{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T11:08:38Z","timestamp":1774264118571,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,7,25]],"date-time":"2017-07-25T00:00:00Z","timestamp":1500940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The optimized dispatch of different distributed generations (DGs) in stand-alone microgrid (MG) is of great significance to the operation\u2019s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL) and combined cooling-heating-power (CCHP) model of micro-gas turbine (MT), a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV), wind turbine (WT), fuel cell (FC), diesel engine (DE), MT and energy storage (ES). Four typical scenarios were designed according to different day types (work day or weekend) and weather conditions (sunny or rainy) in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers\u2019 comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO) to propose modified chaos particle swarm optimization (MCPSO) whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.<\/jats:p>","DOI":"10.3390\/app7080754","type":"journal-article","created":{"date-parts":[[2017,7,25]],"date-time":"2017-07-25T10:04:36Z","timestamp":1500977076000},"page":"754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7332-9726","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China"},{"name":"Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}]},{"given":"Lidong","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Operation and Control of Renewable Energy &amp; Storage Systems, China Electric Power Research Institute, Beijing 100192, China"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Operation and Control of Renewable Energy &amp; Storage Systems, China Electric Power Research Institute, Beijing 100192, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-5355","authenticated-orcid":false,"given":"Miadreza","family":"Shafie-khah","sequence":"additional","affiliation":[{"name":"C-MAST, University of Beira Interior, 6201-001 Covilha\u0303, Portugal"}]},{"given":"Joa\u0303o","family":"Catala\u0303o","sequence":"additional","affiliation":[{"name":"C-MAST, University of Beira Interior, 6201-001 Covilha\u0303, Portugal"},{"name":"INESC TEC and the Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal"},{"name":"INESC-ID, Instituto Superior Te\u0301cnico, University of Lisbon, 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2767","DOI":"10.1109\/TPWRS.2016.2625101","article-title":"Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation","volume":"32","author":"Wan","year":"2017","journal-title":"IEEE Trans. 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