As a promising solution to address the “energy trilemma” confronting human society, peer-to-peer (P2P) energy trading has emerged and rapidly developed in recent years. When carrying out P2P energy trading, customers with distributed energy resources (DERs) are able to directly trade and share energy with each other. This paper summarizes and analyzes the global development of P2P energy trading based on a comprehensive review of related academic papers, research projects, and industrial practice. Key aspects in P2P energy trading are identified and discussed, including market design, trading platforms, physical infrastructure and information and communication technology (ICT) infrastructure, social science perspectives, and policy. For each key aspect, existing research and practice are critically reviewed and insights for future development are presented. Comprehensive concluding remarks are provided at the end, summarizing the major findings and perspectives of this paper. P2P energy trading is a growing field with great potential and opportunities for both academia and industry across the world.
How to comprehensively consider the power flow constraints and various stability constraints in a series of power system optimization problems without affecting the calculation speed is always a problem. The computational burden of probabilistic security assessment is even more unimaginable. In order to solve such problems, a security region (SR) methodology is proposed, which is a brand-new methodology developed on the basis of the classical point-wise method. Tianjin University has been studying the SR methodology since the 1980s, and has achieved a series of original breakthroughs that are described in this paper. The integrated SR introduced in this paper is mainly defined in the power injection space, and includes SRs to ensure steady-state security, transient stability, static voltage stability, and small disturbance stability. These SRs are uniquely determined for a given network topology (as well as location and clearing process for transient faults) and given system component parameters, and are irrelevant to operation states. This paper presents 11 facts and related remarks to introduce the basic concepts, composition, dynamics nature, and topological and geometric characteristics of SRs. It also provides a practical mathematical description of SR boundaries and fast calculation methods to determine them in a concise and systematic way. Thus, this article provides support for the systematic understanding, future research, and applications of SRs. The most critical finding on the topological and geometric characteristics of SRs is that, within the scope of engineering concern, the practical boundaries of SRs in the power injection space can be approximated by one or a few hyperplanes. Based on this finding, the calculation time for power system probabilistic security assessment (i.e., risk analysis) and power system optimization with security constraints can be decreased by orders of magnitude.
A new era of electricity is dawning that combines the decarbonization of the grid with the extensive electrification of all sectors of society. A grid as smart as the internet is needed to harness the full potential of renewables, accommodate technology disruptions, embrace the rise of prosumers, and seamlessly integrate nano-, mini-, and micro-grids. The internet is built upon a layered architecture that facilitates technology innovations, and its intelligence is distributed throughout a hierarchy of networks. Herein, we examine fundamental differences between data flows and power flows. The current operating paradigm of the grid is based on the conviction that a centralized grid operator is necessary to maintain instantaneous power balance on the grid. A new distributed paradigm can be realized by distributing this responsibility to sub-grids and requiring each sub-grid to maintain its net power balance. We present a grid as smart as the internet based on this new paradigm, along with a hierarchical network structure and a layered architecture of operating principles.
The smart grid is an evolving critical infrastructure, which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services. To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid, state estimation, which serves as a basic tool for understanding the true states of a smart grid, should be performed with high frequency. More complete system state data are needed to support high-frequency state estimation. The data completeness problem for smart grid state estimation is therefore studied in this paper. The problem of improving data completeness by recovering high-frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem in this paper. A novel machine-learning-based SRP approach is thereafter proposed. The proposed method, namely the Super Resolution Perception Net for State Estimation (SRPNSE), consists of three steps: feature extraction, information completion, and data reconstruction. Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.
This paper presents a transactive demand response (TDR) scheme for a network of residential customers with generation assets that emphasizes interoperability within a transactive energy architecture. A complete laboratory-based implementation provides the first (to our knowledge) realization of a comprehensive TDR use case that is fully compliant with the Institute of Electrical and Electronics Engineers (IEEE) 2030.5 standard, which addresses interoperability within a cybersecure smart energy profile (SEP) context. Verification is provided by a full system integration with commercial hardware using IP-based (local area network (LAN) and Wi-Fi) communication protocols and transport layer security (TLS) 1.2 cryptographic protocol, and validation is provided by emulation using extensive residential smart meter data. The demand response (DR) scheme is designed to accommodate privacy concerns, allows customers to select their DR compliance level, and provides incentives to maximize their participation. The proposed TDR scheme addresses privacy through the implementation of the SEP 2.0 messaging protocol between a transactive agent (TA) and home energy management system (HEMS) agents. Customer response is handled by a multi-input multi-output (MIMO) fuzzy controller that manages negotiation between the customer agent and the TA. We take a multi-agent system approach to neighborhood coordination, with the TA servicing multiple residences on a common transformer, and use a reward mechanism to maximize customer engagement during the event-based optimization. Based on a set of smart meter data acquired over an extended time period, we engage in multiple TDR scenarios, and demonstrate with a fully-functional IEEE 2030.5-compliant implementation that our scheme can reduce network peak power consumption by 22% under realistic conditions.
Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer's load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSPmax) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV–WT–battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSPmax values as compared with PV–battery and WT–battery systems.
Microseismic source/acoustic emission (MS/AE) localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures. However, the locating errors induced by both the irregular structure and pre-measured velocity are poorly understood in existing methods. To meet the high-accuracy locating requirements in complex three-dimensional hole-containing structures, a velocity-free MS/AE source location method is developed in this paper. It avoids manual repetitive training by using equidistant grid points to search the path, which introduces A* search algorithm and uses grid points to accommodate complex structures with irregular holes. It also takes advantage of the velocity-free source location method. To verify the validity of the proposed method, lead-breaking tests were performed on a cubic concrete test specimen with a size of 10 cm × 10 cm × 10 cm. It was cut out into a cylindrical empty space with a size of ϕ6cm × 10 cm. Based on the arrivals, the classical Geiger method and the proposed method are used to locate lead-breaking sources. Results show that the locating error of the proposed method is 1.20 cm, which is less than 2.02 cm of the Geiger method. Hence, the proposed method can effectively locate sources in the complex three-dimensional structure with holes and achieve higher precision requirements.
The implementation of artificial intelligence (AI) in a smart society, in which the analysis of human habits is mandatory, requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network. In this context, which is characterized by a large gap between training and operative processes, a dedicated method is required to manage and extract the massive amount of data and the related information mining. The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics (AD) for smart management, which is exploitable in any context of Society 5.0, thus reducing the risk factors at all management levels and ensuring quality and sustainability. We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes, for reducing training costs, for improving production yield, and for creating a human–machine cyberspace for smart infrastructure design. The results obtained in 12 international companies demonstrate a possible global standardization of operative processes, leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself. Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions, with the related smart maintenance and education.