Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

A Centralized Energy Coordinator Reliant on BESS and DER-PV Operation

Many existing battery energy storage system (BESS) control schemes focus on mitigating negative impacts resulting from the operation of distributed energy resources-photovoltaic facilities (DER-PV). These include out-of-firm conditions from reverse power flow or extreme variability in the service voltage. Existing control strategies fail to consider how BESS control schemes need to operate in a consecutive day-to-day basis in order for them to be implemented in the field. In this paper, a novel energy management algorithm capable of dispatching a BESS unit upstream of a multi-megawatt DER-PV is introduced. This algorithm referenced as the Master Energy Coordinator (MEC), accepts forecasted DER-PV generation and individual feeder load to create daily charge and discharge rate schedules. Logic is integrated to the cyclic discharging event to sync with the forecasted peak load, even when it will occur during the morning of the next day. To verify the MEC operation, Quasi-Static Time Series (QSTS) simulations are conducted on a 12.47 kV distribution feeder model utilizing historical head-of-feeder and DER-PV analog DSCADA measurements.

Probabilistic Energy and Reserve Co-dispatch for High-renewable Power Systems and Its Convex Reformulation

This paper proposes a probabilistic energy and reserve co-dispatch(PERD) model to address the strong uncertainties in high-renewable power systems. The expected costs of potential renewable energy curtailment/load shedding are fully considered in this model, which avoids insufficient or excessive emergency control capacity to produce more economical reserve decisions than conventional chance-constrained dispatch methods. Furthermore, an analytical reformulation approach of PERD is proposed to make it tractable. We firstly develop an approximation technique with high precision to convert the integral terms in objective functions into analytical ones. Then, the calculation of probabilistic constraints is equivalently transformed into an unconstrained optimization problem by introducing value-at-risk(Va R) representation. Specifically, the Va R formulas can be computed by a computationally-cheap dichotomy search algorithm. Finally, the PERD model is transformed into a convex problem, which can be solved reliably and efficiently using off-the-shelf solvers. Case studies are performed on IEEE test systems and real provincial power grids in China to illustrate the scalability and efficiency of the proposed method.

Resilience-oriented Valuation for Energy Storage Amidst Extreme Events

In power grids,the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate attacks.This has sparked discussions on the resilience of power grids.Energy-storage systems(ESSs)are critical for enhancing the resilience of power grids.ESSs,with their mechanism of flexible charging and discharging,adjust energy usage as needed during disasters,thereby mitigating the impact on the grid and enhancing security and resilience.This,in turn,ensures the power system’s stable operation.Currently,there is limited systematic research quantifying the economic value of energy storage in resilience scenarios.Therefore,a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events.A two-stage stochastic optimization mathematical model was developed.The first stage involves pre-deployment based on day-ahead expectations,and the second stage involves simulating potential failure scenarios through real-time scheduling.Considering the temporal dimension,the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of load-shedding.Here,a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity,as reflected in the emergency dispatch model.This index helps determine the balance between the energy storage investment cost and resilience value.Finally,an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed method.The findings revealed a significant improvement in the resilience value,with a 23.49%increase observed when energy storage systems were implemented compared to the scenario without energy storage systems.The optimal capacity configurations for the flywheel,lithium-ion batteries,and pumped hydro storage were 10 MW,11 MW,and 12 MW,respectively,highlight their potential to maximize value in experimental system.

Optimization for Micro-energy Grid Dispatch Based on Non-supplementary Fired Compressed Air Energy Storage Aided Energy Hub and Hybrid Hyper-spherical Search

Micro-energy grids have shown superiorities over traditional electricity and heating management systems.This paper presents a hybrid optimization strategy for micro-energy grid dispatch with three salient features.First,to enhance the ability to support new storage equipment,an energy hub model is proposed using the non-supplementary fired compressed air energy storage(NSF-CAES).This provides flexible dispatch for cooling,heating and electricity.Second,considering the unique characteristics of the NSF-CAES,a sliding time window(STW)method is designed for simple but effective energy dispatch.Third,for the optimization of energy dispatch,we blend the differential evolution(DE)with the hyper-spherical search(HSS)to formulate a hybrid DE-HSS algorithm,which enhances the global search ability and accuracy.Comparative case studies are performed using real data of scenarios to demonstrate the superiorities of the proposed scheme.