Prosumers are active participants in future energy systems who produce and consume energy. However, the emerging role of prosumers brings challenges of tracing carbon emissions behaviours and formulating pricing scheme targeting on individual prosumption behaviours. This paper proposes a novel blockchain-based peer-to-peer trading framework to trade energy and carbon allowance. The bidding/selling prices of prosumers can directly incentivise the reshaping of prosumption behaviours to achieve regional energy balance and carbon emissions mitigation. A decentralised low carbon incentive mechanism is formulated targeting on specific prosumption behaviours. Case studies using the modified IEEE 37-bus test feeder show that the proposed trading framework can export 0.99kWh of daily energy and save 1465.90g daily carbon emissions, outperforming the existing centralised trading and aggregator-based trading.

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Excessive carbon emissions have posed a threat to sustainable development. An appropriate market-based low carbon policy becomes the essence of regulating strategy for reducing carbon emissions in the energy sector. This study proposes a Stackelberg game-theoretic model to determine an optimal low carbon policy design in energy market. To encourage fuel switching to low-carbon generating sources, the effects of varying carbon price on generator's profit are evaluated. Meanwhile, to reduce carbon emissions caused by energy consumption, carbon tracing and billing incentive methods for consumers are proposed. The efficiency of low carbon policy is ensured through maximising social welfare and the overall carbon reductions from economic and environmental perspectives. A bi-level multiobjective optimisation immune algorithm is designed to dynamically find optimal policy decisions in the leader level, and optimal generation and consumption decisions in the followers level. Case studies demonstrate that the designed model leads to better carbon mitigation and social welfare in the energy market. The proposed methodology can save up to 26.41% of carbon emissions from the consumption side for the UK power sector and promote 31.45% of more electricity generation from renewable energy sources.

Climate change enforces the integration of distributed renewable energy sources and development of carbon price scheme. Whilst the energy is traded among distributed prosumers, the carbon responsibilities and corresponding allowances trading need to be transferred from large-scale energy suppliers to prosumers. During this transformation, the issues of energy imbalance, carbon reduction imbalance, and residential privacy leakage in centralised trading market present serious challenges. In this paper, we propose a fully decentralised blockchain-based peer-to-peer trading scheme coupling energy and carbon markets. We implement pay-to-public-key-hash with multiple signatures as a transaction standard to realise a more secure transaction and reduced storage burden of senders. A script is hashed during the wallet address generation for each new transaction to protect residential privacy. A novel carbon accounting method and corresponding incentive mechanism for carbon reduction are designed to evaluate emission behaviours of distributed prosumers. Case studies demonstrate that the proposed scheme leads a reduced costs and carbon emissions compared to centralised trading systems and existing blockchainbased trading schemes. Index Terms-blockchain, decentralized energy trading, low carbon, distributed energy sources, smart grids.

In this work, an Energy Hub scheduling method is proposed, which could not only meet various energy load demands but also address the transmission line loss and voltage stability influences. The proposed method is then evaluated by case studies, where the electricity network and thermal network are implemented on the Real Time Digital Simulator (RTDS). The proposed scheduling method is run online with real-time monitoring of the whole system operation status. Results show that the proposed Energy Hub scheduling method is able to reduce the hub operating cost while improving the overall voltage profile.

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In order to schedule the virtual power plant and corresponding energy market operation, a two-scenario Stackelberg game-theoretic model is proposed to describe interactions between market operator and VPP operator. During market operation, the market operator is a leader of the game to decide market cleaning prices, considering the power loss minimization of VPP operator, whereas during VPP operation, the VPP operator becomes a leader to facilitate the demand side management (DSM) through proper monetary compensation, considering the market trading balance between power sellers and power buyers. An optimal scheduling strategy including power dispatch and market balance will be realised. Case studies prove the effectiveness of the proposed Stackelberg game-theoretic model through IEEE 30-bus test system. The market scheduling promotes the power exchange among VPPs. The VPP scheduling evaluates the optimal monetary compensation rate to motivate the DSM including load shifting and load curtailment. Index Terms-demand side management (DSM), game theory, virtual power plant (VPP), distributed energy resources (DERs).

In order to alleviate the effects of greenhouse gas emissions, the environmental and economic dispatch (EED) is formulated as multiobjective optimization problem (MOP) solved by multiobjective immune algorithm (MOIA). Building on this model, the virtual power plant (VPP) is proposed involving distributed generation (DG), interruptible load (IL), and energy storage (ES) to participate in joint energy and reserve markets. The uncertainties of load prediction, DG, and IL are treated as an interval-based optimization in this study. The static and real-time simulations are conducted to demonstrate the validity of proposed stochastic EED model through the IEEE 30-bus test system.