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International audience; Microgrid operations are challenging due to variability in loads and renewable energy generation. Advanced tools capable of taking uncertainty into account are essential to maximize microgrid benefits when operating microgrid owned DERs. This paper proposes a novel optimization model for day-ahead economic dispatch of flexible resources within a microgrid environment, considering uncertainty of PV and loads.This model is conceived to support the microgrid supervisory control layer, providing a security-constrained day-ahead strategy to operate three types of microgrid flexible resources: PV, electric storage and controllable loads. The work presented in this paper introduces a novelty in microgrid operations by presenting a stochastic version of the day ahead scheduling of microgrid DERs to deal with uncertainties associated with PV, load and temperature while considering microgrid network limits and end-user comfort as optimization constraints. An annual analysis quantifies the benefits of to the microgrid-owner of a stochastic formulation over a deterministic one both in terms of ensuring end-user comfort and decreasing operation costs.

Purpose The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation. Design/methodology/approach The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation. Findings Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent. Research limitations/implications The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics. Practical implications This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency. Social implications The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping. Originality/value Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research.

International audience; The high variability and uncertainty introduced into modern electrical distribution systems due to decentralized renewable energy generators requires new solutions for grid management and power quality assurance. One of these possible solutions includes grid integrated energy storage. The appropriate size and placement of decentralized storage is highly dependent on purpose of the battery system and expected operational strategy. However, battery operational strategies are difficult to simulate simultaneously during a sizing and placement planning calculation. The motivation of this paper is to propose an algorithm that is capable of integrating sizing, placement and operational strategies of batteries into an Optimal Power Flow (OPF) distribution grid planning tool. The choice of the OPF approach permits to account for grid constraints which is more adapted for grid-connected storage devices compared to other approaches in the state of the art that are based only on an email balance analysis. This paper presents an alternating current (AC) multi-temporal OPF algorithm that uses a convex relaxation of the power flow equations to guarantee exact and optimal solutions with high algorithmic performance. The algorithm is unique and innovative due to the fact that it combines the simultaneous optimization of placement and sizing of storage devices taking into account load curves, photovoltaic (PV) production profiles, and distribution grid power quality constraints. The choice to invest in battery capacity is highly sensitive to the price of battery systems. The investment in battery systems solely for reducing losses an operational costs was proven not to be cost effective, however when battery systems are allowed to buy and sell electricity based on variable market prices they become cost effective. The assumptions used for this study shows that current battery system prices are too high to be cost effective even when allowing battery system market participation.

International audience; The detailed modeling of distribution grids is expected to be critical to understand the current functionality limits and necessary retrofits to satisfy integration of massive amounts of distributed generation, energy storage devices and the electric consumption demand of the future. Due to the highly dimensional non-convex characteristics of the power flow equations, convex relaxations have been used to ensure an efficient calculation time. However, these relaxations have been proven to be inexact during periods of high RES injection. In this paper additional linear constraints were introduced in the power flow formulation to guaranty an exact relaxation. This convex relaxation is then applied within a multi-temporal algorithm in order to evaluate the benefits of storage grid integration. The case study of a French medium voltage feeder is studied to evaluate the maximum capacity of the grid to host RES sources and the advantages of storage systems in reducing curtailment of RES.

In this paper, we analyze a public dataset of electricity consumption collected over 3,800 households for one year and half. We show that some socio-economic factors are critical indicators to forecast households' daily peak (and total) load. By using a random forests model, we show that the daily load can be predicted accurately at a fine temporal granularity. Differently from many state-of-the-art techniques based on support vector machines, our model allows to derive a set of heuristic rules that are highly interpretable and easy to fuse with human experts domain knowledge. Lastly, we quantify the different importance of each socio-economic feature in the prediction task.

We present a methodology to assess the technical feasibility of building thermal energy reduction strategies from an architecturally diverse building stock that is not metered. While carbon emissions forecasting efforts are typically the domain of planning and policy, the process detailed here can inform institutional decision-making relative to investments in renewable energy, infrastructure, and offsets to further reduce carbon footprint. As a case study, we estimated the Smith College campus building thermal energy losses, an analysis which informed our Sustainability and Climate Action Plan [1]. Due to building specific physical constraints and planned renovations, different thermal envelope improvement scenarios were then considered to estimate the heating energy reduction potential of these envelope improvements. The current total heating energy consumption from 79 of our campus buildings was found to be 57,000 MMBTU/yr. Across the three building categories with minimal existing insulation and poor sealing conditions, the nominal annual thermal energy loss per square foot ranged from 27,000–37,000 BTU/ft2 . Should envelope improvements be made targeting a 5 year simple payback, this annual thermal energy loss would be reduced by 40% to 34,000 MMBTU/yr. More extensive and less cost effective envelope improvements suggest further energy reductions approaching 30,000 MMBTU/yr (between 13,000–23,000 BTU/ft2 /yr depending upon the building type).Copyright © 2010 by ASME

International audience; Renewable energy (RE) integration into distribution grids is becoming more common in the context of the energy transition. The management of wind or solar generation due to their variability and low predictability are challenging for distribution system operators (DSO). To that may be added uncertainties related to electric load profiles. The role of flexibility, coming from decentralized storage devices, will be important for DSOs trying to manage uncertain loads as well as high levels of RE penetration. The introduction of automation and smart metering in distribution grids allows for the optimized management of storage devices to maximize the capability of current infrastructure to integrate RE generators. These optimized management strategies can be calculated with optimal power flow (OPF) algorithms. This paper uses a convex relaxation of the power flow equations to expand the multi-temporal deterministic approach presented in [1] to a stochastic one. The stochastic algorithm implies the integration of a scenario tree to plan the charging and discharging schedule of batteries one day in advance. When comparing deterministic and stochastic operation planning strategies, the stochastic method annually increases total economic benefit by 3.1% while requiring lower annual cycling of the battery therefore increasing battery life.