A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study.In this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg game.Operator holds a large-scale ESS that is shared among users in the form of energy transactions.It sells energy to users and sets the selling price first.It maximizes its profit through optimal pricing and ESS dispatching.Users purchase some energy from operator for the reduction of their demand charges after operator's selling price is announced.This game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization model.The upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'costs.The bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing techniques.Case studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

Demand Charge under Nonsinusoidal Conditions

This paper aims to present a new point of view about the active power measurement, for billing purposes, measured at the PCC （point of common coupling） between the utility and the consumer when harmonic distortions are involved. Depending on theorigin of it, the active power can result in higher or lower values in comparison to the fundamental component. The consequences are higher costs for the consumer or losses for the electric utility. Using computational simulations and theoretical analysis, these aspects are evaluated and compared.

Secure and efficient prediction of electric vehicle charging demand usingα^(2)-LSTM and AES-128 cryptography

In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging requirements.This prediction plays a crucial role in evaluating its impact on the power grid,encompassing power management and peak demand management.In this paper,a novel deep neural network based onα^(2)-LSTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time resolution.Additionally,we employ AES-128 for station quantization and secure communication with users.Our proposed algorithm achieves a 9.2%reduction in both the Root Mean Square Error(RMSE)and the mean absolute error compared to LSTM,along with a 13.01%increase in demand accuracy.We present a 12-month prediction of EV charging demand at charging stations,accompanied by an effective comparative analysis of Mean Absolute Percentage Error(MAPE)and Mean Percentage Error(MPE)over the last five years using our proposed model.The prediction analysis has been conducted using Python programming.

An Electricity Demand-Based Planning of Electric Vehicle Charging Infrastructure

The construction of charging infrastructure is an important prerequisite for the development of electric vehicles （EVs）. In this paper, the classification of charging vehicle models and charging infrastructure was firstly summarized, and the optimal charging mode of each type of EV model and the total electicity demand of charging were then analyzed. Combined with the general principle of the development and application of new energy vehicles in the city H, the model of electric vehicle charging infrastructure planning was designed. The case we proposed fully proved the effectiveness of the model.

Estimation of charging demand for electric vehicles by discrete choice models and numerical simulations: Application to a case study in Turin

The electrification of vehicles is considered one of the most important strategies for addressing the issues related to energy dependence and climate change.To meet user needs,electric vehicle(EV)management for charging operations is essential.This study uses modelling and simulation of EV user behaviour to forecast possible scenarios for electric charging in cities and to identify potential management problems and opportunities for improvement of EVs and EV charging infrastructures.The conurbation of Turin was selected as a case study to reproduce realistic scenarios by applying discrete choice modelling based on socio-economic and transport system data.One of objectives of the study was to describe user charging behaviour from a geographic perspective to model where users prefer to charge in the area studied according to the variables that may affect decisions.Another objective was to estimate the number of electric vehicles in Turin and the characteristics of their users,both of which are helpful in understanding electric mobility within a city.Analysing these behavioural issues in a modelling framework can provide a set of tools to compare and evaluate a variety of possible modifications,indicating an adequate network of charging infrastructure to facilitate the diffusion of electric vehicles.

Technical review of electric vehicle charging distribution models with considering driver behaviors impacts

Amassive market penetration of electric vehicles(EVs)associated with nonnegligible energy consumption and environmental issues has imposed a big challenge on evaluating electrical power distribution and related transportation facilities improvement in response to the largescale EV charging service need.Strategical deployment of EV charging stations including location and determination ofnumber of slowcharging stations and fast charging stationshas become an emerging concern and one of the most pressing needs in planning.This paper conducts a comprehensive survey of EV charging demand and distribution models with consideration of realistic driver behaviors impacts.This is currently a shortage in academic literature,but indeed has drawn practical attention in the strategic planning process.To address the need,this paper presents an in-depth literature review of relevant studies that have identified different types of EV charging facilities,needs or concerns that are considered into EV charging demand and distribution modeling,alongside critical impacting factor identification,mathematical relationshipsof the contributing factorsandEVchargingdemand and distribution modeling.Key findings from the current literature are summarized with strategies for optimized plan of charging station deployments(i.e.,location and related number of charging station),in an attempt to provide a valuable reference for interested readers.

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution network operation.Methods:This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs.The solution is designed to ensure the reliable and secure operation of the active power distribution networks,the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand,as the PEVs can be considered as mobile energy storage units.Results:As a result,the charging demands of PEVs are optimally scheduled temporally and spatially,which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing(RTP).Conclusions:The proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.