[Objective] Virtual power plants （VPPs） centered on air-conditioning loads are susceptible to uncertainties， such as control delays and discrepancies between models and measurements， leading to deviations in the efficacy of demand response （DR） strategies from anticipated outcomes. A key contributor to this phenomenon is the reliance of existing DR strategies on static target load profiles， hindering their adaptability to dynamic operational environments.[Methods] To address this issue， this study introduced an adaptive control methodology for flexible-load VPPs participating in peak-shaving DR， utilizing a large-scale split-type inverter air conditioner on campuses as a case study. This approach allowed the adjustment of target load profiles for subsequent DR periods within the permissible range of the DR invitation based on the current operational environment， thereby enhancing the economic and robust nature of peak-shaving DR. In the proposed closed-loop control model， the controlled process was decoupled into a small-scale linear progress deviation model and a peak-shaving electricity correction model， each placed within the controller and feedback loop. The progress deviation model allocated planned peak shaving electricity to air conditioners， ensuring compliance with power constraints and user comfort levels. The peak-shaving electricity correction model， with the actual response to the peak-shaving DR， adaptively adjusted the target load profile for subsequent control moments to mitigate the adverse effects of uncertainties on control effectiveness.[Results] The case study focused on four types of inverter air conditioner clusters and examined the impact of different peak-shaving strategies， models， measurement errors， and control delays on the participation of the VPP in peak-shaving DR under market and invitation modes. This study verified the proposed method’s economic efficiency and robustness.[Conclusions] The results show that the proposed adaptive control method for peak-shaving DR based on a dynamic target load curve can autonomously adjust the target load curve based on the actual response conditions， demonstrating superior performance in terms of control accuracy， economic benefits， and robustness.

[Objective] To improve the low-carbon economic performance of scheduling strategies for virtual power plants， this study proposes a distributionally robust low-carbon scheduling model that incorporates emerging distributed resources and electricity-carbon trading. [Methods] First， this study established an electricity-carbon trading framework for a virtual power plant. Second， two emerging distributed resources （e.g.， electric hydrogen production system and carbon capture system） were modeled within virtual power plants， along with traditional distributed resources （e.g.， energy storage， wind power， and photovoltaics）. Next， to minimize costs and consider the impact of electricity carbon trading， a low-carbon scheduling model for virtual power plants was established. Owing to the difficulty in obtaining accurate probability distributions of wind and solar power outputs and electric hydrogen loads， an uncertainty set of probability distributions was constructed using the 1-norm and infinite-norm. To avoid the complex iterations required in traditional multiple discrete-scenario distributionally robust optimization methods， this study solves the proposed model using a strong duality. Finally， the effectiveness of the proposed model in addressing source-load uncertainty and improving economic and low-carbon performance was verified based on numerical examples.[Results] Electricity-carbon trading reduced costs by approximately 24.7% compared to no electricity-carbon trading. Excess renewable energy could be sold entirely to the electricity market to obtain profitable operational results. Considering both the carbon capture system and the electric hydrogen production system， both abandoned electricity and operating costs are further respectively reduced by about 34.7% and 28.1% when only considering the carbon capture system， and respectively by about 2.6% and 1.8% when only considering the electric hydrogen production system. The total profit error of the proposed distributionally robust optimization method was approximately 1.7%， and the solving speed improved by approximately 40%.[Conclusions] Electricity-carbon trading and the integration of electric hydrogen production system and carbon capture system can jointly reduce scheduling costs， abandoned electricity， and carbon emissions. Moreover， the proposed distributionally robust optimization method showed high accuracy in decision-making results and significantly improved the solving speed.

[Objective] In the context of high renewable energy penetration， the collaborative operation of multiple virtual power plants （VPPs） faces dual challenges： uncertainty risks and conflicts in benefit distribution. This study proposes a collaborative optimization strategy for multiple VPPs that integrates risk quantification with hybrid game theory by combining conditional value-at-risk （CVaR） and a multi-agent game framework. This approach provides a new perspective for collaborative VPP optimization in scenarios with high renewable energy integration.[Methods] First， a scenario analysis method combining Latin hypercube sampling （LHS） and Manhattan probability distance was designed to address the uncertainties in wind and solar output as well as electricity prices. CVaR was adopted to measure the impact of these uncertainty risks. Second， a Stackelberg game framework was constructed between the distribution system operator （DSO） and the VPP alliance， where the VPP alliance， based on cooperative game theory， established an asymmetric Nash bargaining model incorporating energy contributions. The model was then decomposed into two subproblems： maximizing alliance benefits and distributing cooperative benefits. Finally， the hybrid game model was solved using a combination of the bisection method and the alternating direction method of multipliers （ADMM）.[Results] Simulation results demonstrate that the proposed coordinated optimization strategy for VPPs effectively enhances the operational economy of the VPP alliance and improves operational reliability and security under uncertainty.[Conclusions] The proposed strategy increased the flexibility of coordinated operations among multiple VPPs. By incorporating CVaR for risk quantification and multi-agent game theory， the strategy not only enhances overall system benefits but also ensures a fair distribution of cooperative gains. Moreover， VPPs can balance the risk-benefit trade-off based on their risk aversion coefficients， providing a valuable reference for rational dispatch decision-making.

[Objective] The increasing penetration of distributed resources into the distribution grid and their participation in coordinated operations across different levels of the power grid present several challenges. To address these challenges， this study proposes a joint clearing model for virtual power plants （VPPs） participating in local flexibility markets and spot markets. [Methods] Distribution service operators first aggregate the distributed resources into the VPP trading units in distribution network buses. Subsequently， at the distribution network level， a flexibility dispatching model for VPPs on the distribution network is established， which comprehensively considers the constraints of VPPs and the distribution network. Finally， the flexibility dispatching model and spot market-clearing model are co-optimized in a non-iterative manner. To validate the effectiveness of the proposed model， a case study was conducted based on IEEE standard test systems， and a comparative analysis with the market model was performed on the overall cost， transmission-distribution network operation， and clearing efficiency. [Results] Using the proposed method， the expected revenue of the transmission and distribution networks increased by 4.4% in the simulation and computation time reduced by 77.8%， compared with the non-cooperative scenario and iterative calculation method， respectively. [Conclusions] The results demonstrate that the proposed model can meet the flexibility needs of transmission and distribution networks and enhance the integration capacity of the system for renewables.

[Objective] The large-scale integration of electric vehicles （EVs） presents potential flexibility and operational uncertainty in power systems. Virtual power plants （VPPs）， as efficient paradigms for aggregating distributed energy resources， offer a feasible approach for coordinating EV participation in grid operations. This study proposed a bi-level optimization strategy based on a Stackelberg game to manage the interaction between VPPs and EV users under uncertainty.[Methods] A bi-level Stackelberg game model was developed in which the VPP acts as the leader and the EV users as followers. The upper-level model maximized the VPP profit while managing EV-related uncertainties via the conditional value at risk （CVaR）. It sets risk-aware charging and discharging prices. The lower-level model minimized user costs by responding to these prices using a utility function that captures both cost satisfaction and charging experience. A particle swarm optimization algorithm was employed to solve the coupled model and identify the equilibrium strategies.[Results] A case study of a VPP system with wind， solar， storage， and 300 EVs demonstrated the effectiveness of the proposed approach. Compared to benchmark strategies， the model reduced the peak-valley load gap by up to 36.9%， lowered the average user cost by 28.79%， and enhanced profit stability under uncertainty.[Conclusions] The CVaR-based bi-level game framework effectively balances the VPP profit， EV user satisfaction， and system stability. It provides a risk-aware， market-oriented approach for flexible resource management and offers practical insights into future EV-grid integration strategies.

When constructing new power systems，the increasing number of prosumers with varying sizes，such as microgrids and commercial buildings，has to be considered. A virtual power plant formed by the aggregation and interconnection of such prosumer clusters can collaboratively optimize flexible resources and improve the overall efficiency of the cluster through power interactions and power mutual aid. To realize the coordinated operation of multiple prosumers，this study proposes a mixed game strategy between virtual power plant operators and multiple prosumers. First，a master-slave game model of virtual power plant operators and multiple prosumers is constructed. The virtual power plant operator （master） dynamically adjusts the electricity prices，and the prosumers （slave） follow. Considering the uncertainty of new internal energy sources，an optimal dispatch is conducted based on distributed robust opportunity constraints. Subsequently，a multi-prosumer cooperative game model is constructed based on the scheduling strategy and transaction results of the master-slave game. Further，a cooperative income distribution strategy based on energy contributions is proposed to achieve a fair and reasonable distribution of income from multi-prosumer cooperation alliances. Finally，the rationality and superiority of the proposed hybrid game strategy are verified through a numerical example analysis.

To normalize the issuance and trading of green certificates，the National Energy Administration （NEA） recently introduced a new policy. This policy mandates issuing tradable and nontradable green certificates for on-grid renewable energy electricity and spontaneous self-use renewable energy electricity，respectively. Although green certificates contribute in offsetting carbon emissions，maximizing the economic and environmentally friendly aspects of virtual power plants （VPPs） is important. This study proposes an approach to implement both green certificate trading and carbon emissions offsetting in the development of day-ahead operational strategy of the VPP. First，the electricity associated with tradable and nontradable green certificates of the VPP is quantified based on the "use what is generated，and the excess on the grid" mode of renewable energy generation in the VPP. The charging-discharging strategy in electricity storage for the renewable and non-renewable energy electricity is also considered. Second，a method for offsetting carbon emissions using green certificates is proposed，and the total amount of carbon emissions offset by the VPP under the two types of green certificates is calculated. Third，an optimal day-ahead operational strategy model of the VPP under the electricity-carbon-green certificate coupled market is developed to maximize the total revenue in the coupled market. This study demonstrates that the proposed approach enhances the financial performance of the VPP and boosts its renewable energy consumption capacity by engaging in carbon-green certificate trading under the new policy. Moreover，this approach contributes to an overall reduction in carbon emissions within the energy system.

Integrating a high proportion of renewable energy to achieve dual-carbon strategic goals has resulted in new challenges for the regulatory capability of power systems. Virtual power plants （VPPs） have extensive industrial applications as an effective means of exploiting demand-side flexible resources. However，the current application scenarios of VPPs are relatively simple and a market-oriented profit model for sustainable operations has not yet been established. To promote the market-oriented development of VPPs，a series of policies has been introduced by the government at all levels. Focusing on the actual situation in Shanghai，this paper first summarizes the construction and development of VPPs and introduces the achievements and development directions of the various VPP practices followed in Shanghai. Second，according to the domestic market development situation，the profit models of VPPs participating in the demand response，spot markets，and ancillary service markets are analyzed，and a brief outlook is provided on the future expandable business of VPPs. Furthermore，a market-oriented profit model for VPPs participating in multiple market types is established to improve their operating benefits by reusing the regulation capabilities in different scenarios. Finally，the effectiveness and feasibility of the proposed model are verified through case studies. The results of this study are significant for promoting the market-oriented development of VPPs，improving the regulation capability of power systems，and achieving sustainable development goals.

Energy aggregation service providers in the electricity market are highly dependent on the load-forecasting accuracy to maximize their market interests. However，the required forecasting data are typically scattered among different energy suppliers，and owing to data security and privacy concerns，these suppliers are often unwilling to share their data. Inaccurate load forecasting reduces the operational efficiency of energy aggregation service providers and limits the in-depth understanding of supplier operating models. To address this issue，this study proposes a load-forecasting model based on federated learning，which achieves an effective improvement in load-forecasting accuracy while ensuring data security and privacy. First，an artificial neural network with multidimensional environmental feature selection is established according to the task requirements. Subsequently，a weighted-average strategy was used to jointly train the artificial neural network，bridging the gap between individual energy entities and environmental features. Finally，the model features trained separately are sent to the server of the energy-aggregation service provider for the fitting process. The results of a case analysis using actual data from a large energy aggregation service provider in a southern city in China demonstrate that the proposed model significantly improves operational efficiency and forecasting accuracy.