A Hybrid Incentive Program for Managing Electric Vehicle Charging Flexibility

With the mass roll-out of electric vehicles (EVs) and rapid progress in battery technology, utilizing EV charging flexibility has become a promising solution for supporting economic and secured power system operations. This work proposes a novel hybrid incentive program, which encourages EV owners to sell their charging flexibility to a charging station (CS) and achieve a win-win situation for both EV owners and the CS. Unlike existing approaches, the proposed hybrid incentive program is simultaneously featured with simplicity, consistency, and controllability. To determine the incentive payment parameters, an optimal incentive price selection model is developed. In the solution methodology, we first linearize the original problem, then develop an adaptive ADMM algorithm to efficiently solve the formulated problem. Case studies confirm the superiority of the proposed hybrid incentive program over the state-of-the-arts, achieving 22.51% of EV owners’ cost reduction, 31.18% of energy market bill reduction, and 64.13% of potential charging flexibility utilization

マルチチャネルスキームと階層的リソーススケジューリングを備えた車両のインターネットのための効率的なブロックチェーンに向けて

電気通信大学202

No show passengers prediction system based on computational inteligence techniques

Тема овог рада представља предлог система за предвиђање путника који се неће појавити на лету („no-show“), који се заснива на техникама рачунарске интелигенције. Предвиђање броја „no-show“ путника представља специфичан и уско формулисан проблем који је већ дужи низ година веома актуелан у авио индустрији како са теоријског, тако и са практичног аспекта. На основу очекиваног броја „no show“ путника, као и других фактора, авио компаније доносе одлуку о додатном броју места који ће бити доступан кроз резервациони систем. На овај начин, авио компаније могу остварити додатан профит, поготову када се ради о летовима који су попуњени у потпуности. Предложени систем за предвиђање “no-show“ путника се састоји од две компоненте. Прва компонента се односи на избор најпрецизнијег модела за предвиђање, а друга на примену и валидацију система. Модел за предвиђање се састоји из алгоритма који се заснива на техници закључивања на основу случаја и интерполативне Булове алгебре. Даље, модел комбинује предлог који је генерисан од стране алгоритма и предлог који препоручује експерт. На овај начин предложени систем обједињује и узима у обзир објективну и субјективну димензију приликом предвиђања. Сличност између летова се израчунава коришћењем традиционалних мера (Eуклидска и Mенхетн) и ИБА мере сличности. Такође, ИБА приступ употпуњује традиционални алгоритам технике закључивања на основу случаја кроз омогућавање логичке агрегације вредности, односно моделовањем постојећих нелинеарних зависности између података. Примена предложеног система је представљена коришћењем података о лету на релацији Београд - Амстердам, за период од годину дана. Добијени резултати показују да је неопходно укључити препоруку експерта у процес предвиђања, као и да сам алгоритам није довољан да би се добили довољно прецизни резултати. Такође, добијени резултати указују да су модели који су засновани на ИБА приступу и који комбинују резултате алгоритма и препоруку експерта, прецизнији од модела који користе традиционалне мере за израчунавање сличности. Сходно томе, потврђено је да логички приступ моделовању сличности представља перспективан правац примене у оквируviii технике закључивања на основу случаја. Са практичне стране, предложено решење је једноставно за разумевање у погледу функционисања, и може се доста једноставно имплементирати и прилагодити специфичностима и операцијама авио компаније.In this doctorial dissertation no-show passengers prediction system based on computational intelligence techniques is proposed. Predicting no-show passengers represents a specific and concisely formulated problem that actively persists for a longer period of time in the airline industry from both theoretical and practical perspective. Based on the expected number of no-show passengers, as well as some other factors, airlines are making decisions about how many additional seats will be allowed for overbooking through reservation system. This way, airlines could make additional profit, especially when it comes to the high demanding flights that are fully booked. Proposed prediction system for no-show passengers consists of two major components. First component considers selecting the best performing prediction model from the available model pool, and the second component is related to the model validation and application. Prediction model is based on the algorithm that combines case based reasoning technique and interpolative Boolean algebra (IBA) approach. Furthermore, model combines prediction recommendation generated by algorithm and recommendation provided from the domain expert. This way, the proposed system considers and takes into account both objective and subjective dimension. Similarity between flights is determined using traditional metrics (Euclidean and Manhattan) and IBA similarity measure. Also, IBA approach is enhancing the conventional CBR algorithm by enabling logical aggregation of values, i.e. capturing existing nonlinear dependencies in the data. The usage of the proposed system is illustrated in the numerical example regarding a single leg flight on the Belgrade-Amsterdam route and covers a one-year period. The obtained results show the necessity to include expert knowledge in prediction process, i.e. the CBR algorithm used alone is insufficient to produce results that are accurate enough. Furthermore, the results are indicating that the IBA-based models that combine the results of the CBR algorithm and expert recommendations perform better than distance-based models. Therefore, it is confirmed that the logicbased approach of similarity modelling is the prospective direction within the CBR algorithm. From a practical side, proposed solution is easy for understanding from thex functional aspect, and could be easily implemented and adjusted according to airline operations

Economic Operation of Virtual Power Plants with Electric Vehicle Charging Stations

Energy management of distributed energy resources (DERs) is challenging due to the distributed and uncertain nature of DERs. To optimally operate DERs and trade their energy as well as energy flexibility for financial benefits, energy management for virtual power plants (VPPs) and electric vehicle (EV) charging stations are investigated in this thesis. The research in this thesis can be summarized into three parts. Part I provides a VPP operation strategy in the electricity market environment. Part II proposes an EV charging station operation strategy considering EV user incentives. Part III develops a coordinated VPP and EV charging station operation framework based on the methods proposed in parts I and II. (1) Economic VPP operation In this part, an optimal VPP operation regime is proposed considering multiple electricity markets and multiple uncertainties. The proposed operation regime handles both the VPP market bidding and unit dispatching problems. To deal with uncertainties, a hybrid stochastic minimax regret optimization model is proposed. To reduce the conservativeness of the formulated optimization models, a self-adaptive algorithm is proposed. (2) Economic EV charging station operation In this part, an EV charging station operation strategy with an EV user incentive program is proposed to improve the EV charging station economic benefit. To maximize the long-term profit of the EV charging station, an optimal incentive price selection model is developed. In the solution methodology, a problem linearization method is first proposed. Then, a distributed solution methodology is developed based on the proposed adaptive alternating-direction-methodof-multipliers algorithm. (3) Economic VPP operation considering EV charging stations i In this part, a multi-stakeholder VPP-charging station system is investigated. Firstly, a coordinated operation framework is proposed for the VPP-charging station system to maximize the total benefit of the system. Then, an improved EV user incentive program is proposed for acquiring EV energy flexibility. At the cost allocation stage, a τ -value cost allocation method is developed. To alleviate the computation burden in calculating the τ -values, a τ -values estimation approach is proposed. The effectiveness of the energy management methods proposed in this thesis is verified through theoretical analysis and numerical simulations. Significant results suggest high potential for practical application in certain scenarios