of the 3‐phase current signal. The VAMCE methodology is better suited for islanding detection because of its response to current sensitivity under islanding scenarios but not under normal conditions. Several simulations under various settings, including normal and islanded scenarios are used to analyse this method. These simulations have demonstrated different situations, such as when the system works normally and when it does not. The VAMCE along with the quadrature demodulation technique outperforms the others. The proposed solution is not only more accurate but also much faster compared to other methods. The proposed approach can identify normal and islanded situations in just 0.4 s. This paper proposes and demonstrates an efficient and accurate approach to islanding detection based on the Variance Autocorrelation Function of a Modal Current Envelope (VAMCE) technique. The VAMCE methodology is better suited for islanding detection because of its response to current sensitivity under islanding scenarios but not under normal conditions. The proposed solution is not only more accurate but also much faster compared to other methods. The proposed approach can identify normal and islanded situations in just 0.4 s.

A coordinated set point automatic adjustment with correction enabled (C‐SPAACE) framework that uses 5G communication for real‐time control coordination between inverter‐based resources (IBR) in microgrids is proposed. Utilising slicing capability, 5G offers low‐latency communication to C‐SPAACE under normal conditions. However, given the multitude of power grid use cases, a certain 5G slice for C‐SPAACE may have access only to limited radio spectrum resources, which if not managed well, greatly undermines the communication needs of C‐SPAACE framework. Thus, optimally scheduling the available spectrum resources among IBRs in a sliced 5G network‐based C‐SPAACE framework becomes a critical problem. To address this issue, the authors utilise a novel age of information (AoI) metric and designs an AoI‐based 5G scheduler to provide low‐latency communication to C‐SPAACE. Following this, a co‐simulation environment is designed using PSCAD/EMTDC and Python to simulate a microgrid supported by 5G communication. Time‐domain simulation case studies are performed using the proposed co‐simulation environment to evaluate the performance of C‐SPAACE using 5G with both AoI‐based and other baseline (non‐AoI) schedulers. A 5G‐based coordinated set point automatic adjustment with correction enabled (C‐SPAACE) for distributed control of microgrids is proposed. The proposed method uses the age of information to optimally allocate resource blocks to ensure the delivery of the quality of service required by C‐SPAACE. Cosimulation results using PSCAD/EMTDC software and Python verify the validity of the proposed method.