The efficient operation of virtual power plants(VPPs)relies on massive heterogeneous data interactions,facing the fundamental contradiction between data value mining and privacy protection.Addressing the challenges of data silos,single points of failure,and privacy leakage in centralized management,this paper reviews the application status and integration solutions of federated learning and trusted execution environment in VPPs.Through comparative analysis of blockchain and cryptographic collaboration schemes,the advantages of federated learning in privacy protection,collaboration efficiency,and technical adaptability are clarified.The applications of federated learning in VPP load forecasting,distributed energy resource coordination,electricity market trading,and demand response are systematically reviewed,along with an analysis of trusted execution environment mechanisms that ensure computational confidentiality and integrity through hardware isolation.The integration architecture of both technologies is emphasized:trusted execution environment addresses federated learning's vulnerabilities in parameter leakage and poisoning attack defense by protecting model aggregation processes and sensitive data processing,while federated learning's distributed characteristics overcome the performance and scalability bottlenecks of trusted hardware.This integrated technology enables"data availability without visibility",and provides a feasible path for secure and efficient data collaboration in the energy internet.On the basis,this paper further discusses future challenges related to performance optimization,side-channel defense,and standardization.
The proposal of the national"dual carbon"strategic goals and the improvement of the carbon trading market have placed higher demands on the dispatch and operation strategies of microgrids.Aiming at emission reduction and cost lowering,this paper develops a dual-layer optimization model for low-carbon and economic operation of microgrids,which includes photovoltaic,wind power,diesel generator sets,and energy storage modules,while considering load constraints,charging/discharging constraints of storage devices,and carbon quota constraints.The outer-layer planning model introduces a carbon asset operation mechanism and uses an improved genetic algorithm to optimize microgrid operation strategies,improving economic benefits while ensuring low-carbon system operation.The inner-layer demand response model improves user electricity consumption behavior through incentive measures,optimizes the load curve,and feeds back the user response after implementing demand response to the outer-layer model.The inner and outer layers interact iteratively until both equipment configurations and load curves reach optimum,thereby achieving a bidirectional interaction between load and carbon emissions.Simulation results under multiple scenarios demonstrate that the proposed dual-layer optimization model can significantly reduce microgrid operation costs and carbon emissions.Sensitivity analysis results show that carbon trading prices have a significant impact on operation strategies and costs.
The trading behavior of virtual power plant(VPP)in the carbon-electricity market will affect the harmonious development of the market.Therefore,this paper proposes a peer to peer(P2P)transaction matching method for VPPs to participate in the carbon-electricity convergence market considering trading harmony.Firstly,the overall framework for VPPs participating in P2P transaction mechanisms of the carbon-electricity convergence market is designed.Based on the operation plan of each unit of source,load and storage in VPP and the carbon emission coefficient of carbon-emitting units,a transaction capacity evaluation model for VPP to participate in the electricity market and carbon market is constructed.Meanwhile,by analyzing the impact of various historical transaction behaviors and performance behaviors of VPP on the harmonious development of the carbon-electricity trading market,the evaluation index of trading harmony for VPP participating in the carbon-electricity market is proposed.Secondly,a P2P transaction matching model for the primary carbon-electricity convergence market is constructed with the objective function of maximizing the total transaction acceptance of both parties,including the transaction harmony of both parties,the transaction price acceptance of both parties and the carbon neutrality achievement of both parties.Furthermore,a secondary carbon market P2P transaction mechanism is established as a supplementary part of the primary carbon-electricity convergence transaction market for the carbon rights transaction volume corresponding to the deviation between the actual executed electricity and the agreed electricity in the day-ahead electricity market.Finally,the simulation example verifies that VPP with high trading harmony and low carbon emissions are more competitive in the P2P transactions of the carbon-electricity convergence market.
To address the coordinated challenge of economy,low-carbon performance,and robustness in the scheduling of virtual power plants(VPPs)under high-proportion renewable energy grid integration,this paper proposes a VPP optimization scheduling strategy based on electricity-carbon coupling and multi-time-scale distributionally robust optimization.Firstly,a joint electricity-carbon market framework integrating stepwise carbon trading and green certificate trading is constructed,which stimulates the low-carbon scheduling potential of VPPs through price signal linkage.Secondly,a three-level day-ahead-intraday-real-time optimization architecture is designed,with the core of formulating global plans,correcting prediction errors,and eliminating instantaneous deviations respectively,to achieve accurate scheduling across different time scales.Finally,in response to the uncertainties of wind and solar power output and market prices,a distributionally robust optimization method is adopted to build a Wasserstein ambiguity set model,which dynamically adjusts robust parameters to balance risks and benefits.The case study results show that the total cost of the proposed strategy is reduced to 12,300 yuan per day,a decrease of 32.4%compared with the traditional strategy;the renewable energy consumption rate reaches 99.5%,the carbon cost-benefit ratio is increased to 1.8,and the robustness compliance rate is as high as 98.2%.At the same time,the energy storage charge-discharge efficiency and the optimization effect of market revenue structure are significant.This strategy achieves an all-round improvement in economy,low-carbon performance,robustness,and energy utilization efficiency,providing an effective technical path for the optimal operation of VPPs in a multi-market environment.
To enhance the operational flexibility of coal-fired power plants in supporting high penetration of renewable energy, this paper focuses on the coordinated planning of regional coal-fired power flexibility upgrades. Existing research typically optimizes deep peak-shaving modifications on the boiler side of condensing units and thermal-electric decoupling technologies separately, with most studies neglecting the impact of grid structural constraints. this paper first systematically analyses the operational characteristics of four technical approaches: deep peak shaving modifications on the boiler side, zero-output modifications for low-pressure cylinders, electric boilers, and thermal storage devices. Subsequently, a mixed-integer linear programming model incorporating grid topology is constructed. This model aims to minimize total system costs, enabling the coordinated configuration and operational optimization of multiple technical pathways. A case study based on an enhanced IEEE 14-node system demonstrates that integrated optimization of these technologies reduces total system costs by 5.98% and curtailment rates for wind and solar power by 15.68%. The results validate that the proposed collaborative planning approach effectively integrates complementary advantages across different technologies, significantly lowering system costs and alleviating pressure on renewable energy integration. It also reveals that the flexibility regulation capacity of coal-fired power plants is influenced by their node position within the grid.
