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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]

• 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]

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]