The Synergies of Shared Autonomous Electric Vehicles with Renewable Energy in a Virtual Power Plant and Microgrid

An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

Ride-hailing with autonomous electric vehicles and shared autonomous electric vehicle (SAEV) systems are expected to become widely used within this decade. These electrified vehicles can be key enablers of the shift to intermittent renewable energy by providing electricity storage to the grid and offering demand flexibility. In order to accomplish this goal, practical smart charging strategies for fleets of SAEVs must be developed. In this work, we present a scalable, flexible, and practical approach to optimise the operation of SAEVs including smart charging based on dynamic electricity prices. Our approach integrates independent optimisation modules with a simulation model to overcome the complexity and scalability limitations of previous works. We tested our solution on real transport and electricity data over four weeks using a publicly available dataset of taxi trips from New York City. Our approach can significantly lower charging costs and carbon emissions when compared to an uncoordinated charging strategy, and can lead to beneficial synergies for fleet operators, passengers, and the power grid.

Systematic Categorization of Optimization Strategies for Virtual Power Plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

Electric Vehicles and Biofuels Synergies in the Brazilian Energy System

Shaping a secure and sustainable energy future may require a set of transformations in the global energy sector. Although several studies have recognized the importance of Electric Vehicles (EVs) for power systems, no large-scale studies have been performed to assess the impact of this technology in energy systems combining a diverse set of renewable energies for electricity production and biofuels in the transportation sector such as the case of Brazil. This research makes several noteworthy contributions to the current literature, including not only the evaluation of the main impacts of EVs’ penetration in a renewable electricity system but also a Life-Cycle Assessment (LCA) that estimates the overall level of CO2 emissions resulted from the EVs integration. Findings of this study indicated a clear positive effect of increasing the share of EVs on reducing the overall level of CO2 emissions. This is, however, highly dependent on the share of Renewable Energy Sources (RES) in the power system and the use of biofuels in the transport sector but also on the credits resulting from the battery recycling materials credit and battery reuse credit. Our conclusions underline the importance of such studies in providing support for the governmental discussions regarding potential synergies in the use of bioresources between transport and electricity sectors.

Energy Scenario Analysis for the Nordic Transport Sector: A Critical Review

Experiencing the highest growth in emissions since 1990 and relying mainly on oil, transport is considered the most complicated sector to decarbonize. Lately, the Nordic countries have shown remarkable success in reducing greenhouse gas (GHG) emissions, especially in the power and heat sector. However, when it comes to transportation, the greatest source of Nordic GHG emissions, stronger measures are needed. Relying on a rich and diversified portfolio of renewable sources and expertise, the Nordic countries could benefit from a common mitigation strategy by encompassing a larger variety of solutions and potential synergies. This article reviews studies addressing integrated energy and transport scenario analysis for the Nordic region as a whole. The studies targeted are those applying energy system models, given their extensive adoption in supporting scenario analysis. Most notable of these studies is the “Nordic Energy Technology Perspectives 2016” to which a special focus is dedicated. The article reviews the methodological choices and the research content of the selected literature. Challenges/limitations are identified in light of recent transport research, and categorized as: “transport behavior”, “breakthrough technologies”, “domestic energy resources” and “geographical aggregation and system boundaries”. Lastly, a list of suggestions to tackle the identified gaps is provided based on the existing literature.

A Two-Step Framework for Energy Local Area Network Scheduling Problem with Electric Vehicles Based on Global–Local Optimization Method

To reduce the fluctuation of renewable energy (RE) supply and improve the economic efficiency of the power grid, the energy local area network (ELAN), which is a subnetwork of the energy internet (EI), plays an important role in specific regions. Electric vehicles (EVs), as virtual energy storage (VES) in ELANs, are helpful to decrease the fluctuations of RE supply. However, how to use EVs in ELANs is a complex issue, considering the uncertainties of EVs’ charging demand, the forecast data errors of RE sources, etc. In this paper, a typical ELAN structure is established, taking into account RE sources, load response system, and a distributed energy storage (DES) system including EVs. A two-step optimization framework for ELAN scheduling problem is proposed. A global optimization model based on forecast data is built to maximize the income of ELAN, and an online local optimization model is introduced to minimize the correction cost utilizing prior knowledge. Finally, the proposed two-step optimization framework is applied to a series of real-world ELAN scheduling problems. The results show that DES system with EVs can reduce the volatility of RE supply evidently, and the proposed method is able to maximize the income of the ELAN efficiently.

A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses

To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual power plant (VPP) with price-based demand response (PBDR). Firstly, a basic scheduling model for the VPP was proposed in this study with the objective of the maximum operation revenue. Secondly, a risk aversion model for the VPP was constructed based on the conditional value at risk (CVaR) method and robust optimization theory considering the operating risk from WPP and PV. Thirdly, a solution methodology was constructed and three cases were considered for comparative analyses. Finally, an independent micro-grid on an industrial park in East China was utilized for an example analysis. The results show the following: (1) the proposed risk aversion scheduling model could cope with the uncertainty risk via a reasonable confidence degree β and robust coefficient Γ. When Γ ≤ 0.85 or Γ ≥ 0.95, a small uncertainty brought great risk, indicating that the risk attitude of the decision maker will affect the scheduling scheme of the VPP, and the decision maker belongs to the risk extreme aversion type. When Γ ∈ (0.85, 0.95), the decision-making scheme was in a stable state, the growth of β lead to the increase of CVaR, but the magnitude was not large. When the prediction error e was higher, the value of CVaR increased more when Γ increased by the same magnitude, which indicates that a lower prediction accuracy will amplify the uncertainty risk. (2) when the capacity ratio of (WPP, PV): ESS was higher than 1.5:1 and the peak-to-valley price gap was higher than 3:1, the values of revenue, VaR, and CVaR changed slower, indicating that both ESS and PBDR can improve the operating revenue, but the capacity scale of ESS and the peak-valley price gap need to be set properly, considering both economic benefits and operating risks. Therefore, the proposed risk aversion model could maximize the utilization of clean energy to obtain higher economic benefits while rationally controlling risks and provide reliable decision support for developing optimal operation plans for the VPP.