Stochastic Flexibility Evaluation for Virtual Power Plants by Aggregating Distributed Energy Resources

To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.

Allocating Ex-post Deviation Cost of Virtual Power Plants in Distribution Networks

Virtual power plants(VPPs)including distributed generation,energy storage,and elastic load are emerging in distribution networks.Multiple VPPs can participate in electricity market as an aggregated entity and effective cost allocation mechanism among VPPs is a crucial issue.This paper focuses on allocating ex-post cost of VPPs incurred by deviation between actual power and ex-ante schedule in a two-settlement electricity market.We obtain approximate quadratic formulation of ex-post deviation cost considering network loss and develop an analytical cost allocation algorithm based on cooperative game theory.The allocated cost is consistent with cost causation principle and provides VPPs with incentive for aggregation.The proposed allocation method and relevant theoretical result are evaluated and verified by numerical tests.

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.

Data-driven Convexification for Frequency Nadir Constraint of Unit Commitment

The increasing penetration of the renewable energy sources brings new challenges to the frequency security of power systems. In order to guarantee the system frequency security, frequency constraints are incorporated into unit commitment(UC) models. Due to the non-convex form of the frequency nadir constraint which makes the frequency constrained UC(FCUC) intractable, this letter proposes a revised support vector machine(SVM) based system parameter separating plane method to convexify it. Based on this data-driven convexification method, we obtain a tractable FCUC model which is formulated as a mixed-integer quadratic programming(MIQP) problem. Case studies indicate that the proposed method can obtain less conservative solution than the existing methods with higher efficiency.

Data-driven Inverter-based Volt/VAr Control for Partially Observable Distribution Networks

For active distribution networks(ADNs)integrated with massive inverter-based energy resources,it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs.Thus,current models of ADNs usually involve significant errors or even unknown occurances.Moreover,ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant users.To provide a practical Volt/Var control(VVC)strategy for such networks,a data-driven VVC method is proposed in this paper.First,the system response policy,approximating the relationship between the control variables and states of monitoring nodes,is estimated by a recursive regression closed-form solution.Then,based on real-time measurements and the newly updated system response policy,a VVC strategy with convergence guarantee is realized.Since the recursive regression solution is embedded in the control stage,a data-driven closedloop VVC framework is established.The effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads,where not only the rapid and self-adaptive voltage regulation is realized,but also systemwide optimization is achieved.

Laparoscopic versus open pancreaticoduodenectomy for pancreatic ductal adenocarcinoma:a propensity score matching analysis

Background:A growing body of evidence supports the use of laparoscopic pancreaticoduodenectomy(LPD)as an efficient and feasible surgical technique.However,few studies have investigated its applicability in pancreatic ductal adenocarcinoma(PDAC),and the long-term efficacy of LPD on PDAC remains unclear.This study aimed to compare the short-and long-term outcomes between LPD and open pancreaticoduodenectomy(OPD)for PDAC.Methods:The data of patients who had OPD or LPD for PDAC between January 2013 and September 2017 were retrieved.Their postoperative outcomes and survival were compared after propensity score matching.Results:A total of 309 patients were included.After a 2:1 matching,93 cases in the OPD group and 55 in the LPD group were identified.Delayed gastric emptying(DGE),particularly grade B/C DGE,occurred less frequently in the LPD group than in the OPD group(1.8%vs.36.6%,P<0.001;1.8%vs.22.6%,P=0.001).The overall complication rates were significantly lower in the LPD group than in the OPD group(49.1%vs.71.0%,P=0.008),whereas the rates of major complications were similar(10.9%vs.14.0%,P=0.590).In addition,the median overall survival was comparable between the two groups(20.0 vs.18.7 months,P=0.293).Conclusion:LPD was found to be technically feasible with efficacy similar to OPD for patients with PDAC.

Very Short-term Spatial and Temporal Wind Power Forecasting: A Deep Learning Approach

In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting methods at presentonly utilize individual wind farm historical data.However,studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind farms.Current research on spatial-temporal wind power forecasting is based on relatively shallow time series models that,to date,have demonstrated unsatisfactory performance.In this paper,a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms.A novel convolution-based spatial-temporal wind power predictor(CSTWPP)is developed.Due to CSTWPP’s high nonlinearity and deep architecture,wind power variation features and regularities included in the historical data can be more effectively extracted.Furthermore,the online training of CSTWPP enables incremental learning,which makes CSTWPP non-stationary and in conformity with real scenarios.Graphics processing units(GPU)is used to speed up the training process,validating the developed CSTWPP for real-time application.Case studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.

Optimal Aggregation Approach for Virtual Power Plant Considering Network Reconfiguration

As an aggregator of distributed energy resources(DERs) such as distributed generator, energy storage, and load,the virtual power plant(VPP) enables these small DERs participating in system operation. One of the critical issues is how to aggregate DERs to form VPPs appropriately. To improve the controllability and reduce the operation cost of VPP, the complementary DERs with close electrical distances should be aggregated in the same VPP. In this paper, it is formulated as an optimal network partition model for minimizing the voltage deviation inside VPPs and the fluctuation of injection power at the point of common coupling(PCC). A new convex formulation of network reconfiguration strategy is incorporated in this approach which can guarantee the components of the same VPP connected and further improve the performance of VPPs.The proposed approach is cast as an instance of mixed-integer linear programming(MILP) and can be effectively solved.Moreover, a scenario reduction method is developed to reduce the computation burden based on the k-shape algorithm. Numerical tests on the 13-bus and 70-bus distribution networks justify the effectiveness of the proposed approach.

Integrated Distribution Management System:Architecture,Functions,and Application in China

As massive distributed energy resources(DERs)are integrated into distribution networks(DNs)and the distribution automation facilities are widely deployed,the DNs are evolving to active distribution networks(ADNs).This paper introduces the architecture and main function modules of an integrated distribution management system(IDMS)and its applica-tions in China.This system consists of three subsystems,including the real-time operation and control system(OCS),outage management system(OMS),and operator training simulator(OTS).The OCS has a hierarchical architecture with three levels,including the local controller for DER clusters,the optimization of DNs incorporated with multi-clusters,and the coordina-tion operation of integrated transmission&distribution(T&D)networks.The OMS is developed based on the geographical information system(GIS)and coordinated with OCS.While in the OTS,both the ADN and its host transmission network(TN)are simulated to make the simulation results more credible.The main functions of the three subsystems and their interaction data flows are described and some typical application scenarios are also presented.

Optimal Decomposition of Stochastic Dispatch Schedule for Renewable Energy Cluster

The correlated renewable energy farms are usually aggregated as a cluster in economic dispatch to relieve computational burden.This strategy can also achieve better performance since the precision of predicting the power generation of a cluster can be higher than those of individual farms.This paper proposes an optimal decomposition method to allocate dispatch schedules among renewable energy farms(REFs)in the cluster under existing stochastic optimization framework.The proposed model takes advantage of probabilistic characteristics of renewable generation to minimize the curtailment and ensure the feasibility of dispatch schedule of the clusters.Approximated tractable formulation and efficient solution method are the proposed to solve the proposed model.Numerical tests show that the proposed method achieves the optimal decomposition of dispatch schedule among REFs and facilitates the utilization of renewable generation.

Transient outage model considering corrective and preventive maintenance

Traditional outage model for the power equipment usually focus on the behavior of the equipment under random factors,and the availability of the power equipment in system analysis is usually confined to the steady value.However,this model may be inaccurate in the short term analysis,where the transient process of availability has not ended yet.Furthermore,the power equipment in the short term analysis might be influenced by both random factors and deterministic factors,yet the impact of deterministic factors cannot be completely reflected in the traditional outage model.Based on the above issues,a Markov-based transient outage model is proposed in this paper,which describes the deterioration and repair process of an equipment.Both the corrective maintenance and preventive maintenance are concerned in the model.The preventive maintenance in the model is considered as deterministic event,in which the start time and duration are both scheduled.Meanwhile the corrective maintenance and the unexpected failure are modeled as random events.The transient state probability and availability of equipment under preventive maintenance is derived.The effect of deterministic events on the availability of equipment is analyzed on numerical tests.The proposed model can be used in the short-term reliability assessment and maintenance scheduling in actual systems.

A sparse recovery model with fast decoupled solution for distribution state estimation and its performance analysis

This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast decoupled solution is adopted.The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements.The robustness and the computational efficiency of the R-Capped-Ll model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.

Basophils as a potential therapeutic target in cancer

Basophils,which are considered as redundant relatives of mast cells and the rarest granulocytes in peripheral circulation,have been neglected by researchers in the past decades.Previous studies have revealed their vital roles in allergic diseases and parasitic infections.Intriguingly,recent studies even reported that basophils might be associated with cancer development,as activated basophils synthesize and release a variety of cytokines and chemokines in response to cancers.However,it is still subject to debate whether basophils function as tumor-protecting or tumor-promoting components;the answer may depend on the tumor biology and the microenvironment.Herein,we reviewed the role of basophils in cancers,and highlighted some potential and promising therapeutic strategies.

A Multi-parametric Programming Based Analytic Method to Compute Consumer Offer Curve for Reserves

Abstract-An analytic method is proposed to compute the price-reserve offer curve at the consumer level in hierarchical direct load control.The convexification of the consumer reserve provision is examined,and the analytic expression of the optimal solution within each critical region is derived.Then,based on multi-parametric programming,a combinatorial enumeration method in conjunction with efficient reduction and pruning strategy is proposed to compute the optimal response of consumers in the whole price space.Numerical tests along with an application example in the bi-level aggregator pricing problem demonstrate the merit of this method.

Guest Editorial: State Estimation for Future Cyber-physical Power and Energy Systems:Challenges and Solutions

LARGE-SCALE renewable power plants and distributed generators are integrated in the electric power systems on the generation side,meanwhile new demand-response technologies are being deployed on the demand side,such as electric vehicles and energy storage,controllable building energy and integrated energy system of industry park.