This paper proposes a novel Peer-to-Peer (P2P) joint electricity and carbon (E&C) trading model to co-optimize the energy and carbon emissions permit transactions considering the trading preferences in the distribution network. To realize the secure and low-carbon network operation, a decomposable carbon-aware distribution locational marginal pricing (CDLMP)-based operation service pricing scheme of the distribution system operator (DSO) is proposed to guide the P2P transactions among prosumers. In practice, the coordination between the P2P market and the DSO is cast as a bi-level optimization. In the upper level, the network operation optimization of the DSO is modeled as a carbon-aware optimal power flow (COPF) problem based on a carbon emission flow model to ensure the security of network operation and derive the CDLMP. In the lower level, a decentralized P2P joint E&C trading is modeled to simultaneously clear energy and carbon permit. Later, this paper develops an l (p)-Box ADMM method to relax the binary variables originating from the peer-matching process of the carbon trading. Finally, a novel demurrage protocol is designed to facilitate the convergence of the bi-level interaction with an incentive for trading adjustments. Case studies illustrate the effectiveness of the proposed method in motivating “grid-friendly” and “low-carbon” P2P trading.

The proliferation of distributed energy resources entails efficient market mechanisms in distribution-level networks. This paper establishes a local energy market (LEM) framework in which electricity and hydrogen are traded. Players in the LEM consist of renewable distributed generators (DGs), loads, hydrogen vehicles (HVs), and a hydrogen storage system (HSS) operated by a HSS agent (HSSA). An iterative LEM clearing method is proposed based on the merit order principle. Players submit offers/bids with consideration of their own preferences and profiles according to the utility functions. The decentralized LEM clearing process not only avoids complex calculation induced by centralized decision process, but also preserves players' privacy. Case studies are conducted that demonstrate that the LEM promotes local integration of renewable energy, reduces peak demand, and improves players' utilities. Sensitivity analysis is then implemented to discuss the influences on the LEM clearing results of capacities of DGs, Loads, and the HSS, as well as price of hydrogen from the hydrogen station (HS).

Price-based demand response is widely implemented by the load aggregators (LAs) to exploit the flexibility of massive demand-side resources. However, a key problem in real-life implementation is the controllability of price-based demand response, viz, whether the price can guide the demand to the desired value. To bridge this gap, this paper proposes a close-loop reverse Stackelberg game-based pricing method to better regulate the demand response. In the hierarchical game structure, the non-cooperative competition of heterogeneous demand-side resources in participating demand response constitutes the lower-level, while the determination of pricing policy by LA makes the upper-level. Towards a computationally efficient solution, the mean-field game is adopted to approximate the Nash equilibrium of the large-scale lower level competition, and the indirect formulation of close-loop reverse Stackelberg game is adopted. The accuracy of the approximation is proved to be the $(\epsilon _{1}, \epsilon _{2})$ -hierarchical equilibrium of the original game. Finally, a decentralized solution algorithm with sequential linear programming as outer-loop and mean-field iteration as inner-loop is proposed to price the demand response. Numerical tests on a retail electricity market containing 15,000 heterogeneous demand-side flexible resources validate the effectiveness of the proposed pricing scheme.

The rapid growth of distributed energy resources results in the proliferation of prosumers at the distribution level, who can both produce and consume energy. In order to harvest benefits of aggregation to obtain revenues in wholesale market and encourage direct interactions among prosumers and other flexible players at the same time, this paper proposes an aggregation framework for prosumers based on transactive energy, in which prosumers aggregate and trade in an independent, proactive and privacy-preserved manner without relying on a real central entity, while other flexible players could be benefited equally. The aggregation of prosumers as a whole behaves as a retailer that trades in wholesale market and towards end-users in retail market. Therefore, the problem is generalized as a bi-level multi-leader multi-follower game in which prosumers are leaders and end-users are followers. To realize the transactive energy based trading framework, an inner-outer iteration approach is proposed, where the outer iteration involves interactions among prosumers and end-users, and achieves final results including the retail price and optimal behaviors of all players. The inner iteration is conducted during each time of the outer iteration to solve end-users’ optimal consuming profiles. The whole iteration process involves the independent decision makings of players, and only relies on exchange of information of retail price and prosumers’ offers instead of players’ private information. Case studies are then carried out demonstrating the proposed transactive energy based aggregation framework for prosumers could decrease the retail price, increase revenues for both prosumers and end-users, and reduce the peak load.

Carbon-capture-utilization-and-storage (CCUS) system plays a critical role in the process of decarbonization. This paper proposes a cooperative operation model for a CCUS-based thermal power plant and distributed energy resources. The critical purpose is to achieve a higher profit and flexibility together with a lower carbon emission for the CCUS system under the electricity, carbon, ancillary services, and synthesis fuel markets. First, to enhance the operational flexibility in various markets, a critical focus of this paper is on elaborating feasible operation domain of the CCUS system through identifying the physicochemical processes associated with carbon dioxide (CO ₂ ) treatment and multi-energy conversion. Second, a Nash-bargaining framework is developed to describe how the CCUS works with distributed energy resources cooperatively to consume cheap and clean renewables to treat CO ₂ . Third, to solve the cooperative operation problem efficiently and accurately, an adaptive sequential cone programming (ASCP) method together with a progressive hedging variant of the Benders decomposition (PH-BD) algorithm are designed. Case studies indicate the total revenue of the thermal power plant under the cooperative mode is improved by up to 8.85% and 66.53% compared with the individual mode with and without the CCUS. Further, the solution quality and efficiency of proposed methods are verified.

The green hydrogen provides a pathway to the 100% sustainable energy future, rendering hydrogen energy service providers (HESPs) an important role in energy supply. Different from the conventional electricity service providers, HESP is not only responsible for the production, purchase and retailing but also the transportation of hydrogen, which makes its operation a challenging problem. To this end, this paper proposes a bi-level strategic operation model for HESPs. In the operation level, the bidding, hydrogen production and hydrogen transportation are coordinated to minimize the overall cost of HESPs. In the market level, the market clearing is simulated to estimate the influence of HESP behavior on electricity prices in the market. Then, a model reformulation technique is developed to connect the discrete-time based hydrogen production and continuous-time based hydrogen transportation. The bi-level optimization model is transformed into a single-level mixed-integer linear programming (MILP) problem with Karush-Kuhn-Tucker (KKT) conditions. The case studies with the modified IEEE-RTS-79 system were given to validate the proposed model and confirm the necessity to coordinate the hydrogen production and transportation in the strategic operation of HESPs. Sensitivity analysis is also conducted to study the potential influencing factors.