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Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users (consumers and prosumers) in a smart residential locality. In a P2P fashion, prosumers/consumers can export/import the available generation/demand in the LEM at a profit relative to utility prices. A common portal known as the transactive energy market operator (TEMO) is introduced to manage the trading in the LEM. The goal of the TEMO is to develop a transaction agreement among P2P players by establishing a price for each transaction based on the price and trading demand provided by the participants. A few case studies on a location with ten residential P2P participants validate the performance of the proposed TEMO.

With the development and refinement of the carbon emissions trading market, the relationship between the carbon market and the stock market has grown increasingly intertwined. This has led to a surge in research investigating the interactions between the carbon market and related sectors. This study examines the intensity and direction of spillover effects among ten industries associated with carbon emissions, spanning traditional and emerging energy sectors. Through static analysis, we find that spillover effects between industries in the carbon and stock markets are bidirectional and asymmetric. Dynamic analysis reveals that the carbon market, acting as the primary recipient of spillover effects, is notably influenced by traditional energy industries such as coal and oil, followed by photovoltaics, new energy vehicles, and others. The magnitude of these spillover effects is subject to fluctuations influenced by energy crises and events like the COVID-19 pandemic, while policy interventions can alter the overall trends in net spillover effects across various industries.

Peer-to-peer (P2P) energy-trading has emerged as a promising mechanism for decentralized electricity markets, but its practical deployment is often limited by the difficulty of accounting for physical network constraints and transmission losses in real time. This paper presents a decentralized P2P energy trading mechanism that incorporates network constraints and transmission losses directly into the market-clearing process. The framework combines Power Transfer Distribution Factors (PTDFs) for pre-trade feasibility validation with an Enhanced Least Squares Method (ELSM) for loss estimation, enabling loss-aware settlement without computationally intensive and redundant AC power flow calculations. The mechanism is implemented on Hyperledger Fabric using Attribute-Based Access Control, Access Control Lists and Private Data Collections to ensure privacy and auditability. Numerical studies on a 3-bus and the IEEE 39-bus system show that the proposed approach closely reproduces AC Optimal Power Flow dispatch and cost outcomes, while significantly improving simplified DC-based loss models. The results demonstrate that physically feasible and economically efficient decentralized trading can be achieved in a permissioned blockchain environment.

This report summarizes the results of a recent review of the emerging experience with the design and implementation of policy instruments to promote the development of renewable energy (RE) in a sample of six representative developing countries and transition economies ('developing countries') (World Bank 2010). The review focused mainly on price- and quantity-setting policies, but it also covered fiscal and financial incentives, as well as relevant market facilitation measures. The lessons learned were taken from the rapidly growing literature and reports that analyze and discuss RE policy instruments in the context of different types of power market structures. The analysis considered all types of grid-connected RE options except large hydropower: wind (on-shore and off-shore), solar (photovoltaic and concentrated solar power), small hydropower (SHP) (with capacities below 30 megawatts), biomass, bioelectricity (cogeneration), landfill gas, and geothermal. The six countries selected for the review included Brazil, India, Indonesia, Nicaragua, Sri Lanka, and Turkey.

Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

Connections to world markets facilitate local markets developments to support more efficient capital allocation and greater investment and growth opportunities. Under the framework of cross-market rebalancing theory, in this study, we aim to systematically examine the market connections among world financial, energy, renewable energy and European carbon markets by measuring the return spillovers from 2008 to 2021. We find that the renewable energy market is more closely connected to the world financial and energy markets in the sense of the return transmission, while the carbon market is less connected to them. However, due to improved market regulations and determinations related to fighting climate change, the connections between the carbon market and other markets have gradually intensified. Plotting the return spillover indexes, we observe that strong return spillovers from the renewable energy market to other markets occurred when large investment plans were announced. Regarding the carbon market, regulation changes introduced by the EU Commission to improve and stabilize market environment induced intensified return transmission from carbon market to other markets. Another interesting finding is that the highly intensified return transmission among markets due to the COVID-19 crisis started to loosen when COVAX published the first interim distribution forecast on 3 February 2021.

Purpose of Review The fast growth of gas-fired generating units and the new emerging power-to-gas (PtG) technology have intensified the interdependency of the electricity grid and the natural gas network. Indeed, the security and economy of one system could directly and significantly affect that of the other. In observing these new trends and changes, a coordinated optimization between the two energy systems has attracted increasing attentions in recent years, which is believed to derive much more satisfactory solutions than optimized separately. Thus, this paper provides a comprehensive review of existing works on the coordination of interdependent electricity grid and natural gas network. Recent Findings The paper first highlights the modeling of key coupling components and discusses various coordination strategies of the two energy systems. The review then focuses on three major aspects of the coordination: coordinated short-term scheduling, coordinated long-term expansion planning, and energy market and energy hub. Summary Research and practical implementation on coordination of the interdependent electricity and natural gas system (IENS) are still in the infant stage. Challenges and potential future research directions that could further benefit the secure, reliable, and economic operation and planning of future IENS are summarized.

The Middle East and North Africa (MENA) region is one of the most water-stressed parts of the world. In just over 25 years, between 1975 and 2001. Looking to the future, MENA's freshwater outlook is expected to worsen because of continued population growth and projected climate change impacts. The region's population is on the way to doubling to 700 million by 2050. Projections of climate change and variability impacts on the region's water availability are highly uncertain, but they are expected to be largely negative. To offer just one more example, rainfall and freshwater availability could decrease by up to 40 percent for some MENA countries by the end of this century. The urgent challenge is how to adapt to the future as illustrated by these numbers and how to turn the region's economy onto a sustainable path. This volume suggests new ways of thinking about the complex changes and planning needed to achieve this. New thinking will mean making better use of desert land, sun, and salt water the abundant riches of the region which can be harnessed to underpin sustainable growth. More mundane, but just as important, new thinking will also mean planning for dramatically better management of the water already available. Right now, water is very poorly managed in MENA. Inefficiencies are notorious in agriculture, where irrigation consumes up to 81 percent of extracted water. Similarly, municipal and industrial water supply systems have abnormally high losses, and most utilities are financially unsustainable. In addition, many MENA countries overexploit their fossil aquifers to meet growing water demand. None of this is sustainable while water resources decline. This volume hopes to add to the ongoing thinking and planning by presenting methodologies to address the water demand gap. It assesses the viability of desalination powered by renewable energy from economic, social, technical, and environmental viewpoints, and it reviews initiatives attempting to make renewable energy desalination a competitively viable option. The authors also highlight the change required in terms of policy, financing, and regional cooperation to make this alternative method of desalination a success. And as with any leading edge technology, the conversation here is of course about scale, cost, environmental impact, and where countries share water bodies plain good neighborly behavior.

Purpose of Review Integration of distributed energy resources (DERs) brings huge challenges to distribution systems. Among many control room applications, distribution system state estimation (DSSE) is regarded as a key tool to establish the relationship between state variables and abundant measurements for system monitoring and analysis. The emergence of DERs poses multiple uncertainties, resulting in stringent requirements for system modeling and operation practices. This paper summarizes the state-of-the-art approaches, techniques, and challenges in the uncertainty modeling of DERs in practical power system and electricity market operations. Recent Findings DSSE has become increasingly important to realize appropriate monitoring and control for active distribution systems. The current research focuses on more precise and robust uncertainty modeling of multiple DERs in DSSE and the application of big data analytics. Probabilistic methods also emerge as a major research direction for these studies. Summary Accurate and effective modeling of DER uncertainty calls for holistic improvement. Moreover, machine learning and data-driven techniques exhibit great potential in such applications. Future work is expected to accurately capture the stochasticity and variability of DER outputs in the operational and market models, and thus lead to great economic benefits.

This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA futures and ICE Brent oil futures (reflecting the two largest energy sources in Europe), Stoxx600 Europe Oil and Gas Index (the main energy stock index in Europe), EEX Power Futures (representing electricity), and Stoxx600 Europe Renewable Energy index (representing the sunrise energy industry). This paper finds that the Moving Average (MA) technique beats random timing for carbon emission allowances, coal, and renewable energy. In these asset markets, there seems to be significant returns predictability of stochastic trends in prices. The results are mixed for Brent oil, and there are no predictable trends for the Oil and Gas index. Stochastic trends are also missing in the electricity market as there is an ARFIMA-FIGARCH process in the day-ahead power prices. The empirical results are interesting for several reasons. We identified the data generating process in EU electricity prices as fractionally integrated (0.5), with a fractionally integrated Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process in the residual. This is a novel finding. The order of integration of order 0.5 implies that the process is not stationary but less non-stationary than the non-stationary I(1) process, and that the process has long memory. This is probably because electricity cannot be stored. Returns predictability with MA rules requires stochastic trends in price series, indicating that the asset prices should obey the I(1) process, that is, to facilitate long run returns predictability. However, all the other price series tested in the paper are I(1)-processes, so that their returns series are stationary. The empirical results are important because they give a simple answer to the following question: When are MA rules useful? The answer is that, if significant stochastic trends develop in prices, long run returns are predictable, and market timing performs better than does random timing.