Optimal Scheduling Strategy of Source-Load-Storage Based onWind Power Absorption Benefit

In recent years,the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing,but the peak regulation capacity of the power grid in the three north regions of China is limited,resulting in insufficient local wind power consumption capacity.Therefore,this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid’s wind power consumption capacity.The objective of the uppermodel is tominimize the peak-valley difference of the systemload,which ismainly to optimize the system load by using the demand response resources,and to reduce the peak-valley difference of the system load to improve the peak load regulation capacity of the grid.The lower scheduling model is aimed at maximizing the system operation benefit,and the scheduling model is selected based on the rolling schedulingmethod.The load-side schedulingmodel needs to reallocate the absorbed wind power according to the response speed,absorption benefit,and curtailment penalty cost of the two DR dispatching resources.Finally,the measured data of a power grid are simulated by MATLAB,and the results show that:the proposed strategy can improve the power grid’s wind power consumption capacity and get a large wind power consumption benefit.

Optimal Trade-Off Between Regulation and Wind Curtailment in the Economic Dispatch Problem

Generator regulation and wind power curtailment are two conflicting ways to deal with the inaccurate prediction and volatility of wind power.This study focuses on planning the day-ahead schedule based on optimal trade-off between regulation of generators and wind curtailment.Compared to traditional economic dispatch methods,the proposed schedule is more robust and adaptive to multiple forecast scenarios instead of a single forecast scenario.The works of this paper are as follows:First,an economic dispatch problem based on multiple scenarios is formulated with the objective of minimizing both generator regulation and wind curtailment.Next,a forecast method for wind power scenarios is given.Finally,the proposed model is verified by comparing with other dispatch models that are based on single forecast scenario.The simulation results demonstrate the effectiveness of the proposed method with less wind curtailment,generator regulation,and operational cost.In addition,the penalty factors are set as parameters and the influences on the generators’regulation and wind curtailment are analyzed,providing the reference for system operators with different regulatory purposes.

Deep reinforcement learning for wind and energy storage coordination in wholesale energy and ancillary service markets

Wind energy has been increasingly adopted to mitigate climate change.However,the variability of wind energy causes wind curtailment,resulting in considerable economic losses for wind farm owners.Wind curtailment can be reduced using battery energy storage systems(BESS)as onsite backup sources.Yet,this auxiliary role may significantly weaken the economic potential of BESS in energy trading.Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding,but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation.We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets.We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility,enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues.Using realistic wind farm data,we validated the coordinated bidding strategy,with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25%and more wind curtailment reduction by 2.3 times.Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately.Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains.The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.

Benefit Evaluation of the Deep Peak-regulation Market in the Northeast China Grid

In the Northeast China Grid(NCG),the percentage of wind power has reached nearly 20%of the total installed generation capacity,which causes increasing demands for deep peak-regulation capacity(DPC)during the operation of power systems.The shortage of DPC has become a significant problem in the NCG which may lead to wind curtailments and affect the security of power systems as well as the heating needs for inhabitants.In order to cope with this DPC shortage issue,the deep peak-regulation market(DPM)was established and has been running steadily in the past few years in NCG.This paper elaborates on the roles of the market players and the operational processes of the DPM,of which the advancements in terms of management mechanism are summarized.Moreover,benefits of the DPM for social harmony,environmental protection and economic efficiency are analyzed,for which relevant evaluation indices are proposed.A five-unit simulation system is constructed to illustrate the operation and benefits of the DPM.And focusing on comparisons with the previous Two Rules,the case study of Liaoning Power Grid verifies further that the DPM is feasible and able to bring more benefits to grids.

Short-term Transmission Maintenance Scheduling Considering Network Topology Optimization

With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind power.Meanwhile,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power systems.This paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control devices.The problem is formulated as a two-stage stochastic mixed-integer programming model.The first stage arranges the maintenance periods of transmission lines.The second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed scenarios.The proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both stages.Therefore,the progressive hedging algorithm is applied.The results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.

Optimal Energy Reserve Scheduling in Integrated Electricity and Gas Systems Considering Reliability Requirements

With the growing interdependence between the electricity system and the natural gas system,the operation uncertainties in either subsystem,such as wind fluctuations or component failures,could have a magnified impact on the reliability of the whole system due to energy interactions.A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system(IEGS).Therefore,this paper proposes a day-ahead security-constrained unit commitment(SCUC)model for the IEGS to schedule the operation and reserve simultaneously considering reliability requirements.Firstly,the multi-state models for generating units and gas wells are established.Based on the multi-state models,the expected unserved energy cost(EUEC)and the expected wind curtailment cost(EWC)criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS.Furthermore,the EUEC and EWC criteria are incorporated into the day-ahead SCUC model,which is nonconvex and mathematically reformulated into a solvable mixed-integer second-order cone programming(MISOCP)problem.The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system.Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.