Peer-to-peer based energy trading system for heterogeneous small-scale DERs

Author(s):  
Yoon-Sik Yoo ◽  
Taein Hwang ◽  
Shinyuk Kang ◽  
S.H. Shah Newaz ◽  
Il-Woo Lee ◽  
...  
2019 ◽  
Vol 260 ◽  
pp. 01003
Author(s):  
Sang Hyeon Lee ◽  
Myeong-in Choi ◽  
SangHoon Lee ◽  
SoungHoan Park ◽  
Sehyun Park

As small-scale distributed energy is gradually expanding, commercialization of peer to peer(P2P) energy trading that freely exchanges energy among individuals in various countries is being commercialized, and the Microgrids (MGs) are considered to be an optimal platform for P2P energy trading. Although conducting electricity trade among individuals without going through power companies is still in its infancy, it is expected to expand gradually as the awareness of the shared economy grows and the MG spreads. Research on more efficient trading systems is needed while trading energy in MG. Therefore we propose a more efficient energy trading system that minimizes the loss in proportion to the distance of the power line when energy trading is performed in the MG. We have constructed a virtual MG environment and experimented with energy trading scenarios. As a result, when the algorithm is applied, loss in proportion to the distance is reduced by 2.495% and energy trading becomes more active. The amount of energy and the number of trades increased by 1.5 times during the energy trading process.


Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7484
Author(s):  
Yuki Matsuda ◽  
Yuto Yamazaki ◽  
Hiromu Oki ◽  
Yasuhiro Takeda ◽  
Daishi Sagawa ◽  
...  

To further implement decentralized renewable energy resources, blockchain based peer-to-peer (P2P) energy trading is gaining attention and its architecture has been proposed with virtual demonstrations. In this paper, to further socially implement this concept, a blockchain based peer to peer energy trading system which could coordinate with energy control hardware was constructed, and a demonstration experiment was conducted. Previous work focused on virtually matching energy supply and demand via blockchain P2P energy markets, and our work pushes this forward by demonstrating the possibility of actual energy flow control. In this demonstration, Plug-in Hybrid Electrical Vehicles(PHEVs) and Home Energy Management Systems(HEMS) actually used in daily life were controlled in coordination with the blockchain system. In construction, the need of a multi-tagged continuous market was found and proposed. In the demonstration experiment, the proposed blockchain market and hardware control interface was proven capable of securing and stably transmitting energy within the P2P energy system. Also, by the implementation of multi-tagged energy markets, the number of transactions required to secure the required amount of electricity was reduced.


Author(s):  
Nihar Ranjan Pradhan ◽  
Akhilendra Pratap Singh ◽  
Kaibalya Prasad Panda ◽  
Diptendu Sinha Roy

Abstract The vital dependence of peer to peer (P2P) energy trading frameworks on creative Internet of Things (IoT) has been making it more vulnerable against a wide scope of attacks and performance bottlenecks like low throughput, high latency, high CPU, memory use, etc. This hence compromises the energy exchanging information to store, share, oversee, and access. Blockchain innovation as a feasible solution, works with the rule of untrusted members. To alleviate this threat and performance issues, this paper presents a Blockchain based Confidential Consortium (CoCo) P2P energy trading system that works on the trust issues among the energy exchanging networks and limits performance parameters. It reduces the duplicate validation by creating a trusted network on nodes, where participants identities are known and controlled. A Java-script-based smart contract is sent over the Microsoft CoCo system with Proof of Elapsed Time (PoET) consensus protocol. Also, a functional model is designed for the proposed framework and the performance bench-marking has been done considering about latency, throughput, transaction rate control, success and fail transaction, CPU and memory usage, network traffic. Additionally, it is shown that PoET’s performance is superior to proof of work (PoW) for multi-hosting conditions. The measured throughput and latency moving toward database speeds with more flexible, business-specific confidentiality models, network policy management through distributed governance, support for non-deterministic transactions, and reduced energy consumption.


Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1985
Author(s):  
Jae Geun Song ◽  
Eung seon Kang ◽  
Hyeon Woo Shin ◽  
Ju Wook Jang

We implement a peer-to-peer (P2P) energy trading system between prosumers and consumers using a smart contract on Ethereum blockchain. The smart contract resides on a blockchain shared by participants and hence guarantees exact execution of trade and keeps immutable transaction records. It removes high cost and overheads needed against hacking or tampering in traditional server-based P2P energy trade systems. The salient features of our implementation include: 1. Dynamic pricing for automatic balancing of total supply and total demand within a microgrid, 2. prevention of double sale, 3. automatic and autonomous operation, 4. experiment on a testbed (Node.js and web3.js API to access Ethereum Virtual Machine on Raspberry Pis with MATLAB interface), and 5. simulation via personas (virtual consumers and prosumers generated from benchmark). Detailed description of our implementation is provided along with state diagrams and core procedures.


2020 ◽  
Vol 67 (6) ◽  
pp. 4646-4657 ◽  
Author(s):  
Mohsen Khorasany ◽  
Yateendra Mishra ◽  
Gerard Ledwich

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3568
Author(s):  
Min Hee Chung

Since the sharing economy emerged as a new paradigm with the development of technology, the global sharing economy market has grown rapidly. In the energy sector, peer-to-peer energy trading is being conducted to share energy produced through renewable energy systems. In this study, in the situation where energy transactions among individuals are expected to expand in the future, the types of buildings and trading to secure the economics of energy trading were compared. The types of buildings were limited to residential buildings, and the economic efficiency according to energy performance was compared. Because the government has strengthened energy performance regulations, the performance varied depending on the time of construction. Therefore, building types were divided into existing houses, new houses, and zero-energy houses. The trading types were compared to the existing methods, net-metering and feed-in tariff for small-scale distributed PV systems, with P2P trading. Thus, consuming only the amount of electricity in Tier 1 and trading the rest between individuals was the most economical strategy in residential buildings to which the progressive tariff system was applied. As the performance of a building improves, the more electricity that can be traded, and the wider the range for securing economic feasibility.


Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).


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