Salvador Acha

Effects of optimised plug-in hybrid vehicle charging strategies on electric distribution network losses

Distribution network operators (DNOs) require assessment tools on the tradeoffs plug-in hybrid vehicle (PHEV) technology will have on their assets. This paper employs a time coordinated optimal power flow (TCOPF) formulation to show that, through the control of PHEV storage units and tap-changers (OLTCs), electric network operators can influence savings in energy losses. Case studies are performed in which PHEV units are constrained by various charging and discharging strategies. Results indicate how DNOs can value the storage available in their networks by the way it is dispatched for each time interval. The storage resources furthest away from the grid supply point (GSP) are managed more carefully due to their greater ability to reduce transmission losses at moments of peak demand. The TCOPF tool offers a fresh perspective for stakeholders wishing to evaluate the impacts PHEVs can have on operational aspects such as load profile variation, energy loss reduction, and peak shaving.

Optimal charging strategies of electric vehicles in the UK power market

In order to gain the most from their deployment, it is imperative for stakeholders to exploit the main benefits electric vehicles bring to utilities. Therefore, this paper focuses on the aspects required to model the management of electricity supply for electric vehicles. The framework presented details a time coordinated optimal power flow (TCOPF) tool to illustrate the tradeoffs distribution network operators (DNO) might encounter when implementing various load control approaches of electric vehicles. Within an UK context, a case study is performed where the TCOPF tool functions as the intermediary entity that coordinates cost-effective interactions between power markets, network operators, and the plugged vehicles. Results depict the stochastic but optimal charging patterns stakeholders might visualise from electric vehicles in local networks as they are operated to reduce energy and emission costs. Furthermore, results show current emission costs have a negligible weight in the optimisation process when compared to wholesale electricity costs.

Modelling spatial and temporal agent travel patterns for optimal charging of electric vehicles in low carbon networks

The ability to determine optimal charging profiles of electric vehicles (EVs) is paramount in developing an efficient and reliable smart-grid. However, so far the level of analysis proposed to address this issue lacks combined spatial and temporal elements, thus making mobility a key challenge to address for a proper representation of this problem. This paper details the principles applied to represent optimal charging of EVs by employing an agent-based model that simulates the travelling patterns of vehicles on a road network. The output data is used as a reliable forecast so an optimal power flow model can devise optimal charging scenarios of EVs in a local electrical network. The effectiveness of the model is illustrated by presenting a multi-day case study in an urban area. Results show a high level of detail and variability in EV charging when a present-day carbon fuel mix is compared to one with lower carbon intensity.

Impacts of plug-in hybrid vehicles and combined heat and power technologies on electric and gas distribution network losses

Distribution network operators (DNOs) require strategies that can offset the tradeoffs new embedded technologies have on their assets. This paper employs modelling to show that through control device manipulation, gas and electric (G&E) network operators can influence savings in energy losses under the presence of plug-in hybrid vehicles (PHEVs) and combined heat and power technologies (CHPs). An integrated gas and electric optimal power flow (OPF) tool is introduced to undertake various case studies. The OPF tool evaluates the technical impacts experienced in the networks when DNOs apply a “plug and forget” operation strategy and then compares the results against a “loss minimisation” strategy. Results show the benefits in applying different strategies are more considerable in electric networks than in gas networks. The study corroborates that an integrated G&E analysis offers a fresh perspective for stakeholders in evaluating energy service networks performance under different operation strategies.

Techno-economical tradeoffs from embedded technologies with storage capabilities on electric and gas distribution networks

The deployment of plug-in hybrid vehicles (PHEVs) and micro-combined heat and power (μ-CHPs) technologies creates the opportunity for these units to be optimally operated under various control schemes to enhance electric grid operation. In addition, if vehicle-to-grid (V2G) and thermal storage features are considered, these embedded technologies could have even a greater impact on network performance. This paper employs an integrated electric and gas time coordinated optimal power flow (TCOPF) to illustrate the techno-economical tradeoffs that energy service network operators might encounter under various load control approaches. A case study is assessed under various formulations in which the TCOPF acts as the intermediary entity that manages cost-effective interactions between the connected technologies and the distribution network operators (DNOs). Results show considerable benefits in electric networks while simultaneously having mild side-effects in gas networks. The TCOPF offers a fresh perspective for stakeholders wishing to successfully integrate distributed resources with energy utilities.

Simulating residential electricity and heat demand in urban areas using an agent-based modelling approach

Cities account for around 75% of the global energy demand and are responsible for 60-70% of the global greenhouse gasses emissions. To reduce this environmental impact it is important to design efficient energy infrastructures able to deal with high level of renewable energy resources. A crucial element in this design is the quantitative understanding of the dynamics behind energy demands such as transport, electricity and heat. In this paper an agent-based simulation model is developed to generate residential energy demand profiles in urban areas, influenced by factors such as land use, energy infrastructure and user behaviour. Within this framework, impact assessment of low carbon technologies such as plug-in electric vehicles and heat pumps is performed using London as a case study. The results show that the model can generate important insights as a decision support tool for the design and planning of sustainable urban energy systems.

Modelling real-time pricing of electricity for energy conservation measures in the UK commercial sector

Electricity bills in the UK are increasing year after year due to power market conditions and they will most likely continue to rise. These high costs are reducing the profitability of businesses and thus efforts on understanding and mitigating these charges are a key concern for companies in order to improve their bottom line. This paper focuses on detailing a comprehensive bottom-up model of electricity commercial bills that generates real-time price curves; thus allowing customers to comprehend the true cost of the electricity they consume. The model provides profiles for different UK regions across various seasons. These insights are valuable because they can be used to inform more accurately energy efficiency programs in terms such as return on investment. By knowing where energy is more expensive it makes it easier to prioritize investments. Results overall show Yorkshire has the highest rates, while the South West has the most expensive peaks. Meanwhile, London and Southern England have the cheapest rates.

Incorporating life cycle assessment indicators into optimal electric vehicle charging strategies: An integrated modelling approach

Abstract Transport electrification is one of the most attractive solutions for the decarbonisation of light-duty European transport. However, the environmental evaluation of plug-in electric vehicles (PEVs) presents challenges as the precise footprint depends on complex factors such as driving and charging behaviour and the source of the electricity used to recharge the PEVs. In this paper, an integrated modelling approach is developed to assess the environmental impact of different PEV charging strategies using an agent-based simulation combined with a life-cycle assessment and a multi-objective optimisation taking environmental and economic indicators into account. This integrated approach is presented and tested using a case study in London.