17,027 research outputs found

    Simulation support for internet-based energy services

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    The rapidly developing Internet broadband network offers new opportunities for deploying a range of energy, environment and health-related services for people in their homes and workplaces. Several of these services can be enabled or enhanced through the application of building simulation. This paper describes the infrastructure for e-services under test within a European research project and shows the potential for simulation support for these services

    Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

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    The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

    Distributed Market Clearing Approach for Local Energy Trading in Transactive Market

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    This paper proposes a market clearing mechanism for energy trading in a local transactive market, where each player can participate in the market as seller or buyer and tries to maximize its welfare individually. Market players send their demand and supply to a local data center, where clearing price is determined to balance demand and supply. The topology of the grid and associated network constraints are considered to compute a price signal in the data center to keep the system secure by applying this signal to the corresponding players. The proposed approach needs only the demanded/supplied power by each player to reach global optimum which means that utility and cost function parameters would remain private. Also, this approach uses distributed method by applying local market clearing price as coordination information and direct load flow (DLF) for power flow calculation saving computation resources and making it suitable for online and automatic operation for a market with a large number of players. The proposed method is tested on a market with 50 players and simulation results show that the convergence is guaranteed and the proposed distributed method can reach the same result as conventional centralized approach.Comment: Accepted paper. To appear in PESGM 2018, Portland, OR, 201

    Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

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    As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

    Managing Customer Services: Human Resource Practices, Turnover, and Sales Growth

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    This study examines the relationship between human resource practices, employee quit rates, and organizational performance by drawing on a unique nationally representative sample of 354 customer service and sales establishments in the telecommunications industry. Multivariate analyses show that quit rates are lower and sales growth is higher in establishments that emphasize high skills, employee participation in decision-making and in teams, and HR incentives such as high relative pay and employment security. Quit rates partially mediate the relationship between human resource practices and sales growth. These relationships also are moderated by the customer segment that frontline employees serve
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