An incentivized auction based group-selling approach for demand response management in V2G systems

Vehicle-to-grid (V2G) system with efficient demand response management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at electric vehicles (EVs). An incentivized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling-based auction. In the proposed approach, a number of aggregators sit on the first-level auction responsible to communicate with a group of EVs. EVs as bidders consider quality of energy (QoE) requirements, and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate that the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation

Electric vehicle as a service (EVaaS):applications, challenges and enablers

Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system

Bi-directional coordination of plug-in electric vehicles with economic model predictive control

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-To-vehicle (G2V) and vehicle-To-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%

Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid. Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating. In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023

Power peak shaving : how to schedule charging of electric vehicles and organize mutually beneficial vehicle to grid (V2G)

This thesis contributes to a project by the Norwegian University of Life Sciences (NMBU) featuring a pilot Vehicle to Grid (V2G) park at Oslo Gardermoen Airport. The goal of the project is two-fold. On one hand, the goal is to provide the airport, a large power consumer who pays power tariffs, with viable measures to shave power peaks and thereby reduce costs. On the other hand, the example of an airport is used to illustrate how V2G can be implemented in a feasible way for EV owners. If successful, this would be advantageous both to power grid operators, to EV owners, and to large power consumers who facilitate EV charging. The thesis approaches power peak shaving by utilizing electric vehicles (EVs) from two angles: Load shifting by scheduling EV charging, and EVs as alternative power supply through vehicle to grid (V2G). EVs with one-way charging capability can be utilized for the first approach, while EVs with two-way charging capability (currently not many) can be utilized for both. In a setting where a large power consumer facilitates long term parking and charging of EVs on its property, both approaches in combination can contribute to reducing power tariffs for the large power consumer. Before V2G is ready for full scale implementation, scheduling the charging is a step in the right direction, and can be seen as ground work for V2G. This thesis presents a Python program demonstrating a method based on scheduling theory, adjusted to minimize simultaneous power demand from EVs, and schedule it outside of expected power peaks. To this author's knowledge, the theory has not been used for this purpose before. While V2G is most commonly regarded from a grid operation perspective, the focus of this thesis is to organize V2G as a mutually beneficial cooperation between representatives of grid interests and the owners of EVs. The technical process that occurs during V2G can be described in very different business terms, depending on perspective. While control based V2G contracts are most commonly considered, stemming from the perspective that the grid operator takes control over (rents) the EV battery to use for V2G, this thesis explores contract designs that regard EV owners as electricity traders, who own the electricity in their battery until they decide to sell it. This leaves more control in the hands of EV owners. Different demand response mechanisms are explored to trigger electricity sale under different circumstances. The thesis concludes with a volume based V2G contract design for the case at Oslo Gardermoen Airport, where EV owners agree to sell a certain electricity volume during a predefined time frame, that the airport may extract when it suits their purposes. Elements from a price based contract, where EV owners define a sales price that triggers a sale when matched by the market price, is also included for certain circumstances. An approach to design V2G contracts for different circumstances can be derived from the discussion.Denne oppgaven bidrar til et prosjekt i regi av Norges Miljø- og Biovitenskapelige Universitet (NMBU), som omhandler et V2G-pilotprosjekt ved Oslo Lufthavn, Gardermoen. Prosjektets mål er todelt. På den ene siden er målet å tilby flyplassen, en stor strømkunde som betaler effekttariffer, virkemidler for å jevne ut effekttopper og dermed redusere kostnader. På den annen side brukes flyplassen som et eksempel på hvordan V2G kan innføres på en gangbar måte for elbileiere. Hvis dette lykkes, vil det komme både nettoperatører, elbileiere og store strømkunder som fasiliterer elbillading, til gode. Oppgaven tilnærmer seg effekttopputjevning ved hjelp av elbiler fra to ulike vinkler: Lastforflytning gjennom tidsplanlegging av elbillading, og elbiler som alternativ kraftforsyning gjennom vehicle to grid (V2G). Elbiler med batterier som kan lades én vei kan brukes til den første tilnærmingen, og elbiler som kan lade to veier (foreløpig ikke mange) kan brukes til begge deler. I tilfeller der en stor strømkunde fasiliterer langtidsparkering og lading av elbiler på eiendommen sin, kan en kombinasjon av begge tilnærmingene bidra til å redusere effekttariffer for strømkunden. Før V2G er modent for innføring i full skala, er tidsplanlegging av ladingen et steg i riktig retning, og kan ses på som forarbeid for V2G. Denne oppgaven presenterer et Python-program som demonstrerer en metode bygget på scheduling-teori, tilpasset til å sikre at minst mulig effekt trekkes samtidig til lading av elbiler, i tillegg til å planlegge det utenfor forventede effekttopper. Såvidt denne forfatteren vet er ikke teorien blitt brukt til dette formålet tidligere. V2G er oftest diskutert sett fra en nettoperatørs perspektiv. Denne oppgaven fokuserer på å organisere V2G som et samarbeid mellom representater for kraftnettets interesser og elbileiere, til gjensidig nytte for begge. Den tekniske prosessen som skjer ved V2G kan beskrives på flere måter i forretningsøyemed, avhengig av perspektiv. V2G er vanligvis diskutert som en kontrollbasert kontrakt, sprunget ut av et perspektiv der nettoperatøren tar kontroll over (leier) elbilbatteriet til V2G-bruk. Oppgaven utforsker kontraktsutforminger som springer ut av et perspektiv der elbileieren anses som en krafthandler, som eier elektrisiteten i sitt eget batteri, og kan velge å selge den. Dette gir elbileieren mer kontroll. Forskjellige etterspørselsrespons-mekanismer utforskes for å utløse salg av elektrisitet under ulike omstendigheter. Oppgaven konkluderer med en volumbasert kontraktsutforming til case-studien ved Oslo Lufthavn, Gardermoen, der elbileiere forplikter seg til å selge et visst elektrisitetsvolum ila. en forhåndsdefinert tilkoblingsperiode. Flyplassen kan kan kjøpe dette volumet på tidspunkt som passer deres formål innenfor den avtalte perioden. Elementer fra en prisbasert kontrakt, der en forhåndsdefinert salgspris utløser et elektrisitetssalg idet markedsprisen matcher den, er også inkludert for visse tilfeller. En tilnærming til V2G-kontraktsutforming til forskjellige sammenhenger kan utledes fra diskusjonen.M-M