Market-Driven Joint Trading Strategy for Computing Service and Electricity in Cloud-Edge Collaborative Systems

A joint trading mechanism for energy credits and electricity in virtual power plants

With the urgent need to achieve carbon neutrality goals and the rapid development of distributed energy resources, traditional electricity markets face challenges in effectively integrating environmental and economic values, lacking unified mechanisms to simultaneously clear energy and environmental transactions. This study proposes an energy credits–electricity joint trading mechanism for virtual power plants that achieves co-equilibrium through explicit coupling between energy and environmental value markets. First, a multi-dimensional energy credit quantification model is established, integrating energy type, time period, trading volume, and behavioral characteristics to differentiate environmental contributions. An optional reputation assessment enhancement covering prediction accuracy, fulfillment reliability, response timeliness, trading frequency, and anomaly behavior can be integrated for virtual power plant (VPP) operators requiring behavioral differentiation. Second, a unified joint clearing model is constructed that co-optimizes energy and credit trading, employing the alternating direction method of multipliers (ADMM) to decompose large-scale optimization problems into parallelizable prosumer and market coordination subproblems. Simulation results across single-day, multi-day (5-day cycle), and seasonal scenarios demonstrate that the mechanism successfully distinguishes prosumer performance: renewable energy prosumers accumulate substantial positive credits (136.2 credits over 5 days), while fossil fuel users incur credit deficits (−25.1 credits single-day), achieving system-level carbon credit balance over multi-day settlement cycles. The proposed mechanism effectively realizes the principle of “green contributors benefit, polluters pay” and provides a practical pathway for integrating environmental value in electricity markets.

Cost-Sharing Strategy for Distributed P2P Energy Trading Considering Spatial Distance Cost

Data-driven pricing and trusted trading mechanism for park-level flexible resources under energy-carbon coupling

The coupling of electricity and carbon markets presents new challenges for the operation of industrial park integrated energy systems. This paper proposes a comprehensive framework to address inaccurate response modeling, scalability limitations in pricing, and a lack of trust in distributed trading. A dynamic sensitivity matrix based on a hybrid neural network is developed to quantify the real-time response potential of flexible resources. A three-layer Stackelberg game model is then constructed and solved via a distributed algorithm to determine optimal pricing. Furthermore, a blockchain-based verification mechanism is introduced to ensure transaction trust. Validation based on real-world data from an industrial park in the Guangdong-Hong Kong-Macao Greater Bay Area demonstrates that the proposed method effectively captures energy-carbon coupling effects and improves market efficiency and fairness.

Multi-Market Joint Trading of Distributed Resource Aggregators Considering a Carbon–Green Certificate Linkage Mechanism

To address the coupling among bidding decisions, resource allocation, and coordinated utilization of environmental rights for distributed resource aggregators in multiple markets, including the energy market, peak regulation ancillary service market, carbon trading market, and tradable green certificate market, this paper proposes a bi-level optimization model for multi-market joint trading considering a limited carbon–green certificate linkage mechanism. First, a quantitative mapping relationship between the emission reduction attribute of surplus green certificates and carbon emission reduction is established, and an upper limit constraint on the offset ratio is introduced to describe the limited conversion of green certificate environmental attributes into carbon emission reduction value. Second, an upper-level bidding model for the distributed resource aggregator is constructed, aiming at profit maximization while considering revenues from the energy, peak regulation ancillary service, carbon trading, and green certificate markets, as well as the operational constraints of gas turbines, energy storage, and flexible loads. This model characterizes the aggregator’s joint bidding strategy and internal resource coordination. Then, a lower-level unified market clearing model is developed to minimize system operating cost and simulate the segmented bidding and unified clearing process of the aggregator, wind power, and thermal power units in the energy and peak regulation ancillary service markets. Finally, the bi-level model is transformed into a solvable single-level model using the Karush-Kuhn-Tucker (KKT) conditions and the Big-M method, and case studies are conducted to verify its effectiveness. The numerical results show that under the complete multi-market mechanism, the distributed resource aggregator (DRA) obtains a net profit of 2245.03 yuan, which is higher than 1450.33 yuan in the scenario without the carbon–green certificate mechanism and 773.48 yuan in the energy-only scenario. The wind curtailment rate decreases from 8.53% in the energy-only scenario to 2.71%, and the carbon emissions accounted for within the DRA boundary are reduced to 2865.40 kg. Although the price-taker scenario obtains a slightly higher net profit of 2300.20 yuan, its average regulation price reaches 455.20 yuan/MWh, compared with 412.50 yuan/MWh under the proposed model, indicating that the proposed strategy achieves a better balance between aggregator revenue and system-side regulation cost.

Data-Driven Sequential Market Optimization for Front-of-the-Meter Battery Energy Storage Systems

The growing integration of Battery Energy Storage Systems into electricity markets has highlighted the importance of coordinated participation across energy and balancing services to fully exploit their operational flexibility. However, existing revenue-stacking models often simplify market sequences and neglect the impact of rolling forecasts, leading to unrealistic scheduling and overestimated revenues. This paper addresses this gap by introducing a sequential, data-driven optimization framework for Front-of-the-Meter Battery Energy Storage Systems that mirrors actual market operations. The framework explicitly models market mechanisms and its respective Gate Closure Times across Frequency Containment Reserve, automated Frequency Restoration Reserve, Day-Ahead Auction, and Intraday Continuous markets. Each market stage optimizes expected revenue over all remaining markets using updated price forecasts while maintaining feasibility within both technical and regulatory limits. A key contribution is the opportunity-cost-based bidding strategy, which endogenously derives market-consistent bid prices and quantities from residual capacity optimization. Validation for a representative operating day demonstrates that the framework yields consistent and feasible schedules, effectively adapts to updated forecasts, and minimizes deviations between planned and realized dispatch, thereby enhancing the realism and profitability of BESS operation.

Single-level MILP incentive pricing for customer directrix load-based demand response in smart buildings

With the high penetration of renewable energy, tapping the regulation potential of smart buildings in distribution networks plays a pivotal role in enabling orderly renewable energy accommodation. Customer directrix load-based demand response serves as an effective mechanism for guiding smart building interaction. However, its incentive price, derived as the equilibrium point of a leader-follower game between the operator and load entities, relies on a corresponding bilevel optimization model that suffers from low computational efficiency. To address this limitation, an efficient incentive pricing method is proposed. The proposed linear similarity metric and incentive revenue formulation ensure the linearity of the lower-level optimization problem. Subsequently, based on primal-dual feasibility, an aggregated strong-duality equality, and a discrete-price reformulation, the original bilevel model is accurately reformulated into a single-level mixed-integer linear programming problem, enabling a one-shot determination of the incentive price. Simulation results demonstrate that the proposed linear metric is highly consistent with traditional nonlinear metrics in terms of ranking monotonicity. Compared with time-of-use-only demand response and fixed incentive pricing strategies, the proposed method can identify an economically balanced incentive price that effectively guides smart building energy consumption behavior, improves renewable energy accommodation, and maintains favorable economic performance for both the distribution system operator and smart buildings. These findings support the use of the proposed single-level MILP framework for balancing renewable energy accommodation with the economic interests of the distribution system operator and smart buildings.