Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation.

This study presents peer-to-peer (P2P) energy transaction mechanisms to maximize social welfare considering the uncertainty and profit fairness of the players. The P2P energy transaction problem is formulated as a P2P energy transaction pair matching and the determination of the P2P transaction price. To solve the problem, the optimal condition to maximize social welfare is determined using stochastic P2P energy transaction performance analysis based on the uncertainty characteristics. The analysis results show that social welfare is maximized to match the producer and consumer pairs that have similar demand characteristics; the P2P transaction price balances the profit fairness between the pair. Using these results, two centralized P2P energy transaction mechanisms are proposed by modifying the optimization problem. Moreover, a decentralized P2P energy transaction mechanism that operates in a distributed manner is suggested with the operational signal flow for the implementation of the mechanism. The simulation results show that the centralized and decentralized mechanisms have near optimal performance, with less than a 0.5% and 1% optimal gap compared to the optimal solution that requires perfect information including uncertainty, respectively. However, the decentralized mechanism is less computationally complex and uses less information than the centralized mechanisms; consequently, it can alleviate the operational burden and security and privacy problems. In addition, the results show that the performance of P2P energy transaction is related to the relative demand ratio between the producer and consumer. The optimal condition and results suggest a guide to the design of the P2P energy transaction.

The depletion of existing resources and environmental problems led to an increase in distributed energy resources (DERs) and the accordingly emergence of the prosumer. This change has created an issue of power system operations and the coordinating entities of distribution networks. Besides, environmental concerns and the global policy of supporting renewable energy resources has given some electricity consumers a better perception and a willingness to pay (WTP) for green energy at a higher cost. This article presents a bidding-based peer-to-peer (P2P) energy transaction optimization model that considers these green energy preferences based on prosumer and consumer's intention. The objective of the model, which deals with energy transactions between prosumers and consumers within the virtual energy community, is to minimize community operating costs taking into account the depreciation costs of energy storage units while maximizing the social welfare for encouraging the energy trading among consumers and prosumers. The weighted social-welfare terms in the objective function provides diverse community operations reflecting the goal of the energy trading operator (community operator). Case studies were conducted according to the presence and absence of energy transactions and energy storages and the green energy preference. Results of the case study showed that the benefits of prosumers and community have occurred in energy transactions and CO2 emission is reduced according to green energy preference.

Energy management models for buildings have been designed primarily to reduce energy costs and improve efficiency. However, the focus has recently shifted to GEBs with a view toward balancing energy supply and demand while enhancing system flexibility and responsiveness. This paper provides a comprehensive comparative analysis of GEBs and other building energy management models, categorizing their features into internal and external dimensions. This review highlights the evolution of building models, including intelligent buildings, smart buildings, green buildings, and zero-energy buildings, and introduces eight distinct features of GEBs related to their efficient, connected, smart, and flexible aspects. The analysis is based on an extensive literature review and a detailed comparison of building models across the aforementioned features. GEBs prioritize interaction with the power grid, which distinguishes them from traditional models focusing on internal efficiency and occupant comfort. This paper also discusses the technological components and research trends associated with GEBs, providing insights into their development and potential evolution in the context of sustainable and efficient building design.

Photovoltaics (PV) have attracted considerable attention owing to their longer lifespans and higher generation potentials compared with other renewable energy sources. However, the intermittent nature of PV systems can degrade the power quality, hindering their widespread adoption. To mitigate the power-quality degradation resulting from the proliferation of PV, high forecasting accuracy is essential. However, missing data during the development of forecasting models can degrade performance. Therefore, appropriate imputation procedures are required. Typically, linear imputation is used. However, there is a tendency for the performance of the forecasting model to decline owing to errors between the actual and imputed values. In this study, we addressed missing PV power data using direct deletion, linear imputation, k-nearest neighbors imputation, and Generative Adversarial Imputation Nets. Subsequently, to assess the impact of weather variability on the imputation performance, we employed the “sky status” to categorize the replaced data and analyze whether differences in imputation performance emerged. Finally, we developed a PV forecasting model using the replaced data and evaluated its forecasting performance.

Operating virtual power plants (VPP) solely based on economic feasibility can cause stability problems, such as voltage violation and power congestion in the distribution system. A cooperation-based operation between the VPP and distribution system operator (DSO) is regarded one of the methodologies for solving stability problems. In the cooperation-based operation method, a VPP establishes an operation strategy considering the information provided beforehand by the DSO, such as the distribution system constraint (DSC). The DSC is uncertain because it is determined based on the forecasted grid situation. Therefore, operational strategies that rely only on the DSCs provided by DSOs may make the operation of the VPP passive and reduce profits depending on the accuracy of the announced DSCs. This study proposes an operating methodology and optimal operation model considering the uncertainty of the DSC to increase the profit of the VPP. In the proposed model, the VPP independently forecasts the DSC for profit maximization and establishes an operational plan considering penalties. Case studies were conducted to demonstrate the economic improvement of the VPP, considering the uncertainty of the DSC. The case study results demonstrated that the proposed model increased the profit of the VPP by 1.6 % compared with that of the conventional method.