Single and multi-objective operation management of micro-grid using krill herd optimization and ant lion optimizer algorithms

International Journal of Energy and Environmental Engineering https://doi.org/10.1007/s40095-018-0266-8 ORIGINAL RESEARCH Single and multi‑objective operation management of micro‑grid using krill herd optimization and ant lion optimizer algorithms Ahmed Fathy1,2 · Almoataz Y. Abdelaziz3 Received: 5 November 2017 / Accepted: 19 February 2018 © The Author(s) 2018. This article is an open access publication Abstract In this paper, two recent heuristic optimization algorithms are presented to optimally manage the operation of the microgrid (MG) with installed renewable energy sources (RESs); krill herd (KH) optimization and ant lion optimizer (ALO) algorithms. The first algorithm is used for solving single-objective function represents either total operation cost or total pollutant emission injected from the installed generating units while ALO is applied to solve the multi-objective function of both total operating cost and emission. The problem is formulated as nonlinear constrained objective function with equality and inequality constraints. In this work; the devices installed in MGs are photovoltaic panel (PV), wind turbine (WT), microturbine (MT), fuel cell (FC), battery and grid. Two scenarios are studied; the first one is optimizing MG with installing all RESs within specified limits in addition to grid, while the second scenario is operating both PV and WT at their rated powers. The obtained results are compared with different reported algorithms like genetic algorithm (GA), Fuzzy self-adaptive PSO (FSAPSO) and others programmed like particle swarm optimization (PSO), grey-wolf optimizer (GWO) and whale optimization algorithm (WOA). For first scenario; the proposed KH gives the best optimal cost of 105.94 €ct while the best emission is 420.57 kg, the best optimal cost and emission of 592.86 €ct 339.71 kg are obtained via KH in the second scenario. Keywords Krill herd optimization algorithm · Micro-grid operation · Ant lion optimizer Introduction One of the most important problems facing the society is the environmental pollution resulted from the operation of the power plants. In recent times; clean sources are able to generate electric energy without causing any pollution named renewable energy sources (RESs) such as solar and wind energies. Therefore; micro-grids (MGs) with installed RESs have been introduced as alternatives to the utility grid especially in the remote areas. The most important challenge facing micro-grids operation is the sizing process of these RESs. Literature works dealt with the process of management and operation of MGs as an optimization problem for * Ahmed Fathy 1 Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakakah, Saudi Arabia 2 Electric Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt 3 Electric Power and Machine Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt achieving less operating cost and less pollutant emission of the installed RESs. Guo et al. [1] determined the optimal sites and sizes of RESs installed in typical micro-grid via formulating multiobjective function comprising the contract price between distribution company (Disco) and distributed generation owner (DGO). Non-dominated sorting genetic algorithm (NSGA-II) has been used for solving the presented problem. A micro-grid including RESs, plug-in hybrid electric vehicles (PHEVs) and storage device has been optimized in Ref. [2] via Mont-Carlo approach. Alavi et al. [3] performed energy management process for typical MG based on relevant uncertainties modeled by point estimate method. A hybrid optimization algorithm comprises Fuzzy rules and particle swarm optimization (PSO) has been presented by Moghaddam et al. [4] to manage MG optimally. In Ref. [5], the optimal operation of typical MG with RESs has been solved via adaptive modified particle swarm optimization (AMPSO). Gabbar et al. [6] determined the optimal output power of distributed energy resources installed in MGs via multi-objective GA. Borhanazad et al. [7] presented multiobjective PSO for determining the optimal siting and sizing 13 Vol.:(0123456789) International Journal of Energy and Environmental Engineering of RESs installed in MG, the objective function comprises electricity cost and loss of power supply probability (LPSP). Differential evolution (DE) approach has been presented in Ref. [8] to evaluate the optimal site, generation level, tariff incentives for RESs such that Disco’s profit has been maximized. Multi-objective optimal planning process has been introduced in Refs. [9–11] to achieve both Disco and DGO benefits. Moradi et al. [12] treated with optimization of MG as slave-objective and multi-objective problems. Artificial bee colony and its modification via gravity search algorithm have been presented in [13, 14] for optimal management and operation of micro-gird. The total operating cost including emission price and start-up cost has been taken in Ref. [15] as objective function for evaluating the optimum operation of MG; the problem has been solved via mesh adaptive direct search algorithm (MADSA). Stochastic model of coordinating the generating units installed in MG has been introduced in Refs. [16–18]. Bahramara, et al. [19] presented bi-level optimization approach for achieving the objectives of Disco and MGs. Multi-objective function represents the operating cost and emission extracted from the distributed generators (DGs) installed in MG has been optimized via heuristic algorithm [20, 21, 23]. In Ref. [22], wind farm and pumped storage unit installed in MG have been optimized for achieving less cost. Other researchers dealt with the optimal sizing of RESs in stand-alone power system. Ahmadi et al. [24] proposed a hybrid big bang–big crunch for (HBB-BC) for solving the optimal size of photovoltaic (PV) system, wind turbine and battery bank for minimizing the total cost. In Ref. [25], the proposed RESs, PV, diesel and battery, are sized optimally via quadratic programming approach. Kaabeche et al. [26] presented an iterative optimization approach for optimal sizing of RESs such that achieving minimum energy load deficit. In Refs. [27, 28], single and multi-objective optimization problem have been presented to solve the sizing of hybrid system of PV-wind-diesel-battery system for minimizing the net present cost (NPC) of the system. Single and multi-objective problems have been studied in Ref. [29] for optimal sizing of RESs for minimizing the system cost and weight. In [30], adaptive neuro-Fuzzy inference system (ANFIS) has been introduced for optimizing hybrid RESs and calculating the optimum tilt angle for the PV system. A hybrid grid connected RESs system is optimized in Ref. [31]. Fathy [32] used mine blast algorithm (MBA) for evaluating the optimum size of PV, wind and fuel cell to cover a load in remote area with minimum operating cost. Most of the optimization approaches used (...truncated)