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.

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 variability caused by the proliferation of distributed energy resources (DERs) and the significant growth in unbalanced three-phase loads pose unprecedented challenges to distribution network operations. This paper focuses on how a distribution system operator (DSO), taking over the distribution grid and market operations, would develop a risk-aware flexibility market to mitigate uncertainties in an unbalanced three-phase power distribution network. First, a distributionally robust chance constraint (DRCC) method is devised to solve the unbalanced three-phase optimal power flow using a semidefinite programming (SDP) model. The DSO can apply the proposed solution to jointly clear energy and flexibility markets. Then, the DRCC model accuracy is improved by an information-sharing mechanism characterized by spatially-correlated uncertainties in the distribution grid. Further, a novel system-wide response function is derived to make the DRCC model tractable. Using the duality theory, the paper further investigates the physical composition of the DSO’s cleared flexibility prices to guide the unbalanced distribution network operation. Finally, the effectiveness of the risk-aware flexibility market is verified in a modified three-phase IEEE 34-node system. Results demonstrate that the flexibility market can quantify the impact of spatially correlated uncertainties and facilitate the utilization of flexible resources to mitigate uncertainties across the network.

The electrical vehicle charging station (EVCS) paradigm will get to be more proactive progressively owing to the massive deployment of onsite distributed renewable energy (DRE) and battery energy storage system (BESS). In this environment, the coordination of peer-to-peer (P2P) transactive energy (TE) trading among EVCSs would be a critical topic which would demand further investigation. This paper proposes a hierarchical framework for the optimal P2P trading among EVCSs in constrained urban transportation network (UTN) and power distribution network (PDN). The UTN operation is incorporated through multi-period traffic flow assignment problem (MTAP), which is solved by the transportation system operator (TSO). After receiving the corresponding EV assignments, a P2P trading approach is considered among EVCSs, which would emphasize the EVCS privacy while minimizing the TE trading cost. The trading results are subsequently delivered by EVCSs to the distribution system operator (DSO) which would solve a three-phase unbalanced optimal power flow to guarantee the secure PDN operation. If any PDN constraints are violated, a trading adjustment signal will be returned to each EVCS in an iterative process. Numerical experiments based on EVCSs located on a 12-node UTN and the modified IEEE 33-bus PDN validate the effectiveness of the hierarchical P2P coordination approach and solution methodology, where the total operation cost of EVCSS is reduced by 16.61%.

To enhance the resilience of distribution systems, with improving the ability to withstand and rapidly recover in the face of high-impact, low-probability (HILP) events, has become increasingly essential. This paper explores the potential efficacy of peer-to-peer (P2P) transactions in enhancing distribution system resilience. Prosumers equipped with distributed energy resources (DERs) could support power supply in a local manner by forming P2P transactions to improve distribution system resilience. To describe the problem, we present a novel bi-level two-stage robust model considering both the pre-event and post-event stages, in which the distribution system operator (DSO) level generates the distribution locational marginal prices (DLMPs) and the prosumers level conducts P2P transactions incentivized by the DLMPs. The proposed solution draws upon the nested column-and-constraint generation (CCG) algorithm, further coupled with a bi-level interaction method by introducing deviation penalty functions. The case studies validate the effectiveness of the proposed approaches, demonstrating the multifaceted benefits of P2P transactions in enhancing distribution system resilience by reducing load shedding cost and quantity facing the HILP events as well as the operation cost.

In pursuit of achieving carbon neutrality goals, modern power systems are increasingly characterized by low-carbon and low-inertia properties, leading to significant concerns regarding the security of system frequency. These ancillary services for providing frequency regulation (FR) can contribute to the system inertia, FR reserve capacity, and the response rate of FR reserves. However, it could be challenging to motivate low-carbon resources, like carbon-capture-utilization-and-storage (CCUS) systems and grid-forming inverter-based renewable energy systems (RESs), to participate in FR ancillary service markets. A critical focus of this paper lies in assessing the marginal value of diverse FR ancillary services for improving the performance of frequency-secured systems in under-frequency and over-frequency cases. Given the tight relations among energy and FR ancillary services, the frequency-secured performance criteria are introduced, including maximum rate of change of frequency (RoCoF), maximum frequency deviation, and quasi-steady-state (Q-S-S) frequency which are devised in a joint energy, carbon, and FR ancillary service market. To solve this nonconvex and nonlinear market problem, while minimizing the uplift payment, a tractable shrunken convex hull pricing method is presented. Multiple case studies confirm the proposed method's effectiveness in enhancing the system frequency stability, reducing total costs, and curtailing carbon emissions.