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To address the issue of economic dispatch imbalance in virtual power plant (VPP) systems caused by the influence of operators and distribution networks, this study introduces an optimized economic dispatch method based on bi-level game theory. Firstly, a bi-level game model is formulated, which integrates the operational and environmental expenses of VPPs with the revenues of system operators. To avoid local optima during the search process, an enhanced reinforcement learning algorithm is developed to achieve rapid convergence and obtain the optimal solution. Finally, case analyses illustrate that the proposed method effectively accomplishes multi-objective optimization for various decision-making stakeholders, including VPP and system operators, while significantly reducing curtailment costs associated with the extensive integration of distributed renewable energy. Furthermore, the proposed algorithm achieves fast iteration and yields superior dispatch outcomes under the same modeling conditions. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules. [ABSTRACT FROM AUTHOR]

With the booming development of the electricity market, market factors such as electricity trading varieties are growing rapidly. The frequency of transactions has become increasingly real-time, and transaction clearing and settlement tasks have become more complex. The increasing demands for concurrent access and carrying capacity in trading systems have made it increasingly difficult for existing systems to support business. This article proposes a transaction support system for large-scale electricity trading market entities, which solves the problems of high concurrency access and massive access data calculation while ensuring system security through business isolation measures. The system uses microservices to treat various functional modules as independent service modules, thus making service segmentation and composition more flexible. By using read–write separation, caching mechanisms, and several data reliability assurance measures, data can be stored and accessed quickly and securely. The use of a three-layer load balancing module consisting of an OpenResty access entry layer, a gateway routing gateway layer, and a WebClient service inter-resource invocation layer can effectively improve the system's ability to handle concurrent access. [ABSTRACT FROM AUTHOR]

As climate change becomes the greatest issue of this century, countries are looking for sustainable solutions to effectively control their greenhouse gas (GHG) emissions. Uzbekistan, being one of the most vulnerable countries to climate change effects, is taking notable actions toward a low-carbon economy. In this line, there is a significant need for a thorough assessment of the country’s current decarbonization measures, its prospects, and possible directions of CO2 emission reduction in both governmental and academic spheres, which is the primary aim of this research. To reach these objectives, this study estimates, for the first time, the carbon dioxide (CO2) emission trends in Uzbekistan and their sector-by-sector contribution. Additionally, various carbon emission reduction pathways are discussed emphasizing the potential for the transition to renewable energy sources (RES) and carbon capture, storage, and utilization (CCSU) technology integration. According to the results, the annual CO2 emission in Uzbekistan was about 116 Mt with 3.27 t per capita in 2021. Expectedly, with the share of 87%, power and industrial sectors contribute most to it. Over 80% of the country´s CO2 emissions come from natural gas combustion, producing low-concentration flue gas. The resulting increased cost of CO2 separation from large point sources decreases the CCSU application potential in the abundantly present oil and gas reservoirs. Under these circumstances, future investigations can focus on improving CO2 separation, exploring onshore and offshore storage, integrating RES, and promoting collaborative decarbonization actions among neighbouring countries.Highlights: The annual CO2 emissions and their sector-by-sector contribution in Uzbekistan are estimated and compared with other statistical databases In 2021, Uzbekistan's annual CO2 emissions were approximately 115.89 million metric tons, with a per capita rate of 3.274 metric tons. Power and industrial sectors contribute significantly, accounting for 87.7% of emissions. Modernizing and enhancing the existing power transmission infrastructure holds the potential to reduce greenhouse gas emissions by up to 2 million metric tons. Initial CO2 capture cost was calculated based on CO2 concentration in flue gases from various sources. Uzbekistan possesses a valuable opportunity to leverage CO2 for Enhanced Oil Recovery (EOR) and Enhanced Gas Recovery (EGR) due to its abundant natural gas and oil wells. [ABSTRACT FROM AUTHOR]

Microgrids help in the better utilization of distributed energy resources. However, due to the intermittent nature of such resources, the net energy availability within microgrids varies with time. This adversely affects the power balance within microgrids, especially if energy storage systems or grid support are unavailable. In such cases, the microgrids in a locality can form a network to trade power among themselves. The most transparent and fair method is power trading using a double auction with sealed bidding. A novel method for sealed bid power trading using consortium blockchains that is entirely decentralized is introduced, ensuring better operational and data security. Here, all bidding data are shared twice. During bidding time, bids are shared by participating microgrids using one-way encryption and are added to the blockchain by all participants. After the bid closure, the respective microgrids share the same data unencrypted. By encrypting and comparing the bid data with the encrypted data already added to the blockchains, other participants can verify whether the microgrids have kept their bid data unmodified post-bid-closure. If the data has been kept unmodified, all participants add the bid data as verified data to their blockchain. Successful bids are then identified using a smart contract, and power transfer is effected accordingly. All power and monetary transactions are recorded distributively in blockchains. A decentralised framework for the implementation of the method is also envisaged. Further, a novel approach is proposed for distributively authenticating the blocks shared by participating microgrids using public and shared keys. The method and the framework are then successfully tested for decentralization, transparency, resilience, public verifiability, and transaction enforcement using simulations in a benchmark test system for networked microgrids.

The consensus mechanism is a critical technology in the power trading blockchain distributed power trading platforms, which are gradually being applied and promoted to achieve the "dual carbon" goal. Green-certificate trading and carbon emission trading systems help mobilize power generation enterprises to increase the generation of and investment in renewable energy, thereby becoming important factors in distributed power trading. Therefore, they should also be considered as factors in the consensus mechanism. This article first evaluates the green-certificate weight based on whether nodes possess green certificates, evaluates the carbon emission weight based on the carbon emission settlement of nodes in the previous cycle, and constructs a mechanism to evaluate a node's weight. An improved Raft consensus algorithm based on node weight is then proposed. Nodes with greater weights are more likely to become leading nodes, promoting active compliance and green consumption by nodes and reducing carbon emissions. A simulation shows that the improved Raft consensus mechanism enhances the reliability of leading nodes, improves the efficiency of the distributed power transaction, increases the generation of renewable energy, and encourages the consumption of new energy. [ABSTRACT FROM AUTHOR]

With the popularity of solar Photovoltaic (PV) power generation, the real-time interaction between distributed microgrids and large grids has become a new hotspot of concern. In distributed PV power trading, we aim to achieve a dynamic balance between PV users and the grid. This involves real-time power prediction for users, secure blockchain-based recording and protection of trading data, and efficient matching of microgrid and large-grid trading information, including pricing details. System simulations and experimental data analysis have demonstrated the benefits of this transaction model, including enhanced real-time interaction between microgrid and grid. For the buyer, the cost of purchasing electricity can be reduced by about 6%, and for the seller, the income from selling electricity is 1.5 times that of direct Internet access, which has positive significance in improving the income from selling electricity and reducing the cost of electricity for users. [ABSTRACT FROM AUTHOR]

The consensus mechanism is a critical technology in the power trading blockchain distributed power trading platforms, which are gradually being applied and promoted to achieve the “dual carbon” goal. Green-certificate trading and carbon emission trading systems help mobilize power generation enterprises to increase the generation of and investment in renewable energy, thereby becoming important factors in distributed power trading. Therefore, they should also be considered as factors in the consensus mechanism. This article first evaluates the green-certificate weight based on whether nodes possess green certificates, evaluates the carbon emission weight based on the carbon emission settlement of nodes in the previous cycle, and constructs a mechanism to evaluate a node’s weight. An improved Raft consensus algorithm based on node weight is then proposed. Nodes with greater weights are more likely to become leading nodes, promoting active compliance and green consumption by nodes and reducing carbon emissions. A simulation shows that the improved Raft consensus mechanism enhances the reliability of leading nodes, improves the efficiency of the distributed power transaction, increases the generation of renewable energy, and encourages the consumption of new energy.

The introduction of Smart Grid resulted in the development of various applications that are built upon the concept of bi-directional flow of electricity and data. One of the Smart Grids pillars is the Distributed Generation (DG) technologies, where customers turn to be prosumers with power generation capability. Another pillar is the Demand Side Management (DSM), which helps control the energy consumption by changing the power usage slots among other peers. DG and DSM have facilitated the sharing of excess power by customers to the grid, and then to their peers through the grid as a trading agent. Although the concept of integrating Peer-to-Peer energy trading with DSM has been explored by scholars and relatively established trading frameworks, there are very limited research performed in respect to the UAE market in terms of its acceptance and readiness towards this energy trading market. This research aims to explore the perception of Peer-to-Peer electricity trading within the Power Distribution Network in the United Arab Emirates. The study will review the Smart Grid network in the UAE and will obtain insights on people's perception of the transition from classical electricity network to Smart Grid. It will also look into peoples' perception regarding the transition from being electricity consumers to being electricity producers that trade among peers through semi-structured interviews. This will enhance the understanding of the energy trading market between self-generated power producers connected to a network grid, where the consumer will be utilizing the excess power available in the form of electricity trading, by importing and exporting power, without adding any additional power to the grid. The outcome of the study will provide an insight on the UAE electricity market by designing an electricity trading model that is built upon the following vital factors: power quality, supply reliability, type of integration, peers, and trading time. Furthermore, the study will provide a foundation base to the utilities, as well as individuals, when dealing with the changes in the electricity market structure. [ABSTRACT FROM AUTHOR]

In this paper, a novel peer-to-peer (P2P) power trading for power management of nanogrid clusters with renewable energy sources is presented. Unlike power management of smart grids in megawatt power scale, power management of nanogrids in kilowatt power scale requires individual control of electric appliances. P2P trading is characterized by complex elements such as decentralized architecture, participant behaviors, and distributed energy resources. To this end, multi-objective optimization providing Pareto-optimal solutions is applied to the power management of nanogrid clusters capable of P2P trading. The P2P power trading aims at maximizing the profit as well as minimizing the grid power consumption. However, due to the conventional rule for P2P trading and trade-off property of Pareto-optimal solution, the result of P2P trading is not optimal. To improve the performance of P2P trading in terms of total electricity cost, reinforcement learning (RL) algorithms are introduced. For RL, deep neural networks are applied to P2P power trading. The RL agent of each nanogrid cluster learns the behavior of P2P trading and then fulfills P2P trading. In the proposed RL technique, graph convolutional network is employed for analyzing graph-structured data, and bidirectional long short-term memory network is utilized for data prediction, enhancing the performance of P2P trading. It is found from simulations of power management for nanogrid clusters that the electricity cost with the proposed RL technique is the lowest among the RL algorithms considered for benchmark test. The electricity cost is reduced by 36.7%, averaged over ten nanogrid clusters, as compared to the electricity cost obtained from power management without RL algorithms. [ABSTRACT FROM AUTHOR]

In this study, artificial intelligence, the Internet of things, and blockchain technology were combined to develop a vehicle-to-everything blockchain power trading and energy management platform with the objective of enabling multi-level power transactions for electric vehicle charging stations in or between commercial buildings. The proposed platform considers green power transactions across microgrids, bidirectional power and green power charging and discharging transactions in microgrids, demand response bidding, and vehicle-to-vehicle emergency rescue charging. This innovative architecture operates smart contracts and distributed ledgers independently; uses smart contracts to handle bidding, matching, and settlement of the power trading platform; and simultaneously employs the cloud and local distributed ledger nodes to chain the detailed real-time transaction information and power data on the ledger. In this manner, the security and fairness of data can be ensured; furthermore, a large amount of chaining information can be processed, and the chaining latency can be shortened. In addition, the communication protocols are integrated through the artificial intelligence of things. An energy management system (EMS) is proposed, which can perform optimal charging and discharging scheduling according to the power transaction matching results and then control charging piles and energy storage system devices. It can reduce the operating cost of microgrids in commercial buildings, increase the efficiency of green power utilization, and reduce power loss. In this system, power transactions are automatically and simultaneously executed in the EMS to achieve real-time supply and demand balance of regional microgrids.

Integrating Distributed Energy Resources (DERs) and Micro-Grid (MG) into a system evolved the traditional power system. In spite of their significant advantages, MGs may result in volatility and uncertainty in the power systems. For reliable operation of the grid, energy trading among MGs should be optimized to maintain a fair trading price, maximize participants’ profit, and satisfy network constraints. In this paper, the optimal power trading among multiple reconfigurable MGs is formulated as a Mixed-Integer Nonlinear Programming (MINLP) considering all energy resources and their dynamic prices. In spite of the other methods in the literature, the proposed method minimizes the total cost (increase sales and decrease purchases) and transmission loss considering all energy resources in the MGs. In order to flatten the load profile, a time-based load profile is considered for the demand response program. The performance of the proposed model is evaluated on an IEEE 6-bus network as well as a modified IEEE 33-bus test system. The results verify that the proposed method, (i) determines the best configuration among MGs with a switching reduction of about 30%, (ii) optimizes the power generation of energy resources with 12% reduction in energy production, and (iii) optimizes the power trading costs with a 10% reduction in costs compared with the basic model without DR and trade that is introduced as $Scen.1$ in this paper.

Endowed with over 40,000 MW of economic hydropower potential relative to its nascent domestic demand, Nepal can export electricity to its power-deficit neighbours in the BBIN (Bhutan, Bangladesh, India and Nepal) region as a strategic economic opportunity. The untapped hydro potential in Nepal, coupled with BBIN's complementary seasonal demand patterns, difference in peak load timing, and rapidly growing growth of electricity demand in Bangladesh and India, are favourable for regional electricity cooperation, especially for Nepal. This study uses the SWOT-AHP-TOWS approach to identify the key barriers and opportunities for cross-border electricity trade and to provide insights on possible strategies for Nepal. [ABSTRACT FROM AUTHOR]

This article utilizes consortium blockchain to design a decentralized, secure, and privacy-preserving scheme for bidirectional power trading between electric vehicles (EVs) and the power grid. To reduce adverse effects induced by disordered charging of massive EVs to the power grid, we minimize the total load deviation through optimizing the charging and discharging time period of EVs. Our optimization scheduling is a large-scale mixed-integer programming problem of which the number of variables and constraints are enormous. Hence, we propose to adopt the heuristic algorithm, an improved krill herd (KH) algorithm to solve it. Simulation results indicate that our model can effectively smooth the load fluctuations, and improved KH can improve the rate and accuracy of solving this model effectively. Implementation of Hyperledger Fabric evaluates the performance and scalability of our scheme. Qualitative security and privacy analysis demonstrate that our scheme helps to improve the security and privacy of power trading. [ABSTRACT FROM AUTHOR]

Energy management problem is regarded as an important subject in the networked microgrids (MGs) based distribution system, where each entity possesses individual objectives. In this study, risk aversion energy management has been proposed for each MG and the distribution network (DN) in order to assess risks associated with uncertain sources. Conditional value at risk technique is utilised for including variability of profit to the objective function. In addition, this study investigates usefulness of possibility of power trading among MGs. For this aim, separate power line is considered between two different MGs besides of their power lines with DN. Power flow constraints are implemented to energy management formulation of each MG and DN as well. The proposed energy management problem is expressed based on stochastic linear programming. In this study, auxiliary problem principle decentralised approach is utilised to solve the optimisation problem in order to consider computer hardware limitations and privacy constraints. The recommended energy management approach has been applied to the IEEE 33‐bus DN which is modified by MGs. Then, the effectiveness of providing power link between each two different MGs on the obtained profit has been evaluated under the both islanded and grid‐connected modes of operation and various risk aversion parameter of entities. [ABSTRACT FROM AUTHOR]

Highlights • Multi-agent negotiation techniques demonstrated for a transactive energy market. • Microgrid agent behavior differs by instantaneous load, solar, and storage level. • Trading in a 3-node and 9-node network gives 3.6% and 5.4% energy cost reduction. • Nodes with storage were self-sufficient and traded less often with other consumers. Abstract Distributed energy resources are becoming increasingly common and forcing change in conventional energy markets with growing attention given to transactive energy networks that allow power trading between neighboring microgrids or distributed energy resources customers to supplement transactions with an electric utility. This study develops and evaluates a generalizable method for managing energy trading between microgrids in a grid-connected network through multi-agent techniques. The approach is demonstrated for a 3-node network and a 9-node network for a simulated year with hourly load and solar data for each unique microgrid agent. Results are compared against baseline networks without trading enabled to quantify a 3.6% and 5.4% reduction in the levelized cost of energy, respectively, with trading enabled for the 3-node and 9-node cases. Local energy storage capacities are varied to examine impact on the levelized cost of energy and trading behaviors. Results indicate that trading between microgrids reduces the levelized cost of energy for each individual node and the whole network, and that certain trends emerge between agents that allow some microgrids to operate at a lower cost than others. [ABSTRACT FROM AUTHOR]

With smart grid, the power supply will shift from the 1∶N tree structure with centralized power plants to the M∶N structure with various kinds of distributed energy resources based on renewable energy, batteries, and so on. Deregulation of the electricity market will yield a truly competitive market, where anyone can become a power seller or buyer, which will necessitate a real-time multiseller–multibuyer power trading system. However, it is difficult to realize such a system without centralized control, because of the additional trade complexity created by a large number of sellers, including ordinary homes. In this paper, the authors propose a novel distributed power cooperation algorithm that maximizes each home’s welfare based on local information. The proposed algorithm enables each home to calculate the same electricity market price from only local household information, to trade, and to maximize all members’ satisfaction in smart grid by balancing consumption against supply. The authors formulate a distributed optimization problem and logically prove that the authors’ algorithm can obtain the same optimal user welfare as the global optimal approach but within a much shorter time. [ABSTRACT FROM AUTHOR]

Local energy production and distributed energy storage facilities will take a prominent position in the future smart grid along with the challenge of sustainability. To cope with that, smart grid simulation platforms are needed in order to analyze the problems of two-way data and energy flow. The Power Trading Agent Competition (Power TAC) provides an open source, smart grid simulation platform to enable various smart grid studies from the perspective of sustainability. In this paper, we present AgentUDE, which competed in the Power TAC 2014 and 2015 Final games as a broker agent. Here, we create and formalize information system artefacts for the electricity trading problem in a continuous and competitive environment. The main trading problem is partitioned to smaller problems to reduce the complexity. Therefore, wholesale and retail electricity market activities are individually discussed for AgentUDE14. In 2015 competition, we added a capacity control capability to AgentUDE14, which was introduced as AgentUDE15. We show that AgentUDE is a successful broker agent in many metrics, analyzing its performance in controlled experiments and international tournaments. We have also reviewed the tournament data from the winner agent's point of view [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid. [ABSTRACT FROM PUBLISHER]

• Coupling analysis of blockchain technology and electricity trading. • Multi-microgrid transaction model based on blockchain. • Using ant colony algorithm to solve multi-microgrid power market transaction model to obtain local optimal electricity price. • Establish a detailed solution process for multi-microgrid power transactions based on blockchain technology. As a new distributed generation system, multi-microgrid is an important part of Energy Internet. There are a series of problems in the selection of generation resources and power trading which includes obvious concentration, fairness and low credibility. Aiming at the shortcomings of microgrid in real-time electricity trade, a multi-microgrid electricity bidding trading model based on blockchain technology is proposed. A competitive spot market with distributed "multi-seller and multi-buyer" is constructed to realize the integration of "source-sale". This paper compares the traditional trading mode with the power trading mode supported by blockchain technology. In addition, this paper establishes a blockchain-based multi-microgrid power trading model, and uses ant colony algorithm random bidding matching to achieve multi-objective optimization. Through the whole process, the unification of energy flow, information flow and currency flow are realized. Finally, the reasonable allocation of power resources and the openness and transparency of power trading are ensured. [ABSTRACT FROM AUTHOR]

In this article we propose a new wind power forecasting model that does not focus on providing the most precise forecasts, but minimizes the financial loss of forecasting impreciseness. We show that the loss function is asymmetric and therefore account for asymmetry during the estimation stage of our model. The new model's forecasts are compared to two state-of-the-Art models and we are able to show that the new model can increase the financial profit for power producers, power traders and/or network operators by a severe degree. [ABSTRACT FROM AUTHOR]

In this paper, we propose an autonomous microgrid operation using a multiagent approach. The proposed multiagent system consists of seven types of agent: a single microgrid controller agent (MGC), several load agents (LAGs), several gas-turbine (gas-engine) agents (GAGs), several photovoltaic generation agents (PVAGs), several wind-turbine generation agents (WTAGs), several battery agents (BAGs), and a single grid agent (GridAG). In a microgrid, LAGs act as consumers or buyers, GAGs, PVAGs, and WTAGs act as producers or sellers, and BAGs act as prosumers or sellers/buyers. In order to verify the performance of the proposed system, it is applied to a simple model system with different electrical power prices. From the simulation results, it can be seen that the proposed multiagent system can perform microgrid operations efficiently. [ABSTRACT FROM AUTHOR]

The renewable power consumption guarantee mechanism aims to promote local consumption of renewable power through power trading in the inter-provincial market and provincial market. This study aims to investigate whether the renewable power consumption guarantee mechanism can actively affect the power trading market. The difference-in-difference method is proposed to evaluate the impact of by selecting the pilot provinces in power spot trading as the treatment group and the rest provinces as the control group. The empirical results show that the renewable power consumption guarantee mechanism has a positive net effect on the power trading market. Several important factors, such as the power demand response of industrial users and the participation of power sales companies, influence power trading activities. We discuss the possible reasons from the perspective of power generation side, power sale side and power user side. To promote the development of power market, the inter-regional trading mechanism of renewable power need to be improved. When the power market is gradually fully competitive, the establishment of a national unified green power trading market should be considered. Meanwhile, policy incentives should further encourage power sales companies to play a significant role in promoting development of power trading market and renewable energy. • Proposing the difference-in-difference method. • Using the full sample provincial panel data. • The RCGM has a positive net effect on power trading market. • Industrial users and power sales companies are important factors. [ABSTRACT FROM AUTHOR]

• Establish cooperative alliances between multi-microgrids within day-ahead power wholesale market. • Realize the flexible consumption of renewable energy in the region by cooperative game among microgrids. • Reduce the whole operating costs and help microgrids to get satisfactory trading power and tariff. • Improve the multi-microgrid cooperation system and promote the use of renewable energy with the market behaviours. Microgrids are one of the most common forms of distributed energy participation in the electricity market. This paper discusses the lack of market competition among independent microgrids as a factor in setting up a cooperative alliance among microgrids. Independent microgrids aim to minimize the system's overall operating costs. The first principle is to maximize scenery output and consumption. We develop and solve an optimization model to obtain the interactive power with the distribution network and the charging and discharging power arrangement for the energy storage module. We then construct a cooperative game model among multiple microgrids on this basis. Nash bargaining is used to coordinate the distribution of benefits among microgrids, as well as to analyze the optimal trading power and tariffs among microgrids. The research proves that the cooperative game among microgrids can realize the flexible consumption of renewable energy in the region. Microgrids also have lower operating costs. The Nash bargaining helps the members in the coalition to get satisfactory trading power and tariff. Additionally, it effectively improves the overall operational efficiency and market competitiveness of microgrid systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the economic attractiveness of exporting coal-generated electricity from the State of Wyoming to the State of California while investing in large-scale CO2 capture and sequestration to meet California's strict environmental regulations. The project involves partially capturing CO2 from coal power plants, storing it temporarily underground using CO2 Interim Storage, and ultimately using it for Enhanced Oil Recovery. A detailed financial assessment is performed in view of current electricity prices and federal and state regulations in the U.S., especially those related to CCS tax credits. The results show that the project is profitable under current regulations and technical assumptions, and it can be a good opportunity to drive large-scale deployment of CCS, along with the development of EOR activities and the deployment of the newly proposed CO2 Interim Storage technology. [Copyright &y& Elsevier]

The power sector is one of the most critical growth drivers for any country. The power sector in India is highly regulated by the government and is dependent on the policies framed regarding bidding for power projects, regulations regarding transmission of power between states and pricing of power supply to consumer is also decided by the government. In India, the passage of the Electricity Act, 2003 brought about an impetus in the sector which paved way for new reforms in it. India certainly needs a huge jump in its electricity supply to sustain its rapid economic growth and meet the growing demand; it needs to make every effort to efficiently manage all components of value chain. In order to provide adequate support to country's growth aspirations, it is imperative that the sector scales its capacity up across the value chain and push reforms in requisite stages. This paper firstly presents the overview of Indian power sector followed by analysis of value chain of power sector in India -its current scenario as well as future prospects. [ABSTRACT FROM AUTHOR]