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The smart grid is an unprecedented opportunity to shift the current energy industry into a new era of a modernized network where the power generation, transmission, and distribution are intelligently, responsively, and cooperatively managed through a bi-directional automation system. Although the domains of smart grid applications and technologies vary in functions and forms, they generally share common potentials such as intelligent energy curtailment, efficient integration of Demand Response, Distributed Renewable Generation, and Energy Storage. This paper presents a comprehensive review categorically on the recent advances and previous research developments of the smart grid paradigm over the last two decades. The main intent of the study is to provide an application-focused survey where every category and sub-category herein are thoroughly and independently investigated. The preamble of the paper highlights the concept and the structure of the smart grids. The work presented intensively and extensively reviews the recent advances on the energy data management in smart grids, pricing modalities in a modernized power grid, and the predominant components of the smart grid. The paper thoroughly enumerates the recent advances in the area of network reliability. On the other hand, the reliance on smart cities on advanced communication infrastructure promotes more concerns regarding data integrity. Therefore, the paper dedicates a sub-section to highlight the challenges and the state-of-the-art of cybersecurity. Furthermore, highlighting the emerging developments in the pricing mechanisms concludes the review.

Price incentives for reducing fossil fuel related carbon emissions are an important component of effective and efficient climate policy. Current incentives stem from a mixture of energy taxes and carbon pricing (incentivizing less emissions) and diverse support measures for fossil fuels (incentivizing more emissions). We develop a net carbon price indicator that complements existing subsidy and carbon pricing indicators. It can be calculated on different aggregation levels and compared across countries. We calculate the different components and our aggregate indicator for the year 2018 and for eight countries including the worlds’ six largest emitters. Our analysis reveals large differences in net carbon prices across countries and across sectors within countries. We argue that the sectoral differences can inform about adequate national policy reforms while the aggregate national indicator can be useful for international negotiations about comparable national efforts.

Peer-to-peer (P2P) energy trading facilitates both consumers and prosumers to exchange energy without depending on an intermediate medium. This system makes the energy market more decentralized than before, which generates new opportunities in energy-trading enhancements. In recent years, P2P energy trading has emerged as a method for managing renewable energy sources in distribution networks. Studies have focused on creating pricing mechanisms for P2P energy trading, but most of them only consider energy prices. This is because of a lack of understanding of the pricing mechanisms in P2P energy trading. This paper provides a comprehensive overview of pricing mechanisms for energy and network service prices in P2P energy trading, based on the recent advancements in P2P. It suggests that pricing methodology can be categorized by trading process in two categories, namely energy pricing and network service pricing (NSP). Within these categories, network service pricing can be used to identify financial conflicts, and the relationship between energy and network service pricing can be determined by examining interactions within the trading process. This review can provide useful insights for creating a P2P energy market in distribution networks. This review work provides suggestions and future directions for further development in P2P pricing mechanisms.

One of the main sectors that contribute to climate change is the buildings sector. While nearly zero-energy buildings are becoming a new norm in many countries in the world, research is advancing towards energy flexibility and resilience to reach energy efficiency and sustainability goals. Combining the energy flexibility and energy resilience concept is rare. In this article, we aim to investigate the effect of energy efficiency in a new single-family building on the energy flexibility potential and resilience characteristics and compare these with those for an old building in the cold climate of Finland. These two objectives are dependent on the buildings’ respective thermal mass. The heat demands of the two buildings are compared. Their technical and economic performance are calculated to compare their flexibility and resilience characteristics. Dynamic simulation software is used to model the buildings. The results show that the old building has better flexibility and higher energy cost savings when including the energy conservation activation strategy. In the old building, savings can be around EUR 400 and flexibility factor can be around 24–52% depending on the activation duration and strategy. The new building, due to higher efficiency, may not provide higher energy cost savings, and the energy conservation activation strategy is better. In the new building, savings can be around EUR 70 and the flexibility factor reaches around 7–14% depending on the activation duration and strategy. The shifting efficiency of the new house is better compared to that of the old house due to its higher storage capacity. For energy resilience, the new building is shown to be better during power outages. The new building can be habitable for 17 h, while the old building can provide the same conditions for 3 h only. Therefore, it is essential to consider both energy flexibility and resilience as this can impact performance during the energy crisis.

This paper examines the determinants of successful energy subsidy reforms by synthesizing lessons from the literature and elaborating them through an analysis of an original dataset comprising 417 reform attempts and their outcomes. We synthesize six lessons that governments should follow for a successful outcome: (1) preparing a comprehensive strategy, (2) ensuring appropriate timing, (3) communicating effectively, (4) implementing reforms gradually, (5) launching compensatory measures, and (6) moving toward deregulated and depoliticized pricing. Our analysis both validates these lessons and highlights their nuances, showing, for example, that gasoline price hikes above 50% appear to double the occurrence of unsuccessful outcomes. The analysis also provides actionable guidance for policymakers. For example, we show how a failed first attempt makes future reform more challenging, and we discuss how a government can implement partial reversals in response to protests to keep part of the benefits of reform while demonstrating its willingness to compromise. JEL Classification: P18 Energy; Environment; Q41 Energy: Demand and Supply; Prices; Q48 Energy: Government Policy; H23 Taxation and Subsidies: Externalities; Redistributive Effects; Environmental Taxes and Subsidies.

The bi-directional energy flow between prosumers (wind energy) and smart grid (SG) provides pertinent benefits, such as (i) load-sharing, (ii) peak-load shaving, (iii) load reduction with energy market programs, (iv) ancillary services-based energy transactions, and (v) mutual beneficial frameworks based on rewards and penalties. However, the load variations of SG, intermittent wind speed in energy district (ED) of prosumers, and stochastic energy price are the major constraints that must be considered in wind energy prosumers (WEPs) interaction with utility. Further, the interfacing and interactions of WEPs with SG incur an enormous volume of data to be processed, stored, accessed, and managed. Therefore, the authors proposed a stochastic bi-directional energy management model (BEMM) to manage the aforementioned constraints. Moreover, the BEMM is empowered with cloud-based service level agreement (C-SLA) that provides massive storage capabilities to the enormous data incurred due to WEPs interactions with SG. Two sub-models of BEMM are incorporated, namely stochastic wind estimation model and stochastic energy pricing model. The wind estimation model deals the stochasticity of wind speed for energy generation, while energy price model manages and controls the uncertainty of pricing tariffs based on real-time pricing and day-a-head pricing mechanisms for efficient energy trade between SG and WEPs under the principle of C-SLA.

The widespread adoption of Electric Vehicles (EVs) in the smart grid is transforming the traditional grid into a more complex system. EVs have the ability to both charge and discharge, acting as loads that draw high power and sources that inject energy back into the grid. Consequently, energy sharing and management within smart grid communities integrated with EVs have become interesting aspects to study in order to efficiently utilize this energy. However, most existing research focuses solely on energy sharing within single communities, utilizing homogeneous energy profiles and neglecting the potential of heterogeneous energy across multiple communities. EVs also possess the capability to travel to different places and communities, where they can engage in energy sharing with areas that have varying load profiles and prices. In this work, a novel three-level energy sharing management approach is proposed for a multiple community system integrating movable energy storage such as EVs. This model involves three main entities: the Utility Company (UC), Community Energy Aggregator (CEA), and EVs. The energy sharing problem is formulated as a Stackelberg game, with all entities striving to maximize their utility through optimal strategies, including pricing, energy demand, or supply. The proposed model is validated through comparison with typical human charging behavior, as well as single- and multiple-community two-level game models. The findings reveal that the proposed model successfully optimizes pricing and energy strategies, significantly lowering the peak-to-average ratio and smoothing the overall energy profile.

This article mainly focuses on investigating the nonlinear co-integration and nonlinear causality relationships between oil prices and Chinese stock market at the overall and sectoral levels by using nonlinear autoregressive distributed lags (NARDL) model and Diks and Panchenko (DP) test. The empirical results show that there are not significantly asymmetric co-integration effects between oil prices and Chinese stock market for the overall and sectoral levels. However, the significantly nonlinear causality between oil prices and Chinese stock market can be found. Specifically, oil prices can widely affect Chinese stock indices through nonlinear channel. The cases in the reverse also work for overall indices and Mining, Utilities, Financial and Real Estate sectors. Furthermore, the potential sources of these nonlinear causality linkages are examined. The results suggest that volatility persistence rather than asymmetrical co-integration is the major factor that accounts for the nonlinear causality between oil prices and Chinese stock market.

There is a growing need for electricity-system flexibility to maintain real-time balance between energy supply and demand. In this paper, we explore optimal and incentive-compatible scheduling of generators for this purpose. Specifically, we examine a setting wherein each generator has a different operating cost if it is committed in advance (e.g., day- or hour-ahead) as opposed to being reserved as flexible real-time supply. We model an optimal division of generators between advanced commitment and real-time flexible reserves to minimize the expected cost of serving an uncertain demand. Next, we propose an incentive-compatible remuneration scheme with two key properties. First, the remuneration scheme incentivizes generators to reveal their true costs. Second, the scheme aligns generators’ incentives with the market operator’s optimal division of generators between advanced commitment and real-time reserve. We use a simple example to illustrate the market operator’s decision and the remuneration scheme. JEL Classification: C61, D47, D82, L94, Q4

Rapid growth in a number of developing nations’ mobile telecommunications sectors presents network operators with difficulties such as poor service quality and congestion, mostly because these locations lack a dependable and reasonably priced electrical source. In order to provide a sustainable and reasonably priced energy alternative for the developing world, this study provides a detailed examination of the core ideas behind renewable energy technology (RET). A multi-agent-based small-scaled smart base transceiver station (BTS) site reinforcement strategy is presented to manage energy resources by boosting resilience so to supply power to essential loads in peak demand periods by leveraging demand-side management (DSM). Diverse energy sources are combined to create interconnected BTS sites, which enable energy sharing to balance fluctuations by establishing a market that promotes economical energy. A MATLAB simulation model was developed to assess the effectiveness of the proposed system by using real load data and fast electric vehicle charging loads from five different base transceiver stations (BTSs) located throughout Pakistan’s southern area. In this proposed study, the base transceiver station (BTS) sites can share their energy through a multi-agent-based system. From the results, it is observed that, after optimization, the base transceiver station (BTS) sites trade their energy with the grid at rate of 0.08 USD/kWh and with other sites at a rate of 0.04 USD/kWh. Therefore, grid dependency is decreased by 44.3% and carbon emissions are reduced by 71.4% after the optimization of the base transceiver station (BTS) sites.