3,795 research outputs found

    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

    Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles

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    In this paper, a multi-objective optimization method based on the normal boundary intersection is proposed to solve the dynamic economic dispatch with demand side management of individual residential loads and electric vehicles. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times and electric vehicle charging requirements. The multi-objectives of minimizing generation costs, emissions, and energy loss in the system are balanced in a Pareto front approach in which a fuzzy decision making method has been implemented to find the best compromise solution based on desired system operating conditions. The normal boundary intersection method is described and validated

    Coordinated Smart Home Thermal and Energy Management System Using a Co-simulation Framework

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    The increasing demand for electricity especially during the peak hours threaten the grid reliability. Demand response (DR), changing the load pattern of the consumer in response to system conditions, can decrease energy consumption during periods of high wholesale market price and also maintain system reliability. Residential homes consume 38% of the total electric energy in the U.S., making them promising for DR participation. Consumers can be motivated to participate in DR programs by providing incentives (incentive-based DR), or by introducing a time-varying tariff for electricity consumption (price-based DR). A home energy management system (HEMS), an automated system which can alter the residential consumer’s energy consumption pattern based on the price of electricity or financial incentives, enables the consumers to participate in such DR programs. HEMS also should consider consumer comfort during the scheduling of the heating, ventilation, and air conditioning (HVAC) and other appliances. As internal heat gain of appliances and people have a significant effect in the HVAC energy consumption, an integrated HVAC and appliance scheduling are necessary to properly evaluate potential benefits of HEMS. This work presents the formulation of HEMS considering combined scheduling of HVAC and appliances in time-varying tariff. The HEMS also considers the consumer comfort for the HVAC and appliances while minimizing the total electricity cost. Similarly, the HEMS also considers the detailed building model in EnergyPlus, a building energy analysis tool, to evaluate the effectiveness of the HEMS. HEMS+, a communication interface to EnergyPlus, is designed to couple HEMS and EnergyPlus in this work. Furthermore, a co-simulation framework coupling EnergyPlus and GridLAB-D, a distribution system simulation tool, is developed. This framework enables incorporation of the controllers such as HEMS and aggregator, allowing controllers to be tested in detail in both building and power system domains. Lack of coordination among a large number of HEMS responding to same price signal results in peak more severe than the normal operating condition. This work presents an incentive-based hierarchical control framework for coordinating and controlling a large number of residential consumers’ thermostatically controlled loads (TCLs) such as HVAC and electric water heater (EWH). The potential market-level economic benefits of the residential demand reduction are also quantified

    Vehicle-to-anything: a power transfer perspective for vehicle electrification

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    The concept of vehicle-to-anything (V2X) is mainly focused on the bidirectional communication between any technology of vehicle and any external system that can contribute for its operation. However, prospecting the vehicle electrification, this concept can also be associated with the power transfer between an electric vehicle (EV) and any external system, where bidirectional communication is absolutely fundamental. Within the power transfer, the possibility of exchanging active power between an EV and the power grid is considered as a promising operation mode, especially considering the possibility of selling demand response services for the electrical power grid. Contemplating the vehicle electrification context, in addition to the latent possibility of interaction between EVs and the power grid for active power exchange, other possibilities of interaction can also be considered, providing advantageous services for the power grid. Thus, this article approaches the V2X concept for off-board systems in the power transfer perspective for vehicle electrification, aggregating new contributions related with the interaction between an EV and any external electrical system (operating as source or load), and both from on-grid or off-grid point of view. Contributions are meticulously presented, recognizing their advantages and disadvantages in a real-scenario of operation. A comparison in terms of cost of implementation and in terms of efficiency is presented considering the various solutions of the vehicle electrification in a smart grid perspective.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation ‐ COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT‐Fundação para a Ciência e a Tecnologia, within project SAICTPAC/0004/2015‐ POCI‐01‐0145‐FEDER‐016434. Mr. Tiago Sousa is supported by the doctoral scholarship SFRH/BD/134353/2017 granted by the Portuguese FCT agency. This work is part of the FCT project POCI-01-0145-FEDER-030283

    Energy Management System for Smart Homes:Modeling, Control, Performance and Profit Assessment

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