Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Optimizing plug-in electric vehicle charging in interaction with a small office building

This paper considers the integration of plug-in electric vehicles (PEVs) in micro-grids. Extending a theoretical framework for mobile storage connection, the economic analysis here turns to the interactions of commuters and their driving behavior with office buildings. An illustrative example for a real office building is reported. The chosen system includes solar thermal, photovoltaic, combined heat and power generation as well as an array of plug-in electric vehicles with a combined aggregated capaci-ty of 864 kWh. With the benefit-sharing mechanism proposed here and idea-lized circumstances, estimated cost savings of 5% are possible. Different pricing schemes were applied which include flat rates, demand charges, as well as hourly variable final customer tariffs and their effects on the operation of intermittent storage were revealed and examined in detail. Because the plug-in electric vehicle connection coincides with peak heat and electricity loads as well as solar radiation, it is possible to shift energy demand as desired in order to realize cost savings. --Battery storage,building management systems,dispersed storage and generation,electric vehicles,load management,microgrid,optimization methods,power system economics,road vehicle electric propulsion

OPTIMIZING THE USE OF ENERGY STORAGE AS A DEMAND RESPONSE TOOL

The renewable energies expansion over last years, due to the need to bring electricity production towards ever higher levels of green production and the increase of the demand, have brought further stability problems to the main grid. The handling of the integration of these alternative sources and the optimization of the electricity grid have given high attention on the role of demand response program as a key part for the target. The combination of battery storage units with real-time prices is part of the research effort that aims to reduce the instability of the grid and the energy costs of the users. Literature shows good potential for the control strategies as the relative wide range of technologies developed recently for the scope, even if for the residential customers usually the potential is constrained by the limited controllable loads and their significant share of consumption. However, the aspect of user comfort is not always fully considered leading to less realistic conclusions. The objective of the work described in the dissertation was then to obtain a reduction in residential energy costs through the optimal scheduling of user appliances supported by the use of battery storage, under a real-time price scheme, while limiting the discomfort for the customer. Although the first results of applying a real time pricing scheme based on the current variations in price observed in the Iberian wholesale market led only to small profits when not considering additional self-generation, they increased significantly if a small photovoltaic based production is considered, and reached significant cost savings (circa 70%) in periods of high solar generation. But, when applying a real time price following the fluctuations of the renewable energy supply, which produced much higher variations in price, the results improved considerably, reaching cost savings as high as 85%. The implemented model shows the true relevance of Demand Response and Energy Storage, producing meaningful savings if the supply costs change with the availability of renewable energy supply. With self-generation, the obtained value is even higher in the perspective of the individual customer, maximizing the cost-effectiveness of such investment

Factoring Electrochemical and Full-Lifecycle Aging Modes of Battery Participating in Energy and Transportation Systems

Transportation electrification emerges as a pivotal strategy to realize deep decarbonization for many countries, and the central part of this is battery. However, a key challenge often overlooked is the impact of battery aging on the economy and longevity of electric vehicles (EVs). To address this issue, the paper proposes a novel battery full-life degradation (FLD) model and energy management framework that substantially improves the overall economic efficiency of Battery Energy Storage Systems (BESS). In the first stage, battery electrochemical aging features are modeled by learning cell fading rate under various healthy states, capitalized on the Stanford experimental open dataset. Accordingly, a lifecycle degradation model is then developed considering various operational conditions and aging stages to quantitatively assess the effects of depth of discharge, C-rate, state of health, and state of charge. In the second stage, battery electrochemical aging features are integrated into vehicle energy management so that batteries under different fading rates can be flexibly utilized during whole lifecycles. The proposed methods are validated on a practical UK distribution network and a hybrid vehicles hardware-in-the-loop platform. With the proposed methods, EV users can make informed decisions to optimize energy usage and prolong the lifespan of vehicle BESS, thereby fostering a more sustainable and efficient transportation infrastructure.</p

Factoring Electrochemical and Full-Lifecycle Aging Modes of Battery Participating in Energy and Transportation Systems

Transportation electrification emerges as a pivotal strategy to realize deep decarbonization for many countries, and the central part of this is battery. However, a key challenge often overlooked is the impact of battery aging on the economy and longevity of electric vehicles (EVs). To address this issue, the paper proposes a novel battery full-life degradation (FLD) model and energy management framework that substantially improves the overall economic efficiency of Battery Energy Storage Systems (BESS). In the first stage, battery electrochemical aging features are modeled by learning cell fading rate under various healthy states, capitalized on the Stanford experimental open dataset. Accordingly, a lifecycle degradation model is then developed considering various operational conditions and aging stages to quantitatively assess the effects of depth of discharge, C-rate, state of health, and state of charge. In the second stage, battery electrochemical aging features are integrated into vehicle energy management so that batteries under different fading rates can be flexibly utilized during whole lifecycles. The proposed methods are validated on a practical UK distribution network and a hybrid vehicles hardware-in-the-loop platform. With the proposed methods, EV users can make informed decisions to optimize energy usage and prolong the lifespan of vehicle BESS, thereby fostering a more sustainable and efficient transportation infrastructure.</p

Application of a simplified thermal-electric model of a sodium-nickel chloride battery energy storage system to a real case residential prosumer

Recently, power system customers have changed the way they interact with public networks, playing a more and more active role. End-users first installed local small-size generating units, and now they are being equipped with storage devices to increase the self-consumption rate. By suitably managing local resources, the provision of ancillary services and aggregations among several end-users are expected evolutions in the near future. In the upcoming market of household-sized storage devices, sodium-nickel chloride technology seems to be an interesting alternative to lead-acid and lithium-ion batteries. To accurately investigate the operation of the NaNiCl2 battery system at the residential level, a suitable thermoelectric model has been developed by the authors, starting from the results of laboratory tests. The behavior of the battery internal temperature has been characterized. Then, the designed model has been used to evaluate the economic profitability in installing a storage system in the case that end-users are already equipped with a photovoltaic unit. To obtain realistic results, real field measurements of customer consumption and solar radiation have been considered. A concrete interest in adopting the sodium-nickel chloride technology at the residential level is confirmed, taking into account the achievable benefits in terms of economic income, back-up supply, and increased indifference to the evolution of the electricity market

A Real-time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling

With the increasing consumption of energy, it is of high significance to improve energy efficiency and realize optimal operation of the multi-energy system. Among the many energy system modeling methods, the concept of “energy hub (EH)” is an emerging one. However, the previous EH models only included one or a few of constituting components. The construction of an energy hub model that integrates energy storage systems, photovoltaic (PV) components, a combined cooling heating and power (CCHP) system and electric vehicles (EVs) is explained in this thesis. The inclusion of the CCHP system helps to meet the energy demand and improve the mismatch of heat-to-electric ratio between the energy hub and the load. Additionally, vehicle-to-grid (V2G) technology is applied in this EH; that is, EVs are regarded not only as load demands but also as power suppliers. The energy hub optimization scheduling problem is formulated as a multi-period stochastic problem with the minimum total energy cost as the objective. Compared to 24-hour day-ahead scheduling, rolling horizon optimization is used in the EH scheduling and shows its superiority. In real-time rolling horizon scheduling, the optimization principle ensured that the result is optimized each moment, so it avoids energy waste caused by overbuying energy. As part of electricity loads, EVs have certain influence on energy hub scheduling. However, due to the randomness of the driving patterns, it is still very difficult to perfectly predict the driving consumption and the charging availability of the EVs one day in advance. Chance constrained programming can hedge the risk of uncertainty for a big probability and drop the extreme case with a very low probability. By restricting the probability of chance constraints over a specific level, the influence of the uncertainty of electric vehicle charging behavior on energy hub scheduling can be reduced. Simulation results show that the energy hub optimization scheduling with chance constrained programming results in a less energy cost and it can make better use of time-varying PV energy as well as the peak-to-valley electricity price

A Real-Time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling

With the increasing consumption of energy, it is of high significance to improve energy efficiency and realize optimal operation of the multi-energy system. Among the many energy system modeling methods, the concept of “energy hub (EH)” is an emerging one. However, the previous EH models only included one or a few of constituting components. The construction of an energy hub model that integrates energy storage systems, photovoltaic (PV) components, a combined cooling heating and power (CCHP) system and electric vehicles (EVs) is explained in this thesis. The inclusion of the CCHP system helps to meet the energy demand and improve the mismatch of heat-to-electric ratio between the energy hub and the load. Additionally, vehicle-to-grid (V2G) technology is applied in this EH; that is, EVs are regarded not only as load demands but also as power suppliers. The energy hub optimization scheduling problem is formulated as a multi-period stochastic problem with the minimum total energy cost as the objective. Compared to 24-hour day-ahead scheduling, rolling horizon optimization is used in the EH scheduling and shows its superiority. In real-time rolling horizon scheduling, the optimization principle ensured that the result is optimized each moment, so it avoids energy waste caused by overbuying energy. As part of electricity loads, EVs have certain influence on energy hub scheduling. However, due to the randomness of the driving patterns, it is still very difficult to perfectly predict the driving consumption and the charging availability of the EVs one day in advance. Chance constrained programming can hedge the risk of uncertainty for a big probability and drop the extreme case with a very low probability. By restricting the probability of chance constraints over a specific level, the influence of the uncertainty of electric vehicle charging behavior on energy hub scheduling can be reduced. Simulation results show that the energy hub optimization scheduling with chance constrained programming results in a less energy cost and it can make better use of time-varying PV energy as well as the peak-to-valley electricity price

EcoBlock: Grid Impacts, Scaling, and Resilience

Widespread deployment of EcoBlocks has the potential to transform today's electricity system into one that is more resilient, flexible, efficient and sustainable. In this vision, the system will consist of self- su cient, renewable-powered, block-scale entities that can deliberately adjust their net power exchange and can optimize performance, maintain stability, support each other, or disconnect entirely from the grid as needed. This report is intended as an independent analysis of the potential relationships, both constructive and adverse, between EcoBlocks and the grid