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

SEEV4City INTERIM 'Summary of the State of the Art' report

This report summarizes the state-of-the-art on plug-in and full battery electric vehicles (EVs), smart charging and vehicle to grid (V2G) charging. This is in relation to the technology development, the role of EVs in CO2 reduction, their impact on the energy system as a whole, plus potential business models, services and policies to further promote the use of EV smart charging and V2G, relevant to the SEEV4-City project

Prospects for Electric Mobility: Systemic, Economic and Environmental Issues

The transport sector, which is currently almost completely based on fossil fuels, is one of the major contributors to greenhouse gas emissions. Heading towards a more sustainable development of mobility could be possible with more energy efficient automotive technologies such as battery electric vehicles. The number of electric vehicles has been increasing over the last decade, but there are still many challenges that have to be solved in the future. This Special Issue “Prospects for Electric Mobility: Systemic, Economic and Environmental Issues” contributes to the better understanding of the current situation as well as the future prospects and impediments for electro mobility. The published papers range from historical development of electricity use in different transport modes and the recent challenges up to future perspectives

Demand Response Management and Control Strategies for Integrated Smart Electricity Networks

Demand Response (DR) programs are being introduced by some electricity grid operators as resource options for curtailing and reducing the demand of electricity during certain time periods for balancing supply and demand. DR is considered as a class of demand-side management programs, where utilities offer incentives to end-users to reduce their power consumption during peak periods. DR is, indeed, a promising opportunity for consumers to control their energy usage in response to electricity tariffs or other incentives from their energy suppliers. Thus, successful execution of a DR program requires the design of efficient algorithms and strategies to be used in the utility grid to motivate end-users to actively engage in residential DR. This thesis studies DR management using machine learning techniques such as Reinforcement Learning (RL), Fuzzy Logic (FL) and Neural Networks (NN) to develop a Home Energy Management System (HEMS) for customers, construct an energy customer behaviour framework, investigate the integration of Electrical Vehicles (EVs) into DR management at the home level and the provision of ancillary services to the utility grid such as Frequency Regulation (FR), and build effective pricing strategies for Peer-to-Peer (P2P) energy trading. In this thesis, we firstly proposed a new and effective algorithm for residential energy management system using Q-learning method to minimise the electricity bills and maximise the user’s satisfaction. The proposed DR algorithm aims to schedule household appliances considering dynamic electricity prices and different household power consumption patterns. Moreover, a human comfort-based control approach for HEMS has been developed to increase the user’s satisfaction as much as possible while responding to DR schemes. The simulation results presented in this Chapter showed that the proposed algorithm leads to minimising energy consumption, reducing household electricity bills, and maximising the user’s satisfaction. Secondly, with the increasing electrification of vehicles, emerging technologies such as Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, EVs become as distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Therefore, we proposed an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations studies are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue. In addition, the potential benefits of EVs battery and V2G technology to provide grid frequency response services have also been investigated. We have designed an optimal real-time V2G control strategy for EVs to perform supplementary frequency regulation using Deep Deterministic Policy Gradient (DDPG). The main feature that distinguishes the proposed approach from previous related works is that the scheduled charging power of an individual EV is optimally tracked and adjusted in real-time to fulfil the charging demand of EV's battery at the plug-out time without using the forced charging technique to maximise the frequency regulation capacity. Finally, a Peer-to-Peer (P2P) model for energy transaction in a community microgrid has been proposed. The concept of P2P energy trading can promote the implementation of DR by providing consumers with greater control over their energy usage, incentivising them to manage their energy consumption patterns in response to changes in energy supply and demand. It also stimulates the adoption of renewable energy sources. The proposed P2P energy-sharing mechanism for a residential microgrid with price-based DR is designed to engage individual customers to participate in energy trading and ensures that not a single household would be worse off. The proposed pricing mechanism is compared with three popular P2P energy sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and Bill Sharing (BS) considering different types of peers equipped with solar Photovoltaic (PV) panels, EVs, and domestic energy storage systems. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network

Business Models for SEEV4-City Operational Pilots: From a generic SEEV4-City business model towards improved specific OP business models

This report, led by Northumbria University, provides a final analysis by project partners regarding Business Models for SEEV4-City Operational pilots. It is part of a collection of reports published by the project covering a variation of specific and cross-cutting analysis and evaluation perspectives and spans 6 operational pilots