Article, 2024

Hierarchical online energy management for residential microgrids with Hybrid hydrogen–electricity Storage System

Applied Energy, ISSN 0306-2619, 1872-9118, Volume 363, Page 123020, 10.1016/j.apenergy.2024.123020

Contributors

Wu, Jingxuan 0000-0003-4277-8550 (Corresponding author) [1] Li, Shuting 0009-0003-1289-227X [1] Fu, Aihui 0000-0002-6116-2260 [2] Cvetković, Milos 0000-0003-4425-8575 [2] Palensky, Peter 0000-0003-3183-4705 [2] Vasquez, Juan C 0000-0001-6332-385X [1] Guerrero, Josep M 0000-0001-5236-4592 [1] [3] [4]

Affiliations

  1. [1] Aalborg University
    2. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD];
  2. [2] Delft University of Technology
    3. [NORA names: Netherlands; Europe, EU; OECD];
  3. [3] Institució Catalana de Recerca i Estudis Avançats
    4. [NORA names: Spain; Europe, EU; OECD];
  4. [4] Universitat Politècnica de Catalunya
    5. [NORA names: Spain; Europe, EU; OECD]

Abstract

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.

Keywords

ES units, FL controller, accurate prediction technique, allocation, battery, battery stack, comparison, control, dataset, decision, decision process, decision-making, demand, demand side, dependence, electricity, energy, energy allocation, energy estimation model, energy management, energy management system, energy shift, energy sources, energy storage, estimation model, expectations, experiments, forecasting, frequency, fuzzy logic energy management system, hybrid, hydrogen, hydrogen energy storage, implementation, implementation of energy management systems, increasing proportion, increasing proportion of renewable energy, load, load forecasting, long-term, management, management system, manner, method, microgrid, model, numerical experiments, online decision-making, online energy management, online energy management system, operating frequency, operation, optimal operation, optimization method, performance, power, power system, prediction, prediction techniques, price expectations, proportion of renewable energy, real-world datasets, renewable energy, renewable energy sources, residential microgrid, resolution, shift, short-term uncertainty, side, source, stack, status, storage, storage system, superiority, system, system status, technique, uncertainty, units

Funders

  • China Scholarship Council

Data Provider: Digital Science

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