Optimal Peer-to-Peer Energy Sharing Between Prosumers Using Hydrokinetic, Diesel Generator and Pumped Hydro Storage

Published ArticleIn this paper, an optimal Peer-to-Peer energy sharing model between two small prosumers is developed and simulated in the south African context. For this purpose, a case study of a commercial type prosumer sharing power with a residential prosumer, both on the same premise, is used to demonstrate the effectiveness of the model. The commercial prosumer owns a small hydrokinetic system operating in conjunction with a pumped hydro storage while the residential prosumer has a diesel generator. The developed model aims to minimize the resulting cost of energy linked to the diesel generator, while optimizing the power flow between the two prosumers. Using actual data, the developed model has been used to simulate and analyze the complex interaction between the different power sources, the energy storage and the demands within the proposed system sizing and operation constraints. The simulation results show that the power flows can be optimally managed, resulting in a substantial reduction in the residential prosumer's operation cost which can now rely not only on its diesel generator but also on the power shared by the hydrokinetic and pumped hydro system of the commercial prosumer

Peer-to-peer energy trading between wind power producer and demand response aggregators for scheduling joint energy and reserve

In this article, a stochastic decision-making framework is presented in which a wind power producer (WPP) provides some required reserve capacity from demand response aggregators (DRAs) in a peer-to-peer (P2P) structure. In this structure, each DRA is able to choose the most competitive WPP, and purchase energy and sell reserve capacity to that WPP under a bilateral contract-based P2P electricity trading mechanism. Based on this structure, the WPP can determine the optimal buying reserve from DRAs to offset part of wind power deviation. The proposed framework is formulated as a bilevel stochastic model in which the upper level maximizes the WPP's profit based on the optimal bidding in the day-ahead and balancing markets, whereas the lower level minimizes DRAs' costs. In order to incorporate the risk associated with the WPP's decisions and to assess the effect of scheduling reserves on the profit variability, conditional value at risk is employed.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Pool trading model within a local energy community considering flexible loads, photovoltaic generation and energy storage systems

This paper presents a pool trading model within a local energy community considering home energy management systems (HEMSs) and other consumers. A transparent mechanism for market clearing is proposed to incentivise active prosumers to trade their surplus energy within a rule-based pool market in the local energy community. A price-based demand response program (PBDRP) is considered to increase the consumers’ willingness to modify their consumption. The mathematical optimization problem is a standard mixed-integer linear programming (MILP) problem to allow for rapid assessment of the trading market for real energy communities which have a considerable number of consumers. This allows for novel energy trading strategies amongst different clients in the model and for the integration of a pool energy trading model at the level of the local energy community. The objective function of the energy community is to minimize the overall bills of all participants while fulfilling their demands. Two different scenarios have been evaluated, independent and integrated operation modes, to show the impacts of coordination amongst different end-users. Results show that through cooperation, end-users in the local energy community market can reduce the total electricity bill. This is shown in a 16.63% cost reduction in the independent operation and a 21.38% reduction in the integrated case. Revenues for active consumers under coordination increased compared to independent operation of the HEMS.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

BECA: A Blockchain-Based Edge Computing Architecture for Internet of Things Systems

The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage

Blockchain-Based Hardware-in-the-Loop Simulation of a Decentralized Controller for Local Energy Communities

The development of local energy communities observed in the last years requires the reorganization of energy consumption and production. In these newly considered energy systems, the commercial and technical decision processes should be decentralized in order to reduce their maintenance costs. This will be allowed by the progressive spreading of IoT systems capable of interacting with distributed energy resources, giving local sources the ability to be optimally coordinated in terms of network and energy management. In this context, this paper presents a decentralized controlling architecture that performs a wide spectrum of power system optimization procedures oriented to the local market management. The controller framework is based on a decentralized genetic algorithm. The manuscript describes the structure of the tool and its validation, considering an automated distributed resource scheduling for local energy markets. The simulation platform permits implementing the blockchain-based trading process and the automated distributed resource scheduling. The effectiveness of the tool proposed is discussed with a hardware-in-the-loop case study

Impact of local energy markets integration in power systems layer: A comprehensive review

In recent years extensive research has been conducted on the development of different models that enable energy trading between prosumers and consumers due to expected high integration of distributed energy resources. Some of the most researched mechanisms include Peer-to-Peer energy trading, Community Self-Consumption and Transactive Energy Models. To ensure the stable and reliable delivery of electricity as such markets and models grow, this paper aims to understand the impact of these models on grid infrastructure, including impacts on the control, operation, and planning of power systems, interaction between multiple market models and impact on transmission network. Here, we present a comprehensive review of existing research on impact of Local Energy Market integration in power systems layer. We detect and classify most common issues and benefits that the power grid can expect from integrating these models. We also present a detailed overview of methods that are used to integrate physical network constraints into the market mechanisms, their advantages, drawbacks, and scaling potential. In addition, we present different methods to calculate and allocate network tariffs and power losses. We find that financial energy transactions do not directly reflect the physical energy flows imposed by the constraints of the installed electrical infrastructure. In the end, we identify a number of different challenges and detect research gaps that need to be addressed in order to integrate Local Energy Market models into existing infrastructure

VPT: Privacy Preserving Energy Trading and Block Mining Mechanism for Blockchain based Virtual Power Plants

The desire to overcome reliability issues of distributed energy resources (DERs) lead researchers to development of a novel concept named as virtual power plant (VPP). VPPs are supposed to carry out intelligent, secure, and smart energy trading among prosumers, buyers, and generating stations along with providing efficient energy management. Therefore, integrating blockchain in decentralized VPP network emerged out as a new paradigm, and recent experiments over this integration have shown fruitful results. However, this decentralization also suffers with energy management, trust, reliability, and efficiency issues due to the dynamic nature of DERs. In order to overcome this, in this paper, we first work over providing efficient energy management strategy for VPP to enhance demand response, then we propose an energy oriented trading and block mining protocol and named it as proof of energy market (PoEM). To enhance it further, we integrate differential privacy in PoEM and propose a Private PoEM (PPoEM) model. Collectively, we propose a private decentralized VPP trading model and named it as Virtual Private Trading (VPT) model. We further carry out extensive theoretical analysis and derive step-by-step valuations for market race probability, market stability probability, energy trading expectation, winning state probability, and prospective leading time profit values. Afterwards, we carry out simulation-based experiment of our proposed model. The performance evaluation and theoretical analysis of our VPT model make it one of the most viable model for blockchain based VPP network as compared to other state-of-the-art works.Comment: Article Submitted for Revie