Abstract
The energy management in new distribution paradigms are amongst one of core research dimension, particularly in smart grids. This paper proposes a hierarchical energy management system for inter-connected multi-smart buildings with an inclusion of local Power Market. As home appliances have huge contribution in load of buildings, the appliances are scheduled in order to minimize operational cost while taking into account the user comfort and other system constraints. The objectives of this paper aim to minimize operational cost, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, grid dependency while maximize user comfort and revenue. The proposed technique enables a prosumer with two options, either they can sell excess energy to the utility or can bid and sell in market with high price compare to utility. Besides increase in revenue, the consumer is enabled to buy electricity from utility or from local market with low prices compare to utility grid aiming at reducing operational cost. The proposed framework is evaluated across three algorithms namely, JAYA, teacher learning based optimization (TLBO) and Rao1, respectively. As per comparative analysis, the JAYA algorithm outperforms the others in achieving the aimed objectives in-terms of favorable achieved numerical values. Different cases are created in order to test the effectiveness of proposed system. The overall simulation results validate the proposed approach with highest operational cost reduction of 151.48%, peak load reduction 76.76%, grid dependency reduction 95.61%, and minimum emission of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is 3.70 Kg/Day as compare to base case.
Highlights
The Residential buildings are one of the largest consumers of electricity, which uses about 37.4% of total electricity produce in the United States (US) in 2017 and 80% of the electricity in the United Arab Emirates[1] [2]
teacher learning based optimization (TLBO) is better in comparison with achieved cost reduction of 55.18% in case 1
The findings of six different simulation cases shows that by inter-connecting and inclusion of local power market the overall operating cost of system decreases and revenue increases by a prominent amount and this amount depends on the number and combination of energy resources
Summary
The Residential buildings are one of the largest consumers of electricity, which uses about 37.4% of total electricity produce in the United States (US) in 2017 and 80% of the electricity in the United Arab Emirates[1] [2]. An EMS is proposed for interconnected multi energy hubs aimed to minimize the operating costs, carbon emissions and increase in system independency from the utility grid [10]. The proposed mixed integer nonlinear programming (MINLP) model in [15] manages the load and generation in order to minimize the operational cost of energy purchased from the utility grid in buildings. A mixed integer linear programming (MILP) optimization model is proposed to minimize the operational cost and CO2 emissions considering user preference in buildings including DERs [17]. A Grid-Home based EMS framework is proposed for the management of EV charging and discharging with effectively utilizing the photo voltaic (PV) system, in order to minimize consumer operational cost and PV generation curtailment [23].
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